lsqnonlin added

8 files changed, 318 insertions, 440 deletions

diff --git a/macros/fminbnd.bin b/macros/fminbnd.bin
index 97b00fc..de98f29 100644
--- a/macros/fminbnd.bin
+++ b/macros/fminbnd.bin
diff --git a/macros/fminunc.bin b/macros/fminunc.bin
index aa82fc3..3bda54f 100644
--- a/macros/fminunc.bin
+++ b/macros/fminunc.bin
diff --git a/macros/lib b/macros/lib
index 612b878..08d754b 100644
--- a/macros/lib
+++ b/macros/lib
diff --git a/macros/lsqcurvefit.bin b/macros/lsqcurvefit.bin
deleted file mode 100644
index 20a8d0d..0000000
--- a/macros/lsqcurvefit.bin
+++ /dev/null
diff --git a/macros/lsqcurvefit.sci b/macros/lsqcurvefit.sci
deleted file mode 100644
index 10e8e48..0000000
--- a/macros/lsqcurvefit.sci
+++ /dev/null
@@ -1,439 +0,0 @@
-// 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: Harpreet Singh
-// Organization: FOSSEE, IIT Bombay
-// Email: toolbox@scilab.in
-
-function [xopt,resnorm,residual,exitflag,output,lambda] = lsqcurvefit (varargin)
-	// Solves a non linear data fitting problems.
-	//
-	//   Calling Sequence
-	//   xopt = lsqcurvefit(fun,x0,xdata,ydata)
-	//   xopt = lsqcurvefit(fun,x0,xdata,ydata,lb,ub)
-	//   xopt = lsqcurvefit(fun,x0,xdata,ydata,lb,ub,options)
-	//   [xopt,resnorm] = lsqcurvefit( ... )
-	//   
-	//   Parameters
-	//   C : a matrix of double, represents the multiplier of the solution x in the expression C⋅x - d. Number of columns in C is equal to the number of elements in x.
-	//   d : a vector of double, represents the additive constant term in the expression C⋅x - d. Number of elements in d is equal to the number of rows in C matrix.
-	//   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.
-	//   x0 : a vector of double, contains initial guess of variables.
-	//   param : a list containing the parameters to be set.
-	//   xopt : a vector of double, the computed solution of the optimization problem.
-	//   resnorm : a double, objective value returned as the scalar value norm(C⋅x-d)^2.
-	//   residual : a vector of double, solution residuals returned as the vector d-C⋅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 linear least square problem specified by :
-	//
-	//   <latex>
-	//    \begin{eqnarray}
-	//    &\mbox{min}_{x}
-	//    & 1/2||C⋅x - d||_2^2  \\
-	//    & \text{subject to} & A⋅x \leq b \\
-	//    & & Aeq⋅x = beq \\
-	//    & & lb \leq x \leq ub \\
-	//    \end{eqnarray}
-	//   </latex>
-	//   
-	//   The routine calls Ipopt for solving the linear least square 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", [---]);</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>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 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.constrviolation: The max-norm of the constraint violation.</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>
-	// </itemizedlist>
