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Changes to make flow compile with AutoDiffMatrix.

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This commit is contained in:
Atgeirr Flø Rasmussen
2015-08-25 16:15:29 +02:00
committed by babrodtk
parent c712d0070d
commit c795113ce3
11 changed files with 195 additions and 109 deletions
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55
opm/autodiff/AutoDiffHelpers.hpp
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@@ -39,7 +39,8 @@ namespace Opm
/// operations on (AD or regular) vectors of data.
struct HelperOps
{
typedef AutoDiffBlock<double>::M M;
// typedef AutoDiffBlock<double>::M M;
typedef Eigen::SparseMatrix<double> M;
typedef AutoDiffBlock<double>::V V;
/// A list of internal faces.
@@ -250,7 +251,7 @@ struct HelperOps
}
private:
typename ADB::M select_;
Eigen::SparseMatrix<double> select_;
};
@@ -259,17 +260,17 @@ namespace {
template <typename Scalar, class IntVec>
typename AutoDiffBlock<Scalar>::M
typename Eigen::SparseMatrix<Scalar>
constructSupersetSparseMatrix(const int full_size, const IntVec& indices)
{
const int subset_size = indices.size();
if (subset_size == 0) {
typename AutoDiffBlock<Scalar>::M mat(full_size, 0);
Eigen::SparseMatrix<Scalar> mat(full_size, 0);
return mat;
}
typename AutoDiffBlock<Scalar>::M mat(full_size, subset_size);
Eigen::SparseMatrix<Scalar> mat(full_size, subset_size);
mat.reserve(Eigen::VectorXi::Constant(subset_size, 1));
for (int i = 0; i < subset_size; ++i) {
mat.insert(indices[i], i) = 1;
@@ -302,8 +303,8 @@ AutoDiffBlock<Scalar>
subset(const AutoDiffBlock<Scalar>& x,
const IntVec& indices)
{
const typename AutoDiffBlock<Scalar>::M sub
= constructSupersetSparseMatrix<Scalar>(x.value().size(), indices).transpose();
const Eigen::SparseMatrix<Scalar> sub
= constructSupersetSparseMatrix<Scalar>(x.value().size(), indices).transpose().eval();
return sub * x;
}
@@ -336,10 +337,12 @@ superset(const Eigen::Array<Scalar, Eigen::Dynamic, 1>& x,
/// elements of d on the diagonal.
/// Need to mark this as inline since it is defined in a header and not a template.
inline
AutoDiffBlock<double>::M
//AutoDiffBlock<double>::M
Eigen::SparseMatrix<double>
spdiag(const AutoDiffBlock<double>::V& d)
{
typedef AutoDiffBlock<double>::M M;
// typedef AutoDiffBlock<double>::M M;
typedef Eigen::SparseMatrix<double> M;
const int n = d.size();
M mat(n, n);
@@ -456,9 +459,11 @@ collapseJacs(const AutoDiffBlock<double>& x, Matrix& jacobian)
t.reserve(nnz);
int block_col_start = 0;
for (int block = 0; block < nb; ++block) {
const ADB::M& jac = x.derivative()[block];
for (ADB::M::Index k = 0; k < jac.outerSize(); ++k) {
for (ADB::M::InnerIterator i(jac, k); i ; ++i) {
const ADB::M& jac1 = x.derivative()[block];
Eigen::SparseMatrix<double> jac;
jac1.toSparse(jac);
for (Eigen::SparseMatrix<double>::Index k = 0; k < jac.outerSize(); ++k) {
for (Eigen::SparseMatrix<double>::InnerIterator i(jac, k); i ; ++i) {
t.push_back(Tri(i.row(),
i.col() + block_col_start,
i.value()));
@@ -478,16 +483,18 @@ inline
AutoDiffBlock<double>
collapseJacs(const AutoDiffBlock<double>& x)
{
typedef AutoDiffBlock<double> ADB;
Eigen::SparseMatrix<double> comb_jac;
collapseJacs( x, comb_jac );
// Build final jacobian.
typedef AutoDiffBlock<double> ADB;
std::vector<ADB::M> jacs(1);
collapseJacs( x, jacs[ 0 ] );
jacs[0] = AutoDiffMatrix(std::move(comb_jac));
ADB::V val = x.value();
return ADB::function(std::move(val), std::move(jacs));
}
/*
/// Returns the vertical concatenation [ x; y ] of the inputs.
inline
@@ -510,7 +517,7 @@ vertcat(const AutoDiffBlock<double>& x,
}
*/
/// Returns the vertical concatenation [ x[0]; x[1]; ...; x[n-1] ] of the inputs.
@@ -569,6 +576,7 @@ vertcatCollapseJacs(const std::vector<AutoDiffBlock<double> >& x)
}
// Set up for batch insertion of all Jacobian elements.
typedef Eigen::SparseMatrix<double> M;
typedef Eigen::Triplet<double> Tri;
std::vector<Tri> t;
t.reserve(nnz);
@@ -577,9 +585,11 @@ vertcatCollapseJacs(const std::vector<AutoDiffBlock<double> >& x)
int block_col_start = 0;
if (!x[elem].derivative().empty()) {
for (int block = 0; block < num_blocks; ++block) {
const ADB::M& jac = x[elem].derivative()[block];
for (ADB::M::Index k = 0; k < jac.outerSize(); ++k) {
for (ADB::M::InnerIterator i(jac, k); i ; ++i) {
// const ADB::M& jac = x[elem].derivative()[block];
M jac;
x[elem].derivative()[block].toSparse(jac);
for (M::Index k = 0; k < jac.outerSize(); ++k) {
for (M::InnerIterator i(jac, k); i ; ++i) {
t.push_back(Tri(i.row() + block_row_start,
i.col() + block_col_start,
i.value()));
@@ -592,10 +602,11 @@ vertcatCollapseJacs(const std::vector<AutoDiffBlock<double> >& x)
}
// Build final jacobian.
M comb_jac = M(size, num_cols);
comb_jac.reserve(nnz);
comb_jac.setFromTriplets(t.begin(), t.end());
std::vector<ADB::M> jac(1);
jac[0] = Eigen::SparseMatrix<double>(size, num_cols);
jac[0].reserve(nnz);
jac[0].setFromTriplets(t.begin(), t.end());
jac[0] = ADB::M(comb_jac);
// Use move semantics to return result efficiently.
return ADB::function(std::move(val), std::move(jac));