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1745dd615b3897a3ef9896acfdba67eee1739bf4
ceres-solver/internal/ceres/solver.cc
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Sameer Agarwal 1745dd615b Refactor SolverImpl. 
Replace SolverImpl with

 a. A minimizer specific preprocessor class.
 b. A generic Solve function inside solver.cc
 c. Presummarize and Postsummarize functions to handle
    updates to the summary object.

The existing SolverImpl class was a mixture of the above three
things and was increasingly complicated code to follow. This change,
breaks it into its three separate constituents, with the aims of
better separation of concerns and thus better testability and
reliability.

The call to Solver::Solve() now consists of

1. Presummarize - summarize the given state of the problem and solver
   options.
2. Preprocess - Setup everything that is needed to call the minimizer.
   This includes, removing redundant parameter and residual blocks,
   setting up the reordering for the linear solver, creating the
   linear solver, evaluator, inner iteration minimizer etc.
3. Minimize.
4. Post summarize - summarize the result of the preprocessing and the
   solve.

Change-Id: I80f35cfc9f2cbf78f1df4aceace27075779d8a3a
2014-08-18 13:31:31 -07:00

797 lines
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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2014 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: keir@google.com (Keir Mierle)
// sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/solver.h"
#include <algorithm>
#include <sstream> // NOLINT
#include <vector>
#include "ceres/internal/port.h"
#include "ceres/parameter_block_ordering.h"
#include "ceres/preprocessor.h"
#include "ceres/gradient_checking_cost_function.h"
#include "ceres/problem.h"
#include "ceres/problem_impl.h"
#include "ceres/program.h"
#include "ceres/stringprintf.h"
#include "ceres/types.h"
#include "ceres/version.h"
#include "ceres/wall_time.h"
namespace ceres {
namespace {
#define OPTION_OP(x, y, OP) \
if (!(options.x OP y)) { \
std::stringstream ss; \
ss << "Invalid configuration. "; \
ss << string("Solver::Options::" #x " = ") << options.x << ". "; \
ss << "Violated constraint: "; \
ss << string("Solver::Options::" #x " " #OP " "#y); \
*error = ss.str(); \
return false; \
}
#define OPTION_OP_OPTION(x, y, OP) \
if (!(options.x OP options.y)) { \
std::stringstream ss; \
ss << "Invalid configuration. "; \
ss << string("Solver::Options::" #x " = ") << options.x << ". "; \
ss << string("Solver::Options::" #y " = ") << options.y << ". "; \
ss << "Violated constraint: "; \
ss << string("Solver::Options::" #x ); \
ss << string(#OP " Solver::Options::" #y "."); \
*error = ss.str(); \
return false; \
}
#define OPTION_GE(x, y) OPTION_OP(x, y, >=);
#define OPTION_GT(x, y) OPTION_OP(x, y, >);
#define OPTION_LE(x, y) OPTION_OP(x, y, <=);
#define OPTION_LT(x, y) OPTION_OP(x, y, <);
#define OPTION_LE_OPTION(x, y) OPTION_OP_OPTION(x ,y, <=)
#define OPTION_LT_OPTION(x, y) OPTION_OP_OPTION(x ,y, <)
bool CommonOptionsAreValid(const Solver::Options& options, string* error) {
OPTION_GE(max_num_iterations, 0);
OPTION_GE(max_solver_time_in_seconds, 0.0);
OPTION_GE(function_tolerance, 0.0);
OPTION_GE(gradient_tolerance, 0.0);
OPTION_GE(parameter_tolerance, 0.0);
OPTION_GT(num_threads, 0);
OPTION_GT(num_linear_solver_threads, 0);
if (options.check_gradients) {
OPTION_GT(gradient_check_relative_precision, 0.0);
