//------------------------------------------------------------------------
// Initialization subroutine
//------------------------------------------------------------------------
void OptBCEllipsoid::initOpt()
{
NLP1 *nlp = nlprob();
int i,n = nlp->getDim();
double dtmp=0.0;
time_t t;
char *c;
// Get date and print out header
t = time(NULL);
c = asctime(localtime(&t));
*optout << "**********************************************************\n";
*optout << "OPT++ version " << OPT_GLOBALS::OPT_VERSION << "\n";
*optout << "Job run at " << c << "\n";
copyright();
*optout << "**********************************************************\n";
// Read in OPT++ input file if it exists. Be aware that anything in
// the input file will override any variable set so far
nlp->initFcn();
SerialDenseVector<int,double> xc(nlp->getXc().length());
xc = nlp->getXc();
readOptInput();
if (debug_)
nlp->setDebug();
ret_code = 0;
if(nlp->hasConstraints()){
CompoundConstraint* constraints = nlp->getConstraints();
SerialDenseVector<int,double> xstart(nlp->getXc().length());
xstart = nlp->getXc();
double feas_tol = tol.getCTol();
bool feasible = constraints->amIFeasible(xstart, feas_tol);
if (!feasible) {
*optout << "OptBCEllipsoid WARNING: Initial guess not feasible.\n"
<< "Ellipsoid may be unable to make progress." << endl;
}
}
if (ret_code == 0) {
nlp->evalF();
// if initial radius of ellipsoid is not specified, set it to something
if (initial_radius < 0.0e0) {
for (i=1; i<=n; i++) dtmp = max(dtmp, fabs(xc(i)));
initial_radius = 1.0e1 * dtmp + 1.0e5;
}
*optout << "\n Iter F(x) Steplength "
<< "fevals gevals\n\n";
if(debug_)
*optout << "Radius of initial ellipsoid = " << initial_radius << "\n";
}
}
void OptCG::initOpt()
{
time_t t;
char *c;
// get date and print out header
t = time(NULL);
c = asctime(localtime(&t));
*optout << "************************************************************\n";
*optout << "OPT++ version " << OPT_GLOBALS::OPT_VERSION << "\n";
*optout << "Job run at " << c << "\n";
copyright();
*optout << "************************************************************\n";
if (debug_)
nlp->setDebug();
nlp->initFcn();
ret_code = 0;
if(nlp->hasConstraints()){
CompoundConstraint* constraints = nlp->getConstraints();
ColumnVector xstart = nlp->getXc();
double feas_tol = tol.getCTol();
bool feasible = constraints->amIFeasible(xstart, feas_tol);
if (!feasible) {
*optout << "OptCG WARNING: Initial guess not feasible.\n"
<< "CG may be unable to make progress." << endl;
}
//cerr << "Error: OptCG does not support bound, linear, or nonlinear "
// << "constraints.\n Please select a different method for "
// << "constrained problems." << endl;
//abort_handler(-1);
}
if (ret_code == 0) {
double fvalue, gnorm;
int n = nlp->getDim();
nlp->eval();
fvalue = nlp->getF();
fprev = fvalue;
xprev = nlp->getXc();
gprev = nlp->getGrad(); gnorm = Norm2(gprev);
*optout << "\n\t\t\t\tNonlinear CG"
<< "\n Iter F(x) ||grad|| "
<< "||step|| beta gtp fcn\n\n"
<< d(0,5) << " " << e(fvalue,12,4) << " " << e(gnorm,12,4) << endl;
if (debug_) {
nlp->fPrintState(optout, "qnewton: Initial Guess");
*optout << "xc, grad, step\n";
for(int i=1; i<=n; i++)
*optout << d(i,6) << e(xprev(i),24,16) << e(gprev(i),24,16) << "\n";
}
}
}
