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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 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
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// 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)
#ifndef CERES_NO_SUITESPARSE
#include "ceres/suitesparse.h"
#include "cholmod.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/triplet_sparse_matrix.h"
namespace ceres {
namespace internal {
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 = new cholmod_sparse_struct;
m->nrow = A->num_cols();
m->ncol = A->num_rows();
m->nzmax = A->num_nonzeros();
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->stype = 0; // Matrix is not symmetric.
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) {
cholmod_factor* factor = cholmod_analyze(A, &cc_);
CHECK_EQ(cc_.status, CHOLMOD_OK)
<< "Cholmod symbolic analysis failed " << cc_.status;
CHECK_NOTNULL(factor);
return factor;
}
bool SuiteSparse::Cholesky(cholmod_sparse* A, cholmod_factor* L) {
CHECK_NOTNULL(A);
CHECK_NOTNULL(L);
cc_.quick_return_if_not_posdef = 1;
int status = cholmod_factorize(A, L, &cc_);
switch (cc_.status) {
case CHOLMOD_NOT_INSTALLED:
LOG(WARNING) << "Cholmod failure: method not installed.";
return false;
case CHOLMOD_OUT_OF_MEMORY:
LOG(WARNING) << "Cholmod failure: out of memory.";
return false;
case CHOLMOD_TOO_LARGE:
LOG(WARNING) << "Cholmod failure: integer overflow occured.";
return false;
case CHOLMOD_INVALID:
LOG(WARNING) << "Cholmod failure: invalid input.";
return false;
case CHOLMOD_NOT_POSDEF:
// TODO(sameeragarwal): These two warnings require more
// sophisticated handling going forward. For now we will be
// strict and treat them as failures.
LOG(WARNING) << "Cholmod warning: matrix not positive definite.";
return false;
case CHOLMOD_DSMALL:
LOG(WARNING) << "Cholmod warning: D for LDL' or diag(L) or "
<< "LL' has tiny absolute value.";
return false;
case CHOLMOD_OK:
if (status != 0) {
return true;
}
LOG(WARNING) << "Cholmod failure: cholmod_factorize returned zero "
<< "but cholmod_common::status is CHOLMOD_OK."
<< "Please report this to ceres-solver@googlegroups.com.";
return false;
default:
LOG(WARNING) << "Unknown cholmod return code. "
<< "Please report this to ceres-solver@googlegroups.com.";
return false;
}
return false;
}
cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
cholmod_dense* b) {
if (cc_.status != CHOLMOD_OK) {
LOG(WARNING) << "CHOLMOD status NOT OK";
return NULL;
}
return cholmod_solve(CHOLMOD_A, L, b, &cc_);
}
cholmod_dense* SuiteSparse::SolveCholesky(cholmod_sparse* A,
cholmod_factor* L,
cholmod_dense* b) {
CHECK_NOTNULL(A);
CHECK_NOTNULL(L);
CHECK_NOTNULL(b);
if (Cholesky(A, L)) {
return Solve(L, b);
}
return NULL;
}
} // namespace internal
} // namespace ceres
#endif // CERES_NO_SUITESPARSE