internal/ceres/suitesparse.cc - ceres-solver - Git at Google

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2015 Google Inc. All rights reserved.
 // http://ceres-solver.org/
 //
 // Redistribution and use in source and binary forms, with or without
 // modification, are permitted provided that the following conditions are met:
 //
 // * Redistributions of source code must retain the above copyright notice,
 //   this list of conditions and the following disclaimer.
 // * Redistributions in binary form must reproduce the above copyright notice,
 //   this list of conditions and the following disclaimer in the documentation
 //   and/or other materials provided with the distribution.
 // * Neither the name of Google Inc. nor the names of its contributors may be
 //   used to endorse or promote products derived from this software without
 //   specific prior written permission.
 //
 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
 // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 // POSSIBILITY OF SUCH DAMAGE.
 //
 // Author: sameeragarwal@google.com (Sameer Agarwal)

 // This include must come before any #ifndef check on Ceres compile options.
 #include "ceres/internal/port.h"

 #ifndef CERES_NO_SUITESPARSE
 #include <vector>

 #include "ceres/compressed_col_sparse_matrix_utils.h"
 #include "ceres/compressed_row_sparse_matrix.h"
 #include "ceres/linear_solver.h"
 #include "ceres/suitesparse.h"
 #include "ceres/triplet_sparse_matrix.h"
 #include "cholmod.h"

 namespace ceres {
 namespace internal {

 using std::string;
 using std::vector;

 SuiteSparse::SuiteSparse() { cholmod_start(&cc_); }

 SuiteSparse::~SuiteSparse() { cholmod_finish(&cc_); }

 cholmod_sparse* SuiteSparse::CreateSparseMatrix(TripletSparseMatrix* A) {
   cholmod_triplet triplet;

   triplet.nrow = A->num_rows();
   triplet.ncol = A->num_cols();
   triplet.nzmax = A->max_num_nonzeros();
   triplet.nnz = A->num_nonzeros();
   triplet.i = reinterpret_cast<void*>(A->mutable_rows());
   triplet.j = reinterpret_cast<void*>(A->mutable_cols());
   triplet.x = reinterpret_cast<void*>(A->mutable_values());
   triplet.stype = 0;  // Matrix is not symmetric.
   triplet.itype = CHOLMOD_INT;
   triplet.xtype = CHOLMOD_REAL;
   triplet.dtype = CHOLMOD_DOUBLE;

   return cholmod_triplet_to_sparse(&triplet, triplet.nnz, &cc_);
 }

 cholmod_sparse* SuiteSparse::CreateSparseMatrixTranspose(
     TripletSparseMatrix* A) {
   cholmod_triplet triplet;

   triplet.ncol = A->num_rows();  // swap row and columns
   triplet.nrow = A->num_cols();
   triplet.nzmax = A->max_num_nonzeros();
   triplet.nnz = A->num_nonzeros();

   // swap rows and columns
   triplet.j = reinterpret_cast<void*>(A->mutable_rows());
   triplet.i = reinterpret_cast<void*>(A->mutable_cols());
   triplet.x = reinterpret_cast<void*>(A->mutable_values());
   triplet.stype = 0;  // Matrix is not symmetric.
   triplet.itype = CHOLMOD_INT;
   triplet.xtype = CHOLMOD_REAL;
   triplet.dtype = CHOLMOD_DOUBLE;

   return cholmod_triplet_to_sparse(&triplet, triplet.nnz, &cc_);
 }

 cholmod_sparse SuiteSparse::CreateSparseMatrixTransposeView(
     CompressedRowSparseMatrix* A) {
   cholmod_sparse m;
   m.nrow = A->num_cols();
   m.ncol = A->num_rows();
   m.nzmax = A->num_nonzeros();
   m.nz = nullptr;
   m.p = reinterpret_cast<void*>(A->mutable_rows());
   m.i = reinterpret_cast<void*>(A->mutable_cols());
   m.x = reinterpret_cast<void*>(A->mutable_values());
   m.z = nullptr;

