fmincon examples

57 files changed, 1436 insertions, 50 deletions

diff --git a/demos/lsqnonlin.dem.sce b/demos/lsqnonlin.dem.sce
index a650e63..756dd4a 100644
--- a/demos/lsqnonlin.dem.sce
+++ b/demos/lsqnonlin.dem.sce
@@ -53,5 +53,5 @@ v = wm.*exp(-x(2)*tm)
 dy = [v , -x(1)*tm.*v]
 endfunction
 options = list("GradObj", "on")
-[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options)
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[])
 //========= E N D === O F === D E M O =========//
diff --git a/jar/scilab_en_US_help.jar b/jar/scilab_en_US_help.jar
index bc44df7..f06df33 100644
--- a/jar/scilab_en_US_help.jar
+++ b/jar/scilab_en_US_help.jar
diff --git a/macros/fseminf.bin b/macros/fseminf.bin
new file mode 100644
index 0000000..4616b57
--- /dev/null
+++ b/macros/fseminf.bin
diff --git a/macros/fseminf.sci b/macros/fseminf.sci
new file mode 100644
index 0000000..2ffcab5
--- /dev/null
+++ b/macros/fseminf.sci
@@ -0,0 +1,322 @@
+// Copyright (C) 2015 - IIT Bombay - FOSSEE
+//
+// This file must be used under the terms of the CeCILL.
+// This source file is licensed as described in the file COPYING, which
+// you should have received as part of this distribution.  The terms
+// are also available at
+// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt
+// Author: Salman Anis, Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
+function [xopt,fopt,exitflag,output,lambda,gradient,hessian] = fseminf (varargin)
+  	// Solves a multi-variable constrainted optimization problem
+  	//
+  	//   Calling Sequence
+  	//   xopt = fseminf(fun,x0,ntheta,seminfcon)
+  	//   xopt = fseminf(fun,x0,ntheta,seminfcon,A,b)
+  	//   xopt = fseminf(fun,x0,ntheta,seminfcon,A,b,Aeq,beq)
+  	//   xopt = fseminf(fun,x0,ntheta,seminfcon,A,b,Aeq,beq,lb,ub)
+  	//   xopt = fseminf(fun,x0,ntheta,seminfcon,A,b,Aeq,beq,lb,ub,options)
+  	//   [xopt,fopt] = fseminf(.....)
+  	//   [xopt,fopt,exitflag]= fseminf(.....)
+  	//   [xopt,fopt,exitflag,output]= fseminf(.....)
+  	//   [xopt,fopt,exitflag,output,lambda]=fseminf(.....)
+  	//   [xopt,fopt,exitflag,output,lambda]=fseminf(.....)
+  	//   [xopt,fopt,exitflag,output,lambda]=fseminf(.....)
+  	//
+  	//   Parameters
+  	//   fun : a function, representing the objective function of the problem 
+  	//   x0 : 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
+	//	 ntheta : The number of semi-infinite constraints.
+	//	 seminfcon : a function that calculates the vector of nonlinear inequality constraints c, a vector of nonlinear equality constraints ceq, and ntheta semi-infinite constraints. See below for details.
+	//   A : a matrix of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b. 
+	//   b : a vector of double, represents the linear coefficients in the inequality constraints A⋅x ≤ b.
+	//   Aeq : a matrix of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.
+	//   beq : a vector of double, represents the linear coefficients in the equality constraints Aeq⋅x = beq.
+	//   lb : a vector of double, contains lower bounds of the variables.
+	//   ub : a vector of double, contains upper bounds of the variables.
+  	//   options : a list, containing the option for user to specify. See below for details. 
+
+	//   xopt : a vector of double, the computed solution of the optimization problem.
+	//   fopt : a double, the value of the function at x.
+	//   exitflag : The exit status. See below for details.
+	//   output : The structure consist of statistics about the optimization. See below for details.
+	//   lambda : The structure consist of the Lagrange multipliers at the solution of problem. See below for details.
+  	//
+  	//   Description
+  	//   Search the minimum of a constrained optimization problem specified by :
+  	//   Find the minimum of f(x) such that 
+  	//
+  	//   <latex>
+  	//    \begin{eqnarray}
+  	//    &\mbox{min}_{x}
+  	//    & f(x) \\
+  	//    & \text{subject to} & A*x \leq b \\
+  	//    & & Aeq*x \ = beq\\
+  	//    & & lb \leq x \leq ub \\
+  	//	  & & c(x) \leq  0\\
+  	//    & & ceq(x) \ =  0\\
+	//	  & & K_i(x,w_i) \leq 0, 1 \leq i \leq n.
+  	//    \end{eqnarray}
+  	//   </latex>
+  	//
+  	//   The routine calls Ipopt for solving the Constrained Optimization problem, Ipopt is a library written in C++.
+  	//
+  	// 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", ---);</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>Default Values : options = list("MaxIter", [3000], "CpuTime", [600]);</listitem>
+	// </itemizedlist>
+	//
+	// 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>
+	//
+	// For more details on exitflag see the ipopt documentation, go to http://www.coin-or.org/Ipopt/documentation/
+	//
+	// 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>
+	//
+	// 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>
+	//
+  	// Examples    
+	//    function [y] = obj(x)
+	//        y = (x-1)^2;
+	//    endfunction
+	//    function [c, ceq, K1, s] = seminfcon(x,s)
+	//    // No finite nonlinear inequality and equality constraints
+	//    c = [];
+	//    ceq = [];
+	//    // Sample set
+	//    if isnan(s)
+	//        // Initial sampling interval
+	//        s = [0.01 0];
+	//    end
+	//    t = 0:s(1):1;
+	//    // Evaluate the semi-infinite constraint
+	//    K1 = (x - 0.5) - (t - 0.5).^2;
+	//    endfunction
+	//	  x = fseminf(obj,0.2,1,seminfcon)
+	//    
+  	// Authors
+  	// 	Salman Anis, Harpreet Singh
+ 	
+	
+	//To check the number of input and output arguments
+   	[lhs , rhs] = argn();
+	
+	//To check the number of arguments given by the user
+   	if ( rhs<4 | rhs==5 | rhs==7 | rhs>13 ) then
+    		errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while it should be 4,6,8,10,11"), "fseminf", rhs);
+    		error(errmsg)
+   	end
+ 
+	//Storing the Input Parameters  
+   	_fun   	  = varargin(1);
+   	x0   	  = varargin(2);
+	ntheta	  = varargin(3);
+	seminfcon = varargin(4); 
+    nbVar = size(x0,'*');
+
+	if(nbVar == 0) then
+		errmsg = msprintf(gettext("%s: Cannot determine the number of variables because input initial guess is empty"), "lsqcurvefit");