OPTION_GT(numeric_derivative_relative_step_size, 0.0);
}
return true;
}
bool TrustRegionOptionsAreValid(const Solver::Options& options, string* error) {
OPTION_GT(initial_trust_region_radius, 0.0);
OPTION_GT(min_trust_region_radius, 0.0);
OPTION_GT(max_trust_region_radius, 0.0);
OPTION_LE_OPTION(min_trust_region_radius, max_trust_region_radius);
OPTION_LE_OPTION(min_trust_region_radius, initial_trust_region_radius);
OPTION_LE_OPTION(initial_trust_region_radius, max_trust_region_radius);
OPTION_GE(min_relative_decrease, 0.0);
OPTION_GE(min_lm_diagonal, 0.0);
OPTION_GE(max_lm_diagonal, 0.0);
OPTION_LE_OPTION(min_lm_diagonal, max_lm_diagonal);
OPTION_GE(max_num_consecutive_invalid_steps, 0);
OPTION_GT(eta, 0.0);
OPTION_GE(min_linear_solver_iterations, 1);
OPTION_GE(max_linear_solver_iterations, 1);
OPTION_LE_OPTION(min_linear_solver_iterations, max_linear_solver_iterations);
if (options.use_inner_iterations) {
OPTION_GE(inner_iteration_tolerance, 0.0);
}
if (options.use_nonmonotonic_steps) {
OPTION_GT(max_consecutive_nonmonotonic_steps, 0);
}
if (options.preconditioner_type == CLUSTER_JACOBI &&
options.sparse_linear_algebra_library_type != SUITE_SPARSE) {
*error = "CLUSTER_JACOBI requires "
"Solver::Options::sparse_linear_algebra_library_type to be "
"SUITE_SPARSE";
return false;
}
if (options.preconditioner_type == CLUSTER_TRIDIAGONAL &&
options.sparse_linear_algebra_library_type != SUITE_SPARSE) {
*error = "CLUSTER_TRIDIAGONAL requires "
"Solver::Options::sparse_linear_algebra_library_type to be "
"SUITE_SPARSE";
return false;
}
#ifdef CERES_NO_LAPACK
if (options.dense_linear_algebra_library_type == LAPACK) {
if (options.linear_solver_type == DENSE_NORMAL_CHOLESKY) {
*error = "Can't use DENSE_NORMAL_CHOLESKY with LAPACK because "
"LAPACK was not enabled when Ceres was built.";
return false;
}
if (options.linear_solver_type == DENSE_QR) {
*error = "Can't use DENSE_QR with LAPACK because "
"LAPACK was not enabled when Ceres was built.";
return false;
}
if (options.linear_solver_type == DENSE_SCHUR) {
*error = "Can't use DENSE_SCHUR with LAPACK because "
"LAPACK was not enabled when Ceres was built.";
return false;
}
}
#endif
#ifdef CERES_NO_SUITESPARSE
if (options.sparse_linear_algebra_library_type == SUITE_SPARSE) {
if (options.linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
*error = "Can't use SPARSE_NORMAL_CHOLESKY with SUITESPARSE because "
"SuiteSparse was not enabled when Ceres was built.";
return false;
}
if (options.linear_solver_type == SPARSE_SCHUR) {
*error = "Can't use SPARSE_SCHUR with SUITESPARSE because "
"SuiteSparse was not enabled when Ceres was built.";
return false;
}
if (options.preconditioner_type == CLUSTER_JACOBI) {
*error = "CLUSTER_JACOBI preconditioner not supported. "
"SuiteSparse was not enabled when Ceres was built.";
return false;
}
if (options.preconditioner_type == CLUSTER_TRIDIAGONAL) {
*error = "CLUSTER_TRIDIAGONAL preconditioner not supported. "
"SuiteSparse was not enabled when Ceres was built.";
return false;
}
}
#endif
#ifdef CERES_NO_CXSPARSE
if (options.sparse_linear_algebra_library_type == CX_SPARSE) {
if (options.linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
*error = "Can't use SPARSE_NORMAL_CHOLESKY with CX_SPARSE because "
"CXSparse was not enabled when Ceres was built.";
return false;
}
if (options.linear_solver_type == SPARSE_SCHUR) {
*error = "Can't use SPARSE_SCHUR with CX_SPARSE because "
"CXSparse was not enabled when Ceres was built.";