-	//
-	// Examples
-	// //A simple linear least square example
-	// C = [ 2 0;
-	//		-1 1;
-	//		 0 2]
-	// d = [1
-	//		0
-	//	   -1];
-	// A = [10 -2;
-	//		-2 10];
-	// b = [4
-	//     -4];
-	// [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b)
-	// // Press ENTER to continue 
-	//    
-	// Examples
-	// //A basic example for equality, inequality constraints and variable bounds
-	//		C = [1 1 1;
-	//			1 1 0;
-	//			0 1 1;
-	//			1 0 0;
-	//			0 0 1]
-	//		d = [89;
-	//			67;
-	//			53;
-	//			35;
-	//			20;]
-	//		A = [3 2 1;
-	//			2 3 4;
-	//			1 2 3];
-	//		b = [191
-	//			209
-	//			162];
-	//	 Aeq = [1 2 1];
-	//	 beq = 10;
-	//	 lb = repmat(0.1,3,1);
-	//	 ub = repmat(4,3,1);
-	//	 [xopt,resnorm,residual,exitflag,output,lambda] = lsqlin(C,d,A,b,Aeq,beq,lb,ub)
-	// Authors
-	// Harpreet Singh
-
-
-	//To check the number of input and output argument
-	[lhs , rhs] = argn();
-
-	//To check the number of argument given by user
-	if ( rhs < 4 | rhs == 5 | rhs == 7 | rhs > 10 ) then
-		errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be in the set of [4 6 8 9 10]"), "lsqlin", rhs);
-		error(errmsg)
-	end
-
-// Initializing all the values to empty matrix
-	C=[];
-	d=[];
-	A=[];
-	b=[];
-	Aeq=[];
-	beq=[];
-	lb=[];
-	ub=[];
-	x0=[];
-
-	C = varargin(1);
-	d = varargin(2);
-	A = varargin(3);
-	b = varargin(4);
-	nbVar = size(C,2);	
-
-	if(nbVar == 0) then
-		errmsg = msprintf(gettext("%s: Cannot determine the number of variables because input objective coefficients is empty"), "lsqlin");
-		error(errmsg);
-	end
-
-	if ( rhs<5 ) then
-		Aeq = []
-		beq = []
-	else
-		Aeq = varargin(5);
-		beq = varargin(6);
-	end
-
-	if ( rhs<7 ) then
-		lb = repmat(-%inf,nbVar,1);
-		ub = repmat(%inf,nbVar,1);
-	else
-		lb = varargin(7);
-		ub = varargin(8);
-	end
-
-
-	if ( rhs<9 | size(varargin(9)) ==0 ) then
-		x0 = repmat(0,nbVar,1)
-	else
-		x0 = varargin(9);
-	end
-
-	if ( rhs<10 | size(varargin(10)) ==0 ) then
-		param = list();
-	else
-		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
-
-	if (type(param) ~= 15) then
-		errmsg = msprintf(gettext("%s: param should be a list "), "lsqlin");
-		error(errmsg);
-	end
-
-	//Check type of variables
-	Checktype("lsqlin", C, "C", 1, "constant")
-	Checktype("lsqlin", d, "d", 2, "constant")
-	Checktype("lsqlin", A, "A", 3, "constant")
-	Checktype("lsqlin", b, "b", 4, "constant")
-	Checktype("lsqlin", Aeq, "Aeq", 5, "constant")
-	Checktype("lsqlin", beq, "beq", 6, "constant")
-	Checktype("lsqlin", lb, "lb", 7, "constant")
-	Checktype("lsqlin", ub, "ub", 8, "constant")
-	Checktype("lsqlin", x0, "x0", 9, "constant")
-
-	if (modulo(size(param),2)) then
-		errmsg = msprintf(gettext("%s: Size of parameters should be even"), "lsqlin");
-		error(errmsg);
-	end
-
-	options = list(	"MaxIter"   , [3000], ...
-					"CpuTime"   , [600] ...
