Files
ceres-solver/internal/ceres/subset_preconditioner.cc
T
Sameer Agarwal 487c1aa51f Expose SubsetPreconditioner in the API
https://github.com/ceres-solver/ceres-solver/issues/270

Detailed list of changes:

1. Add SUBSET to the PreconditionerType enum.
2. Add Solver::Options::residual_blocks_for_subset_preconditioner
3. Integrate SubsetPreconditioner into the CGNR solver.
4. Add the reordering logic needed for this to TrustRegionPreprocessor.
5. Expect CreateJacobianBlockTranspose to take the starting row block
   so that we can work with subparts of the Jacobian matrix.
6. Extend the denoising example to use this preconditioner.

As an illustration of its performance, we consider the performance of
denoising -input ../data/ceres_noisy.pgm  --foe_file ../data/5x5.foe

tl;dr

For the same cost,

SPARSE_NORMAL_CHOLESKY -  81s
CGNR + JACOBI          - 718s
CGNR + SUBSET          -  57s

SPARSE_NORMAL_CHOLESKY
======================

Cost:
Initial                          2.317806e+05
Final                            2.232323e+04
Change                           2.094574e+05

Minimizer iterations                       10
Successful steps                           10
Unsuccessful steps                          0

Time (in seconds):
Preprocessor                         2.999746

  Residual only evaluation           2.306811 (10)
  Jacobian & residual evaluation     7.421727 (10)
  Linear solver                     65.517273 (10)
Minimizer                           78.731011

Postprocessor                        0.026079
Total                               81.756836

Termination:                      CONVERGENCE (Function tolerance reached. |cost_change|/cost: 8.573046e-04 <= 1.000000e-03)

CGNR + JACOBI
=============
Cost:
Initial                          2.317806e+05
Final                            2.232344e+04
Change                           2.094572e+05

Minimizer iterations                       10
Successful steps                           10
Unsuccessful steps                          0

Time (in seconds):
Preprocessor                         0.648814

  Residual only evaluation           2.297607 (10)
  Jacobian & residual evaluation     7.327886 (10)
  Linear solver                    699.601248 (10)
Minimizer                          712.419493

Postprocessor                        0.024014
Total                              713.092321

Termination:                      CONVERGENCE (Function tolerance reached. |cost_change|/cost: 8.528538e-04 <= 1.000000e-03)

CGNR + SUBSET (random 20% residuals used for the preconditioner)
===============================================================
Cost:
Initial                          2.317806e+05
Final                            2.232327e+04
Change                           2.094574e+05

Minimizer iterations                       10
Successful steps                           10
Unsuccessful steps                          0

Time (in seconds):
Preprocessor                         1.472743

  Residual only evaluation           2.428315 (10)
  Jacobian & residual evaluation     7.367796 (10)
  Linear solver                     42.585999 (10)
Minimizer                           55.664459

Postprocessor                        0.024098
Total                               57.161301

Termination:                      CONVERGENCE (Function tolerance reached. |cost_change|/cost: 8.538277e-04 <= 1.000000e-03)

Change-Id: Ifb011408bd53edbb9439b0b7345649a38f999e18
2019-07-12 16:08:36 +02:00

118 lines
4.2 KiB
C++

// 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
// 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)
#include "ceres/subset_preconditioner.h"
#include <memory>
#include <string>
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/inner_product_computer.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()) {
CHECK_GE(options_.subset_preconditioner_start_row_block, 0)
<< "Congratulations, you found a bug in Ceres. Please report it.";
LinearSolver::Options sparse_cholesky_options;
sparse_cholesky_options.sparse_linear_algebra_library_type =
options_.sparse_linear_algebra_library_type;
sparse_cholesky_options.use_postordering =
options_.use_postordering;
sparse_cholesky_ = SparseCholesky::Create(sparse_cholesky_options);
}
SubsetPreconditioner::~SubsetPreconditioner() {}
void SubsetPreconditioner::RightMultiply(const double* x, double* y) const {
CHECK(x != nullptr);
CHECK(y != nullptr);
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]
std::unique_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