1 files changed, 248 insertions, 0 deletions
diff --git a/sci_gateway/cpp/sci_QuadNLP.cpp b/sci_gateway/cpp/sci_QuadNLP.cpp
new file mode 100644
index 0000000..2c484b7
--- /dev/null
+++ b/sci_gateway/cpp/sci_QuadNLP.cpp
@@ -0,0 +1,248 @@
+/*
+ * Quadratic Programming Toolbox for Scilab using IPOPT library
+ * Authors :
+	Sai Kiran
+	Keyur Joshi
+	Iswarya
+ */
+
+#include "QuadNLP.hpp"
+#include "IpIpoptData.hpp"
+
+extern "C"{
+#include <api_scilab.h>
+#include <Scierror.h>
+#include <BOOL.h>
+#include <localization.h>
+#include <sciprint.h>
+
+
+double x_static,i, *op_obj_x = NULL,*op_obj_value = NULL;
+
+using namespace Ipopt;
+
+QuadNLP::~QuadNLP()
+		 {
+			free(finalX_);
+			free(finalZl_);
+			free(finalZu_);}
+
+//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory
+bool QuadNLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, IndexStyleEnum& index_style){
+	n=numVars_; // Number of variables
+	m=numConstr_; // 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=C_STYLE; // Index style of matrices
+	return true;
+	}
+
+//get variable and constraint bound info
+bool QuadNLP::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;
+	}
+
+//get value of objective function at vector x
+bool QuadNLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value){
+	unsigned int i,j;
+	Number temp;
+	obj_value=0;
+
+	for (i=0;i<n;++i){
+		for (j=0;j<n;++j){
+			obj_value+=x[i]*x[j]*qMatrix_[n*i+j];			
+			}
+		obj_value+=x[i]*lMatrix_[i];
+		}
+	return true;
+	}
+
+//get value of gradient of objective function at vector x.
+bool QuadNLP::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]+qMatrix_[n*j+i])*x[j];
+		}
+	return true;
+	}
+
+//Get the values of constraints at vector x.
+bool QuadNLP::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_[n*i+j];
+		}
+	return true;
+	}
+
+// This method sets initial values for required vectors . For now we are assuming 0 to all values. 
+bool QuadNLP::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]=0.0;//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;
+	}
+/* Return either the sparsity structure of the Jacobian of the constraints, or the values for the Jacobian of the constraints at the point x.
+
+*/ 
+bool QuadNLP::eval_jac_g(Index n, const Number* x, bool new_x,
+			 Index m, Index nele_jac, Index* iRow, Index *jCol,
+			 Number* values){
+	
+	//It asked for structure of 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 asked for values
+	else { 
+		int index=0;
+		for (int var=0;var<m;++var)
+			for (int flag=0;flag<n;++flag)
+				values[index++]=conMatrix_[n*var+flag];
+		}
+	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
+ * x,lambda,obj_factor.
+*/
+bool QuadNLP::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]+qMatrix_[n*col+row]);
+				}
+			}
+		}
+	return true;
+	}
+
+
+void QuadNLP::finalize_solution(SolverReturn status,
+				Index n, const Number* x, const Number* z_L, const Number* z_U,
+				Index m, const Number* g, const Number* lambda, Number obj_value,
+				const IpoptData* ip_data,
+				IpoptCalculatedQuantities* ip_cq){
+	
+	finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1);
+	for (Index i=0; i<n; i++) 
+	{
+    		 finalX_[i] = x[i];
+	}
+	
+	finalZl_ = (double*)malloc(sizeof(double) * numVars_ * 1);
+	for (Index i=0; i<n; i++) 
+	{
+    		 finalZl_[i] = z_L[i];
+	}
+
+	finalZu_ = (double*)malloc(sizeof(double) * numVars_ * 1);
+	for (Index i=0; i<n; i++) 
+	{
+    		 finalZu_[i] = z_U[i];
+	}
+
+	finalLambda_ = (double*)malloc(sizeof(double) * numConstr_ * 1);
+	for (Index i=0; i<m; i++) 
+	{
+    		 finalLambda_[i] = lambda[i];
+	}
+
+	iter_ = ip_data->iter_count();
+	finalObjVal_ = obj_value;
+	status_ = status;
+	
+   }
+
+	const double * QuadNLP::getX()
+	{	
+		return finalX_;
+	}
+
+	const double * QuadNLP::getZl()
+	{	
+		return finalZl_;
+	}
+
+	const double * QuadNLP::getZu()
+	{	
+		return finalZu_;
+	}
+
+	const double * QuadNLP::getLambda()
+	{	
+		return finalLambda_;
+	}
+
+	double QuadNLP::getObjVal()
+	{	
+		return finalObjVal_;
+	}
+
+	double QuadNLP::iterCount()
+	{	
+		return (double)iter_;
+	}
+
+	int QuadNLP::returnStatus()
+	{	
+		return status_;
+	}
+
+}