+		error(errmsg);
+	end
+
+   	A    	 = [];
+   	b    	 = [];
+   	Aeq  	 = [];
+   	beq  	 = [];
+   	lb       = [];
+   	ub       = [];
+   	nlc      = [];
+    param = list();
+   	
+   	if (rhs>4) then
+   		A	 = varargin(5);
+   		b  	 = varargin(6);
+   	end
+
+   	if (rhs>6) then
+   		Aeq       = varargin(7);
+   		beq       = varargin(8);
+   	end
+
+   	if (rhs>8) then
+   		lb      = varargin(9);
+		ub      = varargin(10);
+	end
+
+	if (rhs>10) then
+   		param = varargin(10);
+	end
+
+	if (size(lb,2)==0) then
+		lb = repmat(-%inf,nbVar,1);
+	end
+
+	if (size(ub,2)==0) then
+		ub = repmat(%inf,nbVar,1);
+	end
+
+	//Check type of variables
+	Checktype("fseminf", _fun, "fun", 1, "function")
+	Checktype("fseminf", x0, "x0", 2, "constant")
+	Checktype("fseminf", ntheta, "ntheta", 3, "constant")
+	Checktype("fseminf", seminfcon, "seminfcon", 4, ["function","constant"])
+	Checktype("fseminf", A, "A", 5, "constant")
+	Checktype("fseminf", b, "b", 6, "constant")
+	Checktype("fseminf", Aeq, "Aeq", 7, "constant")
+	Checktype("fseminf", beq, "beq", 8, "constant")
+	Checktype("fseminf", lb, "lb", 9, "constant")
+	Checktype("fseminf", ub, "ub", 10, "constant")
+	Checktype("fseminf", param, "param", 10, "list")
+	
+	//To check the user entry for options and storing it
+   	for i = 1:(size(param))/2
+       	select convstr(param(2*i-1),'l')
+          	case "maxiter" then
+      			Checktype("fseminf", param(2*i), "maxiter", 10, "constant")
+  				options(2) = param(2*i);    //Setting the maximum number of iterations as per user entry
+       		case "cputime" then
+      			Checktype("fseminf", param(2*i), "cputime", 10, "constant")
+  				options(4) = param(2*i);    //Setting the maximum CPU time as per user entry
+        	case "gradobj" then
+      			Checktype("fseminf", param(2*i), "gradobj", 10, "string")
+      			if(convstr(param(2*i),'l') == "on") then
+      				function dy = graObj(x)
+      					
+      				endfunction
+      			end
+       		else
+    	     	 	errmsg = msprintf(gettext("%s: Unrecognized parameter name %s."), "fmincon", param(2*i-1));
+    	      		error(errmsg);
+	     end        					
+     end 	      
+	
+  	//To check and convert the 2nd Input argument (x0) to a row vector 
+   	if((size(x0,1)~=1) & (size(x0,2)~=1)) then
+   		errmsg = msprintf(gettext("%s: Expected Vector for initial guess"), "fseminf");
+   		error(errmsg);
+    end
+
+   	if(size(x0,2)==1) then
+   		x0=x0(:);
+   	end
+   	  	
+  	//To check the match between fun (1st Parameter) and x0 (2nd Parameter)
+   	if(execstr('init=_fun(x0)','errcatch')==21) then
+		errmsg = msprintf(gettext("%s: Objective function and x0 did not match"), "fseminf");
+   		error(errmsg);
+	end
+	   	
+	//Check the size of inequality constraint which should be equal to the number of variables
+	if ( size(A,2) ~= nbVar & size(A,2) ~= 0) then
+		errmsg = msprintf(gettext("%s: The number of columns in A must be the same as the number of elements of x0"), "fseminf");
+		error(errmsg);
+	end
+  	
+	nbConInEq = size(A,"r");
+	  	
+	//Check the size of equality constraint which should be equal to the number of variables
+	if ( size(Aeq,2) ~= nbVar & size(Aeq,2) ~= 0 ) then
+		errmsg = msprintf(gettext("%s: The number of columns in Aeq must be the same as the number of elements of f"), "fseminf");
+		error(errmsg);
+	end
+	
+	b = b(:);
+	beq = beq(:);  	
+	lb = lb(:);
+	ub = ub(:);
+   	
+    //To check the contents of lb & ub
+    for i = 1:nbVar
+		if(ub(i)<lb(i)) then
+			errmsg = msprintf(gettext("%s: Problem has inconsistent variable bounds"), "fseminf");
+			error(errmsg);
+    	end
+	end
+
+	if(typeof(seminfcon) == "function")
+		sample_S = %nan
+		if(execstr('[sample_c,sample_ceq,sample_K,sample_S] = seminfcon(x0,sample_S)','errcatch')==21)
+			errmsg = msprintf(gettext("%s: Semi-Infinite Constraint function & x0 did not match"), "fseminf");
+   			error(errmsg);
+		end
+  		[sample_c, sample_ceq, sample_K, sample_S] = seminfcon(x0,sample_S);
+
+		if (size(sample_c,1)~=1 & size(sample_c,1)~=0) then	
+  			errmsg = msprintf(gettext("%s: c in seminfcon should be a row vector or empty matrix"), "fseminf");
+    		error(errmsg)
+    	end
+  		
+  		if (size(sample_ceq,1)~=1 & size(sample_ceq,1)~=0) then
+  			errmsg = msprintf(gettext("%s: ceq in seminfcon should be a row vector or empty matrix"), "fseminf");
+    		error(errmsg)
+    	end
+		
+  		if (size(sample_K,"r")~=ntheta) then
+  			errmsg = msprintf(gettext("%s: Number of rows in K should be equal to ntheta"), "fseminf");
+    		error(errmsg)
+    	end
+		
+  		if (size(sample_S,"c")~=2) then
+  			errmsg = msprintf(gettext("%s: Number of columns in sampling interval should be equal to 2"), "fseminf");
+    		error(errmsg)
+    	end
+	end
+	
+    ierr = execstr('init=_fun(x0)', "errcatch")
+    if ierr <> 0 then
+    lamsg = lasterror();
+    lclmsg = "%s: Error while evaluating the function: ""%s""\n";
+    error(msprintf(gettext(lclmsg), "fseminf", lamsg));
+    end
+
+	S = %nan;
+    
+    ierr = execstr('init=seminfcon(x0,S)', "errcatch")
+    if ierr <> 0 then
+    lamsg = lasterror();
+    lclmsg = "%s: Error while evaluating the function: ""%s""\n";
+    error(msprintf(gettext(lclmsg), "fseminf", lamsg));
+    end
+
+	function [c, ceq] = _seminfcon(x)
+		[c, ceq, K, S] = seminfcon(x,S)
+		K_max = max(K,"c");
+		c= [c;K_max];
+		ceq = ceq;
+	endfunction
+
+   	//Calling the fmincon function for solving the above problem
+	[xopt,fopt,exitflag,output,lambda,gradient] = fmincon(_fun,x0,A,b,Aeq,beq,lb,ub,_seminfcon,param)
+    		
+endfunction
diff --git a/macros/lib b/macros/lib
index 08d754b..9b74ed8 100644
--- a/macros/lib
+++ b/macros/lib
diff --git a/macros/lsqnonlin.bin b/macros/lsqnonlin.bin
index 32e8417..c90bc0d 100644
--- a/macros/lsqnonlin.bin
+++ b/macros/lsqnonlin.bin
diff --git a/macros/lsqnonlin.sci b/macros/lsqnonlin.sci
index 22df579..823d088 100644
--- a/macros/lsqnonlin.sci
+++ b/macros/lsqnonlin.sci
@@ -175,7 +175,7 @@ function [xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin (va
 		ub = varargin(4);
 	end
 
