functions added

28 files changed, 2596 insertions, 5 deletions

diff --git a/help/en_US/fgoalattain.xml b/help/en_US/fgoalattain.xml
new file mode 100644
index 0000000..29a9923
--- /dev/null
+++ b/help/en_US/fgoalattain.xml
@@ -0,0 +1,232 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from fgoalattain.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="fgoalattain" xml:lang="en"
+          xmlns="http://docbook.org/ns/docbook"
+          xmlns:xlink="http://www.w3.org/1999/xlink"
+          xmlns:svg="http://www.w3.org/2000/svg"
+          xmlns:ns3="http://www.w3.org/1999/xhtml"
+          xmlns:mml="http://www.w3.org/1998/Math/MathML"
+          xmlns:scilab="http://www.scilab.org"
+          xmlns:db="http://docbook.org/ns/docbook">
+
+  <refnamediv>
+    <refname>fgoalattain</refname>
+    <refpurpose>Solves a multiobjective goal attainment problem</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   x = fgoalattain(fun,x0,goal,weight)
+   x = fgoalattain(fun,x0,goal,weight,A,b)
+   x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq)
+   x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub)
+   x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon)
+   x = fgoalattain(fun,x0,goal,weight,A,b,Aeq,beq,lb,ub,nonlcon,options)
+   [x,fval] = fgoalattain(...)
+   [x,fval,attainfactor] = fgoalattain(...)
+   [x,fval,attainfactor,exitflag] = fgoalattain(...)
+   [x,fval,attainfactor,exitflag,output] = fgoalattain(...)
+   [x,fval,attainfactor,exitflag,output,lambda] = fgoalattain(...)
+   
+   </synopsis>
+</refsynopsisdiv>
+
+<refsection>
+   <title>Parameters</title>
+   <variablelist>
+   <varlistentry><term>fun:</term>
+      <listitem><para> a function that accepts a vector x and returns a vector F</para></listitem></varlistentry>
+   <varlistentry><term>x0:</term>
+      <listitem><para> a nx1 or 1xn matrix of double, where n is the number of variables.</para></listitem></varlistentry>
+   <varlistentry><term>A:</term>
+      <listitem><para> a nil x n matrix of double, where n is the number of variables and</para></listitem></varlistentry>
+   <varlistentry><term>b:</term>
+      <listitem><para> a nil x 1 matrix of double, where nil is the number of linear</para></listitem></varlistentry>
+   <varlistentry><term>Aeq:</term>
+      <listitem><para> a nel x n matrix of double, where n is the number of variables</para></listitem></varlistentry>
+   <varlistentry><term>beq:</term>
+      <listitem><para> a nel x 1 matrix of double, where nel is the number of linear</para></listitem></varlistentry>
+   <varlistentry><term>lb:</term>
+      <listitem><para> a nx1 or 1xn matrix of double, where n is the number of variables.</para></listitem></varlistentry>
+   <varlistentry><term>ub:</term>
+      <listitem><para> a nx1 or 1xn matrix of double, where n is the number of variables.</para></listitem></varlistentry>
+   <varlistentry><term>nonlcon:</term>
+      <listitem><para> a function, the nonlinear constraints</para></listitem></varlistentry>
+   <varlistentry><term>options :</term>
+      <listitem><para> a list, containing the option for user to specify. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>x:</term>
+      <listitem><para> a nx1 matrix of double, the computed solution of the optimization problem</para></listitem></varlistentry>
+   <varlistentry><term>fval:</term>
+      <listitem><para> a vector of double, the value of functions at x</para></listitem></varlistentry>
+   <varlistentry><term>attainfactor:</term>
+      <listitem><para> The amount of over- or underachievement of the goals,γ at the solution.</para></listitem></varlistentry>
+   <varlistentry><term>exitflag:</term>
+      <listitem><para> a 1x1 matrix of floating point integers, the exit status</para></listitem></varlistentry>
+   <varlistentry><term>output:</term>
+      <listitem><para> a struct, the details of the optimization process</para></listitem></varlistentry>
+   <varlistentry><term>lambda:</term>
+      <listitem><para> a struct, the Lagrange multipliers at optimum</para></listitem></varlistentry>
+   </variablelist>
+</refsection>
+
+<refsection>
+   <title>Description</title>
+   <para>
+fgoalattain solves the goal attainment problem, which is one formulation for minimizing a multiobjective optimization problem.
+Finds the minimum of a problem specified by:
+Minimise Y such that
+   </para>
+   <para>
+<latex>
+\begin{eqnarray}
+\mbox{min}_{x,\gamma}  &amp; f(x)-weight \ast \gamma \leq goal \\
+\mbox{subject to} &amp; c(x) \leq 0 \\
+&amp; c_{eq}(x) = 0 \\
+&amp; Ax \leq b \\
+&amp; A_{eq} x = b_{eq} \\
+&amp; lb \leq x \leq ub
+\end{eqnarray}
+</latex>
+   </para>
+   <para>
+The solver makes use of fmincon to find the minimum.
+   </para>
+   <para>
+The fgoalattain finds out the maximum value of Y for the objectives evaluated at the starting point and
+adds that as another variable to the vector x
+This is passed to the fmincon function to get the optimised value of Y
+Hence, the algorithm used mainly is "ipopt" to obtain the optimum solution
+The relations between f(x), Y, weights and goals are added as additional non-linear inequality constraints
+   </para>
+   <para>
+The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type "list" and contains the following fields.
+<itemizedlist>
+<listitem>Syntax : options= list("MaxIter", [---], "CpuTime", [---], "GradObj", ---, "GradCon", ---);</listitem>
+<listitem>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</listitem>
+<listitem>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</listitem>
+<listitem>GradObj : a function, representing the gradient function of the Objective in Vector Form.</listitem>
+<listitem>GradCon : a function, representing the gradient of the Non-Linear Constraints (both Equality and Inequality) of the problem. It is declared in such a way that gradient of non-linear inequality constraints are defined first as a separate Matrix (cg of size m2 X n or as an empty), followed by gradient of non-linear equality constraints as a separate Matrix (ceqg of size m2 X n or as an empty) where m2 &amp; m3 are number of non-linear inequality and equality constraints respectively.</listitem>
+<listitem>Default Values : options = list("MaxIter", [3000], "CpuTime", [600]);</listitem>
+</itemizedlist>
+   </para>
+   <para>
+By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of minmaxObjfun. In case the GradObj option is off and GradConstr option is on, fminimax approximates minmaxObjfun gradient using numderivative toolbox.
+   </para>
+   <para>
+If we can provide exact gradients, we should do so since it improves the convergence speed of the optimization algorithm.
+   </para>
+   <para>
+Furthermore, we must enable the "GradObj" option with the statement :
+<programlisting>
+minimaxOptions = list("GradObj",fGrad);
+</programlisting>
+This will let fminimax know that the exact gradient of the objective function is known, so that it can change the calling sequence to the objective function. Note that, fGrad should be mentioned in the form of N x n where n is the number of variables, N is the number of functions in objective function.
+   </para>
+   <para>
+The constraint function must have header :
+<programlisting>
+[c, ceq] = confun(x)
+</programlisting>
+where x is a n x 1 matrix of dominmaxUbles, c is a 1 x nni matrix of doubles and ceq is a 1 x nne matrix of doubles (nni : number of nonlinear inequality constraints, nne : number of nonlinear equality constraints).
+On input, the variable x contains the current point and, on output, the variable c must contain the nonlinear inequality constraints and ceq must contain the nonlinear equality constraints.
+   </para>
+   <para>
+By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of confun. In case the GradObj option is on and GradCons option is off, fminimax approximates confun gradient using numderivative toolbox.
+   </para>
+   <para>
+If we can provide exact gradients, we should do so since it improves the convergence speed of the optimization algorithm.
+   </para>
+   <para>
+Furthermore, we must enable the "GradCon" option with the statement :
+<programlisting>
+minimaxOptions = list("GradCon",confunGrad);
+</programlisting>
+This will let fminimax know that the exact gradient of the objective function is known, so that it can change the calling sequence to the objective function.
+   </para>
+   <para>
+The constraint derivative function must have header :
+<programlisting>
+[dc,dceq] = confungrad(x)
+</programlisting>
+where dc is a nni x n matrix of doubles and dceq is a nne x n matrix of doubles.
+   </para>
+   <para>
+The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<itemizedlist>
+<listitem>exitflag=0 : Optimal Solution Found </listitem>
+<listitem>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=2 : Maximum amount of CPU Time exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=3 : Stop at Tiny Step.</listitem>
+<listitem>exitflag=4 : Solved To Acceptable Level.</listitem>
+<listitem>exitflag=5 : Converged to a point of local infeasibility.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
+   </para>
+   <para>
+The output data structure contains detailed informations about the optimization process.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>output.Iterations: The number of iterations performed during the search</listitem>
+<listitem>output.Cpu_Time: The total cpu-time spend during the search</listitem>
+<listitem>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</listitem>
+<listitem>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>lambda.lower: The Lagrange multipliers for the lower bound constraints.</listitem>
+<listitem>lambda.upper: The Lagrange multipliers for the upper bound constraints.</listitem>
+<listitem>lambda.eqlin: The Lagrange multipliers for the linear equality constraints.</listitem>
+<listitem>lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.</listitem>
+<listitem>lambda.eqnonlin: The Lagrange multipliers for the non-linear equality constraints.</listitem>
+<listitem>lambda.ineqnonlin: The Lagrange multipliers for the non-linear inequality constraints.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+</para>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+function f1 = fun(x)
+f1(1)=2*x(1)*x(1)+x(2)*x(2)-48*x(1)-40*x(2)+304
+f1(2)=-x(1)*x(1)-3*x(2)*x(2)
+f1(3)=x(1)+3*x(2)-18
+f1(4)=-x(1)-x(2)
+f1(5)=x(1)+x(2)-8
+endfunction
+x0=[-1,1];
+
+goal=[-5,-3,-2,-1,-4];
+weight=abs(goal)
+//xopt  = [-0.0000011 -63.999998 -2.0000002 -8 3.485D-08]
+//fval  = [4 3.99]
+
+//Run fgoalattain
+[xopt,fval,attainfactor,exitflag,output,lambda]=fgoalattain(fun,x0,goal,weight)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>Prajwala TM, Sheetal Shalini , 2015</member>
+   </simplelist>
+</refsection>
+</refentry>
diff --git a/help/en_US/fminbnd.xml b/help/en_US/fminbnd.xml
new file mode 100644
index 0000000..baf2f34
--- /dev/null
+++ b/help/en_US/fminbnd.xml
@@ -0,0 +1,197 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from fminbnd.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="fminbnd" xml:lang="en"
+          xmlns="http://docbook.org/ns/docbook"
+          xmlns:xlink="http://www.w3.org/1999/xlink"
+          xmlns:svg="http://www.w3.org/2000/svg"
+          xmlns:ns3="http://www.w3.org/1999/xhtml"
+          xmlns:mml="http://www.w3.org/1998/Math/MathML"
+          xmlns:scilab="http://www.scilab.org"
+          xmlns:db="http://docbook.org/ns/docbook">
+
+  <refnamediv>
+    <refname>fminbnd</refname>
+    <refpurpose>Solves a multi-variable optimization problem on a bounded interval</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   xopt = fminbnd(f,x1,x2)
+   xopt = fminbnd(f,x1,x2,options)
+   [xopt,fopt] = fminbnd(.....)
+   [xopt,fopt,exitflag]= fminbnd(.....)
+   [xopt,fopt,exitflag,output]=fminbnd(.....)
+   [xopt,fopt,exitflag,output,lambda]=fminbnd(.....)
+   
+   </synopsis>
+</refsynopsisdiv>
+
+<refsection>
+   <title>Parameters</title>
+   <variablelist>
+   <varlistentry><term>f :</term>
+      <listitem><para> a function, representing the objective function of the problem</para></listitem></varlistentry>
+   <varlistentry><term>x1 :</term>
+      <listitem><para> a vector, containing the lower bound of the variables of size (1 X n) or (n X 1) where 'n' is the number of Variables, where n is number of Variables</para></listitem></varlistentry>
+   <varlistentry><term>x2 :</term>
+      <listitem><para> a vector, containing the upper bound of the variables of size (1 X n) or (n X 1) or (0 X 0) where 'n' is the number of Variables. If x2 is empty it means upper bound is +infinity</para></listitem></varlistentry>
+   <varlistentry><term>options :</term>
+      <listitem><para> a list, containing the option for user to specify. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>xopt :</term>
+      <listitem><para> a vector of doubles, containing the the computed solution of the optimization problem.</para></listitem></varlistentry>
+   <varlistentry><term>fopt :</term>
+      <listitem><para> a scalar of double, containing the the function value at x.</para></listitem></varlistentry>
+   <varlistentry><term>exitflag :</term>
+      <listitem><para> a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>output :</term>
+      <listitem><para> a structure, containing the information about the optimization. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>lambda :</term>
+      <listitem><para> a structure, containing the Lagrange multipliers of lower bound and upper bound at the optimized point. See below for details.</para></listitem></varlistentry>
+   </variablelist>
+</refsection>
+
+<refsection>
+   <title>Description</title>
+   <para>
+Search the minimum of a multi-variable function on bounded interval specified by :
+Find the minimum of f(x) such that
+   </para>
+   <para>
+<latex>
+\begin{eqnarray}
+&amp;\mbox{min}_{x}
+&amp; f(x)\\
+&amp; \text{subject to} &amp; x1 \ &lt; x \ &lt; x2 \\
+\end{eqnarray}
+</latex>
+   </para>
+   <para>
+The routine calls Ipopt for solving the Bounded Optimization problem, Ipopt is a library written in C++.
+   </para>
+   <para>
+The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type "list" and contains the following fields.
+<itemizedlist>
+<listitem>Syntax : options= list("MaxIter", [---], "CpuTime", [---], TolX, [----]);</listitem>
+<listitem>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</listitem>
+<listitem>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</listitem>
+<listitem>TolX : a Scalar, containing the Tolerance value that the solver should take.</listitem>
+<listitem>Default Values : options = list("MaxIter", [3000], "CpuTime", [600], TolX, [1e-4]);</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<itemizedlist>
+<listitem>exitflag=0 : Optimal Solution Found </listitem>
+<listitem>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=2 : Maximum CPU Time exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=3 : Stop at Tiny Step.</listitem>
+<listitem>exitflag=4 : Solved To Acceptable Level.</listitem>
+<listitem>exitflag=5 : Converged to a point of local infeasibility.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
+   </para>
+   <para>
+The output data structure contains detailed informations about the optimization process.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>output.Iterations: The number of iterations performed during the search</listitem>
+<listitem>output.Cpu_Time: The total cpu-time spend during the search</listitem>
+<listitem>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</listitem>
+<listitem>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>lambda.lower: The Lagrange multipliers for the lower bound constraints.</listitem>
+<listitem>lambda.upper: The Lagrange multipliers for the upper bound constraints.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+</para>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R^6 such that it minimizes:
+//f(x)= sin(x1) + sin(x2) + sin(x3) + sin(x4) + sin(x5) + sin(x6)
+//-2 <= x1,x2,x3,x4,x5,x6 <= 2
+//Objective function to be minimised
+function y=f(x)
+y=0
+for i =1:6
+y=y+sin(x(i));
+end
+endfunction
+//Variable bounds
+x1 = [-2, -2, -2, -2, -2, -2];
+x2 = [2, 2, 2, 2, 2, 2];
+//Options
+options=list("MaxIter",[1500],"CpuTime", [100],"TolX",[1e-6])
+//Calling Ipopt
+[x,fval] =fminbnd(f, x1, x2, options)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R such that it minimizes:
+//f(x)= 1/x^2
+//0 <= x <= 1000
+//Objective function to be minimised
+function y=f(x)
+y=1/x^2
+endfunction
+//Variable bounds
+x1 = [0];
+x2 = [1000];
+//Calling Ipopt
+[x,fval,exitflag,output,lambda] =fminbnd(f, x1, x2)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//The below problem is an unbounded problem:
+//Find x in R^2 such that it minimizes:
+//f(x)= -[(x1-1)^2 + (x2-1)^2]
+//-inf <= x1,x2 <= inf
+//Objective function to be minimised
+function y=f(x)
+y=-((x(1)-1)^2+(x(2)-1)^2);
+endfunction
+//Variable bounds
+x1 = [-%inf , -%inf];
+x2 = [];
+//Options
+options=list("MaxIter",[1500],"CpuTime", [100],"TolX",[1e-6])
+//Calling Ipopt
+[x,fval,exitflag,output,lambda] =fminbnd(f, x1, x2, options)
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>R.Vidyadhar , Vignesh Kannan</member>
+   </simplelist>
+</refsection>
+</refentry>
diff --git a/help/en_US/fmincon.xml b/help/en_US/fmincon.xml
new file mode 100644
index 0000000..d8a7c4b
--- /dev/null
+++ b/help/en_US/fmincon.xml
@@ -0,0 +1,334 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from fmincon.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="fmincon" xml:lang="en"
+          xmlns="http://docbook.org/ns/docbook"
+          xmlns:xlink="http://www.w3.org/1999/xlink"
+          xmlns:svg="http://www.w3.org/2000/svg"
+          xmlns:ns3="http://www.w3.org/1999/xhtml"
+          xmlns:mml="http://www.w3.org/1998/Math/MathML"
+          xmlns:scilab="http://www.scilab.org"
+          xmlns:db="http://docbook.org/ns/docbook">
+
+  <refnamediv>
+    <refname>fmincon</refname>
+    <refpurpose>Solves a multi-variable constrainted optimization problem</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   xopt = fmincon(f,x0,A,b)
+   xopt = fmincon(f,x0,A,b,Aeq,beq)
+   xopt = fmincon(f,x0,A,b,Aeq,beq,lb,ub)
+   xopt = fmincon(f,x0,A,b,Aeq,beq,lb,ub,nlc)
+   xopt = fmincon(f,x0,A,b,Aeq,beq,lb,ub,nlc,options)
+   [xopt,fopt] = fmincon(.....)