-					);
-
-	for i = 1:(size(param))/2
-
-		select convstr(param(2*i-1),'l')
-			case "maxiter" then
-				options(2*i) = param(2*i);
-			case "cputime" then
-				options(2*i) = param(2*i);
-			else
-				errmsg = msprintf(gettext("%s: Unrecognized parameter name ''%s''."), "lsqlin", param(2*i-1));
-				error(errmsg)
-		end
-	end
-
-	nbConInEq = size(A,1);
-	nbConEq = size(Aeq,1);
-
-	// Check if the user gives row vector 
-	// and Changing it to a column matrix
-
-	if (size(d,2)== [nbVar]) then
-		d=d';
-	end
-
-	if (size(lb,2)== [nbVar]) then
-		lb = lb';
-	end
-
-	if (size(ub,2)== [nbVar]) then
-		ub = ub';
-	end
-
-	if (size(b,2)==nbConInEq) then
-		b = b';
-	end
-
-	if (size(beq,2)== nbConEq) then
-		beq = beq';
-	end
-
-	if (size(x0,2)== [nbVar]) then
-		x0=x0';
-	end
-
-	//Check the size of d which should equal to the number of variable
-	if ( size(d,1) ~= size(C,1)) then
-		errmsg = msprintf(gettext("%s: The number of rows in C must be equal the number of elements of d"), "lsqlin");
-		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 columns in C"), "lsqlin");
-		error(errmsg);
-	end
-
-	//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 columns in C"), "lsqlin");
-		error(errmsg);
-	end
-
-	//Check the size of Lower Bound which should be equal to the number of variables
-	if ( size(lb,1) ~= nbVar) then
-		errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "lsqlin");
-		error(errmsg);
-	end
-
-	//Check the size of Upper Bound which should equal to the number of variables
-	if ( size(ub,1) ~= nbVar) then
-		errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "lsqlin");
-		error(errmsg);
-	end
-
-	//Check the size of constraints of Lower Bound which should equal to the number of constraints
-	if ( size(b,1) ~= nbConInEq & size(b,1) ~= 0) then
-		errmsg = msprintf(gettext("%s: The number of rows in A must be the same as the number of elements of b"), "lsqlin");
-		error(errmsg);
-	end
-
-	//Check the size of constraints of Upper Bound which should equal to the number of constraints
-	if ( size(beq,1) ~= nbConEq & size(beq,1) ~= 0) then
-		errmsg = msprintf(gettext("%s: The number of rows in Aeq must be the same as the number of elements of beq"), "lsqlin");
-		error(errmsg);
-	end
-
-	//Check the size of initial of variables which should equal to the number of variables
-	if ( size(x0,1) ~= nbVar) then
-		warnmsg = msprintf(gettext("%s: Ignoring initial guess of variables as it is not equal to the number of variables"), "lsqlin");
-		warning(warnmsg);
-		x0 = repmat(0,nbVar,1);
-	end
-
-	//Check if the user gives a matrix instead of a vector
-
-	if ((size(d,1)~=1)& (size(d,2)~=1)) then
-		errmsg = msprintf(gettext("%s: d should be a vector"), "lsqlin");
-		error(errmsg); 
-	end
-
-	if (size(lb,1)~=1)& (size(lb,2)~=1) then
-		errmsg = msprintf(gettext("%s: Lower Bound should be a vector"), "lsqlin");
-		error(errmsg); 
-	end
-
-	if (size(ub,1)~=1)& (size(ub,2)~=1) then
-		errmsg = msprintf(gettext("%s: Upper Bound should be a vector"), "lsqlin");
-		error(errmsg); 
-	end
-
-	if (nbConInEq) then
-		if ((size(b,1)~=1)& (size(b,2)~=1)) then
-			errmsg = msprintf(gettext("%s: Constraint Lower Bound should be a vector"), "lsqlin");
-			error(errmsg); 
-		end
-	end
-
-	if (nbConEq) then
-		if (size(beq,1)~=1)& (size(beq,2)~=1) then
-			errmsg = msprintf(gettext("%s: Constraint should be a vector"), "lsqlin");
-			error(errmsg); 
-		end
-	end
-
-	for i = 1:nbConInEq
-		if (b(i) == -%inf)
-		   	errmsg = msprintf(gettext("%s: Value of b can not be negative infinity"), "lsqlin");
-            error(errmsg); 
-        end	
-	end
-    
-	for i = 1:nbConEq
-		if (beq(i) == -%inf)
-		   	errmsg = msprintf(gettext("%s: Value of beq can not be negative infinity"), "lsqlin");
-            error(errmsg); 
-        end	
-	end
-
-	for i = 1:nbVar
-		if(lb(i)>ub(i))
-			errmsg = msprintf(gettext("%s: Problem has inconsistent variable bounds"), "lsqlin");
-			error(errmsg);
-		end
-	end
-
-	//Converting it into Quadratic Programming Problem
-
-	H = C'*C;
-	f = [-C'*d]';
-	op_add = d'*d;
-	lb = lb';
-	ub = ub';
-	x0 = x0';
-	conMatrix = [Aeq;A];
-	nbCon = size(conMatrix,1);
-	conLB = [beq; repmat(-%inf,nbConInEq,1)]';
-	conUB = [beq;b]' ; 
-	[xopt,fopt,status,iter,Zl,Zu,lmbda] = solveqp(nbVar,nbCon,H,f,conMatrix,conLB,conUB,lb,ub,x0,options);
-
-	xopt = xopt';
-	residual = d-C*xopt;
-	resnorm = residual'*residual;
-	exitflag = status;
-	output = struct("Iterations"      , [], ..