-	if ( rhs<7 | size(varargin(5)) ==0 ) then
+	if ( rhs<5 | size(varargin(5)) ==0 ) then
 		param = list();
 	else
 		param =varargin(5);
@@ -240,7 +240,7 @@ function [xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin (va
 
   	//To check the match between fun (1st Parameter) and x0 (2nd Parameter)
    	if(execstr('init=_fun(x0)','errcatch')==21) then
-		errmsg = msprintf(gettext("%s: Objective function and x0 did not match"), "fmincon");
+		errmsg = msprintf(gettext("%s: Objective function and x0 did not match"), "lsqnonlin");
    		error(errmsg);
 	end
 	
@@ -309,7 +309,12 @@ function [xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin (va
 	
     options(6) = __fGrad;
 	
-	[xopt,fopt,exitflag,output,lambda_fmincon,gradient] = fmincon(__fun,x0,[],[],[],[],lb,ub,[],options);
+    function [c, ceq] = nlc(x)
+        c = [];
+        ceq = [];
+	endfunction
+
+	[xopt,fopt,exitflag,output,lambda_fmincon,gradient] = fmincon(__fun,x0,[],[],[],[],lb,ub,nlc,options);
 	
 	lambda = struct("lower", lambda_fmincon.lower,"upper",lambda_fmincon.upper);
 	
diff --git a/macros/names b/macros/names
index dc01f55..21c7d4e 100644
--- a/macros/names
+++ b/macros/names
@@ -8,6 +8,7 @@ fminbnd
 fmincon
 fminimax
 fminunc
+fseminf
 linprog
 lsqlin
 lsqnonlin
diff --git a/sci_gateway/cpp/sci_ipoptfmincon.cpp b/sci_gateway/cpp/sci_ipoptfmincon.cpp
index f19bbeb..c19687d 100644
--- a/sci_gateway/cpp/sci_ipoptfmincon.cpp
+++ b/sci_gateway/cpp/sci_ipoptfmincon.cpp
@@ -147,7 +147,7 @@ int sci_solveminconp(char *fname)
 