   if (A->storage_type() == CompressedRowSparseMatrix::LOWER_TRIANGULAR) {
     m.stype = 1;
   } else if (A->storage_type() == CompressedRowSparseMatrix::UPPER_TRIANGULAR) {
     m.stype = -1;
   } else {
     m.stype = 0;
   }

   m.itype = CHOLMOD_INT;
   m.xtype = CHOLMOD_REAL;
   m.dtype = CHOLMOD_DOUBLE;
   m.sorted = 1;
   m.packed = 1;

   return m;
 }

 cholmod_dense SuiteSparse::CreateDenseVectorView(const double* x, int size) {
   cholmod_dense v;
   v.nrow = size;
   v.ncol = 1;
   v.nzmax = size;
   v.d = size;
   v.x = const_cast<void*>(reinterpret_cast<const void*>(x));
   v.xtype = CHOLMOD_REAL;
   v.dtype = CHOLMOD_DOUBLE;
   return v;
 }

 cholmod_dense* SuiteSparse::CreateDenseVector(const double* x,
                                               int in_size,
                                               int out_size) {
   CHECK_LE(in_size, out_size);
   cholmod_dense* v = cholmod_zeros(out_size, 1, CHOLMOD_REAL, &cc_);
   if (x != nullptr) {
     memcpy(v->x, x, in_size * sizeof(*x));
   }
   return v;
 }

 cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A,
                                              string* message) {
   // Cholmod can try multiple re-ordering strategies to find a fill
   // reducing ordering. Here we just tell it use AMD with automatic
   // matrix dependence choice of supernodal versus simplicial
   // factorization.
   cc_.nmethods = 1;
   cc_.method[0].ordering = CHOLMOD_AMD;
   cc_.supernodal = CHOLMOD_AUTO;

   cholmod_factor* factor = cholmod_analyze(A, &cc_);
   if (VLOG_IS_ON(2)) {
     cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
   }

   if (cc_.status != CHOLMOD_OK) {
     *message =
         StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
     return nullptr;
   }

   CHECK(factor != nullptr);
   return factor;
 }

 cholmod_factor* SuiteSparse::BlockAnalyzeCholesky(cholmod_sparse* A,
                                                   const vector<int>& row_blocks,
                                                   const vector<int>& col_blocks,
                                                   string* message) {
   vector<int> ordering;
   if (!BlockAMDOrdering(A, row_blocks, col_blocks, &ordering)) {
     return nullptr;
   }
   return AnalyzeCholeskyWithUserOrdering(A, ordering, message);
 }

 cholmod_factor* SuiteSparse::AnalyzeCholeskyWithUserOrdering(
     cholmod_sparse* A, const vector<int>& ordering, string* message) {
   CHECK_EQ(ordering.size(), A->nrow);

   cc_.nmethods = 1;
   cc_.method[0].ordering = CHOLMOD_GIVEN;

   cholmod_factor* factor =
       cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), nullptr, 0, &cc_);
   if (VLOG_IS_ON(2)) {
     cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
   }
   if (cc_.status != CHOLMOD_OK) {
     *message =
         StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
     return nullptr;
   }

   CHECK(factor != nullptr);
   return factor;
 }

 cholmod_factor* SuiteSparse::AnalyzeCholeskyWithNaturalOrdering(
     cholmod_sparse* A, string* message) {
   cc_.nmethods = 1;
   cc_.method[0].ordering = CHOLMOD_NATURAL;
   cc_.postorder = 0;

   cholmod_factor* factor = cholmod_analyze(A, &cc_);
   if (VLOG_IS_ON(2)) {
     cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
   }
   if (cc_.status != CHOLMOD_OK) {
     *message =
         StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
     return nullptr;
   }

   CHECK(factor != nullptr);
   return factor;
 }

 bool SuiteSparse::BlockAMDOrdering(const cholmod_sparse* A,
                                    const vector<int>& row_blocks,
                                    const vector<int>& col_blocks,
                                    vector<int>* ordering) {
   const int num_row_blocks = row_blocks.size();
   const int num_col_blocks = col_blocks.size();

