diff options
Diffstat (limited to 'extern/ceres/internal/ceres/schur_complement_solver.cc')
| -rw-r--r-- | extern/ceres/internal/ceres/schur_complement_solver.cc | 442 |
1 files changed, 97 insertions, 345 deletions
diff --git a/extern/ceres/internal/ceres/schur_complement_solver.cc b/extern/ceres/internal/ceres/schur_complement_solver.cc index 65449832c4c..0083300b036 100644 --- a/extern/ceres/internal/ceres/schur_complement_solver.cc +++ b/extern/ceres/internal/ceres/schur_complement_solver.cc @@ -28,33 +28,30 @@ // // Author: sameeragarwal@google.com (Sameer Agarwal) -#include "ceres/internal/port.h" +#include "ceres/schur_complement_solver.h" #include <algorithm> #include <ctime> +#include <memory> #include <set> -#include <sstream> #include <vector> +#include "Eigen/Dense" +#include "Eigen/SparseCore" #include "ceres/block_random_access_dense_matrix.h" #include "ceres/block_random_access_matrix.h" #include "ceres/block_random_access_sparse_matrix.h" #include "ceres/block_sparse_matrix.h" #include "ceres/block_structure.h" #include "ceres/conjugate_gradients_solver.h" -#include "ceres/cxsparse.h" #include "ceres/detect_structure.h" #include "ceres/internal/eigen.h" -#include "ceres/internal/scoped_ptr.h" #include "ceres/lapack.h" #include "ceres/linear_solver.h" -#include "ceres/schur_complement_solver.h" -#include "ceres/suitesparse.h" +#include "ceres/sparse_cholesky.h" #include "ceres/triplet_sparse_matrix.h" #include "ceres/types.h" #include "ceres/wall_time.h" -#include "Eigen/Dense" -#include "Eigen/SparseCore" namespace ceres { namespace internal { @@ -70,23 +67,22 @@ class BlockRandomAccessSparseMatrixAdapter : public LinearOperator { public: explicit BlockRandomAccessSparseMatrixAdapter( const BlockRandomAccessSparseMatrix& m) - : m_(m) { - } + : m_(m) {} virtual ~BlockRandomAccessSparseMatrixAdapter() {} // y = y + Ax; - virtual void RightMultiply(const double* x, double* y) const { + void RightMultiply(const double* x, double* y) const final { m_.SymmetricRightMultiply(x, y); } // y = y + A'x; - virtual void LeftMultiply(const double* x, double* y) const { + void LeftMultiply(const double* x, double* y) const final { m_.SymmetricRightMultiply(x, y); } - virtual int num_rows() const { return m_.num_rows(); } - virtual int num_cols() const { return m_.num_rows(); } + int num_rows() const final { return m_.num_rows(); } + int num_cols() const final { return m_.num_rows(); } private: const BlockRandomAccessSparseMatrix& m_; @@ -96,29 +92,28 @@ class BlockRandomAccessDiagonalMatrixAdapter : public LinearOperator { public: explicit BlockRandomAccessDiagonalMatrixAdapter( const BlockRandomAccessDiagonalMatrix& m) - : m_(m) { - } + : m_(m) {} virtual ~BlockRandomAccessDiagonalMatrixAdapter() {} // y = y + Ax; - virtual void RightMultiply(const double* x, double* y) const { + void RightMultiply(const double* x, double* y) const final { m_.RightMultiply(x, y); } // y = y + A'x; - virtual void LeftMultiply(const double* x, double* y) const { + void LeftMultiply(const double* x, double* y) const final { m_.RightMultiply(x, y); } - virtual int num_rows() const { return m_.num_rows(); } - virtual int num_cols() const { return m_.num_rows(); } + int num_rows() const final { return m_.num_rows(); } + int num_cols() const final { return m_.num_rows(); } private: const BlockRandomAccessDiagonalMatrix& m_; }; -} // namespace +} // namespace LinearSolver::Summary SchurComplementSolver::SolveImpl( BlockSparseMatrix* A, @@ -127,20 +122,45 @@ LinearSolver::Summary SchurComplementSolver::SolveImpl( double* x) { EventLogger event_logger("SchurComplementSolver::Solve"); + const CompressedRowBlockStructure* bs = A->block_structure(); if (eliminator_.get() == NULL) { - InitStorage(A->block_structure()); - DetectStructure(*A->block_structure(), - options_.elimination_groups[0], + const int num_eliminate_blocks = options_.elimination_groups[0]; + const int num_f_blocks = bs->cols.size() - num_eliminate_blocks; + + InitStorage(bs); + DetectStructure(*bs, + num_eliminate_blocks, &options_.row_block_size, &options_.e_block_size, &options_.f_block_size); - eliminator_.reset(CHECK_NOTNULL(SchurEliminatorBase::Create(options_))); - eliminator_->Init(options_.elimination_groups[0], A->block_structure()); - }; + + // For the special case of the static structure <2,3,6> with + // exactly one f block use the SchurEliminatorForOneFBlock. + // + // TODO(sameeragarwal): A more scalable template specialization + // mechanism that does not cause binary bloat. + if (options_.row_block_size == 2 && + options_.e_block_size == 3 && + options_.f_block_size == 6 && + num_f_blocks == 1) { + eliminator_.reset(new SchurEliminatorForOneFBlock<2, 3, 6>); + } else { + eliminator_.reset(SchurEliminatorBase::Create(options_)); + } + + CHECK(eliminator_); + const bool kFullRankETE = true; + eliminator_->Init(num_eliminate_blocks, kFullRankETE, bs); + } + std::fill(x, x + A->num_cols(), 0.0); event_logger.AddEvent("Setup"); - eliminator_->Eliminate(A, b, per_solve_options.D, lhs_.get(), rhs_.get()); + eliminator_->Eliminate(BlockSparseMatrixData(*A), + b, + per_solve_options.D, + lhs_.get(), + rhs_.get()); event_logger.AddEvent("Eliminate"); double* reduced_solution = x + A->num_cols() - lhs_->num_cols(); @@ -149,7 +169,8 @@ LinearSolver::Summary SchurComplementSolver::SolveImpl( event_logger.AddEvent("ReducedSolve"); if (summary.termination_type == LINEAR_SOLVER_SUCCESS) { - eliminator_->BackSubstitute(A, b, per_solve_options.D, reduced_solution, x); + eliminator_->BackSubstitute( + BlockSparseMatrixData(*A), b, per_solve_options.D, reduced_solution, x); event_logger.AddEvent("BackSubstitute"); } @@ -164,9 +185,7 @@ void DenseSchurComplementSolver::InitStorage( const int num_col_blocks = bs->cols.size(); vector<int> blocks(num_col_blocks - num_eliminate_blocks, 0); - for (int i = num_eliminate_blocks, j = 0; - i < num_col_blocks; - ++i, ++j) { + for (int i = num_eliminate_blocks, j = 0; i < num_col_blocks; ++i, ++j) { blocks[j] = bs->cols[i].size; } @@ -177,10 +196,8 @@ void DenseSchurComplementSolver::InitStorage( // Solve the system Sx = r, assuming that the matrix S is stored in a // BlockRandomAccessDenseMatrix. The linear system is solved using // Eigen's Cholesky factorization. -LinearSolver::Summary -DenseSchurComplementSolver::SolveReducedLinearSystem( - const LinearSolver::PerSolveOptions& per_solve_options, - double* solution) { +LinearSolver::Summary DenseSchurComplementSolver::SolveReducedLinearSystem( + const LinearSolver::PerSolveOptions& per_solve_options, double* solution) { LinearSolver::Summary summary; summary.num_iterations = 0; summary.termination_type = LINEAR_SOLVER_SUCCESS; @@ -201,8 +218,8 @@ DenseSchurComplementSolver::SolveReducedLinearSystem( if (options().dense_linear_algebra_library_type == EIGEN) { Eigen::LLT<Matrix, Eigen::Upper> llt = ConstMatrixRef(m->values(), num_rows, num_rows) - .selfadjointView<Eigen::Upper>() - .llt(); + .selfadjointView<Eigen::Upper>() + .llt(); if (llt.info() != Eigen::Success) { summary.termination_type = LINEAR_SOLVER_FAILURE; summary.message = @@ -213,11 +230,8 @@ DenseSchurComplementSolver::SolveReducedLinearSystem( VectorRef(solution, num_rows) = llt.solve(ConstVectorRef(rhs(), num_rows)); } else { VectorRef(solution, num_rows) = ConstVectorRef(rhs(), num_rows); - summary.termination_type = - LAPACK::SolveInPlaceUsingCholesky(num_rows, - m->values(), - solution, - &summary.message); + summary.termination_type = LAPACK::SolveInPlaceUsingCholesky( + num_rows, m->values(), solution, &summary.message); } return summary; @@ -225,23 +239,14 @@ DenseSchurComplementSolver::SolveReducedLinearSystem( SparseSchurComplementSolver::SparseSchurComplementSolver( const LinearSolver::Options& options) - : SchurComplementSolver(options), - factor_(NULL), - cxsparse_factor_(NULL) { -} - -SparseSchurComplementSolver::~SparseSchurComplementSolver() { - if (factor_ != NULL) { - ss_.Free(factor_); - factor_ = NULL; - } - - if (cxsparse_factor_ != NULL) { - cxsparse_.Free(cxsparse_factor_); - cxsparse_factor_ = NULL; + : SchurComplementSolver(options) { + if (options.type != ITERATIVE_SCHUR) { + sparse_cholesky_ = SparseCholesky::Create(options); } } +SparseSchurComplementSolver::~SparseSchurComplementSolver() {} + // Determine the non-zero blocks in the Schur Complement matrix, and // initialize a BlockRandomAccessSparseMatrix object. void SparseSchurComplementSolver::InitStorage( @@ -255,7 +260,7 @@ void SparseSchurComplementSolver::InitStorage( blocks_[i - num_eliminate_blocks] = bs->cols[i].size; } - set<pair<int, int> > block_pairs; + set<pair<int, int>> block_pairs; for (int i = 0; i < blocks_.size(); ++i) { block_pairs.insert(make_pair(i, i)); } @@ -293,7 +298,7 @@ void SparseSchurComplementSolver::InitStorage( } } - // Remaing rows do not contribute to the chunks and directly go + // Remaining rows do not contribute to the chunks and directly go // into the schur complement via an outer product. for (; r < num_row_blocks; ++r) { const CompressedRow& row = bs->rows[r]; @@ -313,296 +318,49 @@ void SparseSchurComplementSolver::InitStorage( set_rhs(new double[lhs()->num_rows()]); } -LinearSolver::Summary -SparseSchurComplementSolver::SolveReducedLinearSystem( - const LinearSolver::PerSolveOptions& per_solve_options, - double* solution) { +LinearSolver::Summary SparseSchurComplementSolver::SolveReducedLinearSystem( + const LinearSolver::PerSolveOptions& per_solve_options, double* solution) { if (options().type == ITERATIVE_SCHUR) { - CHECK(options().use_explicit_schur_complement); return SolveReducedLinearSystemUsingConjugateGradients(per_solve_options, solution); } - switch (options().sparse_linear_algebra_library_type) { - case SUITE_SPARSE: - return SolveReducedLinearSystemUsingSuiteSparse(per_solve_options, - solution); - case CX_SPARSE: - return SolveReducedLinearSystemUsingCXSparse(per_solve_options, - solution); - case EIGEN_SPARSE: - return SolveReducedLinearSystemUsingEigen(per_solve_options, - solution); - default: - LOG(FATAL) << "Unknown sparse linear algebra library : " - << options().sparse_linear_algebra_library_type; - } - - return LinearSolver::Summary(); -} - -// Solve the system Sx = r, assuming that the matrix S is stored in a -// BlockRandomAccessSparseMatrix. The linear system is solved using -// CHOLMOD's sparse cholesky factorization routines. -LinearSolver::Summary -SparseSchurComplementSolver::SolveReducedLinearSystemUsingSuiteSparse( - const LinearSolver::PerSolveOptions& per_solve_options, - double* solution) { -#ifdef CERES_NO_SUITESPARSE - - LinearSolver::Summary summary; - summary.num_iterations = 0; - summary.termination_type = LINEAR_SOLVER_FATAL_ERROR; - summary.message = "Ceres was not built with SuiteSparse support. " - "Therefore, SPARSE_SCHUR cannot be used with SUITE_SPARSE"; - return summary; - -#else - LinearSolver::Summary summary; summary.num_iterations = 0; summary.termination_type = LINEAR_SOLVER_SUCCESS; summary.message = "Success."; - TripletSparseMatrix* tsm = - const_cast<TripletSparseMatrix*>( - down_cast<const BlockRandomAccessSparseMatrix*>(lhs())->matrix()); - const int num_rows = tsm->num_rows(); - - // The case where there