 	///////// Initialize the IpoptApplication and process the options /////////
 	ApplicationReturnStatus status;
- 	status = app->Initialize();
+	status = app->Initialize();
 	if (status != Solve_Succeeded) 
 	{
 	  	sciprint("\n*** Error during initialization!\n");
diff --git a/tests/general_tests/fminbnd/fminbnd_logical10.sce b/tests/general_tests/fminbnd/fminbnd_logical10.sce
new file mode 100644
index 0000000..d36072a
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical10.sce
@@ -0,0 +1,30 @@
+
+function y = fun(x)
+	y = -1/(1-x*x);
+endfunction
+x1 = [0];
+x2 = [1000];
+
+//Output
+//
+//Error in step computation (regularization becomes too large?)!
+// lambda  =
+// 
+//   lower: [0x0 constant]
+//   upper: [0x0 constant]
+// output  =
+// 
+//   Iterations: 108
+//   Cpu_Time: 0.064
+//   Message: "Error in step computation (regularization becomes too large?)!"
+// exitflag  =
+// 
+//  10  
+// fopt  =
+// 
+//     []
+// xopt  =
+// 
+//     []
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
+
diff --git a/tests/general_tests/fminbnd/fminbnd_logical11.sce b/tests/general_tests/fminbnd/fminbnd_logical11.sce
new file mode 100644
index 0000000..0681d30
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical11.sce
@@ -0,0 +1,31 @@
+
+function y = fun(x)
+	y = -sqrt(x*x);
+endfunction
+x1 = [-10];
+x2 = [-5];
+
+//Output
+//
+//Optimal Solution Found.
+// lambda  =
+// 
+//   lower: 1.0000018
+//   upper: 0.0000018
+// output  =
+// 
+//   Iterations: 5
+//   Cpu_Time: 0.036
+//   Objective_Evaluation: 6
+//   Dual_Infeasibility: 2.390D-11
+//   Message: "Optimal Solution Found"
+// exitflag  =
+// 
+//  0  
+// fopt  =
+// 
+//  - 9.9999909  
+// xopt  =
+// 
+//  - 9.9999909
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
diff --git a/tests/general_tests/fminbnd/fminbnd_logical12.sce b/tests/general_tests/fminbnd/fminbnd_logical12.sce
new file mode 100644
index 0000000..0613aa8
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical12.sce
@@ -0,0 +1,31 @@
+
+function y = fun(x)
+	y = log(x);
+endfunction
+x1 = [-10];
+x2 = [0];
+
+//Output
+//
+//Optimal Solution Found.
+// lambda  =
+// 
+//   lower: 0.0000018
+//   upper: 0.0000018
+// output  =
+// 
+//   Iterations: 4
+//   Cpu_Time: 0.028
+//   Objective_Evaluation: 5
+//   Dual_Infeasibility: 0
+//   Message: "Optimal Solution Found"
+// exitflag  =
+// 
+//  0  
+// fopt  =
+// 
+//    0.  
+// xopt  =
+// 
+//  - 0.7727429
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
diff --git a/tests/general_tests/fminbnd/fminbnd_logical13.sce b/tests/general_tests/fminbnd/fminbnd_logical13.sce
new file mode 100644
index 0000000..64debea
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical13.sce
@@ -0,0 +1,32 @@
+
+function y = fun(x)
+	y = -sqrt(x^3);
+endfunction
+x1 = [-10];
+x2 = [-5];
+
+//Output
+//
+//Optimal Solution Found.
+// lambda  =
+// 
+//   lower: 0.0000036
+//   upper: 0.0000036
+// output  =
+// 
+//   Iterations: 4
+//   Cpu_Time: 0.028
+//   Objective_Evaluation: 5
+//   Dual_Infeasibility: 0
+//   Message: "Optimal Solution Found"
+// exitflag  =
+// 
+//  0  
+// fopt  =
+// 
+//    0.  
+// xopt  =
+// 
+//  - 5.4668503 
+
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
diff --git a/tests/general_tests/fminbnd/fminbnd_logical3.sce b/tests/general_tests/fminbnd/fminbnd_logical3.sce
new file mode 100644
index 0000000..2687733
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical3.sce
@@ -0,0 +1,33 @@
+
+function y = fun(x)
+	y = -1/x;
+endfunction
+x1 = [0];
+x2 = [1.5];
+
+//Output
+//
+//Stop at Tiny Step
+// lambda  =
+// 
+//   lower: 1.235D+14
+//   upper: 0.0000061
+// output  =
+// 
+//   Iterations: 1.663D+09
+//   Cpu_Time: 0.112
+//   Objective_Evaluation: 34
+//   Dual_Infeasibility: 1747.2543
+//   Message: "Stop at Tiny Step"
+// exitflag  =
+// 
+//  3  
+// fopt  =
+// 
+//  - 11111111.  
+// xopt  =
+// 
+//    1.000D-07 
+
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
+
diff --git a/tests/general_tests/fminbnd/fminbnd_logical4.sce b/tests/general_tests/fminbnd/fminbnd_logical4.sce
new file mode 100644
index 0000000..69c6960
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical4.sce
@@ -0,0 +1,33 @@
+
+function y = fun(x)
+	y = 1/x;
+endfunction
+x1 = [-10];
+x2 = [0];
+
+//Output
+//
+//Optimal Solution Found.
+// lambda  =
+// 
+//   lower: 0.0000013
+//   upper: 1.235D+14
+// output  =
+// 
+//   Iterations: 18
+//   Cpu_Time: 0.096
+//   Objective_Evaluation: 19
+//   Dual_Infeasibility: 0.0232831
+//   Message: "Optimal Solution Found"
+// exitflag  =
+// 
+//  0  
+// fopt  =
+// 
+//  - 11111339.  
+// xopt  =
+// 
+//  - 1.000D-07  
+
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
+
diff --git a/tests/general_tests/fminbnd/fminbnd_logical5.sce b/tests/general_tests/fminbnd/fminbnd_logical5.sce
new file mode 100644
index 0000000..0e9f670
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical5.sce
@@ -0,0 +1,31 @@
+
+function y = fun(x)
+	y = -1/(x*x);
+endfunction
+x1 = [0];
+x2 = [1.5];
+
+//Output
+//
+//Error in step computation (regularization becomes too large?)!
+// lambda  =
+// 
+//   lower: [0x0 constant]
+//   upper: [0x0 constant]
+// output  =
+// 
+//   Iterations: 108
+//   Cpu_Time: 0.06
+//   Message: "Error in step computation (regularization becomes too large?)!"
+// exitflag  =
+// 
+//  10  
+// fopt  =
+// 
+//     []
+// xopt  =
+// 
+//     []
+
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
+
diff --git a/tests/general_tests/fminbnd/fminbnd_logical6.sce b/tests/general_tests/fminbnd/fminbnd_logical6.sce
new file mode 100644
index 0000000..d030a86
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical6.sce
@@ -0,0 +1,31 @@
+
+function y = fun(x)
+	y = 1/(x*x);
+endfunction
+x1 = [-10];
+x2 = [0];
+
+//Output
+//
+//Error in step computation (regularization becomes too large?)!
+// lambda  =
+// 
+//   lower: [0x0 constant]
+//   upper: [0x0 constant]
+// output  =
+// 
+//   Iterations: 108
+//   Cpu_Time: 0.06
+//   Message: "Error in step computation (regularization becomes too large?)!"
+// exitflag  =
+// 
+//  10  
+// fopt  =
+// 
+//     []
+// xopt  =
+// 
+//     []
+
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
+
diff --git a/tests/general_tests/fminbnd/fminbnd_logical7.sce b/tests/general_tests/fminbnd/fminbnd_logical7.sce
new file mode 100644
index 0000000..8207f92
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical7.sce
@@ -0,0 +1,33 @@
+
+function y = fun(x)
+	y = exp(x);
+endfunction
+x1 = [-1000];
+x2 = [1000];
+
+//Output
+//
+//Optimal Solution Found.
+// lambda  =
+// 
+//   lower: 9.183D-09
+//   upper: 9.001D-09
+// output  =
+// 
+//   Iterations: 10
+//   Cpu_Time: 0.064
+//   Objective_Evaluation: 11
+//   Dual_Infeasibility: 0.0000455
+//   Message: "Optimal Solution Found"
+// exitflag  =
+// 
+//  0  
+// fopt  =
+// 
+//    0.0000455  
+// xopt  =
+// 
+//  - 9.9979363  
+
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
+
diff --git a/tests/general_tests/fminbnd/fminbnd_logical8.sce b/tests/general_tests/fminbnd/fminbnd_logical8.sce
new file mode 100644
index 0000000..fa0785c
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical8.sce
@@ -0,0 +1,33 @@
+
+function y = fun(x)
+	y = sin(x)*exp(x);
+endfunction
+x1 = [-1000];
+x2 = [1000];
+
+//Output
+//
+//Optimal Solution Found.
+// lambda  =
+// 
+//   lower: 9.183D-09
+//   upper: 9.001D-09
+// output  =
+// 
+//   Iterations: 10
+//   Cpu_Time: 0.064
+//   Objective_Evaluation: 11
+//   Dual_Infeasibility: 0.0000455
+//   Message: "Optimal Solution Found"
+// exitflag  =
+// 
+//  0  
+// fopt  =
+// 
+//    0.0000455  
+// xopt  =
+// 
+//  - 9.9979363  
+
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
+
diff --git a/tests/general_tests/fminbnd/fminbnd_logical9.sce b/tests/general_tests/fminbnd/fminbnd_logical9.sce
new file mode 100644
index 0000000..7581feb
--- /dev/null
+++ b/tests/general_tests/fminbnd/fminbnd_logical9.sce
@@ -0,0 +1,30 @@
+
+function y = fun(x)
+	y = -1/(x*x);
+endfunction
+x1 = [0];
+x2 = [1000];
+
+//Output
+//
+//Error in step computation (regularization becomes too large?)!
+// lambda  =
+// 
+//   lower: [0x0 constant]
+//   upper: [0x0 constant]
+// output  =
+// 
+//   Iterations: 108
+//   Cpu_Time: 0.064
+//   Message: "Error in step computation (regularization becomes too large?)!"
+// exitflag  =
+// 
+//  10  
+// fopt  =
+// 
+//     []
+// xopt  =
+// 
+//     []
+[xopt,fopt,exitflag,output,lambda] = fminbnd (fun, x1, x2)
+
diff --git a/tests/general_tests/fmincon/fmincon_logical12.sce b/tests/general_tests/fmincon/fmincon_logical12.sce
index 325cd2c..48715a1 100644
--- a/tests/general_tests/fmincon/fmincon_logical12.sce
+++ b/tests/general_tests/fmincon/fmincon_logical12.sce
@@ -1,14 +1,13 @@
-    // Example with objective function and inequality constraints
+// Example with objective function and inequality constraints
 function y=fun(x)
-    k=1
-    y=0
-    for i = 1:20
-       y = y + exp(x(i)) 
-    end
+    y=-sum(exp(x))
 endfunction
+
 x0 = [repmat(1,1,20)];
 A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
 b=[-100 -50]';
+
 lb = repmat(0,1,20);
-k = 0
-[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[])
+ub = repmat(10,1,20);
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,ub)
diff --git a/tests/general_tests/fmincon/fmincon_logical13.sce b/tests/general_tests/fmincon/fmincon_logical13.sce
index 3c6b87d..5c1246c 100644
--- a/tests/general_tests/fmincon/fmincon_logical13.sce
+++ b/tests/general_tests/fmincon/fmincon_logical13.sce
@@ -1,10 +1,7 @@
 // Example with objective function and inequality constraints
 