return false;
}
}
#endif
if (options.trust_region_strategy_type == DOGLEG) {
if (options.linear_solver_type == ITERATIVE_SCHUR ||
options.linear_solver_type == CGNR) {
*error = "DOGLEG only supports exact factorization based linear "
"solvers. If you want to use an iterative solver please "
"use LEVENBERG_MARQUARDT as the trust_region_strategy_type";
return false;
}
}
if (options.trust_region_minimizer_iterations_to_dump.size() > 0 &&
options.trust_region_problem_dump_format_type != CONSOLE &&
options.trust_region_problem_dump_directory.empty()) {
*error = "Solver::Options::trust_region_problem_dump_directory is empty.";
return false;
}
if (options.dynamic_sparsity &&
options.linear_solver_type != SPARSE_NORMAL_CHOLESKY) {
*error = "Dynamic sparsity is only supported with SPARSE_NORMAL_CHOLESKY.";
return false;
}
return true;
}
bool LineSearchOptionsAreValid(const Solver::Options& options, string* error) {
OPTION_GT(max_lbfgs_rank, 0);
OPTION_GT(min_line_search_step_size, 0.0);
OPTION_GT(max_line_search_step_contraction, 0.0);
OPTION_LT(max_line_search_step_contraction, 1.0);
OPTION_LT_OPTION(max_line_search_step_contraction,
min_line_search_step_contraction);
OPTION_LE(min_line_search_step_contraction, 1.0);
OPTION_GT(max_num_line_search_step_size_iterations, 0);
OPTION_GT(line_search_sufficient_function_decrease, 0.0);
OPTION_LT_OPTION(line_search_sufficient_function_decrease,
line_search_sufficient_curvature_decrease);
OPTION_LT(line_search_sufficient_curvature_decrease, 1.0);
OPTION_GT(max_line_search_step_expansion, 1.0);
if ((options.line_search_direction_type == ceres::BFGS ||
options.line_search_direction_type == ceres::LBFGS) &&
options.line_search_type != ceres::WOLFE) {
*error =
string("Invalid configuration: Solver::Options::line_search_type = ")
+ string(LineSearchTypeToString(options.line_search_type))
+ string(". When using (L)BFGS, "
"Solver::Options::line_search_type must be set to WOLFE.");
return false;
}
// Warn user if they have requested BISECTION interpolation, but constraints
// on max/min step size change during line search prevent bisection scaling
// from occurring. Warn only, as this is likely a user mistake, but one which
// does not prevent us from continuing.
LOG_IF(WARNING,
(options.line_search_interpolation_type == ceres::BISECTION &&
(options.max_line_search_step_contraction > 0.5 ||
options.min_line_search_step_contraction < 0.5)))
<< "Line search interpolation type is BISECTION, but specified "
<< "max_line_search_step_contraction: "
<< options.max_line_search_step_contraction << ", and "
<< "min_line_search_step_contraction: "
<< options.min_line_search_step_contraction
<< ", prevent bisection (0.5) scaling, continuing with solve regardless.";
return true;
}
#undef OPTION_OP
#undef OPTION_OP_OPTION
#undef OPTION_GT
#undef OPTION_GE
#undef OPTION_LE
#undef OPTION_LT
#undef OPTION_LE_OPTION
#undef OPTION_LT_OPTION
void StringifyOrdering(const vector<int>& ordering, string* report) {
if (ordering.size() == 0) {
internal::StringAppendF(report, "AUTOMATIC");
return;
}
for (int i = 0; i < ordering.size() - 1; ++i) {
internal::StringAppendF(report, "%d, ", ordering[i]);
}
internal::StringAppendF(report, "%d", ordering.back());
}
void SetSummaryFinalCost(Solver::Summary* summary) {
summary->final_cost = summary->initial_cost;
// We need the loop here, instead of just looking at the last
// iteration because the minimizer maybe making non-monotonic steps.