+   [xopt,fopt,exitflag]= fmincon(.....)
+   [xopt,fopt,exitflag,output]= fmincon(.....)
+   [xopt,fopt,exitflag,output,lambda]=fmincon(.....)
+   [xopt,fopt,exitflag,output,lambda,gradient]=fmincon(.....)
+   [xopt,fopt,exitflag,output,lambda,gradient,hessian]=fmincon(.....)
+   
+   </synopsis>
+</refsynopsisdiv>
+
+<refsection>
+   <title>Parameters</title>
+   <variablelist>
+   <varlistentry><term>f :</term>
+      <listitem><para> a function, representing the objective function of the problem</para></listitem></varlistentry>
+   <varlistentry><term>x0 :</term>
+      <listitem><para> a vector of doubles, containing the starting values of variables of size (1 X n) or (n X 1) where 'n' is the number of Variables</para></listitem></varlistentry>
+   <varlistentry><term>A :</term>
+      <listitem><para> a matrix of doubles, containing the coefficients of linear inequality constraints of size (m X n) where 'm' is the number of linear inequality constraints</para></listitem></varlistentry>
+   <varlistentry><term>b :</term>
+      <listitem><para> a vector of doubles, related to 'A' and containing the the Right hand side equation of the linear inequality constraints of size (m X 1)</para></listitem></varlistentry>
+   <varlistentry><term>Aeq :</term>
+      <listitem><para> a matrix of doubles, containing the coefficients of linear equality constraints of size (m1 X n) where 'm1' is the number of linear equality constraints</para></listitem></varlistentry>
+   <varlistentry><term>beq :</term>
+      <listitem><para> a vector of doubles, related to 'Aeq' and containing the the Right hand side equation of the linear equality constraints of size (m1 X 1)</para></listitem></varlistentry>
+   <varlistentry><term>lb :</term>
+      <listitem><para> a vector of doubles, containing the lower bounds of the variables of size (1 X n) or (n X 1) where 'n' is the number of Variables</para></listitem></varlistentry>
+   <varlistentry><term>ub :</term>
+      <listitem><para> a vector of doubles, containing the upper bounds of the variables of size (1 X n) or (n X 1) where 'n' is the number of Variables</para></listitem></varlistentry>
+   <varlistentry><term>nlc :</term>
+      <listitem><para> a function, representing the Non-linear Constraints functions(both Equality and Inequality) of the problem. It is declared in such a way that non-linear inequality constraints are defined first as a single row vector (c), followed by non-linear equality constraints as another single row vector (ceq). Refer Example for definition of Constraint function.</para></listitem></varlistentry>
+   <varlistentry><term>options :</term>
+      <listitem><para> a list, containing the option for user to specify. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>xopt :</term>
+      <listitem><para> a vector of doubles, cointating the computed solution of the optimization problem</para></listitem></varlistentry>
+   <varlistentry><term>fopt :</term>
+      <listitem><para> a scalar of double, containing the the function value at x</para></listitem></varlistentry>
+   <varlistentry><term>exitflag :</term>
+      <listitem><para> a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>output :</term>
+      <listitem><para> a structure, containing the information about the optimization. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>lambda :</term>
+      <listitem><para> a structure, containing the Lagrange multipliers of lower bound, upper bound and constraints at the optimized point. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>gradient :</term>
+      <listitem><para> a vector of doubles, containing the Objective's gradient of the solution.</para></listitem></varlistentry>
+   <varlistentry><term>hessian  :</term>
+      <listitem><para> a matrix of doubles, containing the Lagrangian's hessian of the solution.</para></listitem></varlistentry>
+   </variablelist>
+</refsection>
+
+<refsection>
+   <title>Description</title>
+   <para>
+Search the minimum of a constrained optimization problem specified by :
+Find the minimum of f(x) such that
+   </para>
+   <para>
+<latex>
+\begin{eqnarray}
+&amp;\mbox{min}_{x}
+&amp; f(x) \\
+&amp; \text{subject to} &amp; A*x \leq b \\
+&amp; &amp; Aeq*x \ = beq\\
+&amp; &amp; c(x) \leq  0\\
+&amp; &amp; ceq(x) \ =  0\\
+&amp; &amp; lb \leq x \leq ub \\
+\end{eqnarray}
+</latex>
+   </para>
+   <para>
+The routine calls Ipopt for solving the Constrained Optimization problem, Ipopt is a library written in C++.
+   </para>
+   <para>
+The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type "list" and contains the following fields.
+<itemizedlist>
+<listitem>Syntax : options= list("MaxIter", [---], "CpuTime", [---], "GradObj", ---, "Hessian", ---, "GradCon", ---);</listitem>
+<listitem>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</listitem>
+<listitem>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</listitem>
+<listitem>GradObj : a function, representing the gradient function of the Objective in Vector Form.</listitem>
+<listitem>Hessian : a function, representing the hessian function of the Lagrange in Symmetric Matrix Form with Input parameters x, Objective factor and Lambda. Refer Example for definition of Lagrangian Hessian function.</listitem>
+<listitem>GradCon : a function, representing the gradient of the Non-Linear Constraints (both Equality and Inequality) of the problem. It is declared in such a way that gradient of non-linear inequality constraints are defined first as a separate Matrix (cg of size m2 X n or as an empty), followed by gradient of non-linear equality constraints as a separate Matrix (ceqg of size m2 X n or as an empty) where m2 &amp; m3 are number of non-linear inequality and equality constraints respectively.</listitem>
+<listitem>Default Values : options = list("MaxIter", [3000], "CpuTime", [600]);</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<itemizedlist>
+<listitem>exitflag=0 : Optimal Solution Found </listitem>
+<listitem>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=2 : Maximum amount of CPU Time exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=3 : Stop at Tiny Step.</listitem>
+<listitem>exitflag=4 : Solved To Acceptable Level.</listitem>
+<listitem>exitflag=5 : Converged to a point of local infeasibility.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
+   </para>
+   <para>
+The output data structure contains detailed informations about the optimization process.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>output.Iterations: The number of iterations performed during the search</listitem>
+<listitem>output.Cpu_Time: The total cpu-time spend during the search</listitem>
+<listitem>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</listitem>
+<listitem>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>lambda.lower: The Lagrange multipliers for the lower bound constraints.</listitem>
+<listitem>lambda.upper: The Lagrange multipliers for the upper bound constraints.</listitem>
+<listitem>lambda.eqlin: The Lagrange multipliers for the linear equality constraints.</listitem>
+<listitem>lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.</listitem>
+<listitem>lambda.eqnonlin: The Lagrange multipliers for the non-linear equality constraints.</listitem>
+<listitem>lambda.ineqnonlin: The Lagrange multipliers for the non-linear inequality constraints.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+</para>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R^2 such that it minimizes:
+//f(x)= -x1 -x2/3
+//x0=[0,0]
+//constraint-1 (c1): x1 + x2 <= 2
+//constraint-2 (c2): x1 + x2/4 <= 1
+//constraint-3 (c3): x1 - x2 <= 2
+//constraint-4 (c4): -x1/4 - x2 <= 1
+//constraint-5 (c5): -x1 - x2 <= -1
+//constraint-6 (c6): -x1 + x2 <= 2
+//constraint-7 (c7): x1 + x2 = 2
+//Objective function to be minimised
+function y=f(x)
+y=-x(1)-x(2)/3;
+endfunction
+//Starting point, linear constraints and variable bounds
+x0=[0 , 0];
+A=[1,1 ; 1,1/4 ; 1,-1 ; -1/4,-1 ; -1,-1 ; -1,1];
+b=[2;1;2;1;-1;2];
+Aeq=[1,1];
+beq=[2];
+lb=[];
+ub=[];
+nlc=[];
+//Gradient of objective function
+function y= fGrad(x)
+y= [-1,-1/3];
+endfunction
+//Hessian of lagrangian
+function y= lHess(x,obj,lambda)
+y= obj*[0,0;0,0]
+endfunction
+//Options
+options=list("GradObj", fGrad, "Hessian", lHess);
+//Calling Ipopt
+[x,fval,exitflag,output,lambda,grad,hessian] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,nlc,options)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R^3 such that it minimizes:
+//f(x)= x1*x2 + x2*x3
+//x0=[0.1 , 0.1 , 0.1]
+//constraint-1 (c1): x1^2 - x2^2 + x3^2 <= 2
+//constraint-2 (c2): x1^2 + x2^2 + x3^2 <= 10
+//Objective function to be minimised
+function y=f(x)
+y=x(1)*x(2)+x(2)*x(3);
+endfunction
+//Starting point, linear constraints and variable bounds
+x0=[0.1 , 0.1 , 0.1];
+A=[];
+b=[];
+Aeq=[];
+beq=[];
+lb=[];
+ub=[];
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+c = [x(1)^2 - x(2)^2 + x(3)^2 - 2 , x(1)^2 + x(2)^2 + x(3)^2 - 10];
+ceq = [];
+endfunction
+//Gradient of objective function
+function y= fGrad(x)
+y= [x(2),x(1)+x(3),x(2)];
+endfunction
+//Hessian of the Lagrange Function
+function y= lHess(x,obj,lambda)
+y= obj*[0,1,0;1,0,1;0,1,0] + lambda(1)*[2,0,0;0,-2,0;0,0,2] + lambda(2)*[2,0,0;0,2,0;0,0,2]
+endfunction
+//Gradient of Non-Linear Constraints
+function [cg,ceqg] = cGrad(x)
+cg=[2*x(1) , -2*x(2) , 2*x(3) ; 2*x(1) , 2*x(2) , 2*x(3)];
+ceqg=[];
+endfunction
+//Options
+options=list("MaxIter", [1500], "CpuTime", [500], "GradObj", fGrad, "Hessian", lHess,"GradCon", cGrad);
+//Calling Ipopt
+[x,fval,exitflag,output] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,nlc,options)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//The below problem is an unbounded problem:
+//Find x in R^3 such that it minimizes:
+//f(x)= -(x1^2 + x2^2 + x3^2)
+//x0=[0.1 , 0.1 , 0.1]
+//  x1 <= 0
+//  x2 <= 0
+//  x3 <= 0
+//Objective function to be minimised
+function y=f(x)
+y=-(x(1)^2+x(2)^2+x(3)^2);
+endfunction
+//Starting point, linear constraints and variable bounds
+x0=[0.1 , 0.1 , 0.1];
+A=[];
+b=[];
+Aeq=[];
+beq=[];
+lb=[];
+ub=[0,0,0];
+//Options
+options=list("MaxIter", [1500], "CpuTime", [500]);
+//Calling Ipopt
+[x,fval,exitflag,output,lambda,grad,hessian] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,[],options)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//The below problem is an infeasible problem:
+//Find x in R^3 such that in minimizes:
+//f(x)=x1*x2 + x2*x3
+//x0=[1,1,1]
+//constraint-1 (c1): x1^2 <= 1
+//constraint-2 (c2): x1^2 + x2^2 <= 1
+//constraint-3 (c3): x3^2 <= 1
+//constraint-4 (c4): x1^3 = 0.5
+//constraint-5 (c5): x2^2 + x3^2 = 0.75
+// 0 <= x1 <=0.6
+// 0.2 <= x2 <= inf
+// -inf <= x3 <= 1
+//Objective function to be minimised
+function y=f(x)
+y=x(1)*x(2)+x(2)*x(3);
+endfunction
+//Starting point, linear constraints and variable bounds
+x0=[1,1,1];
+A=[];
+b=[];
+Aeq=[];
+beq=[];
+lb=[0 0.2,-%inf];
+ub=[0.6 %inf,1];
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+c=[x(1)^2-1,x(1)^2+x(2)^2-1,x(3)^2-1];
+ceq=[x(1)^3-0.5,x(2)^2+x(3)^2-0.75];
+endfunction
+//Gradient of objective function
+function y= fGrad(x)
+y= [x(2),x(1)+x(3),x(2)];
+endfunction
+//Hessian of the Lagrange Function
+function y= lHess(x,obj,lambda)
+y= obj*[0,1,0;1,0,1;0,1,0] + lambda(1)*[2,0,0;0,0,0;0,0,0] + lambda(2)*[2,0,0;0,2,0;0,0,0] +lambda(3)*[0,0,0;0,0,0;0,0,2] + lambda(4)*[6*x(1    ),0,0;0,0,0;0,0,0] + lambda(5)*[0,0,0;0,2,0;0,0,2];
+endfunction
+//Gradient of Non-Linear Constraints
+function [cg,ceqg] = cGrad(x)
+cg = [2*x(1),0,0;2*x(1),2*x(2),0;0,0,2*x(3)];
+ceqg = [3*x(1)^2,0,0;0,2*x(2),2*x(3)];
+endfunction
+//Options
+options=list("MaxIter", [1500], "CpuTime", [500], "GradObj", fGrad, "Hessian", lHess,"GradCon", cGrad);
+//Calling Ipopt
+[x,fval,exitflag,output,lambda,grad,hessian] =fmincon(f, x0,A,b,Aeq,beq,lb,ub,nlc,options)
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>R.Vidyadhar , Vignesh Kannan</member>
+   </simplelist>
+</refsection>
+</refentry>
diff --git a/help/en_US/fminimax.xml b/help/en_US/fminimax.xml
new file mode 100644
index 0000000..ddab078
--- /dev/null
+++ b/help/en_US/fminimax.xml
@@ -0,0 +1,293 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from fminimax.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="fminimax" xml:lang="en"
+          xmlns="http://docbook.org/ns/docbook"
+          xmlns:xlink="http://www.w3.org/1999/xlink"
+          xmlns:svg="http://www.w3.org/2000/svg"
+          xmlns:ns3="http://www.w3.org/1999/xhtml"
+          xmlns:mml="http://www.w3.org/1998/Math/MathML"
+          xmlns:scilab="http://www.scilab.org"
+          xmlns:db="http://docbook.org/ns/docbook">
+
+  <refnamediv>
+    <refname>fminimax</refname>
+    <refpurpose>Solves minimax constraint problem</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   x = fminimax(fun,x0)
+   x = fminimax(fun,x0,A,b)
+   x = fminimax(fun,x0,A,b,Aeq,beq)
+   x = fminimax(fun,x0,A,b,Aeq,beq,lb,ub)
+   x = fminimax(fun,x0,A,b,Aeq,beq,lb,ub,nonlinfun)
+   x = fminimax(fun,x0,A,b,Aeq,beq,lb,ub,nonlinfun,options)
+   [x, fval] = fmincon(.....)
+   [x, fval, maxfval]= fmincon(.....)
+   [x, fval, maxfval, exitflag]= fmincon(.....)
+   [x, fval, maxfval, exitflag, output]= fmincon(.....)
+   [x, fval, maxfval, exitflag, output, lambda]= fmincon(.....)
+   
+   </synopsis>
+</refsynopsisdiv>
+
+<refsection>
+   <title>Parameters</title>
+   <variablelist>
+   <varlistentry><term>fun:</term>
+      <listitem><para> The function to be minimized. fun is a function that accepts a vector x and returns a vector F, the objective functions evaluated at x.</para></listitem></varlistentry>
+   <varlistentry><term>x0:</term>
+      <listitem><para> a nx1 or 1xn matrix of doubles, where n is the number of variables, the initial guess for the optimization algorithm</para></listitem></varlistentry>
+   <varlistentry><term>A:</term>
+      <listitem><para> a nil x n matrix of doubles, where n is the number of variables and nil is the number of linear inequalities. If A==[] and b==[], it is assumed that there is no linear inequality constraints. If (A==[] &amp; b&lt;&gt;[]), fminimax generates an error (the same happens if (A&lt;&gt;[] &amp; b==[]))</para></listitem></varlistentry>
+   <varlistentry><term>b:</term>
+      <listitem><para> a nil x 1 matrix of doubles, where nil is the number of linear inequalities</para></listitem></varlistentry>
+   <varlistentry><term>Aeq:</term>
+      <listitem><para> a nel x n matrix of doubles, where n is the number of variables and nel is the number of linear equalities. If Aeq==[] and beq==[], it is assumed that there is no linear equality constraints. If (Aeq==[] &amp; beq&lt;&gt;[]), fminimax generates an error (the same happens if (Aeq&lt;&gt;[] &amp; beq==[]))</para></listitem></varlistentry>
+   <varlistentry><term>beq:</term>
+      <listitem><para> a nel x 1 matrix of doubles, where nel is the number of linear equalities</para></listitem></varlistentry>
+   <varlistentry><term>lb:</term>
+      <listitem><para> a nx1 or 1xn matrix of doubles, where n is the number of variables. The lower bound for x. If lb==[], then the lower bound is automatically set to -inf</para></listitem></varlistentry>
+   <varlistentry><term>ub:</term>
+      <listitem><para> a nx1 or 1xn matrix of doubles, where n is the number of variables. The upper bound for x. If ub==[], then the upper bound is automatically set to +inf</para></listitem></varlistentry>
+   <varlistentry><term>nonlinfun:</term>
+      <listitem><para> function that computes the nonlinear inequality constraints c(x) &lt;= 0 and nonlinear equality constraints ceq(x) = 0.</para></listitem></varlistentry>
+   <varlistentry><term>x:</term>
+      <listitem><para> a nx1 matrix of doubles, the computed solution of the optimization problem</para></listitem></varlistentry>
+   <varlistentry><term>fval:</term>
+      <listitem><para> a vector of doubles, the value of fun at x</para></listitem></varlistentry>
+   <varlistentry><term>maxfval:</term>
+      <listitem><para> a 1x1 matrix of doubles, the maximum value in vector fval</para></listitem></varlistentry>
+   <varlistentry><term>exitflag:</term>
+      <listitem><para> a 1x1 matrix of floating point integers, the exit status</para></listitem></varlistentry>
+   <varlistentry><term>output:</term>
+      <listitem><para> a struct, the details of the optimization process</para></listitem></varlistentry>
+   <varlistentry><term>lambda:</term>
+      <listitem><para> a struct, the Lagrange multipliers at optimum</para></listitem></varlistentry>
+   <varlistentry><term>options:</term>
+      <listitem><para> a list, containing the option for user to specify. See below for details.</para></listitem></varlistentry>
+   </variablelist>
+</refsection>
+
+<refsection>
+   <title>Description</title>
+   <para>
+fminimax minimizes the worst-case (largest) value of a set of multivariable functions, starting at an initial estimate. This is generally referred to as the minimax problem.