-					"ConstrViolation" ,[]);
-	output.Iterations = iter;
-	output.ConstrViolation = max([0;norm(Aeq*xopt-beq, 'inf');(lb'-xopt);(xopt-ub');(A*xopt-b)]);
-	lambda = struct("lower"           , [], ..
-			       "upper"           , [], ..
-			       "eqlin"           , [], ..
-				   "ineqlin"         , []);
-
-	lambda.lower = Zl;
-	lambda.upper = Zu;
-	lambda.eqlin = lmbda(1:nbConEq);
-	lambda.ineqlin = lmbda(nbConEq+1:nbCon);
-
-	select status 
-		case 0 then
-			printf("\nOptimal Solution Found.\n");
-		case 1 then
-			printf("\nMaximum Number of Iterations Exceeded. Output may not be optimal.\n");
-		case 2 then
-			printf("\nMaximum CPU Time exceeded. Output may not be optimal.\n");
-		case 3 then
-			printf("\nStop at Tiny Step\n");
-		case 4 then
-			printf("\nSolved To Acceptable Level\n");
-		case 5 then
-			printf("\nConverged to a point of local infeasibility.\n");
-		case 6 then
-			printf("\nStopping optimization at current point as requested by user.\n");
-		case 7 then
-			printf("\nFeasible point for square problem found.\n");
-		case 8 then 
-			printf("\nIterates diverging; problem might be unbounded.\n");
-		case 9 then
-			printf("\nRestoration Failed!\n");
-		case 10 then
-			printf("\nError in step computation (regularization becomes too large?)!\n");
-		case 12 then
-			printf("\nProblem has too few degrees of freedom.\n");
-		case 13 then
-			printf("\nInvalid option thrown back by Ipopt\n");
-		case 14 then
-			printf("\nNot enough memory.\n");
-		case 15 then
-			printf("\nINTERNAL ERROR: Unknown SolverReturn value - Notify Ipopt Authors.\n");
-		else
-			printf("\nInvalid status returned. Notify the Toolbox authors\n");
-		break;
-	end
-
-endfunction
diff --git a/macros/lsqnonlin.bin b/macros/lsqnonlin.bin
new file mode 100644
index 0000000..2cc1f1c
--- /dev/null
+++ b/macros/lsqnonlin.bin
diff --git a/macros/lsqnonlin.sci b/macros/lsqnonlin.sci
new file mode 100644
index 0000000..50a1a49
--- /dev/null
+++ b/macros/lsqnonlin.sci
@@ -0,0 +1,317 @@
+// 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: Harpreet Singh
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
+function [xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin (varargin)
+	// Solves a non linear data fitting problems.
+	//
+	//   Calling Sequence
+	//   xopt = lsqnonlin(fun,x0)
+	//   xopt = lsqnonlin(fun,x0,lb,ub)
+	//   xopt = lsqnonlin(fun,x0,lb,ub,options)
+	//   [xopt,resnorm] = lsqnonlin( ... )
+	//   [xopt,resnorm,residual] = lsqnonlin( ... )
+	//   [xopt,resnorm,residual,exitflag] = lsqnonlin( ... )
+	//   [xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin( ... )
+	//   
+	//   Parameters
+  	//   fun : a function, representing the objective function and gradient (if given) of the problem
+	//   x0 : a vector of double, contains initial guess of variables.