   // Arrays storing the compressed column structure of the matrix
   // incoding the block sparsity of A.
   vector<int> block_cols;
   vector<int> block_rows;

   CompressedColumnScalarMatrixToBlockMatrix(reinterpret_cast<const int*>(A->i),
                                             reinterpret_cast<const int*>(A->p),
                                             row_blocks,
                                             col_blocks,
                                             &block_rows,
                                             &block_cols);
   cholmod_sparse_struct block_matrix;
   block_matrix.nrow = num_row_blocks;
   block_matrix.ncol = num_col_blocks;
   block_matrix.nzmax = block_rows.size();
   block_matrix.p = reinterpret_cast<void*>(&block_cols[0]);
   block_matrix.i = reinterpret_cast<void*>(&block_rows[0]);
   block_matrix.x = nullptr;
   block_matrix.stype = A->stype;
   block_matrix.itype = CHOLMOD_INT;
   block_matrix.xtype = CHOLMOD_PATTERN;
   block_matrix.dtype = CHOLMOD_DOUBLE;
   block_matrix.sorted = 1;
   block_matrix.packed = 1;

   vector<int> block_ordering(num_row_blocks);
   if (!cholmod_amd(&block_matrix, nullptr, 0, &block_ordering[0], &cc_)) {
     return false;
   }

   BlockOrderingToScalarOrdering(row_blocks, block_ordering, ordering);
   return true;
 }

 LinearSolverTerminationType SuiteSparse::Cholesky(cholmod_sparse* A,
                                                   cholmod_factor* L,
                                                   string* message) {
   CHECK(A != nullptr);
   CHECK(L != nullptr);

   // Save the current print level and silence CHOLMOD, otherwise
   // CHOLMOD is prone to dumping stuff to stderr, which can be
   // distracting when the error (matrix is indefinite) is not a fatal
   // failure.
   const int old_print_level = cc_.print;
   cc_.print = 0;

   cc_.quick_return_if_not_posdef = 1;
   int cholmod_status = cholmod_factorize(A, L, &cc_);
   cc_.print = old_print_level;

   switch (cc_.status) {
     case CHOLMOD_NOT_INSTALLED:
       *message = "CHOLMOD failure: Method not installed.";
       return LINEAR_SOLVER_FATAL_ERROR;
     case CHOLMOD_OUT_OF_MEMORY:
       *message = "CHOLMOD failure: Out of memory.";
       return LINEAR_SOLVER_FATAL_ERROR;
     case CHOLMOD_TOO_LARGE:
       *message = "CHOLMOD failure: Integer overflow occurred.";
       return LINEAR_SOLVER_FATAL_ERROR;
     case CHOLMOD_INVALID:
       *message = "CHOLMOD failure: Invalid input.";
       return LINEAR_SOLVER_FATAL_ERROR;
     case CHOLMOD_NOT_POSDEF:
       *message = "CHOLMOD warning: Matrix not positive definite.";
       return LINEAR_SOLVER_FAILURE;
     case CHOLMOD_DSMALL:
       *message =
           "CHOLMOD warning: D for LDL' or diag(L) or "
           "LL' has tiny absolute value.";
       return LINEAR_SOLVER_FAILURE;
     case CHOLMOD_OK:
       if (cholmod_status != 0) {
         return LINEAR_SOLVER_SUCCESS;
       }

       *message =
           "CHOLMOD failure: cholmod_factorize returned false "
           "but cholmod_common::status is CHOLMOD_OK."
           "Please report this to ceres-solver@googlegroups.com.";
       return LINEAR_SOLVER_FATAL_ERROR;
     default:
       *message = StringPrintf(
           "Unknown cholmod return code: %d. "
           "Please report this to ceres-solver@googlegroups.com.",
           cc_.status);
       return LINEAR_SOLVER_FATAL_ERROR;
   }

