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// 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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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/iterative_schur_complement_solver.h"
#include <algorithm>
#include <cstring>
#include <vector>
#include "Eigen/Dense"
#include "ceres/block_sparse_matrix.h"
#include "ceres/block_structure.h"
#include "ceres/conjugate_gradients_solver.h"
#include "ceres/detect_structure.h"
#include "ceres/implicit_schur_complement.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_solver.h"
#include "ceres/preconditioner.h"
#include "ceres/schur_jacobi_preconditioner.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "ceres/visibility_based_preconditioner.h"
#include "ceres/wall_time.h"
#include "glog/logging.h"
namespace ceres {
namespace internal {
IterativeSchurComplementSolver::IterativeSchurComplementSolver(
const LinearSolver::Options& options)
: options_(options) {}
IterativeSchurComplementSolver::~IterativeSchurComplementSolver() {}
LinearSolver::Summary IterativeSchurComplementSolver::SolveImpl(
BlockSparseMatrix* A,
const double* b,
const LinearSolver::PerSolveOptions& per_solve_options,
double* x) {
EventLogger event_logger("IterativeSchurComplementSolver::Solve");
CHECK_NOTNULL(A->block_structure());
const int num_eliminate_blocks = options_.elimination_groups[0];
// Initialize a ImplicitSchurComplement object.
if (schur_complement_ == NULL) {
DetectStructure(*(A->block_structure()),
num_eliminate_blocks,
&options_.row_block_size,
&options_.e_block_size,
&options_.f_block_size);
schur_complement_.reset(new ImplicitSchurComplement(options_));
}
schur_complement_->Init(*A, per_solve_options.D, b);
const int num_schur_complement_blocks =
A->block_structure()->cols.size() - num_eliminate_blocks;
if (num_schur_complement_blocks == 0) {
VLOG(2) << "No parameter blocks left in the schur complement.";
LinearSolver::Summary summary;
summary.num_iterations = 0;
summary.termination_type = LINEAR_SOLVER_SUCCESS;
schur_complement_->BackSubstitute(NULL, x);
return summary;
}
// Initialize the solution to the Schur complement system to zero.
reduced_linear_system_solution_.resize(schur_complement_->num_rows());
reduced_linear_system_solution_.setZero();
LinearSolver::Options cg_options;
cg_options.min_num_iterations = options_.min_num_iterations;
cg_options.max_num_iterations = options_.max_num_iterations;
ConjugateGradientsSolver cg_solver(cg_options);
LinearSolver::PerSolveOptions cg_per_solve_options;
cg_per_solve_options.r_tolerance = per_solve_options.r_tolerance;
cg_per_solve_options.q_tolerance = per_solve_options.q_tolerance;
CreatePreconditioner(A);
if (preconditioner_.get() != NULL) {
if (!preconditioner_->Update(*A, per_solve_options.D)) {
LinearSolver::Summary summary;
summary.num_iterations = 0;
summary.termination_type = LINEAR_SOLVER_FAILURE;
summary.message = "Preconditioner update failed.";
return summary;
}
cg_per_solve_options.preconditioner = preconditioner_.get();
}
event_logger.AddEvent("Setup");
LinearSolver::Summary summary =
cg_solver.Solve(schur_complement_.get(),
schur_complement_->rhs().data(),
cg_per_solve_options,
reduced_linear_system_solution_.data());
if (summary.termination_type != LINEAR_SOLVER_FAILURE &&
summary.termination_type != LINEAR_SOLVER_FATAL_ERROR) {
schur_complement_->BackSubstitute(reduced_linear_system_solution_.data(),
x);
}
event_logger.AddEvent("Solve");
return summary;
}
void IterativeSchurComplementSolver::CreatePreconditioner(
BlockSparseMatrix* A) {
if (options_.preconditioner_type == IDENTITY ||
preconditioner_.get() != NULL) {
return;
}
Preconditioner::Options preconditioner_options;
preconditioner_options.type = options_.preconditioner_type;
preconditioner_options.visibility_clustering_type =
options_.visibility_clustering_type;
preconditioner_options.sparse_linear_algebra_library_type =
options_.sparse_linear_algebra_library_type;
preconditioner_options.num_threads = options_.num_threads;
preconditioner_options.row_block_size = options_.row_block_size;
preconditioner_options.e_block_size = options_.e_block_size;
preconditioner_options.f_block_size = options_.f_block_size;
preconditioner_options.elimination_groups = options_.elimination_groups;
switch (options_.preconditioner_type) {
case JACOBI:
preconditioner_.reset(new SparseMatrixPreconditionerWrapper(
schur_complement_->block_diagonal_FtF_inverse()));
break;
case SCHUR_JACOBI:
preconditioner_.reset(new SchurJacobiPreconditioner(
*A->block_structure(), preconditioner_options));
break;
case CLUSTER_JACOBI:
case CLUSTER_TRIDIAGONAL:
preconditioner_.reset(new VisibilityBasedPreconditioner(
*A->block_structure(), preconditioner_options));
break;
default:
LOG(FATAL) << "Unknown Preconditioner Type";
}
};
} // namespace internal
} // namespace ceres