 function y=fun(x)
-    y=0
-    for i = 1:20
-       y = y + exp(x(i)) 
-    end
+    y=-sum(exp(x));
 endfunction
 
 x0 = repmat(1,1,20);
diff --git a/tests/general_tests/fmincon/fmincon_logical14.sce b/tests/general_tests/fmincon/fmincon_logical14.sce
index 2febc72..33e108f 100644
--- a/tests/general_tests/fmincon/fmincon_logical14.sce
+++ b/tests/general_tests/fmincon/fmincon_logical14.sce
@@ -1,10 +1,7 @@
-// Example with objective function and inequality constraints
+// Example with objective function, inequality constraints and non linear constraints
 
 function y=fun(x)
-    y=0
-    for i = 1:20
-       y = y + exp(x(i)) 
-    end
+    y=-sum(exp(x))
 endfunction
 
 x0 = repmat(1,1,20);
@@ -15,11 +12,7 @@ b=[-100 -50]';
 
 //Nonlinear constraints
 function [c,ceq]=nlc(x)
-    cfor = 0;
-    for i = 1:20
-        cfor = cfor + 2*exp(x(i)) 
-    end
-    c = [ cfor + 1];
+    c = [ sum(2*exp(x)) + 1];
     ceq = [];
 endfunction
 