for (int i = 0; i < summary->iterations.size(); ++i) {
const IterationSummary& iteration_summary = summary->iterations[i];
summary->final_cost = min(iteration_summary.cost, summary->final_cost);
}
}
void SummarizeGivenProgram(const internal::Program& program,
Solver::Summary* summary) {
summary->num_parameter_blocks = program.NumParameterBlocks();
summary->num_parameters = program.NumParameters();
summary->num_effective_parameters = program.NumEffectiveParameters();
summary->num_residual_blocks = program.NumResidualBlocks();
summary->num_residuals = program.NumResiduals();
}
void SummarizeReducedProgram(const internal::Program& program,
Solver::Summary* summary) {
summary->num_parameter_blocks_reduced = program.NumParameterBlocks();
summary->num_parameters_reduced = program.NumParameters();
summary->num_effective_parameters_reduced = program.NumEffectiveParameters();
summary->num_residual_blocks_reduced = program.NumResidualBlocks();
summary->num_residuals_reduced = program.NumResiduals();
}
void PreSolveSummarize(const Solver::Options& options,
const internal::ProblemImpl* problem,
Solver::Summary* summary) {
SummarizeGivenProgram(problem->program(), summary);
internal::OrderingToGroupSizes(options.linear_solver_ordering.get(),
&(summary->linear_solver_ordering_given));
internal::OrderingToGroupSizes(options.inner_iteration_ordering.get(),
&(summary->inner_iteration_ordering_given));
summary->dense_linear_algebra_library_type = options.dense_linear_algebra_library_type;
summary->dogleg_type = options.dogleg_type;
summary->inner_iteration_time_in_seconds = 0.0;
summary->inner_iterations_given = options.use_inner_iterations;
summary->line_search_direction_type = options.line_search_direction_type;
summary->line_search_interpolation_type = options.line_search_interpolation_type;
summary->line_search_type = options.line_search_type;
summary->linear_solver_type_given = options.linear_solver_type;
summary->max_lbfgs_rank = options.max_lbfgs_rank;
summary->minimizer_type = options.minimizer_type;
summary->nonlinear_conjugate_gradient_type = options.nonlinear_conjugate_gradient_type;
summary->num_linear_solver_threads_given = options.num_linear_solver_threads;
summary->num_threads_given = options.num_threads;
summary->preconditioner_type_given = options.preconditioner_type;
summary->sparse_linear_algebra_library_type = options.sparse_linear_algebra_library_type;
summary->trust_region_strategy_type = options.trust_region_strategy_type;
summary->visibility_clustering_type = options.visibility_clustering_type;
}
void PostSolveSummarize(const internal::PreprocessedProblem& pp,
Solver::Summary* summary) {
internal::OrderingToGroupSizes(pp.options.linear_solver_ordering.get(),
&(summary->linear_solver_ordering_used));
internal::OrderingToGroupSizes(pp.options.inner_iteration_ordering.get(),
&(summary->inner_iteration_ordering_used));
summary->inner_iterations_used = pp.inner_iteration_minimizer.get() != NULL;
summary->linear_solver_type_used = pp.options.linear_solver_type;
summary->num_linear_solver_threads_used = pp.options.num_linear_solver_threads;
summary->num_threads_used = pp.options.num_threads;
summary->preconditioner_type_used = pp.options.preconditioner_type;
SetSummaryFinalCost(summary);
if (pp.reduced_program.get() != NULL) {
SummarizeReducedProgram(*pp.reduced_program, summary);
}
// It is possible that no evaluator was created. This would be the
// case if the preprocessor failed, or if the reduced problem did
// not contain any parameter blocks. Thus, only extract the
// evaluator statistics if one exists.