   return LINEAR_SOLVER_FATAL_ERROR;
 }

 cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
                                   cholmod_dense* b,
                                   string* message) {
   if (cc_.status != CHOLMOD_OK) {
     *message = "cholmod_solve failed. CHOLMOD status is not CHOLMOD_OK";
     return nullptr;
   }

   return cholmod_solve(CHOLMOD_A, L, b, &cc_);
 }

 bool SuiteSparse::ApproximateMinimumDegreeOrdering(cholmod_sparse* matrix,
                                                    int* ordering) {
   return cholmod_amd(matrix, nullptr, 0, ordering, &cc_);
 }

 bool SuiteSparse::ConstrainedApproximateMinimumDegreeOrdering(
     cholmod_sparse* matrix, int* constraints, int* ordering) {
 #ifndef CERES_NO_CAMD
   return cholmod_camd(matrix, nullptr, 0, constraints, ordering, &cc_);
 #else
   LOG(FATAL) << "Congratulations you have found a bug in Ceres."
              << "Ceres Solver was compiled with SuiteSparse "
              << "version 4.1.0 or less. Calling this function "
              << "in that case is a bug. Please contact the"
              << "the Ceres Solver developers.";
   return false;
 #endif
 }

 std::unique_ptr<SparseCholesky> SuiteSparseCholesky::Create(
     const OrderingType ordering_type) {
   return std::unique_ptr<SparseCholesky>(
       new SuiteSparseCholesky(ordering_type));
 }

 SuiteSparseCholesky::SuiteSparseCholesky(const OrderingType ordering_type)
     : ordering_type_(ordering_type), factor_(nullptr) {}

 SuiteSparseCholesky::~SuiteSparseCholesky() {
   if (factor_ != nullptr) {
     ss_.Free(factor_);
   }
 }

 LinearSolverTerminationType SuiteSparseCholesky::Factorize(
     CompressedRowSparseMatrix* lhs, string* message) {
   if (lhs == nullptr) {
     *message = "Failure: Input lhs is NULL.";
     return LINEAR_SOLVER_FATAL_ERROR;
   }

   cholmod_sparse cholmod_lhs = ss_.CreateSparseMatrixTransposeView(lhs);

   if (factor_ == nullptr) {
     if (ordering_type_ == NATURAL) {
       factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&cholmod_lhs, message);
     } else {
       if (!lhs->col_blocks().empty() && !(lhs->row_blocks().empty())) {
         factor_ = ss_.BlockAnalyzeCholesky(
             &cholmod_lhs, lhs->col_blocks(), lhs->row_blocks(), message);
       } else {
         factor_ = ss_.AnalyzeCholesky(&cholmod_lhs, message);
       }
     }

     if (factor_ == nullptr) {
       return LINEAR_SOLVER_FATAL_ERROR;
     }
   }

   return ss_.Cholesky(&cholmod_lhs, factor_, message);
 }

 CompressedRowSparseMatrix::StorageType SuiteSparseCholesky::StorageType()
     const {
   return ((ordering_type_ == NATURAL)
               ? CompressedRowSparseMatrix::UPPER_TRIANGULAR
               : CompressedRowSparseMatrix::LOWER_TRIANGULAR);
 }

 LinearSolverTerminationType SuiteSparseCholesky::Solve(const double* rhs,
                                                        double* solution,
                                                        string* message) {
   // Error checking
   if (factor_ == nullptr) {
     *message = "Solve called without a call to Factorize first.";
     return LINEAR_SOLVER_FATAL_ERROR;
   }

   const int num_cols = factor_->n;
   cholmod_dense cholmod_rhs = ss_.CreateDenseVectorView(rhs, num_cols);
   cholmod_dense* cholmod_dense_solution =
       ss_.Solve(factor_, &cholmod_rhs, message);

   if (cholmod_dense_solution == nullptr) {
     return LINEAR_SOLVER_FAILURE;
   }

   memcpy(solution, cholmod_dense_solution->x, num_cols * sizeof(*solution));
   ss_.Free(cholmod_dense_solution);
   return LINEAR_SOLVER_SUCCESS;
 }