are no f blocks, and the system is block - // diagonal. - if (num_rows == 0) { + const TripletSparseMatrix* tsm = + down_cast<const BlockRandomAccessSparseMatrix*>(lhs())->matrix(); + if (tsm->num_rows() == 0) { return summary; } - summary.num_iterations = 1; - cholmod_sparse* cholmod_lhs = NULL; - if (options().use_postordering) { - // If we are going to do a full symbolic analysis of the schur - // complement matrix from scratch and not rely on the - // pre-ordering, then the fastest path in cholmod_factorize is the - // one corresponding to upper triangular matrices. - - // Create a upper triangular symmetric matrix. - cholmod_lhs = ss_.CreateSparseMatrix(tsm); - cholmod_lhs->stype = 1; - - if (factor_ == NULL) { - factor_ = ss_.BlockAnalyzeCholesky(cholmod_lhs, - blocks_, - blocks_, - &summary.message); - } + std::unique_ptr<CompressedRowSparseMatrix> lhs; + const CompressedRowSparseMatrix::StorageType storage_type = + sparse_cholesky_->StorageType(); + if (storage_type == CompressedRowSparseMatrix::UPPER_TRIANGULAR) { + lhs.reset(CompressedRowSparseMatrix::FromTripletSparseMatrix(*tsm)); + lhs->set_storage_type(CompressedRowSparseMatrix::UPPER_TRIANGULAR); } else { - // If we are going to use the natural ordering (i.e. rely on the - // pre-ordering computed by solver_impl.cc), then the fastest - // path in cholmod_factorize is the one corresponding to lower - // triangular matrices. - - // Create a upper triangular symmetric matrix. - cholmod_lhs = ss_.CreateSparseMatrixTranspose(tsm); - cholmod_lhs->stype = -1; - - if (factor_ == NULL) { - factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(cholmod_lhs, - &summary.message); - } - } - - if (factor_ == NULL) { - ss_.Free(cholmod_lhs); - summary.termination_type = LINEAR_SOLVER_FATAL_ERROR; - // No need to set message as it has already been set by the - // symbolic analysis routines above. - return summary; + lhs.reset( + CompressedRowSparseMatrix::FromTripletSparseMatrixTransposed(*tsm)); + lhs->set_storage_type(CompressedRowSparseMatrix::LOWER_TRIANGULAR); } - summary.termination_type = - ss_.Cholesky(cholmod_lhs, factor_, &summary.message); - - ss_.Free(cholmod_lhs); - - if (summary.termination_type != LINEAR_SOLVER_SUCCESS) { - // No need to set message as it has already been set by the - // numeric factorization routine above. - return summary; - } - - cholmod_dense* cholmod_rhs = - ss_.CreateDenseVector(const_cast<double*>(rhs()), num_rows, num_rows); - cholmod_dense* cholmod_solution = ss_.Solve(factor_, - cholmod_rhs, - &summary.message); - ss_.Free(cholmod_rhs); - - if (cholmod_solution == NULL) { - summary.message = - "SuiteSparse failure. Unable to perform triangular solve."; - summary.termination_type = LINEAR_SOLVER_FAILURE; - return summary; - } - - VectorRef(solution, num_rows) - = VectorRef(static_cast<double*>(cholmod_solution->x), num_rows); - ss_.Free(cholmod_solution); - return summary; -#endif // CERES_NO_SUITESPARSE -} - -// Solve the system Sx = r, assuming that the matrix S is stored in a -// BlockRandomAccessSparseMatrix. The linear system is solved using -// CXSparse's sparse cholesky factorization routines. -LinearSolver::Summary -SparseSchurComplementSolver::SolveReducedLinearSystemUsingCXSparse( - const LinearSolver::PerSolveOptions& per_solve_options, - double* solution) { -#ifdef CERES_NO_CXSPARSE - - LinearSolver::Summary summary; - summary.num_iterations = 0; - summary.termination_type = LINEAR_SOLVER_FATAL_ERROR; - summary.message = "Ceres was not built with CXSparse support. " - "Therefore, SPARSE_SCHUR cannot be used with CX_SPARSE"; - return summary; - -#else - - LinearSolver::Summary summary; - summary.num_iterations = 0; - summary.termination_type = LINEAR_SOLVER_SUCCESS; - summary.message = "Success."; - - // Extract the TripletSparseMatrix that is used for actually storing S. - TripletSparseMatrix* tsm = - const_cast<TripletSparseMatrix*>( - down_cast<const BlockRandomAccessSparseMatrix*>(lhs())->matrix()); - const int num_rows = tsm->num_rows(); - - // The case where there are no f blocks, and the system is block - // diagonal. - if (num_rows == 0) { - return summary; - } - - cs_di* lhs = CHECK_NOTNULL(cxsparse_.CreateSparseMatrix(tsm)); - VectorRef(solution, num_rows) = ConstVectorRef(rhs(), num_rows); - - // Compute symbolic factorization if not available. - if (cxsparse_factor_ == NULL) { - cxsparse_factor_ = cxsparse_.BlockAnalyzeCholesky(lhs, blocks_, blocks_); - } - - if (cxsparse_factor_ == NULL) { - summary.termination_type = LINEAR_SOLVER_FATAL_ERROR; - summary.message = - "CXSparse failure. Unable to find symbolic factorization."; - } else if (!cxsparse_.SolveCholesky(lhs, cxsparse_factor_, solution)) { - summary.termination_type = LINEAR_SOLVER_FAILURE; - summary.message = "CXSparse::SolveCholesky failed."; - } - - cxsparse_.Free(lhs); - return summary; -#endif // CERES_NO_CXPARSE -} - -// Solve the system Sx = r, assuming that the matrix S is stored in a -// BlockRandomAccessSparseMatrix. The linear system is solved using -// Eigen's sparse cholesky factorization routines. -LinearSolver::Summary -SparseSchurComplementSolver::SolveReducedLinearSystemUsingEigen( - const LinearSolver::PerSolveOptions& per_solve_options, - double* solution) { -#ifndef CERES_USE_EIGEN_SPARSE - - LinearSolver::Summary summary; - summary.num_iterations = 0; - summary.termination_type = LINEAR_SOLVER_FATAL_ERROR; - summary.message = - "SPARSE_SCHUR cannot be used with EIGEN_SPARSE. " - "Ceres was not built with support for " - "Eigen's SimplicialLDLT decomposition. " - "This requires enabling building with -DEIGENSPARSE=ON."; - return summary; - -#else - EventLogger event_logger("SchurComplementSolver::EigenSolve"); - LinearSolver::Summary summary; - summary.num_iterations = 0; - summary.termination_type = LINEAR_SOLVER_SUCCESS; - summary.message = "Success."; - - // Extract the TripletSparseMatrix that is used for actually storing S. - TripletSparseMatrix* tsm = - const_cast<TripletSparseMatrix*>( - down_cast<const BlockRandomAccessSparseMatrix*>(lhs())->matrix()); - const int num_rows = tsm->num_rows(); - - // The case where there are no f blocks, and the system is block - // diagonal. - if (num_rows == 0) { - return summary; - } - - // This is an upper triangular matrix. - CompressedRowSparseMatrix crsm(*tsm); - // Map this to a column major, lower triangular matrix. - Eigen::MappedSparseMatrix<double, Eigen::ColMajor> eigen_lhs( - crsm.num_rows(), - crsm.num_rows(), - crsm.num_nonzeros(), - crsm.mutable_rows(), - crsm.mutable_cols(), - crsm.mutable_values()); - event_logger.AddEvent("ToCompressedRowSparseMatrix"); - - // Compute symbolic factorization if one does not exist. - if (simplicial_ldlt_.get() == NULL) { - simplicial_ldlt_.reset(new SimplicialLDLT); - // This ordering is quite bad. The scalar ordering produced by the - // AMD algorithm is quite bad and can be an order of magnitude - // worse than the one computed using the block version of the - // algorithm. - simplicial_ldlt_->analyzePattern(eigen_lhs); - if (VLOG_IS_ON(2)) { - std::stringstream ss; - simplicial_ldlt_->dumpMemory(ss); - VLOG(2) << "Symbolic Analysis\n" - << ss.str(); - } - event_logger.AddEvent("Analysis"); - if (simplicial_ldlt_->info() != Eigen::Success) { - summary.termination_type = LINEAR_SOLVER_FATAL_ERROR; - summary.message = - "Eigen failure. Unable to find symbolic factorization."; - return