void MathUtilities::createDiagonalMatrix( const scalar& c, SparseMatrixsc& D ) { assert( D.rows() == D.cols() ); D.reserve( VectorXi::Constant( D.cols(), 1 ) ); for( int i = 0; i < D.cols(); ++i ) { D.insert(i,i) = c; } D.makeCompressed(); }
void FrictionOperatorUtilities::formGeneralizedFrictionBasis( const VectorXs& q0, const VectorXs& v0, const std::vector<std::unique_ptr<Constraint>>& K, const int num_samples, SparseMatrixsc& D, VectorXs& drel ) { assert( num_samples > 0 ); assert( D.rows() == v0.size() ); assert( num_samples * int( K.size() ) == D.cols() ); // Reserve space for entries reserveSpaceInBasisMatrix( num_samples, K, D ); // Build the matrix buildLinearFrictionBasis( q0, v0, num_samples, K, D, drel ); D.makeCompressed(); }
void MathUtilities::extractLowerTriangularMatrix( const SparseMatrixsc& A, SparseMatrixsc& B ) { std::vector< Eigen::Triplet<scalar> > triplets; for( int col = 0; col < A.outerSize(); ++col ) { for( SparseMatrixsc::InnerIterator it( A, col ); it; ++it ) { if( col > it.row() ) { continue; } triplets.push_back( Eigen::Triplet<scalar>( it.row(), col, it.value() ) ); } } B.resize( A.rows(), A.cols() ); B.setFromTriplets( triplets.begin(), triplets.end() ); B.makeCompressed(); }
void MathUtilities::convertDenseToSparse( const bool filter_zeros, const MatrixXXsc& dense_matrix, SparseMatrixsc& sparse_matrix ) { std::vector<Eigen::Triplet<scalar>> triplets; for( int row = 0; row < dense_matrix.rows(); ++row ) { for( int col = 0; col < dense_matrix.cols(); ++col ) { if( dense_matrix( row, col ) != 0.0 || !filter_zeros ) { triplets.emplace_back( Eigen::Triplet<scalar>{ row, col, dense_matrix( row, col ) } ); } } } sparse_matrix.resize( dense_matrix.rows(), dense_matrix.cols() ); sparse_matrix.setFromTriplets( std::begin( triplets ), std::end( triplets ) ); sparse_matrix.makeCompressed(); }
void FrictionOperatorUtilities::formLinearFrictionDiskConstraint( const int num_samples, SparseMatrixsc& E ) { { const VectorXi column_nonzeros{ VectorXi::Constant( E.cols(), num_samples ) }; E.reserve( column_nonzeros ); } // For each column for( int col = 0; col < E.cols(); ++col ) { for( int samplenum = 0; samplenum < num_samples; ++samplenum ) { // Note the negative for QL E.insert( num_samples * col + samplenum, col ) = 1.0; } } E.makeCompressed(); assert( E.nonZeros() == E.cols() * num_samples ); assert( E.sum() == E.nonZeros() ); }
// TODO: Pull the outerIndexPtr arithmetic into a helper function void MathUtilities::extractColumns( const SparseMatrixsc& A0, const std::vector<unsigned>& cols, SparseMatrixsc& A1 ) { const unsigned ncols_to_extract{ static_cast<unsigned>( cols.size() ) }; assert( ncols_to_extract <= static_cast<unsigned>( A0.cols() ) ); #ifndef NDEBUG for( unsigned i = 0; i < ncols_to_extract; ++i ) { assert( cols[i] < unsigned( A0.cols() ) ); } #endif // Compute the number of nonzeros in each column of the new matrix VectorXi column_nonzeros{ ncols_to_extract }; for( unsigned i = 0; i < ncols_to_extract; ++i ) { column_nonzeros( i ) = A0.outerIndexPtr()[cols[i]+1] - A0.outerIndexPtr()[cols[i]]; } // Resize A1 and reserve space A1.resize( A0.rows(), ncols_to_extract ); A1.reserve( column_nonzeros ); // Copy the data over, column by column for( unsigned cur_col = 0; cur_col < ncols_to_extract; ++cur_col ) { for( SparseMatrixsc::InnerIterator it( A0, cols[ cur_col ] ); it; ++it ) { A1.insert( it.row(), cur_col ) = it.value(); } } A1.makeCompressed(); #ifndef NDEBUG for( int i = 0 ; i < A1.cols(); ++i ) { assert( ( A1.outerIndexPtr()[i+1] - A1.outerIndexPtr()[i] ) == column_nonzeros( i ) ); } #endif }