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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// 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: strandmark@google.com (Petter Strandmark)
#ifndef CERES_NO_CXSPARSE
#include "ceres/cxsparse.h"
#include <vector>
#include "ceres/compressed_col_sparse_matrix_utils.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/internal/port.h"
#include "ceres/triplet_sparse_matrix.h"
#include "glog/logging.h"
namespace ceres {
namespace internal {
CXSparse::CXSparse() : scratch_(NULL), scratch_size_(0) {
}
CXSparse::~CXSparse() {
if (scratch_size_ > 0) {
cs_di_free(scratch_);
}
}
bool CXSparse::SolveCholesky(cs_di* A,
cs_dis* symbolic_factorization,
double* b) {
// Make sure we have enough scratch space available.
if (scratch_size_ < A->n) {
if (scratch_size_ > 0) {
cs_di_free(scratch_);
}
scratch_ =
reinterpret_cast<CS_ENTRY*>(cs_di_malloc(A->n, sizeof(CS_ENTRY)));
scratch_size_ = A->n;
}
// Solve using Cholesky factorization
csn* numeric_factorization = cs_di_chol(A, symbolic_factorization);
if (numeric_factorization == NULL) {
LOG(WARNING) << "Cholesky factorization failed.";
return false;
}
// When the Cholesky factorization succeeded, these methods are
// guaranteed to succeeded as well. In the comments below, "x"
// refers to the scratch space.
//
// Set x = P * b.
cs_di_ipvec(symbolic_factorization->pinv, b, scratch_, A->n);
// Set x = L \ x.
cs_di_lsolve(numeric_factorization->L, scratch_);
// Set x = L' \ x.
cs_di_ltsolve(numeric_factorization->L, scratch_);
// Set b = P' * x.
cs_di_pvec(symbolic_factorization->pinv, scratch_, b, A->n);
// Free Cholesky factorization.
cs_di_nfree(numeric_factorization);
return true;
}
cs_dis* CXSparse::AnalyzeCholesky(cs_di* A) {
// order = 1 for Cholesky factorization.
return cs_schol(1, A);
}
cs_dis* CXSparse::AnalyzeCholeskyWithNaturalOrdering(cs_di* A) {
// order = 0 for Natural ordering.
return cs_schol(0, A);
}
cs_dis* CXSparse::BlockAnalyzeCholesky(cs_di* A,
const vector<int>& row_blocks,
const vector<int>& col_blocks) {
const int num_row_blocks = row_blocks.size();
const int num_col_blocks = col_blocks.size();
vector<int> block_rows;
vector<int> block_cols;
CompressedColumnScalarMatrixToBlockMatrix(A->i,
A->p,
row_blocks,
col_blocks,
&block_rows,
&block_cols);
cs_di block_matrix;
block_matrix.m = num_row_blocks;
block_matrix.n = num_col_blocks;
block_matrix.nz = -1;
block_matrix.nzmax = block_rows.size();
block_matrix.p = &block_cols[0];
block_matrix.i = &block_rows[0];
block_matrix.x = NULL;
int* ordering = cs_amd(1, &block_matrix);
vector<int> block_ordering(num_row_blocks, -1);
copy(ordering, ordering + num_row_blocks, &block_ordering[0]);
cs_free(ordering);
vector<int> scalar_ordering;
BlockOrderingToScalarOrdering(row_blocks, block_ordering, &scalar_ordering);
cs_dis* symbolic_factorization =
reinterpret_cast<cs_dis*>(cs_calloc(1, sizeof(cs_dis)));
symbolic_factorization->pinv = cs_pinv(&scalar_ordering[0], A->n);
cs* permuted_A = cs_symperm(A, symbolic_factorization->pinv, 0);
symbolic_factorization->parent = cs_etree(permuted_A, 0);
int* postordering = cs_post(symbolic_factorization->parent, A->n);
int* column_counts = cs_counts(permuted_A,
symbolic_factorization->parent,
postordering,
0);
cs_free(postordering);
cs_spfree(permuted_A);
symbolic_factorization->cp = (int*) cs_malloc(A->n+1, sizeof(int));
symbolic_factorization->lnz = cs_cumsum(symbolic_factorization->cp,
column_counts,
A->n);
symbolic_factorization->unz = symbolic_factorization->lnz;
cs_free(column_counts);
if (symbolic_factorization->lnz < 0) {
cs_sfree(symbolic_factorization);
symbolic_factorization = NULL;
}
return symbolic_factorization;
}
cs_di CXSparse::CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A) {
cs_di At;
At.m = A->num_cols();
At.n = A->num_rows();
At.nz = -1;
At.nzmax = A->num_nonzeros();
At.p = A->mutable_rows();
At.i = A->mutable_cols();
At.x = A->mutable_values();
return At;
}
cs_di* CXSparse::CreateSparseMatrix(TripletSparseMatrix* tsm) {
cs_di_sparse tsm_wrapper;
tsm_wrapper.nzmax = tsm->num_nonzeros();
tsm_wrapper.nz = tsm->num_nonzeros();
tsm_wrapper.m = tsm->num_rows();
tsm_wrapper.n = tsm->num_cols();
tsm_wrapper.p = tsm->mutable_cols();
tsm_wrapper.i = tsm->mutable_rows();
tsm_wrapper.x = tsm->mutable_values();
return cs_compress(&tsm_wrapper);
}
void CXSparse::ApproximateMinimumDegreeOrdering(cs_di* A, int* ordering) {
int* cs_ordering = cs_amd(1, A);
copy(cs_ordering, cs_ordering + A->m, ordering);
cs_free(cs_ordering);
}
cs_di* CXSparse::TransposeMatrix(cs_di* A) {
return cs_di_transpose(A, 1);
}
cs_di* CXSparse::MatrixMatrixMultiply(cs_di* A, cs_di* B) {
return cs_di_multiply(A, B);
}
void CXSparse::Free(cs_di* sparse_matrix) {
cs_di_spfree(sparse_matrix);
}
void CXSparse::Free(cs_dis* symbolic_factorization) {
cs_di_sfree(symbolic_factorization);
}
} // namespace internal
} // namespace ceres
#endif // CERES_NO_CXSPARSE
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