if (pp.evaluator.get() != NULL) {
const map<string, double>& evaluator_time_statistics =
pp.evaluator->TimeStatistics();
summary->residual_evaluation_time_in_seconds =
FindWithDefault(evaluator_time_statistics, "Evaluator::Residual", 0.0);
summary->jacobian_evaluation_time_in_seconds =
FindWithDefault(evaluator_time_statistics, "Evaluator::Jacobian", 0.0);
}
// Again, like the evaluator, there may or may not be a linear
// solver from which we can extract run time statistics. In
// particular the line search solver does not use a linear solver.
if (pp.linear_solver.get() != NULL) {
const map<string, double>& linear_solver_time_statistics =
pp.linear_solver->TimeStatistics();
summary->linear_solver_time_in_seconds =
FindWithDefault(linear_solver_time_statistics,
"LinearSolver::Solve",
0.0);
}
}
void Minimize(internal::PreprocessedProblem* pp,
Solver::Summary* summary) {
using internal::Program;
using internal::scoped_ptr;
using internal::Minimizer;
Program* program = pp->reduced_program.get();
if (pp->reduced_program->NumParameterBlocks() == 0) {
summary->message = "Function tolerance reached. "
"No non-constant parameter blocks found.";
summary->termination_type = CONVERGENCE;
VLOG_IF(1, pp->options.logging_type != SILENT) << summary->message;
summary->initial_cost = summary->fixed_cost;
summary->final_cost = summary->fixed_cost;
return;
}
scoped_ptr<Minimizer> minimizer(
Minimizer::Create(pp->options.minimizer_type));
minimizer->Minimize(pp->minimizer_options,
pp->reduced_parameters.data(),
summary);
if (summary->IsSolutionUsable()) {
program->StateVectorToParameterBlocks(pp->reduced_parameters.data());
program->CopyParameterBlockStateToUserState();
}
}
} // namespace
bool Solver::Options::IsValid(string* error) const {
if (!CommonOptionsAreValid(*this, error)) {
return false;
}
if (minimizer_type == TRUST_REGION) {
return TrustRegionOptionsAreValid(*this, error);
}
CHECK_EQ(minimizer_type, LINE_SEARCH);
return LineSearchOptionsAreValid(*this, error);
}
Solver::~Solver() {}
void Solver::Solve(const Solver::Options& options,
Problem* problem,
Solver::Summary* summary) {
using internal::PreprocessedProblem;
using internal::Preprocessor;
using internal::ProblemImpl;
using internal::Program;
using internal::scoped_ptr;
using internal::WallTimeInSeconds;
CHECK_NOTNULL(problem);
CHECK_NOTNULL(summary);
double start_time = WallTimeInSeconds();
*summary = Summary();
if (!options.IsValid(&summary->message)) {
LOG(ERROR) << "Terminating: " << summary->message;
return;
}
ProblemImpl* problem_impl = problem->problem_impl_.get();
Program* program = problem_impl->mutable_program();
PreSolveSummarize(options, problem_impl, summary);
// Make sure that all the parameter blocks states are set to the
// values provided by the user.
program->SetParameterBlockStatePtrsToUserStatePtrs();
scoped_ptr<internal::ProblemImpl> gradient_checking_problem;
if (options.check_gradients) {
gradient_checking_problem.reset(
CreateGradientCheckingProblemImpl(
problem_impl,
options.numeric_derivative_relative_step_size,
options.gradient_check_relative_precision));
problem_impl = gradient_checking_problem.get();
program = problem_impl->mutable_program();
}
scoped_ptr<Preprocessor> preprocessor(
Preprocessor::Create(options.minimizer_type));
PreprocessedProblem pp;
const bool status = preprocessor->Preprocess(options, problem_impl, &pp);
summary->fixed_cost = pp.fixed_cost;
summary->preprocessor_time_in_seconds = WallTimeInSeconds() - start_time;
if (status) {
const double minimizer_start_time = WallTimeInSeconds();
Minimize(&pp, summary);
summary->minimizer_time_in_seconds =
WallTimeInSeconds() - minimizer_start_time;
} else {
summary->message = pp.error;
}
const double postprocessor_start_time = WallTimeInSeconds();
problem_impl = problem->problem_impl_.get();
program = problem_impl->mutable_program();
// On exit, ensure that the parameter blocks again point at the user
// provided values and the parameter blocks are numbered according
// to their position in the original user provided program.
