int main(int argc, char **argv) {
MPI_Init(&argc, &argv);
QUESO::EnvOptionsValues options;
options.m_numSubEnvironments = 1;
QUESO::FullEnvironment env(MPI_COMM_WORLD, "", "", &options);
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", 3, NULL);
// Example RHS
QUESO::GslVector b(paramSpace.zeroVector());
b[0] = 1.0;
b[1] = 2.0;
b[2] = 3.0;
// Set up block sizes for observation covariance matrix
std::vector<unsigned int> blockSizes(2);
blockSizes[0] = 1; // First block is 1x1 (scalar)
blockSizes[1] = 2; // Second block is 2x2
// Set up block (identity) matrix with specified block sizes
QUESO::GslBlockMatrix covariance(blockSizes, b, 1.0);
// The matrix [[1, 0, 0], [0, 1, 2], [0, 2, 8]]
// has inverse 0.25 * [[1, 0, 0], [0, 2, -0.5], [0, -0.5, 0.25]]
covariance.getBlock(0)(0, 0) = 1.0;
covariance.getBlock(1)(0, 0) = 1.0;
covariance.getBlock(1)(0, 1) = 2.0;
covariance.getBlock(1)(1, 0) = 2.0;
covariance.getBlock(1)(1, 1) = 8.0;
// Compute solution
QUESO::GslVector x(paramSpace.zeroVector());
covariance.invertMultiply(b, x);
// This is the analytical solution
QUESO::GslVector sol(paramSpace.zeroVector());
sol[0] = 1.0;
sol[1] = 2.5;
sol[2] = -0.25;
// So if the solve worked, this sucker should be the zero vector
sol -= x;
// So its norm should be zero
if (sol.norm2() > TOL) {
std::cerr << "Computed solution:" << std::endl;
std::cerr << b << std::endl;
std::cerr << "Actual solution:" << std::endl;
std::cerr << sol << std::endl;
queso_error_msg("TEST: GslBlockMatrix::invertMultiply failed.");
}
MPI_Finalize();
return 0;
}
int main(int argc, char ** argv) {
#ifdef QUESO_HAS_MPI
MPI_Init(&argc, &argv);
QUESO::FullEnvironment env(MPI_COMM_WORLD, argv[1], "", NULL);
#else
QUESO::FullEnvironment env(argv[1], "", NULL);
#endif
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> paramSpace(env,
"param_", 1, NULL);
double min_val = 0.0;
double max_val = 1.0;
QUESO::GslVector paramMins(paramSpace.zeroVector());
paramMins.cwSet(min_val);
QUESO::GslVector paramMaxs(paramSpace.zeroVector());
paramMaxs.cwSet(max_val);
QUESO::BoxSubset<QUESO::GslVector, QUESO::GslMatrix> paramDomain("param_",
paramSpace, paramMins, paramMaxs);
QUESO::UniformVectorRV<QUESO::GslVector, QUESO::GslMatrix> priorRv("prior_",
paramDomain);
// Set up observation space
QUESO::VectorSpace<QUESO::GslVector, QUESO::GslMatrix> obsSpace(env,
"obs_", 3, NULL);
// Fill up observation vector
QUESO::GslVector observations(obsSpace.zeroVector());
observations[0] = 1.0;
observations[1] = 1.0;
observations[2] = 1.0;
// Set up block sizes for observation covariance matrix
std::vector<unsigned int> blockSizes(2);
blockSizes[0] = 1; // First block is 1x1 (scalar)
blockSizes[1] = 2; // Second block is 2x2
// Set up block matrix (identity matrix) with specified block sizes
QUESO::GslBlockMatrix covariance(blockSizes, observations, 1.0);
// Pass in observations to Gaussian likelihood object
Likelihood<QUESO::GslVector, QUESO::GslMatrix> lhood("llhd_", paramDomain,
observations, covariance);
QUESO::GenericVectorRV<QUESO::GslVector, QUESO::GslMatrix>
postRv("post_", paramSpace);
QUESO::StatisticalInverseProblem<QUESO::GslVector, QUESO::GslMatrix>
ip("", NULL, priorRv, lhood, postRv);
QUESO::GslVector paramInitials(paramSpace.zeroVector());
paramInitials[0] = 0.0;
QUESO::GslMatrix proposalCovMatrix(paramSpace.zeroVector());
for (unsigned int i = 0; i < 1; i++) {
proposalCovMatrix(i, i) = 0.1;
}
ip.solveWithBayesMetropolisHastings(NULL, paramInitials, &proposalCovMatrix);
#ifdef QUESO_HAS_MPI
MPI_Finalize();
#endif
return 0;
}