diff --git a/tests/general_tests/fmincon/fmincon_logical15.sce b/tests/general_tests/fmincon/fmincon_logical15.sce
new file mode 100644
index 0000000..a95cf8d
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical15.sce
@@ -0,0 +1,19 @@
+// Example with objective function, inequality constraints and non linear constraints
+
+function y=fun(x)
+       y = -prod(exp(x))
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(2*exp(x)) + 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical16.sce b/tests/general_tests/fmincon/fmincon_logical16.sce
new file mode 100644
index 0000000..fa28254
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical16.sce
@@ -0,0 +1,18 @@
+// Example with objective function, inequality constraints and non linear constraints
+
+function y=fun(x)
+    y = exp(prod(x));
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+function [c,ceq]=nlc(x)
+    c = [ sum(2*exp(x)) + 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical17.sce b/tests/general_tests/fmincon/fmincon_logical17.sce
new file mode 100644
index 0000000..520b095
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical17.sce
@@ -0,0 +1,13 @@
+// Example with log objective function, inequality constraints
+
+function y=fun(x)
+       y = sum(log(x))
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[])
diff --git a/tests/general_tests/fmincon/fmincon_logical18.sce b/tests/general_tests/fmincon/fmincon_logical18.sce
new file mode 100644
index 0000000..9fdd9d6
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical18.sce
@@ -0,0 +1,14 @@
+// Example with log objective function, inequality constraints
+
+function y=fun(x)
+   y = sum(log(x(i)))
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+ub = repmat(10,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,ub)
diff --git a/tests/general_tests/fmincon/fmincon_logical19.sce b/tests/general_tests/fmincon/fmincon_logical19.sce
new file mode 100644
index 0000000..733e1ec
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical19.sce
@@ -0,0 +1,19 @@
+// Example with log objective function, inequality constraints and non linear constraints
+
+function y=fun(x)
+    y=sum(log(x))
+endfunction
+
+x0 = repmat(1,1,3);
+lb = repmat(0,1,3);
+ub = repmat(10,1,3);
+
+A=[-1,-5,-3;];
+b=[-100]';
+
+function [c,ceq]=nlc(x)
+c = [- 0.5*log(x(1)) - 2.5*log(x(2)) - 1.5*log(x(3)) + 50];
+ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,ub)
diff --git a/tests/general_tests/fmincon/fmincon_logical20.sce b/tests/general_tests/fmincon/fmincon_logical20.sce
new file mode 100644
index 0000000..6a2d92a
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical20.sce
@@ -0,0 +1,19 @@
+// Example with log objective function, inequality constraints and non linear constraints
+
+function y=fun(x)
+   y = sum(log(x))
+endfunction
+
+x0 = repmat(1,1,3);
+lb = repmat(0,1,3);
+ub = repmat(10,1,3);
+
+A=[-1,-5,-3;];
+b=[-100]';
+
+function [c,ceq]=nlc(x)
+c = [log(x(1)) + log(x(2)) + log(x(3)) + 1];
+ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,ub)
diff --git a/tests/general_tests/fmincon/fmincon_logical21.sce b/tests/general_tests/fmincon/fmincon_logical21.sce
new file mode 100644
index 0000000..856fa3a
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical21.sce
@@ -0,0 +1,18 @@
+// Example with log objective function, inequality constraints
+
+function y=fun(x)
+   y = -prod(log(x))
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+function [c,ceq]=nlc(x)
+    c = [ sum(log(x)) + 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical22.sce b/tests/general_tests/fmincon/fmincon_logical22.sce
new file mode 100644
index 0000000..037e9b1
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical22.sce
@@ -0,0 +1,19 @@
+// Example with log objective function, inequality constraints
+
+function y=fun(x)
+    y = log(prod(x));
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(log(x)) + 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical23.sce b/tests/general_tests/fmincon/fmincon_logical23.sce
new file mode 100644
index 0000000..59bd9dd
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical23.sce
@@ -0,0 +1,11 @@
+// Example with objective function and inequality constraints
+function y=fun(x)
+    y=sum((sin(x)).^2 + (cos(x)).^2)
+endfunction
+
+x0 = [repmat(1,1,3)];
+A=[-1,-5,-3; -0.5,-2.5 -1.5;];
+b=[-100 -50]';
+lb = repmat(0,1,3);
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[])
diff --git a/tests/general_tests/fmincon/fmincon_logical24.sce b/tests/general_tests/fmincon/fmincon_logical24.sce
new file mode 100644
index 0000000..7f3c42e
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical24.sce
@@ -0,0 +1,16 @@
+// Example with objective function and inequality constraints
+function y=fun(x)
+    y=sum((sin(x)).^2 + (cos(x)).^2)
+endfunction
+
+x0 = [repmat(1,1,3)];
+A=[-1,-5,-3; -0.5,-2.5 -1.5;];
+b=[-100 -50]';
+lb = repmat(0,1,3);
+
+function [c,ceq]=nlc(x)
+    c = [ -sum((sin(x)).^2 + (cos(x)).^2) + 1.5];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical25.sce b/tests/general_tests/fmincon/fmincon_logical25.sce
new file mode 100644
index 0000000..bc94124
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical25.sce
@@ -0,0 +1,12 @@
+// Example with objective function and inequality constraints
+function y=fun(x)
+    y=sum(1/(cos(x)))
+endfunction
+
+x0 = [repmat(1,1,3)];
+A=[-1,-5,-3;];
+b=[-100]';
+lb = repmat(0,1,3);
+ub = repmat(10,1,3);
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,ub)
diff --git a/tests/general_tests/fmincon/fmincon_logical26.sce b/tests/general_tests/fmincon/fmincon_logical26.sce
new file mode 100644
index 0000000..9535ed8
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical26.sce
@@ -0,0 +1,17 @@
+// Example with objective function and inequality constraints
+function y=fun(x)
+    y=sum(sin(x))
+endfunction
+
+x0 = [repmat(1,1,3)];
+A=[-1,-5,-3; -0.5,-2.5 -1.5;];
+b=[-100 -50]';
+lb = repmat(0,1,3);
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(sin(x)./cos(x)) + 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical27.sce b/tests/general_tests/fmincon/fmincon_logical27.sce
new file mode 100644
index 0000000..2934f2a
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical27.sce
@@ -0,0 +1,19 @@
+// Example with objective function, inequality constraints and non linear constraints
+
+function y=fun(x)
+       y = -prod(tan(x))
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(2*cos(x)) - 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical28.sce b/tests/general_tests/fmincon/fmincon_logical28.sce
new file mode 100644
index 0000000..08f3b06
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical28.sce
@@ -0,0 +1,19 @@
+// Example with objective function, inequality constraints and non linear constraints
+
+function y=fun(x)
+       y = cos(prod(x))