program->SetParameterBlockStatePtrsToUserStatePtrs();
program->SetParameterOffsetsAndIndex();
PostSolveSummarize(pp, summary);
summary->postprocessor_time_in_seconds =
WallTimeInSeconds() - postprocessor_start_time;
summary->total_time_in_seconds = WallTimeInSeconds() - start_time;
}
void Solve(const Solver::Options& options,
Problem* problem,
Solver::Summary* summary) {
Solver solver;
solver.Solve(options, problem, summary);
}
Solver::Summary::Summary()
// Invalid values for most fields, to ensure that we are not
// accidentally reporting default values.
: minimizer_type(TRUST_REGION),
termination_type(FAILURE),
message("ceres::Solve was not called."),
initial_cost(-1.0),
final_cost(-1.0),
fixed_cost(-1.0),
num_successful_steps(-1),
num_unsuccessful_steps(-1),
num_inner_iteration_steps(-1),
preprocessor_time_in_seconds(-1.0),
minimizer_time_in_seconds(-1.0),
postprocessor_time_in_seconds(-1.0),
total_time_in_seconds(-1.0),
linear_solver_time_in_seconds(-1.0),
residual_evaluation_time_in_seconds(-1.0),
jacobian_evaluation_time_in_seconds(-1.0),
inner_iteration_time_in_seconds(-1.0),
num_parameter_blocks(-1),
num_parameters(-1),
num_effective_parameters(-1),
num_residual_blocks(-1),
num_residuals(-1),
num_parameter_blocks_reduced(-1),
num_parameters_reduced(-1),
num_effective_parameters_reduced(-1),
num_residual_blocks_reduced(-1),
num_residuals_reduced(-1),
num_threads_given(-1),
num_threads_used(-1),
num_linear_solver_threads_given(-1),
num_linear_solver_threads_used(-1),
linear_solver_type_given(SPARSE_NORMAL_CHOLESKY),
linear_solver_type_used(SPARSE_NORMAL_CHOLESKY),
inner_iterations_given(false),
inner_iterations_used(false),
preconditioner_type_given(IDENTITY),
preconditioner_type_used(IDENTITY),
visibility_clustering_type(CANONICAL_VIEWS),
trust_region_strategy_type(LEVENBERG_MARQUARDT),
dense_linear_algebra_library_type(EIGEN),
sparse_linear_algebra_library_type(SUITE_SPARSE),
line_search_direction_type(LBFGS),
line_search_type(ARMIJO),
line_search_interpolation_type(BISECTION),
nonlinear_conjugate_gradient_type(FLETCHER_REEVES),
max_lbfgs_rank(-1) {
}
using internal::StringAppendF;
using internal::StringPrintf;
string Solver::Summary::BriefReport() const {
return StringPrintf("Ceres Solver Report: "
"Iterations: %d, "
"Initial cost: %e, "
"Final cost: %e, "
"Termination: %s",
num_successful_steps + num_unsuccessful_steps,
initial_cost,
final_cost,
TerminationTypeToString(termination_type));
};
string Solver::Summary::FullReport() const {
string report =
"\n"
"Ceres Solver v" CERES_VERSION_STRING " Solve Report\n"
"----------------------------------\n";
StringAppendF(&report, "%45s %21s\n", "Original", "Reduced");
StringAppendF(&report, "Parameter blocks % 25d% 25d\n",
num_parameter_blocks, num_parameter_blocks_reduced);
StringAppendF(&report, "Parameters % 25d% 25d\n",
num_parameters, num_parameters_reduced);
if (num_effective_parameters_reduced != num_parameters_reduced) {
StringAppendF(&report, "Effective parameters% 25d% 25d\n",
num_effective_parameters, num_effective_parameters_reduced);