 }  // namespace internal
 }  // namespace ceres

 #endif  // CERES_NO_SUITESPARSE
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2015 Google Inc. All rights reserved.
	// http://ceres-solver.org/
	//
	// Redistribution and use in source and binary forms, with or without
	// modification, are permitted provided that the following conditions are met:
	//
	// * Redistributions of source code must retain the above copyright notice,
	// this list of conditions and the following disclaimer.
	// * Redistributions in binary form must reproduce the above copyright notice,
	// this list of conditions and the following disclaimer in the documentation
	// and/or other materials provided with the distribution.
	// * Neither the name of Google Inc. nor the names of its contributors may be
	// used to endorse or promote products derived from this software without
	// specific prior written permission.
	//
	// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
	// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
	// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
	// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
	// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
	// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
	// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
	// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
	// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
	// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
	// POSSIBILITY OF SUCH DAMAGE.
	//
	// Author: sameeragarwal@google.com (Sameer Agarwal)

	// This include must come before any #ifndef check on Ceres compile options.
	#include "ceres/internal/port.h"

	#ifndef CERES_NO_SUITESPARSE
	#include <vector>

	#include "ceres/compressed_col_sparse_matrix_utils.h"
	#include "ceres/compressed_row_sparse_matrix.h"
	#include "ceres/linear_solver.h"
	#include "ceres/suitesparse.h"
	#include "ceres/triplet_sparse_matrix.h"
	#include "cholmod.h"

	namespace ceres {
	namespace internal {

	using std::string;
	using std::vector;

	SuiteSparse::SuiteSparse() { cholmod_start(&cc_); }

	SuiteSparse::~SuiteSparse() { cholmod_finish(&cc_); }

	cholmod_sparse* SuiteSparse::CreateSparseMatrix(TripletSparseMatrix* A) {
	cholmod_triplet triplet;

	triplet.nrow = A->num_rows();
	triplet.ncol = A->num_cols();
	triplet.nzmax = A->max_num_nonzeros();
	triplet.nnz = A->num_nonzeros();
	triplet.i = reinterpret_cast<void*>(A->mutable_rows());
	triplet.j = reinterpret_cast<void*>(A->mutable_cols());
	triplet.x = reinterpret_cast<void*>(A->mutable_values());
	triplet.stype = 0; // Matrix is not symmetric.
	triplet.itype = CHOLMOD_INT;
	triplet.xtype = CHOLMOD_REAL;
	triplet.dtype = CHOLMOD_DOUBLE;

	return cholmod_triplet_to_sparse(&triplet, triplet.nnz, &cc_);
	}

	cholmod_sparse* SuiteSparse::CreateSparseMatrixTranspose(
	TripletSparseMatrix* A) {
	cholmod_triplet triplet;

	triplet.ncol = A->num_rows(); // swap row and columns
	triplet.nrow = A->num_cols();
	triplet.nzmax = A->max_num_nonzeros();
	triplet.nnz = A->num_nonzeros();

	// swap rows and columns
	triplet.j = reinterpret_cast<void*>(A->mutable_rows());
	triplet.i = reinterpret_cast<void*>(A->mutable_cols());
	triplet.x = reinterpret_cast<void*>(A->mutable_values());
	triplet.stype = 0; // Matrix is not symmetric.
	triplet.itype = CHOLMOD_INT;
	triplet.xtype = CHOLMOD_REAL;
	triplet.dtype = CHOLMOD_DOUBLE;

	return cholmod_triplet_to_sparse(&triplet, triplet.nnz, &cc_);
	}

	cholmod_sparse SuiteSparse::CreateSparseMatrixTransposeView(
	CompressedRowSparseMatrix* A) {
	cholmod_sparse m;
	m.nrow = A->num_cols();
	m.ncol = A->num_rows();
	m.nzmax = A->num_nonzeros();
	m.nz = nullptr;
	m.p = reinterpret_cast<void*>(A->mutable_rows());
	m.i = reinterpret_cast<void*>(A->mutable_cols());
	m.x = reinterpret_cast<void*>(A->mutable_values());
	m.z = nullptr;

