mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
487c1aa51f
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
439 lines
9.9 KiB
C++
439 lines
9.9 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#include <algorithm>
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#include <cctype>
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#include <string>
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#include "ceres/types.h"
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#include "glog/logging.h"
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namespace ceres {
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using std::string;
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#define CASESTR(x) case x: return #x
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#define STRENUM(x) if (value == #x) { *type = x; return true;}
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static void UpperCase(string* input) {
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std::transform(input->begin(), input->end(), input->begin(), ::toupper);
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}
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const char* LinearSolverTypeToString(LinearSolverType type) {
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switch (type) {
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CASESTR(DENSE_NORMAL_CHOLESKY);
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CASESTR(DENSE_QR);
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CASESTR(SPARSE_NORMAL_CHOLESKY);
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CASESTR(DENSE_SCHUR);
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CASESTR(SPARSE_SCHUR);
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CASESTR(ITERATIVE_SCHUR);
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CASESTR(CGNR);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToLinearSolverType(string value, LinearSolverType* type) {
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UpperCase(&value);
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STRENUM(DENSE_NORMAL_CHOLESKY);
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STRENUM(DENSE_QR);
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STRENUM(SPARSE_NORMAL_CHOLESKY);
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STRENUM(DENSE_SCHUR);
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STRENUM(SPARSE_SCHUR);
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STRENUM(ITERATIVE_SCHUR);
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STRENUM(CGNR);
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return false;
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}
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const char* PreconditionerTypeToString(PreconditionerType type) {
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switch (type) {
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CASESTR(IDENTITY);
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CASESTR(JACOBI);
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CASESTR(SCHUR_JACOBI);
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CASESTR(CLUSTER_JACOBI);
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CASESTR(CLUSTER_TRIDIAGONAL);
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CASESTR(SUBSET);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToPreconditionerType(string value, PreconditionerType* type) {
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UpperCase(&value);
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STRENUM(IDENTITY);
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STRENUM(JACOBI);
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STRENUM(SCHUR_JACOBI);
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STRENUM(CLUSTER_JACOBI);
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STRENUM(CLUSTER_TRIDIAGONAL);
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STRENUM(SUBSET);
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return false;
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}
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const char* SparseLinearAlgebraLibraryTypeToString(
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SparseLinearAlgebraLibraryType type) {
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switch (type) {
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CASESTR(SUITE_SPARSE);
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CASESTR(CX_SPARSE);
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CASESTR(EIGEN_SPARSE);
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CASESTR(ACCELERATE_SPARSE);
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CASESTR(NO_SPARSE);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToSparseLinearAlgebraLibraryType(
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string value,
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SparseLinearAlgebraLibraryType* type) {
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UpperCase(&value);
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STRENUM(SUITE_SPARSE);
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STRENUM(CX_SPARSE);
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STRENUM(EIGEN_SPARSE);
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STRENUM(ACCELERATE_SPARSE);
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STRENUM(NO_SPARSE);
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return false;
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}
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const char* DenseLinearAlgebraLibraryTypeToString(
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DenseLinearAlgebraLibraryType type) {
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switch (type) {
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CASESTR(EIGEN);
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CASESTR(LAPACK);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToDenseLinearAlgebraLibraryType(
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string value,
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DenseLinearAlgebraLibraryType* type) {
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UpperCase(&value);
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STRENUM(EIGEN);
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STRENUM(LAPACK);
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return false;
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}
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const char* TrustRegionStrategyTypeToString(TrustRegionStrategyType type) {
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switch (type) {
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CASESTR(LEVENBERG_MARQUARDT);
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CASESTR(DOGLEG);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToTrustRegionStrategyType(string value,
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TrustRegionStrategyType* type) {
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UpperCase(&value);
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STRENUM(LEVENBERG_MARQUARDT);
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STRENUM(DOGLEG);
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return false;
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}
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const char* DoglegTypeToString(DoglegType type) {
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switch (type) {
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CASESTR(TRADITIONAL_DOGLEG);
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CASESTR(SUBSPACE_DOGLEG);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToDoglegType(string value, DoglegType* type) {
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UpperCase(&value);
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STRENUM(TRADITIONAL_DOGLEG);
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STRENUM(SUBSPACE_DOGLEG);
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return false;
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}
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const char* MinimizerTypeToString(MinimizerType type) {
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switch (type) {
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CASESTR(TRUST_REGION);
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CASESTR(LINE_SEARCH);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToMinimizerType(string value, MinimizerType* type) {
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UpperCase(&value);
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STRENUM(TRUST_REGION);
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STRENUM(LINE_SEARCH);
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return false;
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}
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const char* LineSearchDirectionTypeToString(LineSearchDirectionType type) {
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switch (type) {
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CASESTR(STEEPEST_DESCENT);
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CASESTR(NONLINEAR_CONJUGATE_GRADIENT);
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CASESTR(LBFGS);
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CASESTR(BFGS);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToLineSearchDirectionType(string value,
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LineSearchDirectionType* type) {
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UpperCase(&value);
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STRENUM(STEEPEST_DESCENT);
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STRENUM(NONLINEAR_CONJUGATE_GRADIENT);
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STRENUM(LBFGS);
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STRENUM(BFGS);
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return false;
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}
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const char* LineSearchTypeToString(LineSearchType type) {
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switch (type) {
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CASESTR(ARMIJO);
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CASESTR(WOLFE);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToLineSearchType(string value, LineSearchType* type) {
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UpperCase(&value);
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STRENUM(ARMIJO);
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STRENUM(WOLFE);
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return false;
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}
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const char* LineSearchInterpolationTypeToString(
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LineSearchInterpolationType type) {
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switch (type) {
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CASESTR(BISECTION);
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CASESTR(QUADRATIC);
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CASESTR(CUBIC);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToLineSearchInterpolationType(
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string value,
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LineSearchInterpolationType* type) {
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UpperCase(&value);