+   </para>
+   <para>
+<latex>
+\min_{x} \max_{i} F_{i}(x)\: \textrm{such that} \:\begin{cases}
+&amp; c(x) \leq 0 \\
+&amp; ceq(x) = 0 \\
+&amp; A.x \leq b \\
+&amp; Aeq.x = beq \\
+&amp; minmaxLb \leq x \leq minmaxUb
+\end{cases}
+</latex>
+   </para>
+   <para>
+Currently, fminimax calls fmincon which uses the ip-opt algorithm.
+   </para>
+   <para>
+max-min problems can also be solved with fminimax, using the identity
+   </para>
+   <para>
+<latex>
+\max_{x} \min_{i} F_{i}(x) = -\min_{x} \max_{i} \left( -F_{i}(x) \right)
+</latex>
+   </para>
+   <para>
+The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type "list" and contains the following fields.
+<itemizedlist>
+<listitem>Syntax : options= list("MaxIter", [---], "CpuTime", [---], "GradObj", ---, "GradCon", ---);</listitem>
+<listitem>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</listitem>
+<listitem>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</listitem>
+<listitem>GradObj : a function, representing the gradient function of the Objective in Vector Form.</listitem>
+<listitem>GradCon : a function, representing the gradient of the Non-Linear Constraints (both Equality and Inequality) of the problem. It is declared in such a way that gradient of non-linear inequality constraints are defined first as a separate Matrix (cg of size m2 X n or as an empty), followed by gradient of non-linear equality constraints as a separate Matrix (ceqg of size m2 X n or as an empty) where m2 &amp; m3 are number of non-linear inequality and equality constraints respectively.</listitem>
+<listitem>Default Values : options = list("MaxIter", [3000], "CpuTime", [600]);</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The objective function must have header :
+<programlisting>
+F = fun(x)
+</programlisting>
+where x is a n x 1 matrix of doubles and F is a m x 1 matrix of doubles where m is the total number of objective functions inside F.
+On input, the variable x contains the current point and, on output, the variable F must contain the objective function values.
+   </para>
+   <para>
+By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of minmaxObjfun. In case the GradObj option is off and GradConstr option is on, fminimax approximates minmaxObjfun gradient using numderivative toolbox.
+   </para>
+   <para>
+If we can provide exact gradients, we should do so since it improves the convergence speed of the optimization algorithm.
+   </para>
+   <para>
+Furthermore, we must enable the "GradObj" option with the statement :
+<programlisting>
+minimaxOptions = list("GradObj",fGrad);
+</programlisting>
+This will let fminimax know that the exact gradient of the objective function is known, so that it can change the calling sequence to the objective function. Note that, fGrad should be mentioned in the form of N x n where n is the number of variables, N is the number of functions in objective function.
+   </para>
+   <para>
+The constraint function must have header :
+<programlisting>
+[c, ceq] = confun(x)
+</programlisting>
+where x is a n x 1 matrix of dominmaxUbles, c is a 1 x nni matrix of doubles and ceq is a 1 x nne matrix of doubles (nni : number of nonlinear inequality constraints, nne : number of nonlinear equality constraints).
+On input, the variable x contains the current point and, on output, the variable c must contain the nonlinear inequality constraints and ceq must contain the nonlinear equality constraints.
+   </para>
+   <para>
+By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of confun. In case the GradObj option is on and GradCons option is off, fminimax approximates confun gradient using numderivative toolbox.
+   </para>
+   <para>
+If we can provide exact gradients, we should do so since it improves the convergence speed of the optimization algorithm.
+   </para>
+   <para>
+Furthermore, we must enable the "GradCon" option with the statement :
+<programlisting>
+minimaxOptions = list("GradCon",confunGrad);
+</programlisting>
+This will let fminimax know that the exact gradient of the objective function is known, so that it can change the calling sequence to the objective function.
+   </para>
+   <para>
+The constraint derivative function must have header :
+<programlisting>
+[dc,dceq] = confungrad(x)
+</programlisting>
+where dc is a nni x n matrix of doubles and dceq is a nne x n matrix of doubles.
+   </para>
+   <para>
+The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<itemizedlist>
+<listitem>exitflag=0 : Optimal Solution Found </listitem>
+<listitem>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=2 : Maximum amount of CPU Time exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=3 : Stop at Tiny Step.</listitem>
+<listitem>exitflag=4 : Solved To Acceptable Level.</listitem>
+<listitem>exitflag=5 : Converged to a point of local infeasibility.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
+   </para>
+   <para>
+The output data structure contains detailed informations about the optimization process.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>output.Iterations: The number of iterations performed during the search</listitem>
+<listitem>output.Cpu_Time: The total cpu-time spend during the search</listitem>
+<listitem>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</listitem>
+<listitem>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>lambda.lower: The Lagrange multipliers for the lower bound constraints.</listitem>
+<listitem>lambda.upper: The Lagrange multipliers for the upper bound constraints.</listitem>
+<listitem>lambda.eqlin: The Lagrange multipliers for the linear equality constraints.</listitem>
+<listitem>lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.</listitem>
+<listitem>lambda.eqnonlin: The Lagrange multipliers for the non-linear equality constraints.</listitem>
+<listitem>lambda.ineqnonlin: The Lagrange multipliers for the non-linear inequality constraints.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+</para>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+// A basic case :
+// we provide only the objective function and the nonlinear constraint
+// function
+function f = myfun(x)
+f(1)= 2*x(1)^2 + x(2)^2 - 48*x(1) - 40*x(2) + 304;     //Objectives
+f(2)= -x(1)^2 - 3*x(2)^2;
+f(3)= x(1) + 3*x(2) -18;
+f(4)= -x(1) - x(2);
+f(5)= x(1) + x(2) - 8;
+endfunction
+// The initial guess
+x0 = [0.1,0.1];
+// The expected solution : only 4 digits are guaranteed
+//xopt = [4 4]
+//fopt = [0 -64 -2 -8 0]
+maxfopt = 0
+// Run fminimax
+[xopt,fopt,maxfval,exitflag,output,lambda] = fminimax(myfun, x0)
+// Press ENTER to continue
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+// A case where we provide the gradient of the objective
+// functions and the Jacobian matrix of the constraints.
+// The objective function and its gradient
+function f = myfun(x)
+f(1)= 2*x(1)^2 + x(2)^2 - 48*x(1) - 40*x(2) + 304;
+f(2)= -x(1)^2 - 3*x(2)^2;
+f(3)= x(1) + 3*x(2) -18;
+f(4)= -x(1) - x(2);
+f(5)= x(1) + x(2) - 8;
+endfunction
+// Defining gradient of myfun
+function G = myfungrad(x)
+G = [ 4*x(1) - 48, -2*x(1), 1, -1, 1;
+2*x(2) - 40, -6*x(2), 3, -1, 1; ]'
+endfunction
+// The nonlinear constraints and the Jacobian
+// matrix of the constraints
+function [c,ceq] = confun(x)
+// Inequality constraints
+c = [1.5 + x(1)*x(2) - x(1) - x(2), -x(1)*x(2) - 10]
+// No nonlinear equality constraints
+ceq=[]
+endfunction
+// Defining gradient of confungrad
+function [DC,DCeq] = cgrad(x)
+// DC(:,i) = gradient of the i-th constraint
+// DC = [
+//   Dc1/Dx1  Dc1/Dx2
+//   Dc2/Dx1  Dc2/Dx2
+//   ]
+DC= [
+x(2)-1, -x(2)
+x(1)-1, -x(1)
+]'
+DCeq = []'
+endfunction
+// Test with both gradient of objective and gradient of constraints
+minimaxOptions = list("GradObj",myfungrad,"GradCon",cgrad);
+// The initial guess
+x0 = [0,10];
+// The expected solution : only 4 digits are guaranteed
+//xopt = [0.92791 7.93551]
+//fopt = [6.73443  -189.778  6.73443  -8.86342  0.86342]
+maxfopt = 6.73443
+// Run fminimax
+[xopt,fopt,maxfval,exitflag,output] = fminimax(myfun,x0,[],[],[],[],[],[], confun, minimaxOptions)
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>Animesh Baranawal</member>
+   </simplelist>
+</refsection>
+</refentry>
diff --git a/help/en_US/fminunc.xml b/help/en_US/fminunc.xml
new file mode 100644
index 0000000..a28a82a
--- /dev/null
+++ b/help/en_US/fminunc.xml
@@ -0,0 +1,196 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from fminunc.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="fminunc" xml:lang="en"
+          xmlns="http://docbook.org/ns/docbook"
+          xmlns:xlink="http://www.w3.org/1999/xlink"
+          xmlns:svg="http://www.w3.org/2000/svg"
+          xmlns:ns3="http://www.w3.org/1999/xhtml"
+          xmlns:mml="http://www.w3.org/1998/Math/MathML"
+          xmlns:scilab="http://www.scilab.org"
+          xmlns:db="http://docbook.org/ns/docbook">
+
+  <refnamediv>
+    <refname>fminunc</refname>
+    <refpurpose>Solves a multi-variable unconstrainted optimization problem</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   xopt = fminunc(f,x0)
+   xopt = fminunc(f,x0,options)
+   [xopt,fopt] = fminunc(.....)
+   [xopt,fopt,exitflag]= fminunc(.....)
+   [xopt,fopt,exitflag,output]= fminunc(.....)
+   [xopt,fopt,exitflag,output,gradient]=fminunc(.....)
+   [xopt,fopt,exitflag,output,gradient,hessian]=fminunc(.....)
+   
+   </synopsis>
+</refsynopsisdiv>
+
+<refsection>
+   <title>Parameters</title>
+   <variablelist>
+   <varlistentry><term>f :</term>
+      <listitem><para> a function, representing the objective function of the problem</para></listitem></varlistentry>
+   <varlistentry><term>x0 :</term>
+      <listitem><para> a vector of doubles, containing the starting of variables.</para></listitem></varlistentry>
+   <varlistentry><term>options:</term>
+      <listitem><para> a list, containing the option for user to specify. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>xopt :</term>
+      <listitem><para> a vector of doubles, the computed solution of the optimization problem.</para></listitem></varlistentry>
+   <varlistentry><term>fopt :</term>
+      <listitem><para> a scalar of double, the function value at x.</para></listitem></varlistentry>
+   <varlistentry><term>exitflag :</term>
+      <listitem><para> a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>output   :</term>
+      <listitem><para> a structure, containing the information about the optimization. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>gradient :</term>
+      <listitem><para> a vector of doubles, containing the the gradient of the solution.</para></listitem></varlistentry>
+   <varlistentry><term>hessian  :</term>
+      <listitem><para> a matrix of doubles, containing the the hessian of the solution.</para></listitem></varlistentry>
+   </variablelist>
+</refsection>
+
+<refsection>
+   <title>Description</title>
+   <para>
+Search the minimum of an unconstrained optimization problem specified by :
+Find the minimum of f(x) such that
+   </para>
+   <para>
+<latex>
+\begin{eqnarray}
+&amp;\mbox{min}_{x}
+&amp; f(x)\\
+\end{eqnarray}
+</latex>
+   </para>
+   <para>
+The routine calls Ipopt for solving the Un-constrained Optimization problem, Ipopt is a library written in C++.
+   </para>
+   <para>
+The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type "list" and contains the following fields.
+<itemizedlist>
+<listitem>Syntax : options= list("MaxIter", [---], "CpuTime", [---], "Gradient", ---, "Hessian", ---);</listitem>
+<listitem>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</listitem>
+<listitem>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</listitem>
+<listitem>Gradient : a function, representing the gradient function of the Objective in Vector Form.</listitem>
+<listitem>Hessian : a function, representing the hessian function of the Objective in Symmetric Matrix Form.</listitem>
+<listitem>Default Values : options = list("MaxIter", [3000], "CpuTime", [600]);</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<itemizedlist>
+<listitem>exitflag=0 : Optimal Solution Found </listitem>
+<listitem>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=2 : Maximum CPU Time exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=3 : Stop at Tiny Step.</listitem>
+<listitem>exitflag=4 : Solved To Acceptable Level.</listitem>
+<listitem>exitflag=5 : Converged to a point of local infeasibility.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
+   </para>
+   <para>
+The output data structure contains detailed informations about the optimization process.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>output.Iterations: The number of iterations performed during the search</listitem>
+<listitem>output.Cpu_Time: The total cpu-time spend during the search</listitem>
+<listitem>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</listitem>
+<listitem>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</listitem>
+</itemizedlist>
+   </para>
+   <para>
+</para>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R^2 such that it minimizes the Rosenbrock function
+//f = 100*(x2 - x1^2)^2 + (1-x1)^2
+//Objective function to be minimised
+function y= f(x)
+y= 100*(x(2) - x(1)^2)^2 + (1-x(1))^2;
+endfunction
+//Starting point
+x0=[-1,2];
+//Gradient of objective function
+function y= fGrad(x)
+y= [-400*x(1)*x(2) + 400*x(1)^3 + 2*x(1)-2, 200*(x(2)-x(1)^2)];
+endfunction
+//Hessian of Objective Function
+function y= fHess(x)
+y= [1200*x(1)^2- 400*x(2) + 2, -400*x(1);-400*x(1), 200 ];
+endfunction
+//Options
+options=list("MaxIter", [1500], "CpuTime", [500], "Gradient", fGrad, "Hessian", fHess);
+//Calling Ipopt
+[xopt,fopt,exitflag,output,gradient,hessian]=fminunc(f,x0,options)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Find x in R^2 such that the below function is minimum
+//f = x1^2 + x2^2
+//Objective function to be minimised
+function y= f(x)
+y= x(1)^2 + x(2)^2;
+endfunction
+//Starting point
+x0=[2,1];
+//Calling Ipopt
+[xopt,fopt]=fminunc(f,x0)
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//The below problem is an unbounded problem:
+//Find x in R^2 such that the below function is minimum
+//f = - x1^2 - x2^2
+//Objective function to be minimised
+function y= f(x)
+y= -x(1)^2 - x(2)^2;
+endfunction
+//Starting point
+x0=[2,1];
+//Gradient of objective function
+function y= fGrad(x)
+y= [-2*x(1),-2*x(2)];
+endfunction
+//Hessian of Objective Function
+function y= fHess(x)
+y= [-2,0;0,-2];
+endfunction
+//Options
+options=list("MaxIter", [1500], "CpuTime", [500], "Gradient", fGrad, "Hessian", fHess);
+//Calling Ipopt
+[xopt,fopt,exitflag,output,gradient,hessian]=fminunc(f,x0,options)
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>R.Vidyadhar , Vignesh Kannan</member>
+   </simplelist>
+</refsection>
+</refentry>
diff --git a/help/en_US/linprog.xml b/help/en_US/linprog.xml
new file mode 100755
index 0000000..1aa6e7c
--- /dev/null
+++ b/help/en_US/linprog.xml
@@ -0,0 +1,222 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ *
+ * This help file was generated from linprog.sci using help_from_sci().