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(2*cos(x)) - 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical29.sce b/tests/general_tests/fmincon/fmincon_logical29.sce
new file mode 100644
index 0000000..649d7b0
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical29.sce
@@ -0,0 +1,11 @@
+// Example with objective function and inequality constraints
+function y=fun(x)
+    y=sum(exp(x) + cos(x).^2)
+endfunction
+
+x0 = [repmat(1,1,3)];
+A=[-1,-5,-3; -0.5,-2.5 -1.5;];
+b=[-100 -50]';
+lb = repmat(0,1,3);
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[])
diff --git a/tests/general_tests/fmincon/fmincon_logical30.sce b/tests/general_tests/fmincon/fmincon_logical30.sce
new file mode 100644
index 0000000..cbcbac2
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical30.sce
@@ -0,0 +1,17 @@
+// Example with objective function and inequality constraints
+function y=fun(x)
+    y=sum(log(x) + cos(x).^2)
+endfunction
+
+x0 = [repmat(1,1,3)];
+A=[-1,-5,-3; -0.5,-2.5 -1.5;];
+b=[-100 -50]';
+lb = repmat(0,1,3);
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ -sum((sin(x)).^2 + (cos(x)).^2) + 1.5];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical31.sce b/tests/general_tests/fmincon/fmincon_logical31.sce
new file mode 100644
index 0000000..4adfa1d
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical31.sce
@@ -0,0 +1,17 @@
+// Example with objective function and inequality constraints
+function y=fun(x)
+    y=sum(1/exp(x))
+endfunction
+
+x0 = [repmat(1,1,3)];
+A=[-1,-5,-3;];
+b=[-100];
+lb = repmat(0,1,3);
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(log(x)) - 10];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical32.sce b/tests/general_tests/fmincon/fmincon_logical32.sce
new file mode 100644
index 0000000..3684613
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical32.sce
@@ -0,0 +1,17 @@
+// Example with objective function and inequality constraints
+function y=fun(x)
+    y=sum(sin(x))
+endfunction
+
+x0 = [repmat(3,1,3)];
+A=[-1,-5,-3; -0.5,-2.5 -1.5;];
+b=[-100 -50]';
+lb = repmat(0,1,3);
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(exp(x)./log(x)) - 10];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical33.sce b/tests/general_tests/fmincon/fmincon_logical33.sce
new file mode 100644
index 0000000..468aa94
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical33.sce
@@ -0,0 +1,19 @@
+// Example with objective function, inequality constraints and non linear constraints
+
+function y=fun(x)
+       y = -prod(tan(x))
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(2*exp(x)) - 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/fmincon/fmincon_logical34.sce b/tests/general_tests/fmincon/fmincon_logical34.sce
new file mode 100644
index 0000000..b615af8
--- /dev/null
+++ b/tests/general_tests/fmincon/fmincon_logical34.sce
@@ -0,0 +1,19 @@
+// Example with objective function, inequality constraints and non linear constraints
+
+function y=fun(x)
+       y = exp(prod(x))
+endfunction
+
+x0 = repmat(1,1,20);
+lb = repmat(0,1,20);
+
+A=[-1,-5,-3 repmat(0,1,17); -0.5,-2.5 -1.5 repmat(0,1,17);];
+b=[-100 -50]';
+
+//Nonlinear constraints
+function [c,ceq]=nlc(x)
+    c = [ sum(2*cos(x) + log(x)) - 1];
+    ceq = [];
+endfunction
+
+[xopt,fopt,exitflag,output,lambda,gradient,hessian] = fmincon (fun, x0,A,b,[],[],lb,[],nlc)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_A1.sce b/tests/general_tests/lsqnonlin/lsqnonlin_A1.sce
deleted file mode 100644
index 1c2128b..0000000
--- a/tests/general_tests/lsqnonlin/lsqnonlin_A1.sce
+++ /dev/null
@@ -1,19 +0,0 @@
-// Check for elements in A
-C = [2 0;
-	-1 1;
-	 0 2]
-d = [1
-	 0
-    -1];
-A = [10 -2 0;
-	 -2 10 0];
-b = [4
-    -4];
-
-//Error
-//lsqlin: The number of columns in A must be the same as the number of columns in C
-//at line     213 of function lsqlin called by :  
-//[xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
-
-[xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
-
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_input1.sce b/tests/general_tests/lsqnonlin/lsqnonlin_input1.sce
new file mode 100644
index 0000000..6a4ec5a
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_input1.sce
@@ -0,0 +1,29 @@
+// Check for the number of input arguments
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+//Error
+//lsqnonlin: Unexpected number of input arguments : 1 provided while should be in the set of [2 4 5]
+//at line     139 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_input2.sce b/tests/general_tests/lsqnonlin/lsqnonlin_input2.sce
new file mode 100644
index 0000000..67a0b4b
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_input2.sce
@@ -0,0 +1,29 @@
+// Check for the number of input arguments
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+//Error
+//lsqnonlin: Unexpected number of input arguments : 6 provided while should be in the set of [2 4 5]
+//at line     139 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],[],[])
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],[],[])
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_lb1.sce b/tests/general_tests/lsqnonlin/lsqnonlin_lb1.sce
new file mode 100644
index 0000000..9d24f57
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_lb1.sce
@@ -0,0 +1,32 @@
+// Check for elements in lb
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+lb = [0 0 0]
+ub = [10 10]
+
+//Error
+//lsqnonlin: The Lower Bound is not equal to the number of variables
+//at line     246 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_lb2.sce b/tests/general_tests/lsqnonlin/lsqnonlin_lb2.sce
new file mode 100644
index 0000000..f5ea221
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_lb2.sce
@@ -0,0 +1,32 @@
+// Check for elements in lb
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+lb = [0]
+ub = [10 10]
+
+//Error
+//lsqnonlin: The Lower Bound is not equal to the number of variables
+//at line     246 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_lbub.sce b/tests/general_tests/lsqnonlin/lsqnonlin_lbub.sce
new file mode 100644
index 0000000..eabc439
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_lbub.sce
@@ -0,0 +1,32 @@
+// Check for elements in lb and ub
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+lb = [10 10]
+ub = [0 0]
+
+//Error
+//lsqnonlin: Problem has inconsistent variable bounds
+//at line     270 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_logical1.sce b/tests/general_tests/lsqnonlin/lsqnonlin_logical1.sce
index aef546f..ab4a7c7 100644
--- a/tests/general_tests/lsqnonlin/lsqnonlin_logical1.sce
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_logical1.sce
@@ -1,7 +1,10 @@
-function retF = testmyfun(x)
+function [y,dy] = testmyfun(x)
 	km = [1:10]';
-	retF = 2 + 2*km-exp(km*x(1))-exp(km*x(2));
+	y = 2 + 2*km-exp(km*x(1))-exp(km*x(2));
 endfunction
 