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STRENUM(BISECTION);
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STRENUM(QUADRATIC);
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STRENUM(CUBIC);
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return false;
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}
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const char* NonlinearConjugateGradientTypeToString(
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NonlinearConjugateGradientType type) {
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switch (type) {
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CASESTR(FLETCHER_REEVES);
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CASESTR(POLAK_RIBIERE);
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CASESTR(HESTENES_STIEFEL);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToNonlinearConjugateGradientType(
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string value,
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NonlinearConjugateGradientType* type) {
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UpperCase(&value);
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STRENUM(FLETCHER_REEVES);
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STRENUM(POLAK_RIBIERE);
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STRENUM(HESTENES_STIEFEL);
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return false;
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}
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const char* CovarianceAlgorithmTypeToString(
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CovarianceAlgorithmType type) {
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switch (type) {
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CASESTR(DENSE_SVD);
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CASESTR(SPARSE_QR);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToCovarianceAlgorithmType(
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string value,
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CovarianceAlgorithmType* type) {
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UpperCase(&value);
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STRENUM(DENSE_SVD);
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STRENUM(SPARSE_QR);
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return false;
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}
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const char* NumericDiffMethodTypeToString(
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NumericDiffMethodType type) {
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switch (type) {
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CASESTR(CENTRAL);
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CASESTR(FORWARD);
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CASESTR(RIDDERS);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToNumericDiffMethodType(
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string value,
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NumericDiffMethodType* type) {
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UpperCase(&value);
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STRENUM(CENTRAL);
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STRENUM(FORWARD);
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STRENUM(RIDDERS);
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return false;
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}
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const char* VisibilityClusteringTypeToString(
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VisibilityClusteringType type) {
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switch (type) {
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CASESTR(CANONICAL_VIEWS);
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CASESTR(SINGLE_LINKAGE);
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default:
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return "UNKNOWN";
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}
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}
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bool StringToVisibilityClusteringType(
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string value,
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VisibilityClusteringType* type) {
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UpperCase(&value);
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STRENUM(CANONICAL_VIEWS);
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STRENUM(SINGLE_LINKAGE);
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return false;
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}
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const char* TerminationTypeToString(TerminationType type) {
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switch (type) {
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CASESTR(CONVERGENCE);
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CASESTR(NO_CONVERGENCE);
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CASESTR(FAILURE);
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CASESTR(USER_SUCCESS);
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CASESTR(USER_FAILURE);
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default:
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return "UNKNOWN";
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}
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}
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const char* LoggingTypeToString(LoggingType type) {
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switch (type) {
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CASESTR(SILENT);
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CASESTR(PER_MINIMIZER_ITERATION);
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default:
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return "UNKNOWN";
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}
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}
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bool StringtoLoggingType(std::string value, LoggingType* type) {
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UpperCase(&value);
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STRENUM(SILENT);
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STRENUM(PER_MINIMIZER_ITERATION);
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return false;
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}
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const char* DumpFormatTypeToString(DumpFormatType type) {
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switch (type) {
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CASESTR(CONSOLE);
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CASESTR(TEXTFILE);
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default:
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return "UNKNOWN";
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}
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}
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bool StringtoDumpFormatType(std::string value, DumpFormatType* type) {
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UpperCase(&value);
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STRENUM(CONSOLE);
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STRENUM(TEXTFILE);
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return false;
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}
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#undef CASESTR
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#undef STRENUM
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bool IsSchurType(LinearSolverType type) {
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return ((type == SPARSE_SCHUR) ||
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(type == DENSE_SCHUR) ||
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(type == ITERATIVE_SCHUR));
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}
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bool IsSparseLinearAlgebraLibraryTypeAvailable(
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SparseLinearAlgebraLibraryType type) {
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if (type == SUITE_SPARSE) {
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#ifdef CERES_NO_SUITESPARSE
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return false;
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#else
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return true;
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#endif
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}
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if (type == CX_SPARSE) {
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#ifdef CERES_NO_CXSPARSE
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return false;
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#else
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return true;
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#endif
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}
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if (type == ACCELERATE_SPARSE) {
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#ifdef CERES_NO_ACCELERATE_SPARSE
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return false;
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#else
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return true;
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#endif
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}
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if (type == EIGEN_SPARSE) {
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#ifdef CERES_USE_EIGEN_SPARSE
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return true;
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#else
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return false;
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#endif
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}
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LOG(WARNING) << "Unknown sparse linear algebra library " << type;
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return false;
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}
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bool IsDenseLinearAlgebraLibraryTypeAvailable(
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DenseLinearAlgebraLibraryType type) {
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if (type == EIGEN) {
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return true;
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}
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if (type == LAPACK) {
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#ifdef CERES_NO_LAPACK
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return false;
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#else
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return true;
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#endif
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}
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LOG(WARNING) << "Unknown dense linear algebra library " << type;
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return false;
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}
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} // namespace ceres
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