intfminimax added and fmincon updated

5 files changed, 136 insertions, 79 deletions

diff --git a/build/cpp/minbndTMINLP.hpp b/build/cpp/minbndTMINLP.hpp
index 7c9070b..581d5ce 100644
--- a/build/cpp/minbndTMINLP.hpp
+++ b/build/cpp/minbndTMINLP.hpp
@@ -5,7 +5,7 @@
 // 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, Pranav Deshpande and Akshay Miterani
+// Author: Harpreet Singh
 // Organization: FOSSEE, IIT Bombay
 // Email: toolbox@scilab.in
 
diff --git a/build/cpp/sci_minbndTMINLP.cpp b/build/cpp/sci_minbndTMINLP.cpp
index 405c4c3..f26c089 100644
--- a/build/cpp/sci_minbndTMINLP.cpp
+++ b/build/cpp/sci_minbndTMINLP.cpp
@@ -1,6 +1,6 @@
 // Copyright (C) 2015 - IIT Bombay - FOSSEE
 //
-// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani
+// Author: Harpreet Singh
 // Organization: FOSSEE, IIT Bombay
 // Email: toolbox@scilab.in
 // This file must be used under the terms of the CeCILL.
@@ -29,7 +29,7 @@ using namespace Bonmin;
 
 minbndTMINLP::~minbndTMINLP()
 {
-	free(finalX_);
+	if(finalX_) delete[] finalX_;
 }
 
 // Set the type of every variable - CONTINUOUS or INTEGER
@@ -193,7 +193,7 @@ void minbndTMINLP::finalize_solution(SolverReturn status,Index n, const Number*
 	status_ = status;
 	if(status==0 ||status== 3)
 	{
-		finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1);
+		finalX_ =  new double[n];
 		for (Index i=0; i<numVars_; i++) 
 		{
 	    		 finalX_[i] = x[i];
diff --git a/build/cpp/sci_minconTMINLP.cpp b/build/cpp/sci_minconTMINLP.cpp
index 7885083..350594d 100644
--- a/build/cpp/sci_minconTMINLP.cpp
+++ b/build/cpp/sci_minconTMINLP.cpp
@@ -31,7 +31,7 @@ using namespace Bonmin;
 
 minconTMINLP::~minconTMINLP()
 {
-	free(finalX_);
+	if(finalX_) delete[] finalX_;
 }
 
 // Set the type of every variable - CONTINUOUS or INTEGER
@@ -300,7 +300,7 @@ void minconTMINLP::finalize_solution(SolverReturn status,Index n, const Number*
 	status_ = status;
 	if(status==0 ||status== 3)
 	{
-		finalX_ = (double*)malloc(sizeof(double) * numVars_*1);
+		finalX_ =  new double[n];
 		for (Index i=0; i<numVars_; i++) 
 		{
 	    		 finalX_[i] = x[i];
diff --git a/build/cpp/sci_minconTMINLP.cpp~ b/build/cpp/sci_minconTMINLP.cpp~
index 2b9cbc3..9d4ccd3 100644
--- a/build/cpp/sci_minconTMINLP.cpp~
+++ b/build/cpp/sci_minconTMINLP.cpp~
@@ -27,14 +27,19 @@ extern "C"
 using namespace Ipopt;
 using namespace Bonmin;
 
+#define DEBUG 0
+
 minconTMINLP::~minconTMINLP()
 {
-	free(finalX_);
+	if(finalX_) delete[] finalX_;
 }
 
 // Set the type of every variable - CONTINUOUS or INTEGER
 bool minconTMINLP::get_variables_types(Index n, VariableType* var_types)
 {
+	#ifdef DEBUG
+  		sciprint("Code is in get_variables_types\n");
+	#endif
   n = numVars_;
   for(int i=0; i < n; i++)
     var_types[i] = CONTINUOUS;
@@ -45,19 +50,44 @@ bool minconTMINLP::get_variables_types(Index n, VariableType* var_types)
 
 // The linearity of the variables - LINEAR or NON_LINEAR
 bool minconTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types)
-{  return true; }
+{  
+	#ifdef DEBUG
+  		sciprint("Code is in get_variables_linearity\n");
+	#endif
+	for(int i=0;i<n;i++)
+	{
+		  var_types[i] = Ipopt::TNLP::NON_LINEAR;
+	}
+	return true; }
 
 // The linearity of the constraints - LINEAR or NON_LINEAR
 bool minconTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types)
-{  return true;}
+{	
+
+	#ifdef DEBUG
+  		sciprint("Code is in get_constraints_linearity\n");
+	#endif
+	for(int i=0;i<numLC_;i++)
+	{
+		const_types[i] = Ipopt::TNLP::LINEAR;
+	}
+
+	for(int i=numLC_;i<m;i++)
+	{
+		const_types[i] = Ipopt::TNLP::NON_LINEAR;
+	}
+	  return true;}
 
