// 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
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// 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 "ceres/suitesparse.h"
#include <vector>
#include "ceres/compressed_col_sparse_matrix_utils.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/linear_solver.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 = NULL;
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 = NULL;
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::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 != NULL) {
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 NULL;
}
return CHECK_NOTNULL(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 NULL;
}
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]), NULL, 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 NULL;
}
return CHECK_NOTNULL(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 NULL;
}
return CHECK_NOTNULL(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 = NULL;
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, NULL, 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_NOTNULL(A);
CHECK_NOTNULL(L);
// 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 NULL;
}
return cholmod_solve(CHOLMOD_A, L, b, &cc_);
}
bool SuiteSparse::ApproximateMinimumDegreeOrdering(cholmod_sparse* matrix,
int* ordering) {
return cholmod_amd(matrix, NULL, 0, ordering, &cc_);
}
bool SuiteSparse::ConstrainedApproximateMinimumDegreeOrdering(
cholmod_sparse* matrix, int* constraints, int* ordering) {
#ifndef CERES_NO_CAMD
return cholmod_camd(matrix, NULL, 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
}
SuiteSparseCholesky* SuiteSparseCholesky::Create(
const OrderingType ordering_type) {
return new SuiteSparseCholesky(ordering_type);
}
SuiteSparseCholesky::SuiteSparseCholesky(const OrderingType ordering_type)
: ordering_type_(ordering_type), factor_(NULL) {}
SuiteSparseCholesky::~SuiteSparseCholesky() {
if (factor_ != NULL) {
ss_.Free(factor_);
}
}
LinearSolverTerminationType SuiteSparseCholesky::Factorize(
CompressedRowSparseMatrix* lhs, string* message) {
if (lhs == NULL) {
*message = "Failure: Input lhs is NULL.";
return LINEAR_SOLVER_FATAL_ERROR;
}
cholmod_sparse cholmod_lhs = ss_.CreateSparseMatrixTransposeView(lhs);
if (factor_ == NULL) {
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_ == NULL) {
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_ == NULL) {
*message = "Solve called without a call to Factorize first.";
return LINEAR_SOLVER_FATAL_ERROR;
}
const int num_cols = factor_->n;
cholmod_dense* cholmod_dense_rhs =
ss_.CreateDenseVector(rhs, num_cols, num_cols);
cholmod_dense* cholmod_dense_solution =
ss_.Solve(factor_, cholmod_dense_rhs, message);
ss_.Free(cholmod_dense_rhs);
if (cholmod_dense_solution == NULL) {
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