int gauss(dvec& x, dvec& w) {
int n = x.size();
double dist = 1;
double tol = 1e-15;
int iter = 0;
// Use Chebyshev-Gauss nodes and initial guess
initguess(x);
// chebnodes(x);
dvec x0(x);
dvec L(n,0.0);
dvec Lp(n,0.0);
dvec a(n,0.0);
dvec b(n,0.0);
rec_legendre(a,b);
// Iteratively correct nodes with Newton's method
while(dist>tol) {
newton_step(a,b,x,L,Lp);
dist = dist_max(x,x0);
++iter;
x0 = x;
}
// Compute Weights
for(int i=0;i<n;++i){
w[i] = 2.0/((1-x[i]*x[i])*(Lp[i]*Lp[i]));
}
return iter;
}
int main(int argc, char *argv[]) {
Teuchos::GlobalMPISession mpiSession(&argc, &argv);
// This little trick lets us print to std::cout only if a (dummy) command-line argument is provided.
int iprint = argc - 1;
Teuchos::RCP<std::ostream> outStream;
Teuchos::oblackholestream bhs; // outputs nothing
if (iprint > 0)
outStream = Teuchos::rcp(&std::cout, false);
else
outStream = Teuchos::rcp(&bhs, false);
int errorFlag = 0;
// *** Example body.
try {
int dim = 128; // Set problem dimension.
ROL::ZOO::Objective_PoissonInversion<RealT> obj(dim, 1e-6);
Teuchos::ParameterList parlist;
// Basic algorithm.
parlist.set("Trust-Region Subproblem Solver Type", "Truncated CG");
// Krylov parameters.
parlist.set("Absolute Krylov Tolerance", 1.e-4);
parlist.set("Relative Krylov Tolerance", 1.e-2);
parlist.set("Maximum Number of Krylov Iterations", 50);
// Define step.
ROL::TrustRegionStep<RealT> step(parlist);
// Define status test.
RealT gtol = 1e-12; // norm of gradient tolerance
RealT stol = 1e-14; // norm of step tolerance
int maxit = 100; // maximum number of iterations
ROL::StatusTest<RealT> status(gtol, stol, maxit);
// Define algorithm.
ROL::DefaultAlgorithm<RealT> algo(step,status,false);
// Iteration vector.
Teuchos::RCP<std::vector<RealT> > x_rcp = Teuchos::rcp( new std::vector<RealT> (dim, 0.0) );
// Set initial guess.
for (int i=0; i<dim; i++) {
(*x_rcp)[i] = 0.1;
}
ROL::StdVector<RealT> x(x_rcp);
// Run algorithm.
std::vector<std::string> output = algo.run(x, obj, false);
for ( unsigned i = 0; i < output.size(); i++ ) {
std::cout << output[i];
}
// Compute dense Hessian matrix.
Teuchos::SerialDenseMatrix<int, RealT> H(x.dimension(), x.dimension());
H = ROL::computeDenseHessian<RealT>(obj, x);
//H.print(*outStream);
// Compute and print eigenvalues.
std::vector<std::vector<RealT> > eigenvals = ROL::computeEigenvalues<RealT>(H);
*outStream << "\nEigenvalues:\n";
for (unsigned i=0; i<(eigenvals[0]).size(); i++) {
if (i==0) {
*outStream << std::right
<< std::setw(28) << "Real"
<< std::setw(28) << "Imag"
<< "\n";
}
*outStream << std::scientific << std::setprecision(16) << std::right
<< std::setw(28) << (eigenvals[0])[i]
<< std::setw(28) << (eigenvals[1])[i]
<< "\n";
}
// Compute and print generalized eigenvalues.