+ *
+ -->
+
+<refentry version="5.0-subset Scilab" xml:id="linprog" xml:lang="en"
+          xmlns="http://docbook.org/ns/docbook"
+          xmlns:xlink="http://www.w3.org/1999/xlink"
+          xmlns:svg="http://www.w3.org/2000/svg"
+          xmlns:ns3="http://www.w3.org/1999/xhtml"
+          xmlns:mml="http://www.w3.org/1998/Math/MathML"
+          xmlns:scilab="http://www.scilab.org"
+          xmlns:db="http://docbook.org/ns/docbook">
+
+  <refnamediv>
+    <refname>linprog</refname>
+    <refpurpose>Solves a linear programming problem.</refpurpose>
+  </refnamediv>
+
+
+<refsynopsisdiv>
+   <title>Calling Sequence</title>
+   <synopsis>
+   xopt = linprog(c,A,b)
+   xopt = linprog(c,A,b,Aeq,beq)
+   xopt = linprog(c,A,b,Aeq,beq,lb,ub)
+   xopt = linprog(c,A,b,Aeq,beq,lb,ub,param)
+   [xopt, fopt, exitflag, output, lambda] = linprog(file)
+   [xopt,fopt,exitflag,output,lambda] = linprog( ... )
+   
+   </synopsis>
+</refsynopsisdiv>
+
+<refsection>
+   <title>Parameters</title>
+   <variablelist>
+   <varlistentry><term>c :</term>
+      <listitem><para> a vector of double, contains coefficients of the variables in the objective</para></listitem></varlistentry>
+   <varlistentry><term>A :</term>
+      <listitem><para> a matrix of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.</para></listitem></varlistentry>
+   <varlistentry><term>b :</term>
+      <listitem><para> a vector of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.</para></listitem></varlistentry>
+   <varlistentry><term>Aeq :</term>
+      <listitem><para> a matrix of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.</para></listitem></varlistentry>
+   <varlistentry><term>beq :</term>
+      <listitem><para> a vector of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.</para></listitem></varlistentry>
+   <varlistentry><term>lb :</term>
+      <listitem><para> Lower bounds, specified as a vector or array of double. lb represents the lower bounds elementwise in lb ≤ x ≤ ub.</para></listitem></varlistentry>
+   <varlistentry><term>ub :</term>
+      <listitem><para> Upper bounds, specified as a vector or array of double. ub represents the upper bounds elementwise in lb ≤ x ≤ ub.</para></listitem></varlistentry>
+   <varlistentry><term>options :</term>
+      <listitem><para> a list containing the parameters to be set.</para></listitem></varlistentry>
+   <varlistentry><term>file :</term>
+      <listitem><para> a string describing the path to the mps file.</para></listitem></varlistentry>
+   <varlistentry><term>xopt :</term>
+      <listitem><para> a vector of double, the computed solution of the optimization problem.</para></listitem></varlistentry>
+   <varlistentry><term>fopt :</term>
+      <listitem><para> a double, the value of the function at x.</para></listitem></varlistentry>
+   <varlistentry><term>status :</term>
+      <listitem><para> status flag returned from symphony. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>output :</term>
+      <listitem><para> The output data structure contains detailed information about the optimization process. See below for details.</para></listitem></varlistentry>
+   <varlistentry><term>lambda :</term>
+      <listitem><para> The structure consist of the Lagrange multipliers at the solution of problem. See below for details.</para></listitem></varlistentry>
+   </variablelist>
+</refsection>
+
+<refsection>
+   <title>Description</title>
+   <para>
+OSI-CLP is used for solving the linear programming problems, OSI-CLP is a library written in C++.
+Search the minimum of a constrained linear programming problem specified by :
+   </para>
+   <para>
+<latex>
+\begin{eqnarray}
+&amp;\mbox{min}_{x}
+&amp; c^T⋅x  \\
+&amp; \text{subject to} &amp; A⋅x \leq b \\
+&amp; &amp; Aeq⋅x = beq \\
+&amp; &amp; lb \leq x \leq ub \\
+\end{eqnarray}
+</latex>
+The routine calls Clp for solving the linear programming problem, Clp is a library written in C++.
+   </para>
+   <para>
+The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<itemizedlist>
+<listitem>exitflag=0 : Optimal Solution Found </listitem>
+<listitem>exitflag=1 : Primal Infeasible </listitem>
+<listitem>exitflag=2 : Dual Infeasible</listitem>
+<listitem>exitflag=3 : Maximum Number of Iterations Exceeded. Output may not be optimal.</listitem>
+<listitem>exitflag=4 : Solution Abandoned</listitem>
+<listitem>exitflag=5 : Primal objective limit reached.</listitem>
+<listitem>exitflag=6 : Dual objective limit reached.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
+   </para>
+   <para>
+The output data structure contains detailed informations about the optimization process.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>output.iterations: The number of iterations performed during the search</listitem>
+<listitem>output.constrviolation: The max-norm of the constraint violation.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type "struct" and contains the following fields.
+<itemizedlist>
+<listitem>lambda.lower: The Lagrange multipliers for the lower bound constraints.</listitem>
+<listitem>lambda.upper: The Lagrange multipliers for the upper bound constraints.</listitem>
+<listitem>lambda.eqlin: The Lagrange multipliers for the linear equality constraints.</listitem>
+<listitem>lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.</listitem>
+</itemizedlist>
+   </para>
+   <para>
+</para>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Optimal problems
+//Linear program, linear inequality constraints
+c=[-1,-1/3]'
+A=[1,1;1,1/4;1,-1;-1/4,-1;-1,-1;-1,1]
+b=[2,1,2,1,-1,2]
+[xopt,fopt,exitflag,output,lambda]=linprog(c, A, b)
+// Press ENTER to continue
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Linear program with Linear Inequalities and Equalities`
+c=[-1,-1/3]'
+A=[1,1;1,1/4;1,-1;-1/4,-1;-1,-1;-1,1]
+b=[2,1,2,1,-1,2]
+Aeq=[1,1/4]
+beq=[1/2]
+[xopt,fopt,exitflag,output,lambda]=linprog(c, A, b, Aeq, beq)
+// Press ENTER to continue
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Linear program with all constraint types
+c=[-1,-1/3]'
+A=[1,1;1,1/4;1,-1;-1/4,-1;-1,-1;-1,1]
+b=[2,1,2,1,-1,2]
+Aeq=[1,1/4]
+beq=[1/2]
+lb=[-1,-0.5]
+ub=[1.5,1.25]
+[xopt,fopt,exitflag,output,lambda]=linprog(c, A, b, Aeq, beq, lb, ub)
+// Press ENTER to continue
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Primal Infeasible Problem
+c=[-1,-1,-1]'
+A=[1,2,-1]
+b=[-4]
+Aeq=[1,5,3;1,1,0]
+beq=[10,100]
+lb=[0,0,0]
+ub=[%inf,%inf,%inf]
+[xopt,fopt,exitflag,output,lambda]= linprog(c,A,b,Aeq,beq,lb,ub)
+// Press ENTER to continue
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+//Dual Infeasible Problem
+c=[3,5,-7]'
+A=[-1,-1,4;1,1,4]
+b=[-8,5]
+Aeq=[]
+beq=[]
+lb=[-%inf,-%inf,-%inf]
+ub=[%inf,%inf,%inf]
+[xopt,fopt,exitflag,output,lambda]= linprog(c,A,b,Aeq,beq,lb,ub)
+// Press ENTER to continue
+
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Examples</title>
+   <programlisting role="example"><![CDATA[
+filepath = get_absolute_file_path('linprog.dem.sce');
+filepath = filepath + "exmip1.mps"
+[xopt,fopt,exitflag,output,lambda] =linprog(filepath);
+   ]]></programlisting>
+</refsection>
+
+<refsection>
+   <title>Authors</title>
+   <simplelist type="vert">
+   <member>Bhanu Priya Sayal, Guru Pradeep Reddy</member>
+   </simplelist>
+</refsection>
+</refentry>
diff --git a/help/en_US/master_help.xml b/help/en_US/master_help.xml
index e59ac6c..0e162f7 100644
--- a/help/en_US/master_help.xml
+++ b/help/en_US/master_help.xml
@@ -1,6 +1,11 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <!DOCTYPE book [
 <!--Begin Entities-->
+<!ENTITY a745e19a6383796e6f5680cdcc44cfcce SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/fgoalattain.xml">
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+<!ENTITY a52664d077cac340a0384efe1ac107088 SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/fmincon.xml">
+<!ENTITY a14f1077f437dbe35eb1cac51fed7a9fc SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/fminunc.xml">
+<!ENTITY aa809ed678033fc05c9b60a71de55b2ce SYSTEM "/home/harpreet/symphony_work/symphony/help/en_US/linprog.xml">
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@@ -81,6 +86,11 @@
 
 <part xml:id='section_19f4f1e5726c01d683e8b82be0a7e910'>
 <title>Symphony Toolbox</title>
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diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS
index 2a1feb2..5ece28b 100644
--- a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS
+++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS
diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB
index 386e0dd..5ba83c4 100644
--- a/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB
+++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/DOCS.TAB
diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS
index 465e052..bfdb148 100644
--- a/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS
+++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/OFFSETS
diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS
index 629f1cc..c20654a 100644
--- a/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS
+++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/POSITIONS
diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA b/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA
index dab0526..0648b6b 100644
--- a/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA
+++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/SCHEMA
@@ -1,2 +1,2 @@
 JavaSearch 1.0
-TMAP bs=2048 rt=1 fl=-1 id1=1279 id2=1
+TMAP bs=2048 rt=1 fl=-1 id1=1494 id2=1
diff --git a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP
index 775c132..af633ee 100644
--- a/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP
+++ b/help/en_US/scilab_en_US_help/JavaHelpSearch/TMAP
diff --git a/help/en_US/scilab_en_US_help/_LaTeX_fgoalattain.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_fgoalattain.xml_1.png
new file mode 100644
index 0000000..7d7a4b6
--- /dev/null
+++ b/help/en_US/scilab_en_US_help/_LaTeX_fgoalattain.xml_1.png
diff --git a/help/en_US/scilab_en_US_help/_LaTeX_fminbnd.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_fminbnd.xml_1.png
new file mode 100644
index 0000000..2634ce3
--- /dev/null
+++ b/help/en_US/scilab_en_US_help/_LaTeX_fminbnd.xml_1.png
diff --git a/help/en_US/scilab_en_US_help/_LaTeX_fmincon.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_fmincon.xml_1.png
new file mode 100644
index 0000000..07910ed
--- /dev/null
+++ b/help/en_US/scilab_en_US_help/_LaTeX_fmincon.xml_1.png
diff --git a/help/en_US/scilab_en_US_help/_LaTeX_fminunc.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_fminunc.xml_1.png
new file mode 100644
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--- /dev/null
+++ b/help/en_US/scilab_en_US_help/_LaTeX_fminunc.xml_1.png
diff --git a/help/en_US/scilab_en_US_help/_LaTeX_linprog.xml_1.png b/help/en_US/scilab_en_US_help/_LaTeX_linprog.xml_1.png
new file mode 100644
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--- /dev/null
+++ b/help/en_US/scilab_en_US_help/_LaTeX_linprog.xml_1.png
diff --git a/help/en_US/scilab_en_US_help/fgoalattain.html b/help/en_US/scilab_en_US_help/fgoalattain.html
new file mode 100644
index 0000000..a9aa43d
--- /dev/null
+++ b/help/en_US/scilab_en_US_help/fgoalattain.html
@@ -0,0 +1,195 @@
+<html><head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+    <title>fgoalattain</title>
+    <style type="text/css" media="all">
+      @import url("scilab_code.css");
+      @import url("xml_code.css");
+      @import url("c_code.css");
+      @import url("style.css");
+    </style>
+  </head>
+  <body>
+    <div class="manualnavbar">
+    <table width="100%"><tr>
+      <td width="30%">
+    	<span class="previous"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">&lt;&lt; Symphony Toolbox</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="fminbnd.html">fminbnd &gt;&gt;</a></span>
+
+      </td>
+    </tr></table>
+      <hr />
+    </div>
+
+
+
+    <span class="path"><a href="index.html">Symphony Toolbox</a> &gt;&gt; <a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a> &gt; fgoalattain</span>
+
+    <br /><br />
+    <div class="refnamediv"><h1 class="refname">fgoalattain</h1>
+    <p class="refpurpose">Solves a multiobjective goal attainment problem</p></div>
+
+
+<div class="refsynopsisdiv"><h3 class="title">Calling Sequence</h3>
+   <div class="synopsis"><pre><span class="default">x</span><span class="default"> = </span><span class="functionid">fgoalattain</span><span class="default">(</span><span class="default">fun</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">goal</span><span class="default">,</span><span class="default">weight</span><span class="default">)</span>
+<span class="default">x</span><span class="default"> = </span><span class="functionid">fgoalattain</span><span class="default">(</span><span class="default">fun</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">goal</span><span class="default">,</span><span class="default">weight</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">)</span>
+<span class="default">x</span><span class="default"> = </span><span class="functionid">fgoalattain</span><span class="default">(</span><span class="default">fun</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">goal</span><span class="default">,</span><span class="default">weight</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">)</span>
+<span class="default">x</span><span class="default"> = </span><span class="functionid">fgoalattain</span><span class="default">(</span><span class="default">fun</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">goal</span><span class="default">,</span><span class="default">weight</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">,</span><span class="default">lb</span><span class="default">,</span><span class="default">ub</span><span class="default">)</span>
+<span class="default">x</span><span class="default"> = </span><span class="functionid">fgoalattain</span><span class="default">(</span><span class="default">fun</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">goal</span><span class="default">,</span><span class="default">weight</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">,</span><span class="default">lb</span><span class="default">,</span><span class="default">ub</span><span class="default">,</span><span class="default">nonlcon</span><span class="default">)</span>
+<span class="default">x</span><span class="default"> = </span><span class="functionid">fgoalattain</span><span class="default">(</span><span class="default">fun</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">goal</span><span class="default">,</span><span class="default">weight</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">,</span><span class="default">lb</span><span class="default">,</span><span class="default">ub</span><span class="default">,</span><span class="default">nonlcon</span><span class="default">,</span><span class="default">options</span><span class="default">)</span>
+<span class="default">[</span><span class="default">x</span><span class="default">,</span><span class="default">fval</span><span class="default">] = </span><span class="functionid">fgoalattain</span><span class="default">(...)</span>
+<span class="default">[</span><span class="default">x</span><span class="default">,</span><span class="default">fval</span><span class="default">,</span><span class="default">attainfactor</span><span class="default">] = </span><span class="functionid">fgoalattain</span><span class="default">(...)</span>
+<span class="default">[</span><span class="default">x</span><span class="default">,</span><span class="default">fval</span><span class="default">,</span><span class="default">attainfactor</span><span class="default">,</span><span class="default">exitflag</span><span class="default">] = </span><span class="functionid">fgoalattain</span><span class="default">(...)</span>
+<span class="default">[</span><span class="default">x</span><span class="default">,</span><span class="default">fval</span><span class="default">,</span><span class="default">attainfactor</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">] = </span><span class="functionid">fgoalattain</span><span class="default">(...)</span>
+<span class="default">[</span><span class="default">x</span><span class="default">,</span><span class="default">fval</span><span class="default">,</span><span class="default">attainfactor</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">lambda</span><span class="default">] = </span><span class="functionid">fgoalattain</span><span class="default">(...)</span></pre></div></div>
+
+<div class="refsection"><h3 class="title">Parameters</h3>
+   <dl><dt><span class="term">fun:</span>
+      <dd><p class="para">a function that accepts a vector x and returns a vector F</p></dd></dt>
+   <dt><span class="term">x0:</span>
+      <dd><p class="para">a nx1 or 1xn matrix of double, where n is the number of variables.</p></dd></dt>
+   <dt><span class="term">A:</span>
+      <dd><p class="para">a nil x n matrix of double, where n is the number of variables and</p></dd></dt>
+   <dt><span class="term">b:</span>
+      <dd><p class="para">a nil x 1 matrix of double, where nil is the number of linear</p></dd></dt>
+   <dt><span class="term">Aeq:</span>
+      <dd><p class="para">a nel x n matrix of double, where n is the number of variables</p></dd></dt>
+   <dt><span class="term">beq:</span>
+      <dd><p class="para">a nel x 1 matrix of double, where nel is the number of linear</p></dd></dt>
+   <dt><span class="term">lb:</span>
+      <dd><p class="para">a nx1 or 1xn matrix of double, where n is the number of variables.</p></dd></dt>
+   <dt><span class="term">ub:</span>
+      <dd><p class="para">a nx1 or 1xn matrix of double, where n is the number of variables.</p></dd></dt>
+   <dt><span class="term">nonlcon:</span>
+      <dd><p class="para">a function, the nonlinear constraints</p></dd></dt>
+   <dt><span class="term">options :</span>
+      <dd><p class="para">a list, containing the option for user to specify. See below for details.</p></dd></dt>
+   <dt><span class="term">x:</span>
+      <dd><p class="para">a nx1 matrix of double, the computed solution of the optimization problem</p></dd></dt>
+   <dt><span class="term">fval:</span>
+      <dd><p class="para">a vector of double, the value of functions at x</p></dd></dt>
+   <dt><span class="term">attainfactor:</span>
+      <dd><p class="para">The amount of over- or underachievement of the goals,γ at the solution.</p></dd></dt>
+   <dt><span class="term">exitflag:</span>
+      <dd><p class="para">a 1x1 matrix of floating point integers, the exit status</p></dd></dt>
+   <dt><span class="term">output:</span>
+      <dd><p class="para">a struct, the details of the optimization process</p></dd></dt>
+   <dt><span class="term">lambda:</span>
+      <dd><p class="para">a struct, the Lagrange multipliers at optimum</p></dd></dt></dl></div>
+
+<div class="refsection"><h3 class="title">Description</h3>
+   <p class="para">fgoalattain solves the goal attainment problem, which is one formulation for minimizing a multiobjective optimization problem.
