void cmd_nlls()
{
Real *work, *y, *bounds, *covar, *p;
int nQ = count_data();
int ndim = fit[0].pars.n;
int i;
/* Allocate storage: y, covar, bounds, p */
work = (Real *)malloc(sizeof(Real)*(nQ + ndim*ndim + 3*ndim));
assert(work != NULL);
y = work;
bounds = y + nQ;
covar = bounds + 2*ndim;
p = covar + ndim*ndim;
write_pop(&set); /* In case fit crashes */
/* Record what we are doing */
if (parFD != NULL) {
fprintf(parFD,"# %15d Starting Levenberg-Marquardt\n", GetGen(&set));
fflush(parFD);
}
/* Copy normalized data values */
copy_normalized_data(y);
/* Set bounds */
for (i=0; i < ndim; i++) {
bounds[i] = 0.;
bounds[i+ndim] = 1.;
}
/* Get best into p */
pars_set(&fit[0].pars, bestpars);
pars_get01(&fit[0].pars, p);
/* Call nlls */
tic();
#if 1
box_nlls(step_nlls, ndim, nQ, fit, y, bounds, p, covar);
#else
nlls(step_nlls, ndim, nQ, fit, y, p, covar);
#endif
printf("Done LM\n");fflush(stdout);
toc();
print_covar(ndim,covar);
/* Record LM results */
log_best();
/* Inject new p into the GA */
setChromosome(&set, 0, p);
/* Done */
free(work);
}
TEST(MiniTensor_ROL, NLLS01)
{
bool const
print_output = ::testing::GTEST_FLAG(print_time);
// outputs nothing
Teuchos::oblackholestream
bhs;
std::ostream &
os = (print_output == true) ? std::cout : bhs;
constexpr Intrepid2::Index
NUM_CONSTR{3};
constexpr Intrepid2::Index
NUM_VAR{5};
using MSEC = Intrepid2::Nonlinear01<Real, NUM_CONSTR>;
MSEC
msec;
ROL::MiniTensor_EqualityConstraint<MSEC, Real, NUM_CONSTR, NUM_VAR>
constr(msec);
Intrepid2::Vector<Real, NUM_VAR>
xval(Intrepid2::ZEROS);
Intrepid2::Vector<Real, NUM_CONSTR>
cval(Intrepid2::ZEROS);
Intrepid2::Vector<Real, NUM_VAR>
solval(Intrepid2::ZEROS);
// Set initial guess.
xval(0) = -1.8;
xval(1) = 1.7;
xval(2) = 1.9;
xval(3) = -0.8;
xval(4) = -0.8;
// Set solution.
solval(0) = -1.717143570394391e+00;
solval(1) = 1.595709690183565e+00;
solval(2) = 1.827245752927178e+00;
solval(3) = -7.636430781841294e-01;
solval(4) = -7.636430781841294e-01;
Real const
error_full_hess{2.3621708067012991e-02};
Real const
error_gn_hess{2.3669791103726853e-02};
Real const
tol{1.0e-08};
ROL::MiniTensorVector<Real, NUM_VAR>
x(xval);
ROL::MiniTensorVector<Real, NUM_CONSTR>
c(cval);
ROL::MiniTensorVector<Real, NUM_VAR>
sol(solval);
Teuchos::RCP<ROL::EqualityConstraint<Real>>
pconstr = Teuchos::rcp(&constr, false);
// Define algorithm.
Teuchos::ParameterList
params;
std::string
step{"Trust Region"};
params.sublist("Step").sublist(step).set("Subproblem Solver", "Truncated CG");
params.sublist("Status Test").set("Gradient Tolerance", 1.0e-10);
params.sublist("Status Test").set("Constraint Tolerance", 1.0e-10);
params.sublist("Status Test").set("Step Tolerance", 1.0e-18);
params.sublist("Status Test").set("Iteration Limit", 128);
ROL::Algorithm<Real>
algo(step, params);
ROL::NonlinearLeastSquaresObjective<Real>
nlls(pconstr, x, c, false);
os << "\nSOLVE USING FULL HESSIAN\n";
algo.run(x, nlls, true, os);
Intrepid2::Vector<Real, NUM_VAR>
xfinal = ROL::MTfromROL<Real, NUM_VAR>(x);
os << "\nfinal x : " << xfinal << "\n";
Real
error = std::abs(Intrepid2::norm(xfinal - solval) - error_full_hess);
os << "\nerror : " << error << "\n";
ASSERT_LE(error, tol);
algo.reset();
x.set(xval);
ROL::NonlinearLeastSquaresObjective<Real>
gnnlls(pconstr, x, c, true);
os << "\nSOLVE USING GAUSS-NEWTON HESSIAN\n";
algo.run(x, gnnlls, true, os);
xfinal = ROL::MTfromROL<Real, NUM_VAR>(x);
os << "\nfinal x : " << xfinal << "\n";
error = std::abs(Intrepid2::norm(xfinal - solval) - error_gn_hess);
os << "\nerror : " << error << "\n";
ASSERT_LE(error, tol);
}
