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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2017 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
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// 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
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// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/subset_preconditioner.h"
#include <string>
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/inner_product_computer.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_solver.h"
#include "ceres/sparse_cholesky.h"
#include "ceres/types.h"
namespace ceres {
namespace internal {
SubsetPreconditioner::SubsetPreconditioner(
const Preconditioner::Options& options, const BlockSparseMatrix& A)
: options_(options), num_cols_(A.num_cols()) {
sparse_cholesky_.reset(
SparseCholesky::Create(options_.sparse_linear_algebra_library_type,
options_.use_postordering ? AMD : NATURAL));
CHECK_GE(options_.subset_preconditioner_start_row_block, 0);
}
SubsetPreconditioner::~SubsetPreconditioner() {}
void SubsetPreconditioner::RightMultiply(const double* x, double* y) const {
CHECK_NOTNULL(x);
CHECK_NOTNULL(y);
std::string message;
sparse_cholesky_->Solve(x, y, &message);
}
bool SubsetPreconditioner::UpdateImpl(const BlockSparseMatrix& A,
const double* D) {
BlockSparseMatrix* m = const_cast<BlockSparseMatrix*>(&A);
const CompressedRowBlockStructure* bs = m->block_structure();
// A = [P]
// [Q]
// Now add D to A if needed.
if (D != NULL) {
// A = [P]
// [Q]
// [D]
scoped_ptr<BlockSparseMatrix> regularizer(
BlockSparseMatrix::CreateDiagonalMatrix(D, bs->cols));
m->AppendRows(*regularizer);
}
if (inner_product_computer_.get() == NULL) {
inner_product_computer_.reset(InnerProductComputer::Create(
*m,
options_.subset_preconditioner_start_row_block,
bs->rows.size(),
sparse_cholesky_->StorageType()));
}
// Compute inner_product = [Q'*Q + D'*D]
inner_product_computer_->Compute();
// Unappend D if needed.
if (D != NULL) {
// A = [P]
// [Q]
m->DeleteRowBlocks(bs->cols.size());
}
std::string message;
// Compute L. s.t., LL' = Q'*Q + D'*D
const LinearSolverTerminationType termination_type =
sparse_cholesky_->Factorize(inner_product_computer_->mutable_result(),
&message);
if (termination_type != LINEAR_SOLVER_SUCCESS) {
LOG(ERROR) << "Preconditioner factorization failed: " << message;
return false;
}
return true;
}
} // namespace internal
} // namespace ceres