summary; - } - } - - simplicial_ldlt_->factorize(eigen_lhs); - event_logger.AddEvent("Factorize"); - if (simplicial_ldlt_->info() != Eigen::Success) { - summary.termination_type = LINEAR_SOLVER_FAILURE; - summary.message = "Eigen failure. Unable to find numeric factoriztion."; - return summary; - } - - VectorRef(solution, num_rows) = - simplicial_ldlt_->solve(ConstVectorRef(rhs(), num_rows)); - event_logger.AddEvent("Solve"); - if (simplicial_ldlt_->info() != Eigen::Success) { - summary.termination_type = LINEAR_SOLVER_FAILURE; - summary.message = "Eigen failure. Unable to do triangular solve."; - } + *lhs->mutable_col_blocks() = blocks_; + *lhs->mutable_row_blocks() = blocks_; + summary.num_iterations = 1; + summary.termination_type = sparse_cholesky_->FactorAndSolve( + lhs.get(), rhs(), solution, &summary.message); return summary; -#endif // CERES_USE_EIGEN_SPARSE } LinearSolver::Summary SparseSchurComplementSolver::SolveReducedLinearSystemUsingConjugateGradients( - const LinearSolver::PerSolveOptions& per_solve_options, - double* solution) { + const LinearSolver::PerSolveOptions& per_solve_options, double* solution) { + CHECK(options().use_explicit_schur_complement); const int num_rows = lhs()->num_rows(); // The case where there are no f blocks, and the system is block // diagonal. @@ -621,27 +379,24 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingConjugateGradients( preconditioner_.reset(new BlockRandomAccessDiagonalMatrix(blocks_)); } - BlockRandomAccessSparseMatrix* sc = - down_cast<BlockRandomAccessSparseMatrix*>( - const_cast<BlockRandomAccessMatrix*>(lhs())); + BlockRandomAccessSparseMatrix* sc = down_cast<BlockRandomAccessSparseMatrix*>( + const_cast<BlockRandomAccessMatrix*>(lhs())); // Extract block diagonal from the Schur complement to construct the // schur_jacobi preconditioner. - for (int i = 0; i < blocks_.size(); ++i) { + for (int i = 0; i < blocks_.size(); ++i) { const int block_size = blocks_[i]; int sc_r, sc_c, sc_row_stride, sc_col_stride; CellInfo* sc_cell_info = - CHECK_NOTNULL(sc->GetCell(i, i, - &sc_r, &sc_c, - &sc_row_stride, &sc_col_stride)); + sc->GetCell(i, i, &sc_r, &sc_c, &sc_row_stride, &sc_col_stride); + CHECK(sc_cell_info != nullptr); MatrixRef sc_m(sc_cell_info->values, sc_row_stride, sc_col_stride); int pre_r, pre_c, pre_row_stride, pre_col_stride; - CellInfo* pre_cell_info = CHECK_NOTNULL( - preconditioner_->GetCell(i, i, - &pre_r, &pre_c, - &pre_row_stride, &pre_col_stride)); + CellInfo* pre_cell_info = preconditioner_->GetCell( + i, i, &pre_r, &pre_c, &pre_row_stride, &pre_col_stride); + CHECK(pre_cell_info != nullptr); MatrixRef pre_m(pre_cell_info->values, pre_row_stride, pre_col_stride); pre_m.block(pre_r, pre_c, block_size, block_size) = @@ -651,12 +406,11 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingConjugateGradients( VectorRef(solution, num_rows).setZero(); - scoped_ptr<LinearOperator> lhs_adapter( + std::unique_ptr<LinearOperator> lhs_adapter( new BlockRandomAccessSparseMatrixAdapter(*sc)); - scoped_ptr<LinearOperator> preconditioner_adapter( + std::unique_ptr<LinearOperator> preconditioner_adapter( new BlockRandomAccessDiagonalMatrixAdapter(*preconditioner_)); - LinearSolver::Options cg_options; cg_options.min_num_iterations = options().min_num_iterations; cg_options.max_num_iterations = options().max_num_iterations; @@ -667,10 +421,8 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingConjugateGradients( cg_per_solve_options.q_tolerance = per_solve_options.q_tolerance; cg_per_solve_options.preconditioner = preconditioner_adapter.get(); - return cg_solver.Solve(lhs_adapter.get(), - rhs(), - cg_per_solve_options, - solution); + return cg_solver.Solve( + lhs_adapter.get(), rhs(), cg_per_solve_options, solution); } } // namespace internal |