Teuchos::SerialDenseMatrix<int, RealT> M = computeDotMatrix(x);
//M.print(*outStream);
std::vector<std::vector<RealT> > genEigenvals = ROL::computeGenEigenvalues<RealT>(H, M);
*outStream << "\nGeneralized eigenvalues:\n";
for (unsigned i=0; i<(genEigenvals[0]).size(); i++) {
if (i==0) {
*outStream << std::right
<< std::setw(28) << "Real"
<< std::setw(28) << "Imag"
<< "\n";
}
*outStream << std::scientific << std::setprecision(16) << std::right
<< std::setw(28) << (genEigenvals[0])[i]
<< std::setw(28) << (genEigenvals[1])[i]
<< "\n";
}
// Sort and compare eigenvalues and generalized eigenvalues - should be close.
std::sort((eigenvals[0]).begin(), (eigenvals[0]).end());
std::sort((eigenvals[1]).begin(), (eigenvals[1]).end());
std::sort((genEigenvals[0]).begin(), (genEigenvals[0]).end());
std::sort((genEigenvals[1]).begin(), (genEigenvals[1]).end());
RealT errtol = std::sqrt(ROL::ROL_EPSILON);
for (unsigned i=0; i<(eigenvals[0]).size(); i++) {
if ( std::abs( (genEigenvals[0])[i] - (eigenvals[0])[i] ) > errtol*((eigenvals[0])[i]+ROL::ROL_THRESHOLD) ) {
errorFlag++;
*outStream << std::scientific << std::setprecision(20) << "Real genEigenvals - eigenvals (" << i << ") = " << std::abs( (genEigenvals[0])[i] - (eigenvals[0])[i] ) << " > " << errtol*((eigenvals[0])[i]+1e4*ROL::ROL_THRESHOLD) << "\n";
}
if ( std::abs( (genEigenvals[1])[i] - (eigenvals[1])[i] ) > errtol*((eigenvals[1])[i]+ROL::ROL_THRESHOLD) ) {
errorFlag++;
*outStream << std::scientific << std::setprecision(20) << "Imag genEigenvals - eigenvals (" << i << ") = " << std::abs( (genEigenvals[1])[i] - (eigenvals[1])[i] ) << " > " << errtol*((eigenvals[1])[i]+ROL::ROL_THRESHOLD) << "\n";
}
}
// Compute inverse of Hessian.
Teuchos::SerialDenseMatrix<int, RealT> invH = ROL::computeInverse<RealT>(H);
Teuchos::SerialDenseMatrix<int, RealT> HinvH(H);
// Multiply with Hessian and verify that it gives the identity (l2 dot matrix M from above).
HinvH.multiply(Teuchos::NO_TRANS, Teuchos::NO_TRANS, 1.0, H, invH, 0.0);
//*outStream << std::scientific << std::setprecision(6); HinvH.print(*outStream);
HinvH -= M;
if (HinvH.normOne() > errtol) {
errorFlag++;
*outStream << std::scientific << std::setprecision(20) << "1-norm of H*inv(H) - I = " << HinvH.normOne() << " > " << errtol << "\n";
}
// Use Newton algorithm.
parlist.set("Descent Type", "Newton's Method");
// Define step.
ROL::LineSearchStep<RealT> newton_step(parlist);
// Define algorithm.
ROL::DefaultAlgorithm<RealT> newton_algo(newton_step,status,false);
// Reset initial guess.
for (int i=0; i<dim; i++) {
(*x_rcp)[i] = 0.1;
}
// Run Newton algorithm.
output = newton_algo.run(x, obj, false);
for ( unsigned i = 0; i < output.size(); i++ ) {
std::cout << output[i];
}
Teuchos::RCP<const ROL::AlgorithmState<RealT> > new_state = newton_algo.getState();
Teuchos::RCP<const ROL::AlgorithmState<RealT> > old_state = algo.getState();
if ( std::abs(new_state->value - old_state->value) > errtol ) {
errorFlag++;
*outStream << std::scientific << std::setprecision(20) << "\nabs(new_optimal_value - old_optimal_value) = " << std::abs(new_state->value - old_state->value) << " > " << errtol << "\n";
}
}
catch (std::logic_error err) {
*outStream << err.what() << "\n";
errorFlag = -1000;
}; // end try
if (errorFlag != 0)
std::cout << "End Result: TEST FAILED\n";
else
std::cout << "End Result: TEST PASSED\n";
return 0;
}