+Finds the minimum of a problem specified by:
+Minimise Y such that</p>
+   <p class="para"><span><img src='./_LaTeX_fgoalattain.xml_1.png' style='position:relative;top:64px;width:276px;height:136px'/></span></p>
+   <p class="para">The solver makes use of fmincon to find the minimum.</p>
+   <p class="para">The fgoalattain finds out the maximum value of Y for the objectives evaluated at the starting point and
+adds that as another variable to the vector x
+This is passed to the fmincon function to get the optimised value of Y
+Hence, the algorithm used mainly is &#0034;ipopt&#0034; to obtain the optimum solution
+The relations between f(x), Y, weights and goals are added as additional non-linear inequality constraints</p>
+   <p class="para">The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type &#0034;list&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>Syntax : options= list(&#0034;MaxIter&#0034;, [---], &#0034;CpuTime&#0034;, [---], &#0034;GradObj&#0034;, ---, &#0034;GradCon&#0034;, ---);</li>
+<li>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</li>
+<li>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</li>
+<li>GradObj : a function, representing the gradient function of the Objective in Vector Form.</li>
+<li>GradCon : a function, representing the gradient of the Non-Linear Constraints (both Equality and Inequality) of the problem. It is declared in such a way that gradient of non-linear inequality constraints are defined first as a separate Matrix (cg of size m2 X n or as an empty), followed by gradient of non-linear equality constraints as a separate Matrix (ceqg of size m2 X n or as an empty) where m2 &amp; m3 are number of non-linear inequality and equality constraints respectively.</li>
+<li>Default Values : options = list(&#0034;MaxIter&#0034;, [3000], &#0034;CpuTime&#0034;, [600]);</li></ul></p>
+   <p class="para">By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of minmaxObjfun. In case the GradObj option is off and GradConstr option is on, fminimax approximates minmaxObjfun gradient using numderivative toolbox.</p>
+   <p class="para">If we can provide exact gradients, we should do so since it improves the convergence speed of the optimization algorithm.</p>
+   <p class="para">Furthermore, we must enable the &#0034;GradObj&#0034; option with the statement :
+<div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabid">minimaxOptions</span> <span class="scilaboperator">=</span> <a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">GradObj</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span><span class="scilabid">fGrad</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div>
+This will let fminimax know that the exact gradient of the objective function is known, so that it can change the calling sequence to the objective function. Note that, fGrad should be mentioned in the form of N x n where n is the number of variables, N is the number of functions in objective function.</p>
+   <p class="para">The constraint function must have header :
+<div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabopenclose">[</span><span class="scilabid">c</span><span class="scilabdefault">,</span> <span class="scilabid">ceq</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span> <span class="scilabid">confun</span><span class="scilabopenclose">(</span><span class="scilabid">x</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div>
+where x is a n x 1 matrix of dominmaxUbles, c is a 1 x nni matrix of doubles and ceq is a 1 x nne matrix of doubles (nni : number of nonlinear inequality constraints, nne : number of nonlinear equality constraints).
+On input, the variable x contains the current point and, on output, the variable c must contain the nonlinear inequality constraints and ceq must contain the nonlinear equality constraints.</p>
+   <p class="para">By default, the gradient options for fminimax are turned off and and fmincon does the gradient opproximation of confun. In case the GradObj option is on and GradCons option is off, fminimax approximates confun gradient using numderivative toolbox.</p>
+   <p class="para">If we can provide exact gradients, we should do so since it improves the convergence speed of the optimization algorithm.</p>
+   <p class="para">Furthermore, we must enable the &#0034;GradCon&#0034; option with the statement :
+<div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabid">minimaxOptions</span> <span class="scilaboperator">=</span> <a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">GradCon</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span><span class="scilabid">confunGrad</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div>
+This will let fminimax know that the exact gradient of the objective function is known, so that it can change the calling sequence to the objective function.</p>
+   <p class="para">The constraint derivative function must have header :
+<div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabopenclose">[</span><span class="scilabid">dc</span><span class="scilabdefault">,</span><span class="scilabid">dceq</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span> <span class="scilabid">confungrad</span><span class="scilabopenclose">(</span><span class="scilabid">x</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div>
+where dc is a nni x n matrix of doubles and dceq is a nne x n matrix of doubles.</p>
+   <p class="para">The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<ul class="itemizedlist"><li>exitflag=0 : Optimal Solution Found</li>
+<li>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</li>
+<li>exitflag=2 : Maximum amount of CPU Time exceeded. Output may not be optimal.</li>
+<li>exitflag=3 : Stop at Tiny Step.</li>
+<li>exitflag=4 : Solved To Acceptable Level.</li>
+<li>exitflag=5 : Converged to a point of local infeasibility.</li></ul></p>
+   <p class="para">For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/</p>
+   <p class="para">The output data structure contains detailed informations about the optimization process.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>output.Iterations: The number of iterations performed during the search</li>
+<li>output.Cpu_Time: The total cpu-time spend during the search</li>
+<li>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</li>
+<li>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</li></ul></p>
+   <p class="para">The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>lambda.lower: The Lagrange multipliers for the lower bound constraints.</li>
+<li>lambda.upper: The Lagrange multipliers for the upper bound constraints.</li>
+<li>lambda.eqlin: The Lagrange multipliers for the linear equality constraints.</li>
+<li>lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.</li>
+<li>lambda.eqnonlin: The Lagrange multipliers for the non-linear equality constraints.</li>
+<li>lambda.ineqnonlin: The Lagrange multipliers for the non-linear inequality constraints.</li></ul></p>
+   <p class="para"></p></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">f1</span><span class="scilaboperator">=</span><span class="scilabfunctionid">gattainObjfun</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">f1</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">=</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabnumber">48</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabnumber">40</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">+</span><span class="scilabnumber">304</span>
+<span class="scilabinputoutputargs">f1</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">=</span><span class="scilaboperator">-</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabnumber">3</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">f1</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilaboperator">=</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">+</span><span class="scilabnumber">3</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabnumber">18</span>
+<span class="scilabinputoutputargs">f1</span><span class="scilabopenclose">(</span><span class="scilabnumber">4</span><span class="scilabopenclose">)</span><span class="scilaboperator">=</span><span class="scilaboperator">-</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">f1</span><span class="scilabopenclose">(</span><span class="scilabnumber">5</span><span class="scilabopenclose">)</span><span class="scilaboperator">=</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabnumber">8</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabid">x0</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+
+<span class="scilabid">goal</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">5</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">3</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">4</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">weight</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://abs">abs</a><span class="scilabopenclose">(</span><span class="scilabid">goal</span><span class="scilabopenclose">)</span>
+<span class="scilabid">gval</span>  <span class="scilaboperator">=</span>
+<span class="scilabopenclose">[</span><span class="scilaboperator">-</span> <span class="scilabnumber">0.0000011</span>
+<span class="scilaboperator">-</span> <span class="scilabnumber">63.999998</span>
+<span class="scilaboperator">-</span> <span class="scilabnumber">2.0000002</span>
+<span class="scilaboperator">-</span> <span class="scilabnumber">8.</span>
+<span class="scilabnumber">3.485D-08</span><span class="scilabopenclose">]</span>
+<span class="scilabid">z</span>  <span class="scilaboperator">=</span>
+<span class="scilabopenclose">[</span><span class="scilabnumber">4.</span>    <span class="scilabnumber">3.99</span><span class="scilabopenclose">]</span>
+
+<span class="scilabid">Run</span> <span class="scilabid">fgoalattain</span>
+<span class="scilabopenclose">[</span><span class="scilabid">x</span><span class="scilabdefault">,</span><span class="scilabid">fval</span><span class="scilabdefault">,</span><span class="scilabid">attainfactor</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabid">fgoalattain</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">gattainObjfun</span><span class="scilabdefault">,</span><span class="scilabid">x0</span><span class="scilabdefault">,</span><span class="scilabid">goal</span><span class="scilabdefault">,</span><span class="scilabid">weight</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Authors</h3>
+   <ul class="itemizedlist"><li class="member">Prajwala TM, Sheetal Shalini , 2015</li></ul></div>
+    <br />
+
+    <div class="manualnavbar">
+    <table width="100%">
+    <tr><td colspan="3" class="next"><a href="http://bugzilla.scilab.org/enter_bug.cgi?product=Scilab%20software&component=Documentation%20pages" class="ulink">Report an issue</a></td></tr>
+<tr>
+      <td width="30%">
+    	<span class="previous"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">&lt;&lt; Symphony Toolbox</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="fminbnd.html">fminbnd &gt;&gt;</a></span>
+
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+    </tr></table>
+      <hr />
+    </div>
+  </body>
+</html>
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+<html><head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+    <title>fminbnd</title>
+    <style type="text/css" media="all">
+      @import url("scilab_code.css");
+      @import url("xml_code.css");
+      @import url("c_code.css");
+      @import url("style.css");
+    </style>
+  </head>
+  <body>
+    <div class="manualnavbar">
+    <table width="100%"><tr>
+      <td width="30%">
+    	<span class="previous"><a href="fgoalattain.html">&lt;&lt; fgoalattain</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="fmincon.html">fmincon &gt;&gt;</a></span>
+
+      </td>
+    </tr></table>
+      <hr />
+    </div>
+
+
+
+    <span class="path"><a href="index.html">Symphony Toolbox</a> &gt;&gt; <a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a> &gt; fminbnd</span>
+
+    <br /><br />
+    <div class="refnamediv"><h1 class="refname">fminbnd</h1>
+    <p class="refpurpose">Solves a multi-variable optimization problem on a bounded interval</p></div>
+
+
+<div class="refsynopsisdiv"><h3 class="title">Calling Sequence</h3>
+   <div class="synopsis"><pre><span class="default">xopt</span><span class="default"> = </span><span class="functionid">fminbnd</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x1</span><span class="default">,</span><span class="default">x2</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">fminbnd</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x1</span><span class="default">,</span><span class="default">x2</span><span class="default">,</span><span class="default">options</span><span class="default">)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">] = </span><span class="functionid">fminbnd</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">]= </span><span class="functionid">fminbnd</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">]=</span><span class="functionid">fminbnd</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">lambda</span><span class="default">]=</span><span class="functionid">fminbnd</span><span class="default">(.....)</span></pre></div></div>
+
+<div class="refsection"><h3 class="title">Parameters</h3>
+   <dl><dt><span class="term">f :</span>
+      <dd><p class="para">a function, representing the objective function of the problem</p></dd></dt>
+   <dt><span class="term">x1 :</span>
+      <dd><p class="para">a vector, containing the lower bound of the variables of size (1 X n) or (n X 1) where &#0039;n&#0039; is the number of Variables, where n is number of Variables</p></dd></dt>
+   <dt><span class="term">x2 :</span>
+      <dd><p class="para">a vector, containing the upper bound of the variables of size (1 X n) or (n X 1) or (0 X 0) where &#0039;n&#0039; is the number of Variables. If x2 is empty it means upper bound is +infinity</p></dd></dt>
+   <dt><span class="term">options :</span>
+      <dd><p class="para">a list, containing the option for user to specify. See below for details.</p></dd></dt>
+   <dt><span class="term">xopt :</span>
+      <dd><p class="para">a vector of doubles, containing the the computed solution of the optimization problem.</p></dd></dt>
+   <dt><span class="term">fopt :</span>
+      <dd><p class="para">a scalar of double, containing the the function value at x.</p></dd></dt>
+   <dt><span class="term">exitflag :</span>
+      <dd><p class="para">a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.</p></dd></dt>
+   <dt><span class="term">output :</span>
+      <dd><p class="para">a structure, containing the information about the optimization. See below for details.</p></dd></dt>
+   <dt><span class="term">lambda :</span>
+      <dd><p class="para">a structure, containing the Lagrange multipliers of lower bound and upper bound at the optimized point. See below for details.</p></dd></dt></dl></div>
+
+<div class="refsection"><h3 class="title">Description</h3>
+   <p class="para">Search the minimum of a multi-variable function on bounded interval specified by :
+Find the minimum of f(x) such that</p>
+   <p class="para"><span><img src='./_LaTeX_fminbnd.xml_1.png' style='position:relative;top:20px;width:219px;height:48px'/></span></p>
+   <p class="para">The routine calls Ipopt for solving the Bounded Optimization problem, Ipopt is a library written in C++.</p>
+   <p class="para">The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type &#0034;list&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>Syntax : options= list(&#0034;MaxIter&#0034;, [---], &#0034;CpuTime&#0034;, [---], TolX, [----]);</li>
+<li>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</li>
+<li>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</li>
+<li>TolX : a Scalar, containing the Tolerance value that the solver should take.</li>
+<li>Default Values : options = list(&#0034;MaxIter&#0034;, [3000], &#0034;CpuTime&#0034;, [600], TolX, [1e-4]);</li></ul></p>
+   <p class="para">The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<ul class="itemizedlist"><li>exitflag=0 : Optimal Solution Found</li>
+<li>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</li>
+<li>exitflag=2 : Maximum CPU Time exceeded. Output may not be optimal.</li>
+<li>exitflag=3 : Stop at Tiny Step.</li>
+<li>exitflag=4 : Solved To Acceptable Level.</li>
+<li>exitflag=5 : Converged to a point of local infeasibility.</li></ul></p>
+   <p class="para">For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/</p>
+   <p class="para">The output data structure contains detailed informations about the optimization process.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>output.Iterations: The number of iterations performed during the search</li>
+<li>output.Cpu_Time: The total cpu-time spend during the search</li>
+<li>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</li>
+<li>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</li></ul></p>
+   <p class="para">The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>lambda.lower: The Lagrange multipliers for the lower bound constraints.</li>
+<li>lambda.upper: The Lagrange multipliers for the upper bound constraints.</li></ul></p>
+   <p class="para"></p></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Find x in R^6 such that it minimizes:</span>
+<span class="scilabcomment">//f(x)= sin(x1) + sin(x2) + sin(x3) + sin(x4) + sin(x5) + sin(x6)</span>
+<span class="scilabcomment">//-2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= x1,x2,x3,x4,x5,x6 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 2</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabnumber">0</span>
+<span class="scilabskeyword">for</span> <span class="scilabid">i</span> <span class="scilaboperator">=</span><span class="scilabnumber">1</span><span class="scilabspecial">:</span><span class="scilabnumber">6</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabinputoutputargs">y</span><span class="scilaboperator">+</span><a class="scilabcommand" href="scilab://sin">sin</a><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabid">i</span><span class="scilabopenclose">)</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabskeyword">end</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Variable bounds</span>
+<span class="scilabid">x1</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">x2</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilabnumber">2</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Options</span>
+<span class="scilabid">options</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">MaxIter</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span><span class="scilabopenclose">[</span><span class="scilabnumber">1500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span><span class="scilabstring">&#0034;</span><span class="scilabstring">CpuTime</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">100</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span><span class="scilabstring">&#0034;</span><span class="scilabstring">TolX</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span><span class="scilabopenclose">[</span><span class="scilabnumber">1e-6</span><span class="scilabopenclose">]</span><span class="scilabopenclose">)</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">x</span><span class="scilabdefault">,</span><span class="scilabid">fval</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span><span class="scilabid">fminbnd</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span> <span class="scilabid">x1</span><span class="scilabdefault">,</span> <span class="scilabid">x2</span><span class="scilabdefault">,</span> <span class="scilabid">options</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Find x in R such that it minimizes:</span>
+<span class="scilabcomment">//f(x)= 1/x^2</span>
+<span class="scilabcomment">//0 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= x </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 1000</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabinputoutputargs">x</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Variable bounds</span>
+<span class="scilabid">x1</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">x2</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabnumber">1000</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">x</span><span class="scilabdefault">,</span><span class="scilabid">fval</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span><span class="scilabid">fminbnd</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span> <span class="scilabid">x1</span><span class="scilabdefault">,</span> <span class="scilabid">x2</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//The below problem is an unbounded problem:</span>
+<span class="scilabcomment">//Find x in R^2 such that it minimizes:</span>
+<span class="scilabcomment">//f(x)= -[(x1-1)^2 + (x2-1)^2]</span>
+<span class="scilabcomment">//-inf </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= x1,x2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= inf</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilaboperator">-</span><span class="scilabopenclose">(</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">+</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Variable bounds</span>
+<span class="scilabid">x1</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabconstants">%inf</span> <span class="scilabdefault">,</span> <span class="scilaboperator">-</span><span class="scilabconstants">%inf</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">x2</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Options</span>
+<span class="scilabid">options</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">MaxIter</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span><span class="scilabopenclose">[</span><span class="scilabnumber">1500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span><span class="scilabstring">&#0034;</span><span class="scilabstring">CpuTime</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">100</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span><span class="scilabstring">&#0034;</span><span class="scilabstring">TolX</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span><span class="scilabopenclose">[</span><span class="scilabnumber">1e-6</span><span class="scilabopenclose">]</span><span class="scilabopenclose">)</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">x</span><span class="scilabdefault">,</span><span class="scilabid">fval</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span><span class="scilabid">fminbnd</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span> <span class="scilabid">x1</span><span class="scilabdefault">,</span> <span class="scilabid">x2</span><span class="scilabdefault">,</span> <span class="scilabid">options</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Authors</h3>
+   <ul class="itemizedlist"><li class="member">R.Vidyadhar , Vignesh Kannan</li></ul></div>
+    <br />
+
+    <div class="manualnavbar">
+    <table width="100%">
+    <tr><td colspan="3" class="next"><a href="http://bugzilla.scilab.org/enter_bug.cgi?product=Scilab%20software&component=Documentation%20pages" class="ulink">Report an issue</a></td></tr>
+<tr>
+      <td width="30%">
+    	<span class="previous"><a href="fgoalattain.html">&lt;&lt; fgoalattain</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="fmincon.html">fmincon &gt;&gt;</a></span>
+
+      </td>
+    </tr></table>
+      <hr />
+    </div>
+  </body>
+</html>
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@@ -0,0 +1,302 @@
+<html><head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+    <title>fmincon</title>
+    <style type="text/css" media="all">
+      @import url("scilab_code.css");
+      @import url("xml_code.css");
+      @import url("c_code.css");
+      @import url("style.css");
+    </style>
+  </head>
+  <body>
+    <div class="manualnavbar">
+    <table width="100%"><tr>
+      <td width="30%">
+    	<span class="previous"><a href="fminbnd.html">&lt;&lt; fminbnd</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="fminunc.html">fminunc &gt;&gt;</a></span>
+
+      </td>
+    </tr></table>
+      <hr />
+    </div>
+
+
+
+    <span class="path"><a href="index.html">Symphony Toolbox</a> &gt;&gt; <a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a> &gt; fmincon</span>
+
+    <br /><br />
+    <div class="refnamediv"><h1 class="refname">fmincon</h1>
+    <p class="refpurpose">Solves a multi-variable constrainted optimization problem</p></div>
+
+
+<div class="refsynopsisdiv"><h3 class="title">Calling Sequence</h3>