}
StringAppendF(&report, "Residual blocks % 25d% 25d\n",
num_residual_blocks, num_residual_blocks_reduced);
StringAppendF(&report, "Residual % 25d% 25d\n",
num_residuals, num_residuals_reduced);
if (minimizer_type == TRUST_REGION) {
// TRUST_SEARCH HEADER
StringAppendF(&report, "\nMinimizer %19s\n",
"TRUST_REGION");
if (linear_solver_type_used == DENSE_NORMAL_CHOLESKY ||
linear_solver_type_used == DENSE_SCHUR ||
linear_solver_type_used == DENSE_QR) {
StringAppendF(&report, "\nDense linear algebra library %15s\n",
DenseLinearAlgebraLibraryTypeToString(
dense_linear_algebra_library_type));
}
if (linear_solver_type_used == SPARSE_NORMAL_CHOLESKY ||
linear_solver_type_used == SPARSE_SCHUR ||
(linear_solver_type_used == ITERATIVE_SCHUR &&
(preconditioner_type_used == CLUSTER_JACOBI ||
preconditioner_type_used == CLUSTER_TRIDIAGONAL))) {
StringAppendF(&report, "\nSparse linear algebra library %15s\n",
SparseLinearAlgebraLibraryTypeToString(
sparse_linear_algebra_library_type));
}
StringAppendF(&report, "Trust region strategy %19s",
TrustRegionStrategyTypeToString(
trust_region_strategy_type));
if (trust_region_strategy_type == DOGLEG) {
if (dogleg_type == TRADITIONAL_DOGLEG) {
StringAppendF(&report, " (TRADITIONAL)");
} else {
StringAppendF(&report, " (SUBSPACE)");
}
}
StringAppendF(&report, "\n");
StringAppendF(&report, "\n");
StringAppendF(&report, "%45s %21s\n", "Given", "Used");
StringAppendF(&report, "Linear solver %25s%25s\n",
LinearSolverTypeToString(linear_solver_type_given),
LinearSolverTypeToString(linear_solver_type_used));
if (linear_solver_type_given == CGNR ||
linear_solver_type_given == ITERATIVE_SCHUR) {
StringAppendF(&report, "Preconditioner %25s%25s\n",
PreconditionerTypeToString(preconditioner_type_given),
PreconditionerTypeToString(preconditioner_type_used));
}
if (preconditioner_type_used == CLUSTER_JACOBI ||
preconditioner_type_used == CLUSTER_TRIDIAGONAL) {
StringAppendF(&report, "Visibility clustering%24s%25s\n",
VisibilityClusteringTypeToString(
visibility_clustering_type),
VisibilityClusteringTypeToString(
visibility_clustering_type));
}
StringAppendF(&report, "Threads % 25d% 25d\n",
num_threads_given, num_threads_used);
StringAppendF(&report, "Linear solver threads % 23d% 25d\n",
num_linear_solver_threads_given,
num_linear_solver_threads_used);
if (IsSchurType(linear_solver_type_used)) {
string given;
StringifyOrdering(linear_solver_ordering_given, &given);
string used;
StringifyOrdering(linear_solver_ordering_used, &used);
StringAppendF(&report,
"Linear solver ordering %22s %24s\n",
given.c_str(),
used.c_str());
}
if (inner_iterations_given) {
StringAppendF(&report,
"Use inner iterations %20s %20s\n",
inner_iterations_given ? "True" : "False",
inner_iterations_used ? "True" : "False");
}
if (inner_iterations_used) {
string given;
StringifyOrdering(inner_iteration_ordering_given, &given);
string used;
StringifyOrdering(inner_iteration_ordering_used, &used);
StringAppendF(&report,
"Inner iteration ordering %20s %24s\n",
given.c_str(),
used.c_str());
}
} else {
// LINE_SEARCH HEADER
