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-rw-r--r--build/cpp/sci_minconTMINLP.cpp139
1 files changed, 76 insertions, 63 deletions
diff --git a/build/cpp/sci_minconTMINLP.cpp b/build/cpp/sci_minconTMINLP.cpp
index ac688d4..7885083 100644
--- a/build/cpp/sci_minconTMINLP.cpp
+++ b/build/cpp/sci_minconTMINLP.cpp
@@ -27,7 +27,7 @@ extern "C"
using namespace Ipopt;
using namespace Bonmin;
-#define DEBUG 0
+//#define DEBUG 0
minconTMINLP::~minconTMINLP()
{
@@ -37,6 +37,9 @@ minconTMINLP::~minconTMINLP()
// 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;
@@ -48,6 +51,9 @@ 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)
{
+ #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;
@@ -57,6 +63,10 @@ bool minconTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType*
// The linearity of the constraints - LINEAR or NON_LINEAR
bool minconTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types)
{
+
+ #ifdef DEBUG
+ sciprint("Code is in get_constraints_linearity\n");
+ #endif
for(int i=0;i<numLC_;i++)
{
const_types[i] = Ipopt::TNLP::LINEAR;
@@ -71,6 +81,9 @@ bool minconTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType
//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 = n*m; // No. of elements in Jacobian of constraints
@@ -99,6 +112,57 @@ bool minconTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, N
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)
{
@@ -145,7 +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;
@@ -182,58 +246,6 @@ bool minconTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Inde
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;
-}
-
-// 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;
-}
-
/*
* 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
@@ -251,26 +263,27 @@ bool minconTMINLP::eval_h(Index n, const Number* x, bool new_x,Number obj_factor
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 (getHessFromScilab(n,m,name,x, &obj_factor, lambda, 7, 3,2,&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++]=(resh[numVars_*row+col]);
+ values[index++]=resCh[numVars_*row+col];
}
}
}
@@ -281,18 +294,18 @@ void minconTMINLP::finalize_solution(SolverReturn status,Index n, const Number*
{
#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_ = (double*)malloc(sizeof(double) * numVars_*1);
for (Index i=0; i<numVars_; i++)
{
finalX_[i] = x[i];
}
}
-
}
const double * minconTMINLP::getX()
generated by cgit (git 2.34.1) at 2024-12-19 09:03:55 +0000