1 files changed, 230 insertions, 0 deletions
diff --git a/newstructure/sci_gateway/cpp/sci_QuadTMINLP.cpp b/newstructure/sci_gateway/cpp/sci_QuadTMINLP.cpp
new file mode 100644
index 0000000..a424b47
--- /dev/null
+++ b/newstructure/sci_gateway/cpp/sci_QuadTMINLP.cpp
@@ -0,0 +1,230 @@
+// Copyright (C) 2016 - 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, Pranav Deshpande and Akshay Miterani
+// Organization: FOSSEE, IIT Bombay
+// Email: toolbox@scilab.in
+
+#include "QuadTMINLP.hpp"
+#include "IpIpoptData.hpp"
+
+extern "C"{
+#include <sciprint.h>
+}
+
+// Go to http://coin-or.org/Ipopt and http://coin-or.org/Bonmin for the details of the below methods
+
+// Set the type of every variable - CONTINUOUS or INTEGER
+bool QuadTMINLP::get_variables_types(Index n, VariableType* var_types)
+{
+  n = numVars_;
+  for(int i=0; i < n; i++)
+    var_types[i] = CONTINUOUS;
+  for(int i=0 ; i < intconSize_ ; ++i)
+  	var_types[(int)(intcon_[i]-1)] = INTEGER;
+  return true;
+}
+
+// The linearity of the variables - LINEAR or NON_LINEAR
+bool QuadTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types)
+{
+	/*
+	n = numVars_;
+	for(int i = 0; i < n; i++)
+		var_types[i] = Ipopt::TNLP::LINEAR;
+  */
+  return true;
+}
+
+// The linearity of the constraints - LINEAR or NON_LINEAR
+bool QuadTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types)
+{
+  m = numCons_;
+  for(int i = 0; i < m; i++)
+    const_types[i] = Ipopt::TNLP::LINEAR;
+  return true;
+}
+
+// Get MINLP info such as the number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
+bool QuadTMINLP::get_nlp_info(Index& n, Index&m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style)
+{
+  n=numVars_; // Number of variables
+	m=numCons_; // Number of constraints
+	nnz_jac_g = n*m; // 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.
+	index_style=TNLP::C_STYLE; // Index style of matrices
+	return true;
+}
+
+// Get the variables and constraints bound info
+bool QuadTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u)
+{
+  unsigned int i;
+	for(i=0;i<n;i++){
+		x_l[i]=varLB_[i];
+		x_u[i]=varUB_[i];
+		}
+
+	for(i=0;i<m;i++){
+		g_l[i]=conLB_[i];
+		g_u[i]=conUB_[i];
+		}
+	return true;
+}
+
+// This method sets initial values for all the required vectors. We take 0 by default. 
+bool QuadTMINLP::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)
+{
+  if (init_x == true){ //we need to set initial values for vector x
+		for (Index var=0;var<n;var++)
+			x[var]=varGuess_[var];//initialize with 0 or we can change.
+		}
+
+	if (init_z == true){ //we need to provide initial values for vector bound multipliers
+		for (Index var=0;var<n;++var){
+			z_L[var]=0.0; //initialize with 0 or we can change.
+			z_U[var]=0.0;//initialize with 0 or we can change.
+			}
+		}
+	
+	if (init_lambda == true){ //we need to provide initial values for lambda values.
+		for (Index var=0;var<m;++var){
+			lambda[var]=0.0; //initialize with 0 or we can change.
+			}
+		}
+
+	return true;
+}
+
+// Evaluate the objective function at a point
+bool QuadTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value)
+{
+  unsigned int i,j;
+	obj_value=0;
+	for (i=0;i<n;i++){
+		for (j=0;j<n;j++){
+			obj_value+=0.5*x[i]*x[j]*qMatrix_[n*i+j];		
+			}
+  	obj_value+=x[i]*lMatrix_[i];
+		}
+	return true;
+}
+
+// Get the value of gradient of objective function at vector x.
+bool QuadTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f)
+{
+  unsigned int i,j;
+	for(i=0;i<n;i++)
+	{
+		grad_f[i]=lMatrix_[i];
+		for(j=0;j<n;j++)
+			{
+				grad_f[i]+=(qMatrix_[n*i+j])*x[j];
+			}
+	}
+	return true;
+}
+
+// Get the values of constraints at vector x.
+bool QuadTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g)
+{
+  unsigned int i,j;
+	for(i=0;i<m;i++)
+	{
+		g[i]=0;
+		for(j=0;j<n;j++)
+		{
+			g[i]+=x[j]*conMatrix_[i+j*m];	
+		}
+	}
+	return true;
+}
+
+// The Jacobian Matrix
+bool QuadTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,
+                     Index m, Index nnz_jac, Index* iRow, Index *jCol,
+                     Number* values)
+{
+	//It asks for the structure of the jacobian.
+	if (values==NULL){ //Structure of jacobian (full structure)
+		int index=0;
+		for (int var=0;var<m;++var)//no. of constraints
+			for (int flag=0;flag<n;++flag){//no. of variables
+				iRow[index]=var;
+				jCol[index]=flag;
+				index++;
+				}
+		}
+	//It asks for values
+	else { 
+		int index=0;
+		for (int var=0;var<m;++var)
+			for (int flag=0;flag<n;++flag)
+				values[index++]=conMatrix_[var+flag*m];
+		}
+	return true;
+}
+
+/*
+The structure of the Hessain matrix and the values
+*/
+bool QuadTMINLP::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)
+{
+	if (values==NULL){
+		Index idx=0;
+		for (Index row = 0; row < n; row++) {
+			for (Index col = 0; col <= row; col++) {
+				iRow[idx] = row;
+				jCol[idx] = col;
+				idx++;
+		  		}
+			}
+		}
+	else {
+		Index index=0;
+		for (Index row=0;row < n;++row){
+			for (Index col=0; col <= row; ++col){
+				values[index++]=obj_factor*(qMatrix_[n*row+col]);
+				}
+			}
+		}
+	return true;
+}
+
+void QuadTMINLP::finalize_solution(TMINLP::SolverReturn status, Index n, const Number* x,Number obj_value)
+{
+	
+	finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1);
+	for (Index i=0; i<n; i++) 
+	{
+    		 finalX_[i] = x[i];
+	}
+
+	finalObjVal_ = obj_value;
+	status_ = status;
+}
+
+const double * QuadTMINLP::getX()
+{	
+	return finalX_;
+}
+
+double QuadTMINLP::getObjVal()
+{	
+	return finalObjVal_;
+}
+
+int QuadTMINLP::returnStatus()
+{	
+	return status_;
+}