StringAppendF(&report, "\nMinimizer %19s\n", "LINE_SEARCH");
string line_search_direction_string;
if (line_search_direction_type == LBFGS) {
line_search_direction_string = StringPrintf("LBFGS (%d)", max_lbfgs_rank);
} else if (line_search_direction_type == NONLINEAR_CONJUGATE_GRADIENT) {
line_search_direction_string =
NonlinearConjugateGradientTypeToString(
nonlinear_conjugate_gradient_type);
} else {
line_search_direction_string =
LineSearchDirectionTypeToString(line_search_direction_type);
}
StringAppendF(&report, "Line search direction %19s\n",
line_search_direction_string.c_str());
const string line_search_type_string =
StringPrintf("%s %s",
LineSearchInterpolationTypeToString(
line_search_interpolation_type),
LineSearchTypeToString(line_search_type));
StringAppendF(&report, "Line search type %19s\n",
line_search_type_string.c_str());
StringAppendF(&report, "\n");
StringAppendF(&report, "%45s %21s\n", "Given", "Used");
StringAppendF(&report, "Threads % 25d% 25d\n",
num_threads_given, num_threads_used);
}
StringAppendF(&report, "\nCost:\n");
StringAppendF(&report, "Initial % 30e\n", initial_cost);
if (termination_type != FAILURE &&
termination_type != USER_FAILURE) {
StringAppendF(&report, "Final % 30e\n", final_cost);
StringAppendF(&report, "Change % 30e\n",
initial_cost - final_cost);
}
StringAppendF(&report, "\nMinimizer iterations % 16d\n",
num_successful_steps + num_unsuccessful_steps);
// Successful/Unsuccessful steps only matter in the case of the
// trust region solver. Line search terminates when it encounters
// the first unsuccessful step.
if (minimizer_type == TRUST_REGION) {
StringAppendF(&report, "Successful steps % 14d\n",
num_successful_steps);
StringAppendF(&report, "Unsuccessful steps % 14d\n",
num_unsuccessful_steps);
}
if (inner_iterations_used) {
StringAppendF(&report, "Steps with inner iterations % 14d\n",
num_inner_iteration_steps);
}
StringAppendF(&report, "\nTime (in seconds):\n");
StringAppendF(&report, "Preprocessor %25.3f\n",
preprocessor_time_in_seconds);
StringAppendF(&report, "\n Residual evaluation %23.3f\n",
residual_evaluation_time_in_seconds);
StringAppendF(&report, " Jacobian evaluation %23.3f\n",
jacobian_evaluation_time_in_seconds);
if (minimizer_type == TRUST_REGION) {
StringAppendF(&report, " Linear solver %23.3f\n",
linear_solver_time_in_seconds);
}
if (inner_iterations_used) {
StringAppendF(&report, " Inner iterations %23.3f\n",
inner_iteration_time_in_seconds);
}
StringAppendF(&report, "Minimizer %25.3f\n\n",
minimizer_time_in_seconds);
StringAppendF(&report, "Postprocessor %24.3f\n",
postprocessor_time_in_seconds);
StringAppendF(&report, "Total %25.3f\n\n",
total_time_in_seconds);
StringAppendF(&report, "Termination: %25s (%s)\n",
TerminationTypeToString(termination_type), message.c_str());
return report;
};
bool Solver::Summary::IsSolutionUsable() const {
return (termination_type == CONVERGENCE ||
termination_type == NO_CONVERGENCE ||
termination_type == USER_SUCCESS);
}
} // namespace ceres
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