CollOfScalar EquelleRuntimeCPU::solveForUpdate(const CollOfScalar& residual) const
{
Eigen::SparseMatrix<double, Eigen::RowMajor> matr = residual.derivative()[0];
CollOfScalar::V du = CollOfScalar::V::Zero(residual.size());
Opm::time::StopWatch clock;
clock.start();
// solve(n, # nonzero values ("val"), ptr to col indices
// ("col_ind"), ptr to row locations in val array ("row_ind")
// (these two may be swapped, not sure about the naming convention
// here...), array of actual values ("val") (I guess... '*sa'...),
// rhs, solution)
Opm::LinearSolverInterface::LinearSolverReport rep
= linsolver_.solve(matr.rows(), matr.nonZeros(),
matr.outerIndexPtr(), matr.innerIndexPtr(), matr.valuePtr(),
residual.value().data(), du.data());
if (verbose_ > 2) {
std::cout << " solveForUpdate: Linear solver took: " << clock.secsSinceLast() << " seconds." << std::endl;
}
if (!rep.converged) {
OPM_THROW(std::runtime_error, "Linear solver convergence failure.");
}
return du;
}
/// This function is not provided by AutoDiffBlock, so we must add it here.
inline CollOfScalar sqrt(const CollOfScalar& x)
{
// d(sqrt(x))/dy = 1/(2*sqrt(x)) * dx/dy
const auto& xjac = x.derivative();
if (xjac.empty()) {
return CollOfScalar(sqrt(x.value()));
}
const int num_blocks = xjac.size();
std::vector<CollOfScalar::M> jac(num_blocks);
const auto sqrt_x = sqrt(x.value());
const CollOfScalar::M one_over_two_sqrt_x((0.5/sqrt_x).matrix().asDiagonal());
for (int block = 0; block < num_blocks; ++block) {
jac[block] = one_over_two_sqrt_x * xjac[block];
}
return CollOfScalar::ADB::function(sqrt_x, jac);
}
CollOfScalar EquelleRuntimeCUDA::newtonSolve(const ResidualFunctor& rescomp,
const CollOfScalar& u_initialguess)
{
Opm::time::StopWatch clock;
clock.start();
// Set up Newton loop.
// Define the primary variable
CollOfScalar u = CollOfScalar(u_initialguess, true);
if (verbose_ > 2) {
output("Initial u", u);
output(" newtonSolve: norm (initial u)", twoNorm(u));
}
CollOfScalar residual = rescomp(u);
if (verbose_ > 2) {
output("Initial residual", residual);
output(" newtonSolve: norm (initial residual)", twoNorm(residual));
}
int iter = 0;
// Debugging output not specified in Equelle.
if (verbose_ > 1) {
std::cout << " newtonSolve: iter = " << iter << " (max = " << max_iter_
<< "), norm(residual) = " << twoNorm(residual)
<< " (tol = " << abs_res_tol_ << ")" << std::endl;
}
CollOfScalar du;
// Execute newton loop until residual is small or we have used too many iterations.
while ( (twoNorm(residual) > abs_res_tol_) && (iter < max_iter_) ) {
if ( solver_.getSolver() == CPU ) {
du = serialSolveForUpdate(residual);
}
else {
// Solve linear equations for du, apply update.
du = solver_.solve(residual.derivative(),
residual.value(),
verbose_);
}
// du is a constant, hence, u is still a primary variable with an identity
// matrix as its derivative.
u = u - du;
// Recompute residual.
residual = rescomp(u);
if (verbose_ > 2) {
// Debugging output not specified in Equelle.
output("u", u);
output(" newtonSolve: norm(u)", twoNorm(u));
output("residual", residual);
output(" newtonSolve: norm(residual)", twoNorm(residual));
}
++iter;
// Debugging output not specified in Equelle.
if (verbose_ > 1) {
std::cout << " newtonSolve: iter = " << iter << " (max = " << max_iter_
<< "), norm(residual) = " << twoNorm(residual)
<< " (tol = " << abs_res_tol_ << ")" << std::endl;
}
}
if (verbose_ > 0) {
if (twoNorm(residual) > abs_res_tol_) {
std::cout << "Newton solver failed to converge in " << max_iter_ << " iterations" << std::endl;
} else {
std::cout << "Newton solver converged in " << iter << " iterations" << std::endl;
}
}
if (verbose_ > 1) {
std::cout << "Newton solver took: " << clock.secsSinceLast() << " seconds." << std::endl;
}
return CollOfScalar(u.value());
}