 //get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
 bool minconTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style)
 {
+	#ifdef DEBUG
+  		sciprint("Code is in get_nlp_info\n");
+	#endif
 	n=numVars_; // Number of variables
 	m=numCons_; // Number of constraints
-	nnz_jac_g = 0; // No. of elements in Jacobian of constraints 
-	nnz_h_lag = n*(n+1)/2; // No. of elements in lower traingle of Hessian of the Lagrangian.
+	nnz_jac_g = n*m; // No. of elements in Jacobian of constraints 
+	nnz_h_lag = n*n; // No. of elements in Hessian of the Lagrangian.
 	index_style=TNLP::C_STYLE; // Index style of matrices
 	return true;
 }
@@ -65,23 +95,81 @@ bool minconTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz
 //get variable and constraint bound info
 bool minconTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u)
 {
+	#ifdef DEBUG
+  		sciprint("Code is in get_bounds_info\n");
+	#endif
 	unsigned int i;
 	for(i=0;i<n;i++)
 	{
 		x_l[i]=lb_[i];
 		x_u[i]=ub_[i];
 	}
-
 	for(i=0;i<m;i++)
-        g_l=conLb_[i];
-        g_u=conUb_[i];
+	{
+		g_l[i]=conLb_[i];
+        g_u[i]=conUb_[i];
+	}
 	return true;
 }
 
+// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
+bool minconTMINLP::get_starting_point(Index n, bool init_x, Number* x,bool init_z, Number* z_L, Number* z_U,Index m, bool init_lambda,Number* lambda)
+{
+ 	assert(init_x == true);
+  	assert(init_z == false);
+  	assert(init_lambda == false);
+	if (init_x == true)
+	{ //we need to set initial values for vector x
+		for (Index var=0;var<n;var++)
+			{x[var]=x0_[var];}//initialize with 0.
+	}
+	return true;
+}
+
+//get value of objective function at vector x
+bool minconTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)
+{	
+	#ifdef DEBUG
+  		sciprint("Code is eval_f\n");
+	#endif	
+  	char name[20]="_f";
+	Number *obj;
+	if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj))
+	{
+		return false;
+	}
+	obj_value = *obj;
+  	return true;
+}
+
+//get value of gradient of objective function at vector x.
+bool minconTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)
+{
+	#ifdef DEBUG
+  		sciprint("Code is in eval_grad_f\n");
+	#endif	
+	char name[20]="_gradf";
+  	Number *resg;
+	if (getFunctionFromScilab(n,name,x, 7, 1,2,&resg))
+	{
+		return false;
+	}
+	
+	Index i;
+	for(i=0;i<numVars_;i++)
+	{
+		grad_f[i]=resg[i];
+	}
+  	return true;
+}
+
 // return the value of the constraints: g(x)
 bool minconTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)
 {
-  	// return the value of the constraints: g(x)
+	#ifdef DEBUG
+  		sciprint("Code is in eval_g\n");
+	#endif
+ 	// return the value of the constraints: g(x)
   	if(m==0)
   	{
   		g=NULL;
@@ -101,12 +189,16 @@ bool minconTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number*
 			g[i]=con[i];
 		}
 	}
+
   	return true;
 }
 
 // return the structure or values of the jacobian
 bool minconTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values)
 {
+	#ifdef DEBUG
+  		sciprint("Code is in eval_jac_g\n");
+	#endif	
  	if (values == NULL) 
  	{
     	if(m==0)// return the structure of the jacobian of the constraints
@@ -117,7 +209,7 @@ bool minconTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Inde
 		else
 		{
 			unsigned int i,j,idx=0;
-			for(int i=0;i<m;i++)
+			for(i=0;i<m;i++)
 				for(j=0;j<n;j++)
 				{
 					iRow[idx]=i;
@@ -129,72 +221,31 @@ bool minconTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Inde
 	else 
 	{
 		if(m==0)
+		{
 			values=NULL;
-
+		}
 		else
 		{
 			double* resj;
+			char name[20]="_gradnlc";
 			if (getFunctionFromScilab(n,name,x, 7, 1,2,&resj))
 			{
 				return false;
 			}
-			for(j=0;j<n;j++)
+			int c = 0;
+			for(int i=0;i<m;i++)
 			{
-				values[c] = resj[j*(int)nonlinCon_+i];
-				c++;
+				for(int j=0;j<n;j++)
+					{
+						values[c] = resj[j*(int)m+i];
+						c++;
+					}
 			}
 		}	
-	}	
-
-
-  	return true;
-}
-
-//get value of objective function at vector x
-bool minconTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)
-{	
-  	char name[20]="_f";
-	Number *obj;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj))
-	{
-		return false;
 	}
-	obj_value = *obj;
   	return true;
 }
 