+	//   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 parameters to be set.
+	//   xopt : a vector of double, the computed solution of the optimization problem.
+	//   resnorm : a double, objective value returned as the scalar value i.e. sum(fun(x).^2).
+	//   residual : a vector of double, solution of objective function i.e. fun(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.
+  	//   gradient : a vector of doubles, containing the Objective's gradient of the solution.
+	//   
+	//   Description
+	//   Search the minimum of a constrained non-linear least square problem specified by :
+	//
+	//   <latex>
+	//    \begin{eqnarray}
+	//    &\mbox{min}_{x}
+	//    & (f_1(x)^2 + f_2(x)^2 + ... + f_n(x)^2) \\
+	//    & lb \leq x \leq ub \\
+	//    \end{eqnarray}
+	//   </latex>
+	//   
+	//   The routine calls fmincon which calls Ipopt for solving the non-linear least square 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", "on");</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 string, representing the gradient function is on or off.</listitem>
+	//   <listitem>Default Values : options = list("MaxIter", [3000], "CpuTime", [600], "GradObj", "off");</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 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.constrviolation: The max-norm of the constraint violation.</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>
+	// </itemizedlist>
+	//
+	// Examples
+	// //A simple non-linear least square example taken from leastsq default present in scilab
+	//	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
+	//	// the simplest call
+	//	[xopt,resnorm,residual,exitflag,output,lambda,gradient] = lsqnonlin(myfun,x0)
+	// // Press ENTER to continue 
+	//    
+	// Examples
+	// //A basic example taken from leastsq default present in scilab with gradient
+	//	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,dy]=myfun(x, tm, ym, wm)
+	//	   y = wm.*( yth(tm, x) - ym )
+	//	   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)
+	// Authors
+	// Harpreet Singh
+
+
+	//To check the number of input and output argument
+	[lhs , rhs] = argn();
+
+	//To check the number of argument given by user
+	if ( rhs < 2 | rhs == 3 | rhs > 5  ) then
+		errmsg = msprintf(gettext("%s: Unexpected number of input arguments : %d provided while should be in the set of [2 4 5]"), "lsqnonlin", rhs);
+		error(errmsg)
+	end
+
+// Initializing all the values to empty matrix
+	
+
+	_fun = varargin(1);
+	x0 = varargin(2);
+	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
+
+    if (size(x0,2)~=1) then
+		errmsg = msprintf(gettext("%s: Initial Guess should be a column matrix"), "lsqcurvefit");
+		error(errmsg);
+    end
+
+	if ( rhs<3 ) then
+		lb = repmat(-%inf,nbVar,1);
+		ub = repmat(%inf,nbVar,1);
+	else
+		lb = varargin(3);
+		ub = varargin(4);
+	end
+
+	if ( rhs<7 | size(varargin(5)) ==0 ) then
+		param = list();
+	else
+		param =varargin(5);
+	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
+
+	if (type(param) ~= 15) then
+		errmsg = msprintf(gettext("%s: param should be a list "), "lsqnonlin");
+		error(errmsg);
+	end
+
+	//Check type of variables
+	Checktype("lsqnonlin", _fun, "fun", 1, "function")
+	Checktype("lsqnonlin", x0, "x0", 2, "constant")
+	Checktype("lsqnonlin", lb, "lb", 3, "constant")
+	Checktype("lsqnonlin", ub, "ub", 4, "constant")
+
+	if (modulo(size(param),2)) then
+		errmsg = msprintf(gettext("%s: Size of parameters should be even"), "lsqnonlin");
+		error(errmsg);
+	end
+
+	options = list(	"MaxIter"   , [3000], ...
+					"CpuTime"   , [600], ...