	if (A->storage_type() == CompressedRowSparseMatrix::LOWER_TRIANGULAR) {
	m.stype = 1;
	} else if (A->storage_type() == CompressedRowSparseMatrix::UPPER_TRIANGULAR) {
	m.stype = -1;
	} else {
	m.stype = 0;
	}

	m.itype = CHOLMOD_INT;
	m.xtype = CHOLMOD_REAL;
	m.dtype = CHOLMOD_DOUBLE;
	m.sorted = 1;
	m.packed = 1;

	return m;
	}

	cholmod_dense SuiteSparse::CreateDenseVectorView(const double* x, int size) {
	cholmod_dense v;
	v.nrow = size;
	v.ncol = 1;
	v.nzmax = size;
	v.d = size;
	v.x = const_cast<void>(reinterpret_cast<const void>(x));
	v.xtype = CHOLMOD_REAL;
	v.dtype = CHOLMOD_DOUBLE;
	return v;
	}

	cholmod_dense* SuiteSparse::CreateDenseVector(const double* x,
	int in_size,
	int out_size) {
	CHECK_LE(in_size, out_size);
	cholmod_dense* v = cholmod_zeros(out_size, 1, CHOLMOD_REAL, &cc_);
	if (x != nullptr) {
	memcpy(v->x, x, in_size * sizeof(*x));
	}
	return v;
	}

	cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A,
	string* message) {
	// Cholmod can try multiple re-ordering strategies to find a fill
	// reducing ordering. Here we just tell it use AMD with automatic
	// matrix dependence choice of supernodal versus simplicial
	// factorization.
	cc_.nmethods = 1;
	cc_.method[0].ordering = CHOLMOD_AMD;
	cc_.supernodal = CHOLMOD_AUTO;

	cholmod_factor* factor = cholmod_analyze(A, &cc_);
	if (VLOG_IS_ON(2)) {
	cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
	}

	if (cc_.status != CHOLMOD_OK) {
	*message =
	StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
	return nullptr;
	}

	CHECK(factor != nullptr);
	return factor;
	}

	cholmod_factor* SuiteSparse::BlockAnalyzeCholesky(cholmod_sparse* A,
	const vector<int>& row_blocks,
	const vector<int>& col_blocks,
	string* message) {
	vector<int> ordering;
	if (!BlockAMDOrdering(A, row_blocks, col_blocks, &ordering)) {
	return nullptr;
	}
	return AnalyzeCholeskyWithUserOrdering(A, ordering, message);
	}

	cholmod_factor* SuiteSparse::AnalyzeCholeskyWithUserOrdering(
	cholmod_sparse* A, const vector<int>& ordering, string* message) {
	CHECK_EQ(ordering.size(), A->nrow);

	cc_.nmethods = 1;
	cc_.method[0].ordering = CHOLMOD_GIVEN;

	cholmod_factor* factor =
	cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), nullptr, 0, &cc_);
	if (VLOG_IS_ON(2)) {
	cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
	}
	if (cc_.status != CHOLMOD_OK) {
	*message =
	StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
	return nullptr;
	}

	CHECK(factor != nullptr);
	return factor;
	}

	cholmod_factor* SuiteSparse::AnalyzeCholeskyWithNaturalOrdering(
	cholmod_sparse* A, string* message) {
	cc_.nmethods = 1;
	cc_.method[0].ordering = CHOLMOD_NATURAL;
	cc_.postorder = 0;

	cholmod_factor* factor = cholmod_analyze(A, &cc_);
	if (VLOG_IS_ON(2)) {
	cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
	}
	if (cc_.status != CHOLMOD_OK) {
	*message =
	StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
	return nullptr;
	}

	CHECK(factor != nullptr);
	return factor;
	}

	bool SuiteSparse::BlockAMDOrdering(const cholmod_sparse* A,
	const vector<int>& row_blocks,
	const vector<int>& col_blocks,
	vector<int>* ordering) {
	const int num_row_blocks = row_blocks.size();
	const int num_col_blocks = col_blocks.size();