-x0 = [0.3 0.4]'
-[x,resnorm] = lsqnonlin(testmyfun,x0) 
+x0 = [0.2 0.2]'
+
+options = list("GradObj","off")
+
+[x,resnorm,residual] = lsqnonlin(testmyfun,x0,[],[],options)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_maxiter.sce b/tests/general_tests/lsqnonlin/lsqnonlin_maxiter.sce
new file mode 100644
index 0000000..3c13ff4
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_maxiter.sce
@@ -0,0 +1,65 @@
+// Check for the maximum iteration
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+options = list("MaxIter",10)
+
+//Error
+//Maximum Number of Iterations Exceeded. Output may not be optimal.
+// gradient  =
+// 
+//    512.91855  - 4714.171  
+// lambda  =
+// 
+//   lower: [0,0]
+//   upper: [0,0]
+// output  =
+// 
+//   Iterations: 10
+//   Cpu_Time: 0.12
+//   Objective_Evaluation: 11
+//   Dual_Infeasibility: 4714.171
+//   Message: "Maximum Number of Iterations Exceeded. Output may not be optimal"
+// exitflag  =
+// 
+//  1  
+// residual  =
+// 
+//    4.8006782  
+//    5.767661   
+//    6.7598659  
+//    7.8617282  
+//    9.0596638  
+//    10.40234   
+//    11.920987  
+//    13.599744  
+//    15.516066  
+//    17.701171  
+// resnorm  =
+// 
+//    1235.2439  
+// xopt  =
+// 
+//    4.9162235  
+//  - 0.5142398
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_param1.sce b/tests/general_tests/lsqnonlin/lsqnonlin_param1.sce
new file mode 100644
index 0000000..e303099
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_param1.sce
@@ -0,0 +1,32 @@
+// Check for the params
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+options = 0;
+
+//Error
+//lsqnonlin: Expected type ["list"] for input argument param at input #5, but got "constant" instead.
+//at line      56 of function Checktype called by :  
+//at line     186 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_param2.sce b/tests/general_tests/lsqnonlin/lsqnonlin_param2.sce
new file mode 100644
index 0000000..659294a
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_param2.sce
@@ -0,0 +1,31 @@
+// Check for the params
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+options = list("Maxiter")
+
+//Error
+//lsqnonlin: Size of parameters should be even
+//at line     190 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,[],[],options)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_ub1.sce b/tests/general_tests/lsqnonlin/lsqnonlin_ub1.sce
new file mode 100644
index 0000000..37ca056
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_ub1.sce
@@ -0,0 +1,32 @@
+// Check for elements in ub
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+lb = [0 0]
+ub = [10 10 10]
+
+//Error
+//lsqnonlin: The Upper Bound is not equal to the number of variables
+//at line     252 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_ub2.sce b/tests/general_tests/lsqnonlin/lsqnonlin_ub2.sce
new file mode 100644
index 0000000..2206b2f
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_ub2.sce
@@ -0,0 +1,32 @@
+// Check for elements in ub
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5; 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+lb = [0 0]
+ub = [10]
+
+//Error
+//lsqnonlin: The Upper Bound is not equal to the number of variables
+//at line     252 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0,lb,ub)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_x01.sce b/tests/general_tests/lsqnonlin/lsqnonlin_x01.sce
new file mode 100644
index 0000000..41dfb26
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_x01.sce
@@ -0,0 +1,29 @@
+// Check if the x0 is column matrix
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5 0.8];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+//Error
+//lsqcurvefit: Initial Guess should be a column matrix
+//at line     156 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0')
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0)
diff --git a/tests/general_tests/lsqnonlin/lsqnonlin_x02.sce b/tests/general_tests/lsqnonlin/lsqnonlin_x02.sce
new file mode 100644
index 0000000..00df3d4
--- /dev/null
+++ b/tests/general_tests/lsqnonlin/lsqnonlin_x02.sce
@@ -0,0 +1,29 @@
+// Check for elements in x0
+
+function y=yth(t, x)
+y  = x(1)*exp(-x(2)*t)
+endfunction
+// we have the m measures (ti, yi):
+m = 10;
+tm = [0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5]';
+ym = [0.79, 0.59, 0.47, 0.36, 0.29, 0.23, 0.17, 0.15, 0.12, 0.08]';
+// measure weights (here all equal to 1...)
+wm = ones(m,1);
+// and we want to find the parameters x such that the model fits the given
+// data in the least square sense:
+//
+//  minimize  f(x) = sum_i  wm(i)^2 ( yth(tm(i),x) - ym(i) )^2
+// initial parameters guess
+x0 = [1.5];
+// in the first examples, we define the function fun and dfun
+// in scilab language
+function y=myfun(x, tm, ym, wm)
+y = wm.*( yth(tm, x) - ym )
+endfunction
+
+//Error
+//lsqnonlin: Objective function and x0 did not match
+//at line     233 of function lsqnonlin called by :  
+//[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0)
+
+[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0)