int OptCGLike::checkConvg() // check convergence
{
NLP1* nlp = nlprob();
ColumnVector xc(nlp->getXc());
// Test 1. step tolerance
double step_tol = tol.getStepTol();
double snorm = stepTolNorm();
double xnorm = Norm2(xc);
double stol = step_tol*max(1.0,xnorm);
if (snorm <= stol) {
strcpy(mesg,"Algorithm converged - Norm of last step is less than step tolerance");
*optout << "checkConvg: snorm = " << e(snorm,12,4)
<< " stol = " << e(stol,12,4) << "\n";
return 1;
}
// Test 2. function tolerance
double ftol = tol.getFTol();
double fvalue = nlp->getF();
double rftol = ftol*max(1.0,fabs(fvalue));
Real deltaf = fprev - fvalue;
if (deltaf <= rftol) {
strcpy(mesg,"Algorithm converged - Difference in successive fcn values less than tolerance");
*optout << "checkConvg: deltaf = " << e(deltaf,12,4)
<< " ftol = " << e(ftol,12,4) << "\n";
return 2;
}
// Test 3. gradient tolerance
ColumnVector grad(nlp->getGrad());
double gtol = tol.getGTol();
double rgtol = gtol*max(1.0,fabs(fvalue));
double gnorm = Norm2(grad);
if (gnorm <= rgtol) {
strcpy(mesg,"Algorithm converged - Norm of gradient is less than gradient tolerance");
*optout << "checkConvg: gnorm = " << e(gnorm,12,4)
<< " gtol = " << e(rgtol, 12,4) << "\n";
return 3;
}
// Test 4. absolute gradient tolerance
if (gnorm <= gtol) {
strcpy(mesg,"Algorithm converged - Norm of gradient is less than gradient tolerance");
*optout << "checkConvg: gnorm = " << e(gnorm,12,4)
<< " gtol = " << e(gtol, 12,4) << "\n";
return 4;
}
// Nothing to report
return 0;
}