	// Arrays storing the compressed column structure of the matrix
	// incoding the block sparsity of A.
	vector<int> block_cols;
	vector<int> block_rows;

	CompressedColumnScalarMatrixToBlockMatrix(reinterpret_cast<const int*>(A->i),
	reinterpret_cast<const int*>(A->p),
	row_blocks,
	col_blocks,
	&block_rows,
	&block_cols);
	cholmod_sparse_struct block_matrix;
	block_matrix.nrow = num_row_blocks;
	block_matrix.ncol = num_col_blocks;
	block_matrix.nzmax = block_rows.size();
	block_matrix.p = reinterpret_cast<void*>(&block_cols[0]);
	block_matrix.i = reinterpret_cast<void*>(&block_rows[0]);
	block_matrix.x = nullptr;
	block_matrix.stype = A->stype;
	block_matrix.itype = CHOLMOD_INT;
	block_matrix.xtype = CHOLMOD_PATTERN;
	block_matrix.dtype = CHOLMOD_DOUBLE;
	block_matrix.sorted = 1;
	block_matrix.packed = 1;

	vector<int> block_ordering(num_row_blocks);
	if (!cholmod_amd(&block_matrix, nullptr, 0, &block_ordering[0], &cc_)) {
	return false;
	}

	BlockOrderingToScalarOrdering(row_blocks, block_ordering, ordering);
	return true;
	}

	LinearSolverTerminationType SuiteSparse::Cholesky(cholmod_sparse* A,
	cholmod_factor* L,
	string* message) {
	CHECK(A != nullptr);
	CHECK(L != nullptr);

	// Save the current print level and silence CHOLMOD, otherwise
	// CHOLMOD is prone to dumping stuff to stderr, which can be
	// distracting when the error (matrix is indefinite) is not a fatal
	// failure.
	const int old_print_level = cc_.print;
	cc_.print = 0;

	cc_.quick_return_if_not_posdef = 1;
	int cholmod_status = cholmod_factorize(A, L, &cc_);
	cc_.print = old_print_level;

	switch (cc_.status) {
	case CHOLMOD_NOT_INSTALLED:
	*message = "CHOLMOD failure: Method not installed.";
	return LINEAR_SOLVER_FATAL_ERROR;
	case CHOLMOD_OUT_OF_MEMORY:
	*message = "CHOLMOD failure: Out of memory.";
	return LINEAR_SOLVER_FATAL_ERROR;
	case CHOLMOD_TOO_LARGE:
	*message = "CHOLMOD failure: Integer overflow occurred.";
	return LINEAR_SOLVER_FATAL_ERROR;
	case CHOLMOD_INVALID:
	*message = "CHOLMOD failure: Invalid input.";
	return LINEAR_SOLVER_FATAL_ERROR;
	case CHOLMOD_NOT_POSDEF:
	*message = "CHOLMOD warning: Matrix not positive definite.";
	return LINEAR_SOLVER_FAILURE;
	case CHOLMOD_DSMALL:
	*message =
	"CHOLMOD warning: D for LDL' or diag(L) or "
	"LL' has tiny absolute value.";
	return LINEAR_SOLVER_FAILURE;
	case CHOLMOD_OK:
	if (cholmod_status != 0) {
	return LINEAR_SOLVER_SUCCESS;
	}

	*message =
	"CHOLMOD failure: cholmod_factorize returned false "
	"but cholmod_common::status is CHOLMOD_OK."
	"Please report this to ceres-solver@googlegroups.com.";
	return LINEAR_SOLVER_FATAL_ERROR;
	default:
	*message = StringPrintf(
	"Unknown cholmod return code: %d. "
	"Please report this to ceres-solver@googlegroups.com.",
	cc_.status);
	return LINEAR_SOLVER_FATAL_ERROR;
	}

	return LINEAR_SOLVER_FATAL_ERROR;
	}

	cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
	cholmod_dense* b,
	string* message) {
	if (cc_.status != CHOLMOD_OK) {
	*message = "cholmod_solve failed. CHOLMOD status is not CHOLMOD_OK";
	return nullptr;
	}