+   <div class="synopsis"><pre><span class="default">xopt</span><span class="default"> = </span><span class="functionid">fmincon</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">fmincon</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">fmincon</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">,</span><span class="default">lb</span><span class="default">,</span><span class="default">ub</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">fmincon</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">,</span><span class="default">lb</span><span class="default">,</span><span class="default">ub</span><span class="default">,</span><span class="default">nlc</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">fmincon</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">,</span><span class="default">lb</span><span class="default">,</span><span class="default">ub</span><span class="default">,</span><span class="default">nlc</span><span class="default">,</span><span class="default">options</span><span class="default">)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">] = </span><span class="functionid">fmincon</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">]= </span><span class="functionid">fmincon</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">]= </span><span class="functionid">fmincon</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">lambda</span><span class="default">]=</span><span class="functionid">fmincon</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">lambda</span><span class="default">,</span><span class="default">gradient</span><span class="default">]=</span><span class="functionid">fmincon</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">lambda</span><span class="default">,</span><span class="default">gradient</span><span class="default">,</span><span class="default">hessian</span><span class="default">]=</span><span class="functionid">fmincon</span><span class="default">(.....)</span></pre></div></div>
+
+<div class="refsection"><h3 class="title">Parameters</h3>
+   <dl><dt><span class="term">f :</span>
+      <dd><p class="para">a function, representing the objective function of the problem</p></dd></dt>
+   <dt><span class="term">x0 :</span>
+      <dd><p class="para">a vector of doubles, containing the starting values of variables of size (1 X n) or (n X 1) where &#0039;n&#0039; is the number of Variables</p></dd></dt>
+   <dt><span class="term">A :</span>
+      <dd><p class="para">a matrix of doubles, containing the coefficients of linear inequality constraints of size (m X n) where &#0039;m&#0039; is the number of linear inequality constraints</p></dd></dt>
+   <dt><span class="term">b :</span>
+      <dd><p class="para">a vector of doubles, related to &#0039;A&#0039; and containing the the Right hand side equation of the linear inequality constraints of size (m X 1)</p></dd></dt>
+   <dt><span class="term">Aeq :</span>
+      <dd><p class="para">a matrix of doubles, containing the coefficients of linear equality constraints of size (m1 X n) where &#0039;m1&#0039; is the number of linear equality constraints</p></dd></dt>
+   <dt><span class="term">beq :</span>
+      <dd><p class="para">a vector of doubles, related to &#0039;Aeq&#0039; and containing the the Right hand side equation of the linear equality constraints of size (m1 X 1)</p></dd></dt>
+   <dt><span class="term">lb :</span>
+      <dd><p class="para">a vector of doubles, containing the lower bounds of the variables of size (1 X n) or (n X 1) where &#0039;n&#0039; is the number of Variables</p></dd></dt>
+   <dt><span class="term">ub :</span>
+      <dd><p class="para">a vector of doubles, containing the upper bounds of the variables of size (1 X n) or (n X 1) where &#0039;n&#0039; is the number of Variables</p></dd></dt>
+   <dt><span class="term">nlc :</span>
+      <dd><p class="para">a function, representing the Non-linear Constraints functions(both Equality and Inequality) of the problem. It is declared in such a way that non-linear inequality constraints are defined first as a single row vector (c), followed by non-linear equality constraints as another single row vector (ceq). Refer Example for definition of Constraint function.</p></dd></dt>
+   <dt><span class="term">options :</span>
+      <dd><p class="para">a list, containing the option for user to specify. See below for details.</p></dd></dt>
+   <dt><span class="term">xopt :</span>
+      <dd><p class="para">a vector of doubles, cointating the computed solution of the optimization problem</p></dd></dt>
+   <dt><span class="term">fopt :</span>
+      <dd><p class="para">a scalar of double, containing the the function value at x</p></dd></dt>
+   <dt><span class="term">exitflag :</span>
+      <dd><p class="para">a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.</p></dd></dt>
+   <dt><span class="term">output :</span>
+      <dd><p class="para">a structure, containing the information about the optimization. See below for details.</p></dd></dt>
+   <dt><span class="term">lambda :</span>
+      <dd><p class="para">a structure, containing the Lagrange multipliers of lower bound, upper bound and constraints at the optimized point. See below for details.</p></dd></dt>
+   <dt><span class="term">gradient :</span>
+      <dd><p class="para">a vector of doubles, containing the Objective&#0039;s gradient of the solution.</p></dd></dt>
+   <dt><span class="term">hessian  :</span>
+      <dd><p class="para">a matrix of doubles, containing the Lagrangian&#0039;s hessian of the solution.</p></dd></dt></dl></div>
+
+<div class="refsection"><h3 class="title">Description</h3>
+   <p class="para">Search the minimum of a constrained optimization problem specified by :
+Find the minimum of f(x) such that</p>
+   <p class="para"><span><img src='./_LaTeX_fmincon.xml_1.png' style='position:relative;top:63px;width:221px;height:134px'/></span></p>
+   <p class="para">The routine calls Ipopt for solving the Constrained Optimization problem, Ipopt is a library written in C++.</p>
+   <p class="para">The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type &#0034;list&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>Syntax : options= list(&#0034;MaxIter&#0034;, [---], &#0034;CpuTime&#0034;, [---], &#0034;GradObj&#0034;, ---, &#0034;Hessian&#0034;, ---, &#0034;GradCon&#0034;, ---);</li>
+<li>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</li>
+<li>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</li>
+<li>GradObj : a function, representing the gradient function of the Objective in Vector Form.</li>
+<li>Hessian : a function, representing the hessian function of the Lagrange in Symmetric Matrix Form with Input parameters x, Objective factor and Lambda. Refer Example for definition of Lagrangian Hessian function.</li>
+<li>GradCon : a function, representing the gradient of the Non-Linear Constraints (both Equality and Inequality) of the problem. It is declared in such a way that gradient of non-linear inequality constraints are defined first as a separate Matrix (cg of size m2 X n or as an empty), followed by gradient of non-linear equality constraints as a separate Matrix (ceqg of size m2 X n or as an empty) where m2 &amp; m3 are number of non-linear inequality and equality constraints respectively.</li>
+<li>Default Values : options = list(&#0034;MaxIter&#0034;, [3000], &#0034;CpuTime&#0034;, [600]);</li></ul></p>
+   <p class="para">The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<ul class="itemizedlist"><li>exitflag=0 : Optimal Solution Found</li>
+<li>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</li>
+<li>exitflag=2 : Maximum amount of CPU Time exceeded. Output may not be optimal.</li>
+<li>exitflag=3 : Stop at Tiny Step.</li>
+<li>exitflag=4 : Solved To Acceptable Level.</li>
+<li>exitflag=5 : Converged to a point of local infeasibility.</li></ul></p>
+   <p class="para">For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/</p>
+   <p class="para">The output data structure contains detailed informations about the optimization process.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>output.Iterations: The number of iterations performed during the search</li>
+<li>output.Cpu_Time: The total cpu-time spend during the search</li>
+<li>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</li>
+<li>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</li></ul></p>
+   <p class="para">The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>lambda.lower: The Lagrange multipliers for the lower bound constraints.</li>
+<li>lambda.upper: The Lagrange multipliers for the upper bound constraints.</li>
+<li>lambda.eqlin: The Lagrange multipliers for the linear equality constraints.</li>
+<li>lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.</li>
+<li>lambda.eqnonlin: The Lagrange multipliers for the non-linear equality constraints.</li>
+<li>lambda.ineqnonlin: The Lagrange multipliers for the non-linear inequality constraints.</li></ul></p>
+   <p class="para"></p></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Find x in R^2 such that it minimizes:</span>
+<span class="scilabcomment">//f(x)= -x1 -x2/3</span>
+<span class="scilabcomment">//x0=[0,0]</span>
+<span class="scilabcomment">//constraint-1 (c1): x1 + x2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 2</span>
+<span class="scilabcomment">//constraint-2 (c2): x1 + x2/4 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 1</span>
+<span class="scilabcomment">//constraint-3 (c3): x1 - x2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 2</span>
+<span class="scilabcomment">//constraint-4 (c4): -x1/4 - x2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 1</span>
+<span class="scilabcomment">//constraint-5 (c5): -x1 - x2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= -1</span>
+<span class="scilabcomment">//constraint-6 (c6): -x1 + x2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 2</span>
+<span class="scilabcomment">//constraint-7 (c7): x1 + x2 = 2</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilaboperator">-</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">/</span><span class="scilabnumber">3</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Starting point, linear constraints and variable bounds</span>
+<span class="scilabid">x0</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span> <span class="scilabdefault">,</span> <span class="scilabnumber">0</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span> <span class="scilabdefault">;</span> <span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span> <span class="scilabdefault">;</span> <span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span> <span class="scilabdefault">;</span> <span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span> <span class="scilabdefault">;</span> <span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span> <span class="scilabdefault">;</span> <span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilabnumber">2</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">Aeq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">beq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">lb</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">ub</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">nlc</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Gradient of objective function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">fGrad</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">3</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Hessian of lagrangian</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">lHess</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">obj</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabinputoutputargs">obj</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Options</span>
+<span class="scilabid">options</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">GradObj</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">fGrad</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">Hessian</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">lHess</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">x</span><span class="scilabdefault">,</span><span class="scilabid">fval</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabdefault">,</span><span class="scilabid">grad</span><span class="scilabdefault">,</span><span class="scilabid">hessian</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span><span class="scilabid">fmincon</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span> <span class="scilabid">x0</span><span class="scilabdefault">,</span><span class="scilabid">A</span><span class="scilabdefault">,</span><span class="scilabid">b</span><span class="scilabdefault">,</span><span class="scilabid">Aeq</span><span class="scilabdefault">,</span><span class="scilabid">beq</span><span class="scilabdefault">,</span><span class="scilabid">lb</span><span class="scilabdefault">,</span><span class="scilabid">ub</span><span class="scilabdefault">,</span><span class="scilabid">nlc</span><span class="scilabdefault">,</span><span class="scilabid">options</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Find x in R^3 such that it minimizes:</span>
+<span class="scilabcomment">//f(x)= x1*x2 + x2*x3</span>
+<span class="scilabcomment">//x0=[0.1 , 0.1 , 0.1]</span>
+<span class="scilabcomment">//constraint-1 (c1): x1^2 - x2^2 + x3^2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 2</span>
+<span class="scilabcomment">//constraint-2 (c2): x1^2 + x2^2 + x3^2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 10</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Starting point, linear constraints and variable bounds</span>
+<span class="scilabid">x0</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">0.1</span> <span class="scilabdefault">,</span> <span class="scilabnumber">0.1</span> <span class="scilabdefault">,</span> <span class="scilabnumber">0.1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">Aeq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">beq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">lb</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">ub</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Nonlinear constraints</span>
+<span class="scilabfkeyword">function</span> <span class="scilabopenclose">[</span><span class="scilabinputoutputargs">c</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">ceq</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabfunctionid">nlc</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">c</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">-</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">-</span> <span class="scilabnumber">2</span> <span class="scilabdefault">,</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">-</span> <span class="scilabnumber">10</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabinputoutputargs">ceq</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Gradient of objective function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">fGrad</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Hessian of the Lagrange Function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">lHess</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">obj</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabinputoutputargs">obj</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Gradient of Non-Linear Constraints</span>
+<span class="scilabfkeyword">function</span> <span class="scilabopenclose">[</span><span class="scilabinputoutputargs">cg</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">ceqg</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabfunctionid">cGrad</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">cg</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span> <span class="scilabdefault">,</span> <span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span> <span class="scilabdefault">,</span> <span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span> <span class="scilabdefault">;</span> <span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span> <span class="scilabdefault">,</span> <span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span> <span class="scilabdefault">,</span> <span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabinputoutputargs">ceqg</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Options</span>
+<span class="scilabid">options</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">MaxIter</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">1500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">CpuTime</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">GradObj</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">fGrad</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">Hessian</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">lHess</span><span class="scilabdefault">,</span><span class="scilabstring">&#0034;</span><span class="scilabstring">GradCon</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">cGrad</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">x</span><span class="scilabdefault">,</span><span class="scilabid">fval</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span><span class="scilabid">fmincon</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span> <span class="scilabid">x0</span><span class="scilabdefault">,</span><span class="scilabid">A</span><span class="scilabdefault">,</span><span class="scilabid">b</span><span class="scilabdefault">,</span><span class="scilabid">Aeq</span><span class="scilabdefault">,</span><span class="scilabid">beq</span><span class="scilabdefault">,</span><span class="scilabid">lb</span><span class="scilabdefault">,</span><span class="scilabid">ub</span><span class="scilabdefault">,</span><span class="scilabfunctionid">nlc</span><span class="scilabdefault">,</span><span class="scilabid">options</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//The below problem is an unbounded problem:</span>
+<span class="scilabcomment">//Find x in R^3 such that it minimizes:</span>
+<span class="scilabcomment">//f(x)= -(x1^2 + x2^2 + x3^2)</span>
+<span class="scilabcomment">//x0=[0.1 , 0.1 , 0.1]</span>
+<span class="scilabcomment">//  x1 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 0</span>
+<span class="scilabcomment">//  x2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 0</span>
+<span class="scilabcomment">//  x3 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 0</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilaboperator">-</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Starting point, linear constraints and variable bounds</span>
+<span class="scilabid">x0</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">0.1</span> <span class="scilabdefault">,</span> <span class="scilabnumber">0.1</span> <span class="scilabdefault">,</span> <span class="scilabnumber">0.1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">Aeq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">beq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">lb</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">ub</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Options</span>
+<span class="scilabid">options</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">MaxIter</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">1500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">CpuTime</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">500</span><span class="scilabopenclose">]</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">x</span><span class="scilabdefault">,</span><span class="scilabid">fval</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabdefault">,</span><span class="scilabid">grad</span><span class="scilabdefault">,</span><span class="scilabid">hessian</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span><span class="scilabid">fmincon</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span> <span class="scilabid">x0</span><span class="scilabdefault">,</span><span class="scilabid">A</span><span class="scilabdefault">,</span><span class="scilabid">b</span><span class="scilabdefault">,</span><span class="scilabid">Aeq</span><span class="scilabdefault">,</span><span class="scilabid">beq</span><span class="scilabdefault">,</span><span class="scilabid">lb</span><span class="scilabdefault">,</span><span class="scilabid">ub</span><span class="scilabdefault">,</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span><span class="scilabid">options</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//The below problem is an infeasible problem:</span>
+<span class="scilabcomment">//Find x in R^3 such that in minimizes:</span>
+<span class="scilabcomment">//f(x)=x1*x2 + x2*x3</span>
+<span class="scilabcomment">//x0=[1,1,1]</span>
+<span class="scilabcomment">//constraint-1 (c1): x1^2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 1</span>
+<span class="scilabcomment">//constraint-2 (c2): x1^2 + x2^2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 1</span>
+<span class="scilabcomment">//constraint-3 (c3): x3^2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 1</span>
+<span class="scilabcomment">//constraint-4 (c4): x1^3 = 0.5</span>
+<span class="scilabcomment">//constraint-5 (c5): x2^2 + x3^2 = 0.75</span>
+<span class="scilabcomment">// 0 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= x1 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">=0.6</span>
+<span class="scilabcomment">// 0.2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= x2 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= inf</span>
+<span class="scilabcomment">// -inf </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= x3 </span><span class="scilabcomment">&#0060;</span><span class="scilabcomment">= 1</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Starting point, linear constraints and variable bounds</span>
+<span class="scilabid">x0</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">Aeq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">beq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">lb</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span> <span class="scilabnumber">0.2</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabconstants">%inf</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabid">ub</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">0.6</span> <span class="scilabconstants">%inf</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Nonlinear constraints</span>
+<span class="scilabfkeyword">function</span> <span class="scilabopenclose">[</span><span class="scilabinputoutputargs">c</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">ceq</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabfunctionid">nlc</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">c</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabinputoutputargs">ceq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">3</span><span class="scilaboperator">-</span><span class="scilabnumber">0.5</span><span class="scilabdefault">,</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">-</span><span class="scilabnumber">0.75</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Gradient of objective function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">fGrad</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">+</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Hessian of the Lagrange Function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">lHess</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">obj</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabinputoutputargs">obj</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span> <span class="scilaboperator">+</span><span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">(</span><span class="scilabnumber">4</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">6</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span>    <span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">lambda</span><span class="scilabopenclose">(</span><span class="scilabnumber">5</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Gradient of Non-Linear Constraints</span>
+<span class="scilabfkeyword">function</span> <span class="scilabopenclose">[</span><span class="scilabinputoutputargs">cg</span><span class="scilabdefault">, </span><span class="scilabinputoutputargs">ceqg</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabfunctionid">cGrad</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">cg</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabinputoutputargs">ceqg</span> <span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabnumber">3</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">3</span><span class="scilabopenclose">)</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Options</span>