+					"GradObj"	, ["off"]);
+
+	for i = 1:(size(param))/2
+
+		select convstr(param(2*i-1),'l')
+			case "maxiter" then
+				options(2*i) = param(2*i);
+				Checktype("lsqcurvefit", param(2*i), "maxiter", i, "constant")
+			case "cputime" then
+				options(2*i) = param(2*i);
+				Checktype("lsqcurvefit", param(2*i), "cputime", i, "constant")
+        	case "gradobj" then
+        		if (convstr(param(2*i))=="on") then
+    				options(2*i) = "on";
+        		else
+					errmsg = msprintf(gettext("%s: Unrecognized String [%s] entered for gradobj."), "lsqnonlin",param(2*i));
+      				error(errmsg);
+        		end
+			else
+				errmsg = msprintf(gettext("%s: Unrecognized parameter name ''%s''."), "lsqnonlin", param(2*i-1));
+				error(errmsg)
+		end
+	end
+
+	// Check if the user gives row vector 
+	// and Changing it to a column matrix
+
+	if (size(lb,2)== [nbVar]) then
+		lb = lb(:);
+	end
+
+	if (size(ub,2)== [nbVar]) then
+		ub = ub(:);
+	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"), "fmincon");
+   		error(errmsg);
+	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), "lsqnonlin", lamsg));
+    end
+        
+	//Check the size of Lower Bound which should be equal to the number of variables
+	if ( size(lb,1) ~= nbVar) then
+		errmsg = msprintf(gettext("%s: The Lower Bound is not equal to the number of variables"), "lsqnonlin");
+		error(errmsg);
+	end
+
+	//Check the size of Upper Bound which should equal to the number of variables
+	if ( size(ub,1) ~= nbVar) then
+		errmsg = msprintf(gettext("%s: The Upper Bound is not equal to the number of variables"), "lsqnonlin");
+		error(errmsg);
+	end
+
+	//Check if the user gives a matrix instead of a vector
+
+	if (size(lb,1)~=1)& (size(lb,2)~=1) then
+		errmsg = msprintf(gettext("%s: Lower Bound should be a vector"), "lsqnonlin");
+		error(errmsg); 
+	end
+
+	if (size(ub,1)~=1)& (size(ub,2)~=1) then
+		errmsg = msprintf(gettext("%s: Upper Bound should be a vector"), "lsqnonlin");
+		error(errmsg); 
+	end
+
+	for i = 1:nbVar
+		if(lb(i)>ub(i))
+			errmsg = msprintf(gettext("%s: Problem has inconsistent variable bounds"), "lsqnonlin");
+			error(errmsg);
+		end
+	end
+
+	//Converting it into fmincon Problem
+	function [y] = __fun(x)
+		[xVal] = _fun(x);
+		y = sum(xVal.^2);
+	endfunction
+
+	if (options(6) == "on")
+        ierr = execstr('[initx initdx]=_fun(x0)', "errcatch")
+        if ierr <> 0 then
+        lamsg = lasterror();
+        lclmsg = "%s: Error while evaluating the function: ""%s""\n";
+        error(msprintf(gettext(lclmsg), "lsqnonlin", lamsg));
+        end
+		function [dy] = __fGrad(x)
+			[x_user,dx_user] = _fun(x)
+			dy = 2*dx_user*(x_user');
+		endfunction
+	    options(6) = __fGrad;
+	end
+
+	
+	[xopt,fopt,exitflag,output,lambda_fmincon,gradient] = fmincon(__fun,x0,[],[],[],[],lb,ub,[],options);
+	
+	lambda = struct("lower", lambda_fmincon.lower,"upper",lambda_fmincon.upper);
+	
+	resnorm = sum(_fun(xopt).^2);
+	residual = _fun(xopt);
+
+endfunction
diff --git a/macros/names b/macros/names
index 36793aa..dc01f55 100644
--- a/macros/names
+++ b/macros/names
@@ -9,8 +9,8 @@ fmincon
 fminimax
 fminunc
 linprog
-lsqcurvefit
 lsqlin
+lsqnonlin
 lsqnonneg
 matrix_linprog
 mps_linprog