-//get value of gradient of objective function at vector x.
-bool minconTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)
-{
-	char name[20]="_gradf";
-  	Number *resg;
-	if (getFunctionFromScilab(n,name,x, 7, 1,2,&resg))
-	{
-		return false;
-	}
-	
-	Index i;
-	for(i=0;i<numVars_;i++)
-	{
-		grad_f[i]=resg[i];
-	}
-  	return true;
-}
-
-// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
-bool minconTMINLP::get_starting_point(Index n, bool init_x, Number* x,bool init_z, Number* z_L, Number* z_U,Index m, bool init_lambda,Number* lambda)
-{
- 	assert(init_x == true);
-  	assert(init_z == false);
-  	assert(init_lambda == false);
-	if (init_x == true)
-	{ //we need to set initial values for vector x
-		for (Index var=0;var<n;var++)
-			{x[var]=x0_[i];}//initialize with 0.
-	}
-	return true;
-}
-
 /*
  * Return either the sparsity structure of the Hessian of the Lagrangian, 
  * or the values of the Hessian of the Lagrangian  for the given values for
@@ -203,33 +254,36 @@ bool minconTMINLP::get_starting_point(Index n, bool init_x, Number* x,bool init_
 
 bool minconTMINLP::eval_h(Index n, const Number* x, bool new_x,Number obj_factor, Index m, const Number* lambda,bool new_lambda, Index nele_hess, Index* iRow,Index* jCol, Number* values)
 {
+	#ifdef DEBUG
+  		sciprint("Code is in eval_h\n");
+	#endif	
 	double check;
 	if (values==NULL)
 	{
 		Index idx=0;
 		for (Index row = 0; row < numVars_; row++) 
 		{
-			for (Index col = 0; col <= row; col++)
-			{	iRow[idx] = row;
+			for (Index col = 0; col < numVars_; col++)
+			{
+				iRow[idx] = row;
 				jCol[idx] = col;
 				idx++;
 		  	}
 		}
-	}
-
+	}	
 	else 
 	{	char name[20]="_gradhess";
-	  	Number *resh;
-		if (getFunctionFromScilab(n,name,x, 7, 1,2,&resh))
+	  	Number *resCh;
+		if (getHessFromScilab(n,m,name,x, &obj_factor, lambda, 7, 3,2,&resCh))
 		{
 			return false;
 		}
 		Index index=0;
 		for (Index row=0;row < numVars_ ;++row)
 		{
-			for (Index col=0; col <= row; ++col)
+			for (Index col=0; col < numVars_; ++col)
 			{
-				values[index++]=obj_factor*(resh[numVars_*row+col]);
+				values[index++]=resCh[numVars_*row+col];
 			}
 		}
 	}
@@ -238,17 +292,20 @@ bool minconTMINLP::eval_h(Index n, const Number* x, bool new_x,Number obj_factor
 
 void minconTMINLP::finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value)
 {
+	#ifdef DEBUG
+  		sciprint("Code is in finalize_solution\n");
+  		sciprint("%d",status);
+	#endif	
 	finalObjVal_ = obj_value;
 	status_ = status;
 	if(status==0 ||status== 3)
 	{
-		finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1);
+		finalX_ =  new double[n];
 		for (Index i=0; i<numVars_; i++) 
 		{
 	    		 finalX_[i] = x[i];
 		}
 	}
-
 }
 
 const double * minconTMINLP::getX()
diff --git a/build/cpp/sci_minuncTMINLP.cpp b/build/cpp/sci_minuncTMINLP.cpp
index b02ab8e..696c5ef 100644
--- a/build/cpp/sci_minuncTMINLP.cpp
+++ b/build/cpp/sci_minuncTMINLP.cpp
@@ -30,7 +30,7 @@ using namespace Bonmin;
 
 minuncTMINLP::~minuncTMINLP()
 {
-	free(finalX_);
+	if(finalX_) delete[] finalX_;
 }
 
 // Set the type of every variable - CONTINUOUS or INTEGER
@@ -212,7 +212,7 @@ void minuncTMINLP::finalize_solution(SolverReturn status,Index n, const Number*
 	status_ = status;
 	if(status==0 ||status== 3)
 	{
-		finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1);
+		finalX_ = new double[n];
 		for (Index i=0; i<numVars_; i++) 
 		{
 	    		 finalX_[i] = x[i];