+<span class="scilabid">options</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">MaxIter</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">1500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">CpuTime</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">GradObj</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">fGrad</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">Hessian</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">lHess</span><span class="scilabdefault">,</span><span class="scilabstring">&#0034;</span><span class="scilabstring">GradCon</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">cGrad</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">x</span><span class="scilabdefault">,</span><span class="scilabid">fval</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabdefault">,</span><span class="scilabid">grad</span><span class="scilabdefault">,</span><span class="scilabid">hessian</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span><span class="scilabid">fmincon</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span> <span class="scilabid">x0</span><span class="scilabdefault">,</span><span class="scilabid">A</span><span class="scilabdefault">,</span><span class="scilabid">b</span><span class="scilabdefault">,</span><span class="scilabid">Aeq</span><span class="scilabdefault">,</span><span class="scilabid">beq</span><span class="scilabdefault">,</span><span class="scilabid">lb</span><span class="scilabdefault">,</span><span class="scilabid">ub</span><span class="scilabdefault">,</span><span class="scilabfunctionid">nlc</span><span class="scilabdefault">,</span><span class="scilabid">options</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Authors</h3>
+   <ul class="itemizedlist"><li class="member">R.Vidyadhar , Vignesh Kannan</li></ul></div>
+    <br />
+
+    <div class="manualnavbar">
+    <table width="100%">
+    <tr><td colspan="3" class="next"><a href="http://bugzilla.scilab.org/enter_bug.cgi?product=Scilab%20software&component=Documentation%20pages" class="ulink">Report an issue</a></td></tr>
+<tr>
+      <td width="30%">
+    	<span class="previous"><a href="fminbnd.html">&lt;&lt; fminbnd</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="fminunc.html">fminunc &gt;&gt;</a></span>
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+    </tr></table>
+      <hr />
+    </div>
+  </body>
+</html>
diff --git a/help/en_US/scilab_en_US_help/fminunc.html b/help/en_US/scilab_en_US_help/fminunc.html
new file mode 100644
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+++ b/help/en_US/scilab_en_US_help/fminunc.html
@@ -0,0 +1,178 @@
+<html><head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+    <title>fminunc</title>
+    <style type="text/css" media="all">
+      @import url("scilab_code.css");
+      @import url("xml_code.css");
+      @import url("c_code.css");
+      @import url("style.css");
+    </style>
+  </head>
+  <body>
+    <div class="manualnavbar">
+    <table width="100%"><tr>
+      <td width="30%">
+    	<span class="previous"><a href="fmincon.html">&lt;&lt; fmincon</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="linprog.html">linprog &gt;&gt;</a></span>
+
+      </td>
+    </tr></table>
+      <hr />
+    </div>
+
+
+
+    <span class="path"><a href="index.html">Symphony Toolbox</a> &gt;&gt; <a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a> &gt; fminunc</span>
+
+    <br /><br />
+    <div class="refnamediv"><h1 class="refname">fminunc</h1>
+    <p class="refpurpose">Solves a multi-variable unconstrainted optimization problem</p></div>
+
+
+<div class="refsynopsisdiv"><h3 class="title">Calling Sequence</h3>
+   <div class="synopsis"><pre><span class="default">xopt</span><span class="default"> = </span><span class="functionid">fminunc</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x0</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">fminunc</span><span class="default">(</span><span class="default">f</span><span class="default">,</span><span class="default">x0</span><span class="default">,</span><span class="default">options</span><span class="default">)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">] = </span><span class="functionid">fminunc</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">]= </span><span class="functionid">fminunc</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">]= </span><span class="functionid">fminunc</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">gradient</span><span class="default">]=</span><span class="functionid">fminunc</span><span class="default">(.....)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">gradient</span><span class="default">,</span><span class="default">hessian</span><span class="default">]=</span><span class="functionid">fminunc</span><span class="default">(.....)</span></pre></div></div>
+
+<div class="refsection"><h3 class="title">Parameters</h3>
+   <dl><dt><span class="term">f :</span>
+      <dd><p class="para">a function, representing the objective function of the problem</p></dd></dt>
+   <dt><span class="term">x0 :</span>
+      <dd><p class="para">a vector of doubles, containing the starting of variables.</p></dd></dt>
+   <dt><span class="term">options:</span>
+      <dd><p class="para">a list, containing the option for user to specify. See below for details.</p></dd></dt>
+   <dt><span class="term">xopt :</span>
+      <dd><p class="para">a vector of doubles, the computed solution of the optimization problem.</p></dd></dt>
+   <dt><span class="term">fopt :</span>
+      <dd><p class="para">a scalar of double, the function value at x.</p></dd></dt>
+   <dt><span class="term">exitflag :</span>
+      <dd><p class="para">a scalar of integer, containing the flag which denotes the reason for termination of algorithm. See below for details.</p></dd></dt>
+   <dt><span class="term">output   :</span>
+      <dd><p class="para">a structure, containing the information about the optimization. See below for details.</p></dd></dt>
+   <dt><span class="term">gradient :</span>
+      <dd><p class="para">a vector of doubles, containing the the gradient of the solution.</p></dd></dt>
+   <dt><span class="term">hessian  :</span>
+      <dd><p class="para">a matrix of doubles, containing the the hessian of the solution.</p></dd></dt></dl></div>
+
+<div class="refsection"><h3 class="title">Description</h3>
+   <p class="para">Search the minimum of an unconstrained optimization problem specified by :
+Find the minimum of f(x) such that</p>
+   <p class="para"><span><img src='./_LaTeX_fminunc.xml_1.png' style='position:relative;top:9px;width:115px;height:26px'/></span></p>
+   <p class="para">The routine calls Ipopt for solving the Un-constrained Optimization problem, Ipopt is a library written in C++.</p>
+   <p class="para">The options allows the user to set various parameters of the Optimization problem.
+It should be defined as type &#0034;list&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>Syntax : options= list(&#0034;MaxIter&#0034;, [---], &#0034;CpuTime&#0034;, [---], &#0034;Gradient&#0034;, ---, &#0034;Hessian&#0034;, ---);</li>
+<li>MaxIter : a Scalar, containing the Maximum Number of Iteration that the solver should take.</li>
+<li>CpuTime : a Scalar, containing the Maximum amount of CPU Time that the solver should take.</li>
+<li>Gradient : a function, representing the gradient function of the Objective in Vector Form.</li>
+<li>Hessian : a function, representing the hessian function of the Objective in Symmetric Matrix Form.</li>
+<li>Default Values : options = list(&#0034;MaxIter&#0034;, [3000], &#0034;CpuTime&#0034;, [600]);</li></ul></p>
+   <p class="para">The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<ul class="itemizedlist"><li>exitflag=0 : Optimal Solution Found</li>
+<li>exitflag=1 : Maximum Number of Iterations Exceeded. Output may not be optimal.</li>
+<li>exitflag=2 : Maximum CPU Time exceeded. Output may not be optimal.</li>
+<li>exitflag=3 : Stop at Tiny Step.</li>
+<li>exitflag=4 : Solved To Acceptable Level.</li>
+<li>exitflag=5 : Converged to a point of local infeasibility.</li></ul></p>
+   <p class="para">For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/</p>
+   <p class="para">The output data structure contains detailed informations about the optimization process.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>output.Iterations: The number of iterations performed during the search</li>
+<li>output.Cpu_Time: The total cpu-time spend during the search</li>
+<li>output.Objective_Evaluation: The number of Objective Evaluations performed during the search</li>
+<li>output.Dual_Infeasibility: The Dual Infeasiblity of the final soution</li></ul></p>
+   <p class="para"></p></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Find x in R^2 such that it minimizes the Rosenbrock function</span>
+<span class="scilabcomment">//f = 100*(x2 - x1^2)^2 + (1-x1)^2</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabnumber">100</span><span class="scilaboperator">*</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span> <span class="scilaboperator">-</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">+</span> <span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilaboperator">-</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Starting point</span>
+<span class="scilabid">x0</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Gradient of objective function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">fGrad</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">400</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span> <span class="scilaboperator">+</span> <span class="scilabnumber">400</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">3</span> <span class="scilaboperator">+</span> <span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilabnumber">200</span><span class="scilaboperator">*</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">-</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Hessian of Objective Function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">fHess</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilabnumber">1200</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilaboperator">-</span> <span class="scilabnumber">400</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span> <span class="scilaboperator">+</span> <span class="scilabnumber">2</span><span class="scilabdefault">,</span> <span class="scilaboperator">-</span><span class="scilabnumber">400</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">400</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span> <span class="scilabnumber">200</span> <span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Options</span>
+<span class="scilabid">options</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">MaxIter</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">1500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">CpuTime</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">Gradient</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">fGrad</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">Hessian</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">fHess</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">gradient</span><span class="scilabdefault">,</span><span class="scilabid">hessian</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabid">fminunc</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span><span class="scilabid">x0</span><span class="scilabdefault">,</span><span class="scilabid">options</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Find x in R^2 such that the below function is minimum</span>
+<span class="scilabcomment">//f = x1^2 + x2^2</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">+</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Starting point</span>
+<span class="scilabid">x0</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabid">fminunc</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span><span class="scilabid">x0</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//The below problem is an unbounded problem:</span>
+<span class="scilabcomment">//Find x in R^2 such that the below function is minimum</span>
+<span class="scilabcomment">//f = - x1^2 - x2^2</span>
+<span class="scilabcomment">//Objective function to be minimised</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">f</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilaboperator">-</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span> <span class="scilaboperator">-</span> <span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilaboperator">^</span><span class="scilabnumber">2</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Starting point</span>
+<span class="scilabid">x0</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Gradient of objective function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">fGrad</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">1</span><span class="scilabopenclose">)</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilaboperator">*</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">(</span><span class="scilabnumber">2</span><span class="scilabopenclose">)</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Hessian of Objective Function</span>
+<span class="scilabfkeyword">function</span> <span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span><span class="scilabfunctionid">fHess</span><span class="scilabopenclose">(</span><span class="scilabinputoutputargs">x</span><span class="scilabopenclose">)</span>
+<span class="scilabinputoutputargs">y</span><span class="scilaboperator">=</span> <span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">;</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span><span class="scilabdefault">;</span>
+<span class="scilabfkeyword">endfunction</span>
+<span class="scilabcomment">//Options</span>
+<span class="scilabid">options</span><span class="scilaboperator">=</span><a class="scilabcommand" href="scilab://list">list</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0034;</span><span class="scilabstring">MaxIter</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">1500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">CpuTime</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabopenclose">[</span><span class="scilabnumber">500</span><span class="scilabopenclose">]</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">Gradient</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">fGrad</span><span class="scilabdefault">,</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">Hessian</span><span class="scilabstring">&#0034;</span><span class="scilabdefault">,</span> <span class="scilabfunctionid">fHess</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabcomment">//Calling Ipopt</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">gradient</span><span class="scilabdefault">,</span><span class="scilabid">hessian</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabid">fminunc</span><span class="scilabopenclose">(</span><span class="scilabfunctionid">f</span><span class="scilabdefault">,</span><span class="scilabid">x0</span><span class="scilabdefault">,</span><span class="scilabid">options</span><span class="scilabopenclose">)</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Authors</h3>
+   <ul class="itemizedlist"><li class="member">R.Vidyadhar , Vignesh Kannan</li></ul></div>
+    <br />
+
+    <div class="manualnavbar">
+    <table width="100%">
+    <tr><td colspan="3" class="next"><a href="http://bugzilla.scilab.org/enter_bug.cgi?product=Scilab%20software&component=Documentation%20pages" class="ulink">Report an issue</a></td></tr>
+<tr>
+      <td width="30%">
+    	<span class="previous"><a href="fmincon.html">&lt;&lt; fmincon</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="linprog.html">linprog &gt;&gt;</a></span>
+
+      </td>
+    </tr></table>
+      <hr />
+    </div>
+  </body>
+</html>
diff --git a/help/en_US/scilab_en_US_help/index.html b/help/en_US/scilab_en_US_help/index.html
index 03ce98c..fc07de0 100644
--- a/help/en_US/scilab_en_US_help/index.html
+++ b/help/en_US/scilab_en_US_help/index.html
@@ -32,7 +32,37 @@
 <ul class="list-part"><a name="symphony_toolbox_manual"></a><div class="info"></div>
 
 <li><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html" class="part">Symphony Toolbox</a>
-<ul class="list-chapter"><li><a href="lsqlin.html" class="refentry">lsqlin</a> &#8212; <span class="refentry-description">Solves a linear quadratic problem.</span></li>
+<ul class="list-chapter"><li><a href="fgoalattain.html" class="refentry">fgoalattain</a> &#8212; <span class="refentry-description">Solves a multiobjective goal attainment problem</span></li>
+
+
+
+
+
+<li><a href="fminbnd.html" class="refentry">fminbnd</a> &#8212; <span class="refentry-description">Solves a multi-variable optimization problem on a bounded interval</span></li>
+
+
+
+
+
+<li><a href="fmincon.html" class="refentry">fmincon</a> &#8212; <span class="refentry-description">Solves a multi-variable constrainted optimization problem</span></li>
+
+
+
+
+
+<li><a href="fminunc.html" class="refentry">fminunc</a> &#8212; <span class="refentry-description">Solves a multi-variable unconstrainted optimization problem</span></li>
+
+
+
+
+
+<li><a href="linprog.html" class="refentry">linprog</a> &#8212; <span class="refentry-description">Solves a linear programming problem.</span></li>
+
+
+
+
+
+<li><a href="lsqlin.html" class="refentry">lsqlin</a> &#8212; <span class="refentry-description">Solves a linear quadratic problem.</span></li>
 
 
 
diff --git a/help/en_US/scilab_en_US_help/jhelpmap.jhm b/help/en_US/scilab_en_US_help/jhelpmap.jhm
index 0226c5e..be43c6e 100644
--- a/help/en_US/scilab_en_US_help/jhelpmap.jhm
+++ b/help/en_US/scilab_en_US_help/jhelpmap.jhm
@@ -3,6 +3,11 @@
 <map version="1.0">
 <mapID target="index" url="index.html"/>
 <mapID target="section_19f4f1e5726c01d683e8b82be0a7e910" url="section_19f4f1e5726c01d683e8b82be0a7e910.html"/>
+<mapID target="fgoalattain" url="fgoalattain.html"/>
+<mapID target="fminbnd" url="fminbnd.html"/>
+<mapID target="fmincon" url="fmincon.html"/>
+<mapID target="fminunc" url="fminunc.html"/>
+<mapID target="linprog" url="linprog.html"/>
 <mapID target="lsqlin" url="lsqlin.html"/>
 <mapID target="lsqnonneg" url="lsqnonneg.html"/>
 <mapID target="qpipopt" url="qpipopt.html"/>
diff --git a/help/en_US/scilab_en_US_help/jhelptoc.xml b/help/en_US/scilab_en_US_help/jhelptoc.xml
index f53e713..500c6b0 100644
--- a/help/en_US/scilab_en_US_help/jhelptoc.xml
+++ b/help/en_US/scilab_en_US_help/jhelptoc.xml
@@ -3,6 +3,11 @@
 <toc version="1.0">
 <tocitem target="index" text="Symphony Toolbox">
 <tocitem target="section_19f4f1e5726c01d683e8b82be0a7e910" text="Symphony Toolbox">
+<tocitem target="fgoalattain" text="fgoalattain"/>
+<tocitem target="fminbnd" text="fminbnd"/>
+<tocitem target="fmincon" text="fmincon"/>
+<tocitem target="fminunc" text="fminunc"/>
+<tocitem target="linprog" text="linprog"/>
 <tocitem target="lsqlin" text="lsqlin"/>
 <tocitem target="lsqnonneg" text="lsqnonneg"/>
 <tocitem target="qpipopt" text="qpipopt"/>
diff --git a/help/en_US/scilab_en_US_help/linprog.html b/help/en_US/scilab_en_US_help/linprog.html
new file mode 100644
index 0000000..260b8b3
--- /dev/null
+++ b/help/en_US/scilab_en_US_help/linprog.html
@@ -0,0 +1,188 @@
+<html><head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
+    <title>linprog</title>
+    <style type="text/css" media="all">
+      @import url("scilab_code.css");
+      @import url("xml_code.css");
+      @import url("c_code.css");
+      @import url("style.css");
+    </style>
+  </head>
+  <body>
+    <div class="manualnavbar">
+    <table width="100%"><tr>
+      <td width="30%">
+    	<span class="previous"><a href="fminunc.html">&lt;&lt; fminunc</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="lsqlin.html">lsqlin &gt;&gt;</a></span>
+
+      </td>
+    </tr></table>
+      <hr />
+    </div>
+
+
+
+    <span class="path"><a href="index.html">Symphony Toolbox</a> &gt;&gt; <a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a> &gt; linprog</span>
+
+    <br /><br />
+    <div class="refnamediv"><h1 class="refname">linprog</h1>
+    <p class="refpurpose">Solves a linear programming problem.</p></div>
+
+
+<div class="refsynopsisdiv"><h3 class="title">Calling Sequence</h3>
+   <div class="synopsis"><pre><span class="default">xopt</span><span class="default"> = </span><span class="functionid">linprog</span><span class="default">(</span><span class="default">c</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">linprog</span><span class="default">(</span><span class="default">c</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">linprog</span><span class="default">(</span><span class="default">c</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">,</span><span class="default">lb</span><span class="default">,</span><span class="default">ub</span><span class="default">)</span>
+<span class="default">xopt</span><span class="default"> = </span><span class="functionid">linprog</span><span class="default">(</span><span class="default">c</span><span class="default">,</span><span class="default">A</span><span class="default">,</span><span class="default">b</span><span class="default">,</span><span class="default">Aeq</span><span class="default">,</span><span class="default">beq</span><span class="default">,</span><span class="default">lb</span><span class="default">,</span><span class="default">ub</span><span class="default">,</span><span class="default">param</span><span class="default">)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">, </span><span class="default">fopt</span><span class="default">, </span><span class="default">exitflag</span><span class="default">, </span><span class="default">output</span><span class="default">, </span><span class="default">lambda</span><span class="default">] = </span><span class="functionid">linprog</span><span class="default">(</span><span class="default">file</span><span class="default">)</span>
+<span class="default">[</span><span class="default">xopt</span><span class="default">,</span><span class="default">fopt</span><span class="default">,</span><span class="default">exitflag</span><span class="default">,</span><span class="default">output</span><span class="default">,</span><span class="default">lambda</span><span class="default">] = </span><span class="functionid">linprog</span><span class="default">( ... )</span></pre></div></div>
+
+<div class="refsection"><h3 class="title">Parameters</h3>
+   <dl><dt><span class="term">c :</span>
+      <dd><p class="para">a vector of double, contains coefficients of the variables in the objective</p></dd></dt>
+   <dt><span class="term">A :</span>
+      <dd><p class="para">a matrix of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.</p></dd></dt>
+   <dt><span class="term">b :</span>
+      <dd><p class="para">a vector of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.</p></dd></dt>
+   <dt><span class="term">Aeq :</span>
+      <dd><p class="para">a matrix of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.</p></dd></dt>
+   <dt><span class="term">beq :</span>
+      <dd><p class="para">a vector of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.</p></dd></dt>
+   <dt><span class="term">lb :</span>
+      <dd><p class="para">Lower bounds, specified as a vector or array of double. lb represents the lower bounds elementwise in lb ≤ x ≤ ub.</p></dd></dt>
+   <dt><span class="term">ub :</span>
+      <dd><p class="para">Upper bounds, specified as a vector or array of double. ub represents the upper bounds elementwise in lb ≤ x ≤ ub.</p></dd></dt>
+   <dt><span class="term">options :</span>
+      <dd><p class="para">a list containing the parameters to be set.</p></dd></dt>
+   <dt><span class="term">file :</span>
+      <dd><p class="para">a string describing the path to the mps file.</p></dd></dt>
+   <dt><span class="term">xopt :</span>
+      <dd><p class="para">a vector of double, the computed solution of the optimization problem.</p></dd></dt>
+   <dt><span class="term">fopt :</span>
+      <dd><p class="para">a double, the value of the function at x.</p></dd></dt>
+   <dt><span class="term">status :</span>
+      <dd><p class="para">status flag returned from symphony. See below for details.</p></dd></dt>
+   <dt><span class="term">output :</span>
+      <dd><p class="para">The output data structure contains detailed information about the optimization process. See below for details.</p></dd></dt>
+   <dt><span class="term">lambda :</span>
+      <dd><p class="para">The structure consist of the Lagrange multipliers at the solution of problem. See below for details.</p></dd></dt></dl></div>
+
+<div class="refsection"><h3 class="title">Description</h3>
+   <p class="para">OSI-CLP is used for solving the linear programming problems, OSI-CLP is a library written in C++.
