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-rw-r--r--build/cpp/sci_minconTMINLP.cpp~199
1 files changed, 128 insertions, 71 deletions
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()
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