	return cholmod_solve(CHOLMOD_A, L, b, &cc_);
	}

	bool SuiteSparse::ApproximateMinimumDegreeOrdering(cholmod_sparse* matrix,
	int* ordering) {
	return cholmod_amd(matrix, nullptr, 0, ordering, &cc_);
	}

	bool SuiteSparse::ConstrainedApproximateMinimumDegreeOrdering(
	cholmod_sparse* matrix, int* constraints, int* ordering) {
	#ifndef CERES_NO_CAMD
	return cholmod_camd(matrix, nullptr, 0, constraints, ordering, &cc_);
	#else
	LOG(FATAL) << "Congratulations you have found a bug in Ceres."
	<< "Ceres Solver was compiled with SuiteSparse "
	<< "version 4.1.0 or less. Calling this function "
	<< "in that case is a bug. Please contact the"
	<< "the Ceres Solver developers.";
	return false;
	#endif
	}

	std::unique_ptr<SparseCholesky> SuiteSparseCholesky::Create(
	const OrderingType ordering_type) {
	return std::unique_ptr<SparseCholesky>(
	new SuiteSparseCholesky(ordering_type));
	}

	SuiteSparseCholesky::SuiteSparseCholesky(const OrderingType ordering_type)
	: ordering_type_(ordering_type), factor_(nullptr) {}

	SuiteSparseCholesky::~SuiteSparseCholesky() {
	if (factor_ != nullptr) {
	ss_.Free(factor_);
	}
	}

	LinearSolverTerminationType SuiteSparseCholesky::Factorize(
	CompressedRowSparseMatrix* lhs, string* message) {
	if (lhs == nullptr) {
	*message = "Failure: Input lhs is NULL.";
	return LINEAR_SOLVER_FATAL_ERROR;
	}

	cholmod_sparse cholmod_lhs = ss_.CreateSparseMatrixTransposeView(lhs);

	if (factor_ == nullptr) {
	if (ordering_type_ == NATURAL) {
	factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&cholmod_lhs, message);
	} else {
	if (!lhs->col_blocks().empty() && !(lhs->row_blocks().empty())) {
	factor_ = ss_.BlockAnalyzeCholesky(
	&cholmod_lhs, lhs->col_blocks(), lhs->row_blocks(), message);
	} else {
	factor_ = ss_.AnalyzeCholesky(&cholmod_lhs, message);
	}
	}

	if (factor_ == nullptr) {
	return LINEAR_SOLVER_FATAL_ERROR;
	}
	}

	return ss_.Cholesky(&cholmod_lhs, factor_, message);
	}

	CompressedRowSparseMatrix::StorageType SuiteSparseCholesky::StorageType()
	const {
	return ((ordering_type_ == NATURAL)
	? CompressedRowSparseMatrix::UPPER_TRIANGULAR
	: CompressedRowSparseMatrix::LOWER_TRIANGULAR);
	}

	LinearSolverTerminationType SuiteSparseCholesky::Solve(const double* rhs,
	double* solution,
	string* message) {
	// Error checking
	if (factor_ == nullptr) {
	*message = "Solve called without a call to Factorize first.";
	return LINEAR_SOLVER_FATAL_ERROR;
	}

	const int num_cols = factor_->n;
	cholmod_dense cholmod_rhs = ss_.CreateDenseVectorView(rhs, num_cols);
	cholmod_dense* cholmod_dense_solution =
	ss_.Solve(factor_, &cholmod_rhs, message);

	if (cholmod_dense_solution == nullptr) {
	return LINEAR_SOLVER_FAILURE;
	}

	memcpy(solution, cholmod_dense_solution->x, num_cols * sizeof(*solution));
	ss_.Free(cholmod_dense_solution);
	return LINEAR_SOLVER_SUCCESS;
	}

	} // namespace internal
	} // namespace ceres

	#endif // CERES_NO_SUITESPARSE