+Search the minimum of a constrained linear programming problem specified by :</p>
+   <p class="para"><span><img src='./_LaTeX_linprog.xml_1.png' style='position:relative;top:40px;width:212px;height:88px'/></span>
+The routine calls Clp for solving the linear programming problem, Clp is a library written in C++.</p>
+   <p class="para">The exitflag allows to know the status of the optimization which is given back by Ipopt.
+<ul class="itemizedlist"><li>exitflag=0 : Optimal Solution Found</li>
+<li>exitflag=1 : Primal Infeasible</li>
+<li>exitflag=2 : Dual Infeasible</li>
+<li>exitflag=3 : Maximum Number of Iterations Exceeded. Output may not be optimal.</li>
+<li>exitflag=4 : Solution Abandoned</li>
+<li>exitflag=5 : Primal objective limit reached.</li>
+<li>exitflag=6 : Dual objective limit reached.</li></ul></p>
+   <p class="para">For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/</p>
+   <p class="para">The output data structure contains detailed informations about the optimization process.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>output.iterations: The number of iterations performed during the search</li>
+<li>output.constrviolation: The max-norm of the constraint violation.</li></ul></p>
+   <p class="para">The lambda data structure contains the Lagrange multipliers at the end
+of optimization. In the current version the values are returned only when the the solution is optimal.
+It has type &#0034;struct&#0034; and contains the following fields.
+<ul class="itemizedlist"><li>lambda.lower: The Lagrange multipliers for the lower bound constraints.</li>
+<li>lambda.upper: The Lagrange multipliers for the upper bound constraints.</li>
+<li>lambda.eqlin: The Lagrange multipliers for the linear equality constraints.</li>
+<li>lambda.ineqlin: The Lagrange multipliers for the linear inequality constraints.</li></ul></p>
+   <p class="para"></p></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Optimal problems</span>
+<span class="scilabcomment">//Linear program, linear inequality constraints</span>
+<span class="scilabid">c</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">3</span><span class="scilabopenclose">]</span><span class="scilaboperator">&#0039;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabid">linprog</span><span class="scilabopenclose">(</span><span class="scilabid">c</span><span class="scilabdefault">,</span> <span class="scilabid">A</span><span class="scilabdefault">,</span> <span class="scilabid">b</span><span class="scilabopenclose">)</span>
+<span class="scilabcomment">// Press ENTER to continue</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Linear program with Linear Inequalities and Equalities`</span>
+<span class="scilabid">c</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">3</span><span class="scilabopenclose">]</span><span class="scilaboperator">&#0039;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span>
+<span class="scilabid">Aeq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabopenclose">]</span>
+<span class="scilabid">beq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabid">linprog</span><span class="scilabopenclose">(</span><span class="scilabid">c</span><span class="scilabdefault">,</span> <span class="scilabid">A</span><span class="scilabdefault">,</span> <span class="scilabid">b</span><span class="scilabdefault">,</span> <span class="scilabid">Aeq</span><span class="scilabdefault">,</span> <span class="scilabid">beq</span><span class="scilabopenclose">)</span>
+<span class="scilabcomment">// Press ENTER to continue</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Linear program with all constraint types</span>
+<span class="scilabid">c</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">3</span><span class="scilabopenclose">]</span><span class="scilaboperator">&#0039;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">;</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span>
+<span class="scilabid">Aeq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">4</span><span class="scilabopenclose">]</span>
+<span class="scilabid">beq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilaboperator">/</span><span class="scilabnumber">2</span><span class="scilabopenclose">]</span>
+<span class="scilabid">lb</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">0.5</span><span class="scilabopenclose">]</span>
+<span class="scilabid">ub</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1.5</span><span class="scilabdefault">,</span><span class="scilabnumber">1.25</span><span class="scilabopenclose">]</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span><span class="scilabid">linprog</span><span class="scilabopenclose">(</span><span class="scilabid">c</span><span class="scilabdefault">,</span> <span class="scilabid">A</span><span class="scilabdefault">,</span> <span class="scilabid">b</span><span class="scilabdefault">,</span> <span class="scilabid">Aeq</span><span class="scilabdefault">,</span> <span class="scilabid">beq</span><span class="scilabdefault">,</span> <span class="scilabid">lb</span><span class="scilabdefault">,</span> <span class="scilabid">ub</span><span class="scilabopenclose">)</span>
+<span class="scilabcomment">// Press ENTER to continue</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Primal Infeasible Problem</span>
+<span class="scilabid">c</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span><span class="scilaboperator">&#0039;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">2</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabopenclose">]</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">4</span><span class="scilabopenclose">]</span>
+<span class="scilabid">Aeq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">5</span><span class="scilabdefault">,</span><span class="scilabnumber">3</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span>
+<span class="scilabid">beq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">10</span><span class="scilabdefault">,</span><span class="scilabnumber">100</span><span class="scilabopenclose">]</span>
+<span class="scilabid">lb</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabdefault">,</span><span class="scilabnumber">0</span><span class="scilabopenclose">]</span>
+<span class="scilabid">ub</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabconstants">%inf</span><span class="scilabdefault">,</span><span class="scilabconstants">%inf</span><span class="scilabdefault">,</span><span class="scilabconstants">%inf</span><span class="scilabopenclose">]</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span> <span class="scilabid">linprog</span><span class="scilabopenclose">(</span><span class="scilabid">c</span><span class="scilabdefault">,</span><span class="scilabid">A</span><span class="scilabdefault">,</span><span class="scilabid">b</span><span class="scilabdefault">,</span><span class="scilabid">Aeq</span><span class="scilabdefault">,</span><span class="scilabid">beq</span><span class="scilabdefault">,</span><span class="scilabid">lb</span><span class="scilabdefault">,</span><span class="scilabid">ub</span><span class="scilabopenclose">)</span>
+<span class="scilabcomment">// Press ENTER to continue</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabcomment">//Dual Infeasible Problem</span>
+<span class="scilabid">c</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabnumber">3</span><span class="scilabdefault">,</span><span class="scilabnumber">5</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">7</span><span class="scilabopenclose">]</span><span class="scilaboperator">&#0039;</span>
+<span class="scilabid">A</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">4</span><span class="scilabdefault">;</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">1</span><span class="scilabdefault">,</span><span class="scilabnumber">4</span><span class="scilabopenclose">]</span>
+<span class="scilabid">b</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabnumber">8</span><span class="scilabdefault">,</span><span class="scilabnumber">5</span><span class="scilabopenclose">]</span>
+<span class="scilabid">Aeq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span>
+<span class="scilabid">beq</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabopenclose">]</span>
+<span class="scilabid">lb</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilaboperator">-</span><span class="scilabconstants">%inf</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabconstants">%inf</span><span class="scilabdefault">,</span><span class="scilaboperator">-</span><span class="scilabconstants">%inf</span><span class="scilabopenclose">]</span>
+<span class="scilabid">ub</span><span class="scilaboperator">=</span><span class="scilabopenclose">[</span><span class="scilabconstants">%inf</span><span class="scilabdefault">,</span><span class="scilabconstants">%inf</span><span class="scilabdefault">,</span><span class="scilabconstants">%inf</span><span class="scilabopenclose">]</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span><span class="scilaboperator">=</span> <span class="scilabid">linprog</span><span class="scilabopenclose">(</span><span class="scilabid">c</span><span class="scilabdefault">,</span><span class="scilabid">A</span><span class="scilabdefault">,</span><span class="scilabid">b</span><span class="scilabdefault">,</span><span class="scilabid">Aeq</span><span class="scilabdefault">,</span><span class="scilabid">beq</span><span class="scilabdefault">,</span><span class="scilabid">lb</span><span class="scilabdefault">,</span><span class="scilabid">ub</span><span class="scilabopenclose">)</span>
+<span class="scilabcomment">// Press ENTER to continue</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Examples</h3>
+   <div class="programlisting"><table border="0" width="100%"><tr><td width="98%"><pre class="scilabcode"><span class="scilabid">filepath</span> <span class="scilaboperator">=</span> <a class="scilabcommand" href="scilab://get_absolute_file_path">get_absolute_file_path</a><span class="scilabopenclose">(</span><span class="scilabstring">&#0039;</span><span class="scilabstring">linprog.dem.sce</span><span class="scilabstring">&#0039;</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span>
+<span class="scilabid">filepath</span> <span class="scilaboperator">=</span> <span class="scilabid">filepath</span> <span class="scilaboperator">+</span> <span class="scilabstring">&#0034;</span><span class="scilabstring">exmip1.mps</span><span class="scilabstring">&#0034;</span>
+<span class="scilabopenclose">[</span><span class="scilabid">xopt</span><span class="scilabdefault">,</span><span class="scilabid">fopt</span><span class="scilabdefault">,</span><span class="scilabid">exitflag</span><span class="scilabdefault">,</span><span class="scilabid">output</span><span class="scilabdefault">,</span><span class="scilabid">lambda</span><span class="scilabopenclose">]</span> <span class="scilaboperator">=</span><span class="scilabid">linprog</span><span class="scilabopenclose">(</span><span class="scilabid">filepath</span><span class="scilabopenclose">)</span><span class="scilabdefault">;</span></pre></td><td valign="top"><a href="scilab://scilab.execexample/"><img src="ScilabExecute.png" border="0"/></a></td><td valign="top"><a href="scilab://scilab.editexample/"><img src="ScilabEdit.png" border="0"/></a></td><td></td></tr></table></div></div>
+
+<div class="refsection"><h3 class="title">Authors</h3>
+   <ul class="itemizedlist"><li class="member">Bhanu Priya Sayal, Guru Pradeep Reddy</li></ul></div>
+    <br />
+
+    <div class="manualnavbar">
+    <table width="100%">
+    <tr><td colspan="3" class="next"><a href="http://bugzilla.scilab.org/enter_bug.cgi?product=Scilab%20software&component=Documentation%20pages" class="ulink">Report an issue</a></td></tr>
+<tr>
+      <td width="30%">
+    	<span class="previous"><a href="fminunc.html">&lt;&lt; fminunc</a></span>
+
+      </td>
+      <td width="40%" class="center">
+      	<span class="top"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">Symphony Toolbox</a></span>
+
+      </td>
+      <td width="30%" class="next">
+      	<span class="next"><a href="lsqlin.html">lsqlin &gt;&gt;</a></span>
+
+      </td>
+    </tr></table>
+      <hr />
+    </div>
+  </body>
+</html>
diff --git a/help/en_US/scilab_en_US_help/lsqlin.html b/help/en_US/scilab_en_US_help/lsqlin.html
index db24b63..eb1b38d 100644
--- a/help/en_US/scilab_en_US_help/lsqlin.html
+++ b/help/en_US/scilab_en_US_help/lsqlin.html
@@ -12,7 +12,7 @@
     <div class="manualnavbar">
     <table width="100%"><tr>
       <td width="30%">
-    	<span class="previous"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">&lt;&lt; Symphony Toolbox</a></span>
+    	<span class="previous"><a href="linprog.html">&lt;&lt; linprog</a></span>
 
       </td>
       <td width="40%" class="center">
@@ -151,7 +151,7 @@ It has type &#0034;struct&#0034; and contains the following fields.
     <tr><td colspan="3" class="next"><a href="http://bugzilla.scilab.org/enter_bug.cgi?product=Scilab%20software&component=Documentation%20pages" class="ulink">Report an issue</a></td></tr>
 <tr>
       <td width="30%">
-    	<span class="previous"><a href="section_19f4f1e5726c01d683e8b82be0a7e910.html">&lt;&lt; Symphony Toolbox</a></span>
+    	<span class="previous"><a href="linprog.html">&lt;&lt; linprog</a></span>
 
       </td>
       <td width="40%" class="center">
diff --git a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html
index a79bad0..a03f091 100644
--- a/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html
+++ b/help/en_US/scilab_en_US_help/section_19f4f1e5726c01d683e8b82be0a7e910.html
@@ -31,7 +31,37 @@
 
     <br /><br />
     <h3 class="title-part">Symphony Toolbox</h3>
-<ul class="list-chapter"><li><a href="lsqlin.html" class="refentry">lsqlin</a> &#8212; <span class="refentry-description">Solves a linear quadratic problem.</span></li>
+<ul class="list-chapter"><li><a href="fgoalattain.html" class="refentry">fgoalattain</a> &#8212; <span class="refentry-description">Solves a multiobjective goal attainment problem</span></li>
+
+
+
+
+
+<li><a href="fminbnd.html" class="refentry">fminbnd</a> &#8212; <span class="refentry-description">Solves a multi-variable optimization problem on a bounded interval</span></li>
+
+
+
+
+
+<li><a href="fmincon.html" class="refentry">fmincon</a> &#8212; <span class="refentry-description">Solves a multi-variable constrainted optimization problem</span></li>
+
+
+
+
+
+<li><a href="fminunc.html" class="refentry">fminunc</a> &#8212; <span class="refentry-description">Solves a multi-variable unconstrainted optimization problem</span></li>
+
+
+
+
+
+<li><a href="linprog.html" class="refentry">linprog</a> &#8212; <span class="refentry-description">Solves a linear programming problem.</span></li>
+
+
+
+
+
+<li><a href="lsqlin.html" class="refentry">lsqlin</a> &#8212; <span class="refentry-description">Solves a linear quadratic problem.</span></li>