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Diffstat (limited to 'extern/libmv/third_party/ceres/internal/ceres/dogleg_strategy.h')
| -rw-r--r-- | extern/libmv/third_party/ceres/internal/ceres/dogleg_strategy.h | 165 |
1 files changed, 0 insertions, 165 deletions
diff --git a/extern/libmv/third_party/ceres/internal/ceres/dogleg_strategy.h b/extern/libmv/third_party/ceres/internal/ceres/dogleg_strategy.h deleted file mode 100644 index 71c785cc3f7..00000000000 --- a/extern/libmv/third_party/ceres/internal/ceres/dogleg_strategy.h +++ /dev/null @@ -1,165 +0,0 @@ -// Ceres Solver - A fast non-linear least squares minimizer -// Copyright 2012 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: sameeragarwal@google.com (Sameer Agarwal) - -#ifndef CERES_INTERNAL_DOGLEG_STRATEGY_H_ -#define CERES_INTERNAL_DOGLEG_STRATEGY_H_ - -#include "ceres/linear_solver.h" -#include "ceres/trust_region_strategy.h" - -namespace ceres { -namespace internal { - -// Dogleg step computation and trust region sizing strategy based on -// on "Methods for Nonlinear Least Squares" by K. Madsen, H.B. Nielsen -// and O. Tingleff. Available to download from -// -// http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf -// -// One minor modification is that instead of computing the pure -// Gauss-Newton step, we compute a regularized version of it. This is -// because the Jacobian is often rank-deficient and in such cases -// using a direct solver leads to numerical failure. -// -// If SUBSPACE is passed as the type argument to the constructor, the -// DoglegStrategy follows the approach by Shultz, Schnabel, Byrd. -// This finds the exact optimum over the two-dimensional subspace -// spanned by the two Dogleg vectors. -class DoglegStrategy : public TrustRegionStrategy { - public: - explicit DoglegStrategy(const TrustRegionStrategy::Options& options); - virtual ~DoglegStrategy() {} - - // TrustRegionStrategy interface - virtual Summary ComputeStep(const PerSolveOptions& per_solve_options, - SparseMatrix* jacobian, - const double* residuals, - double* step); - virtual void StepAccepted(double step_quality); - virtual void StepRejected(double step_quality); - virtual void StepIsInvalid(); - - virtual double Radius() const; - - // These functions are predominantly for testing. - Vector gradient() const { return gradient_; } - Vector gauss_newton_step() const { return gauss_newton_step_; } - Matrix subspace_basis() const { return subspace_basis_; } - Vector subspace_g() const { return subspace_g_; } - Matrix subspace_B() const { return subspace_B_; } - - private: - typedef Eigen::Matrix<double, 2, 1, Eigen::DontAlign> Vector2d; - typedef Eigen::Matrix<double, 2, 2, Eigen::DontAlign> Matrix2d; - - LinearSolver::Summary ComputeGaussNewtonStep( - const PerSolveOptions& per_solve_options, - SparseMatrix* jacobian, - const double* residuals); - void ComputeCauchyPoint(SparseMatrix* jacobian); - void ComputeGradient(SparseMatrix* jacobian, const double* residuals); - void ComputeTraditionalDoglegStep(double* step); - bool ComputeSubspaceModel(SparseMatrix* jacobian); - void ComputeSubspaceDoglegStep(double* step); - - bool FindMinimumOnTrustRegionBoundary(Vector2d* minimum) const; - Vector MakePolynomialForBoundaryConstrainedProblem() const; - Vector2d ComputeSubspaceStepFromRoot(double lambda) const; - double EvaluateSubspaceModel(const Vector2d& x) const; - - LinearSolver* linear_solver_; - double radius_; - const double max_radius_; - - const double min_diagonal_; - const double max_diagonal_; - - // mu is used to scale the diagonal matrix used to make the - // Gauss-Newton solve full rank. In each solve, the strategy starts - // out with mu = min_mu, and tries values upto max_mu. If the user - // reports an invalid step, the value of mu_ is increased so that - // the next solve starts with a stronger regularization. - // - // If a successful step is reported, then the value of mu_ is - // decreased with a lower bound of min_mu_. - double mu_; - const double min_mu_; - const double max_mu_; - const double mu_increase_factor_; - const double increase_threshold_; - const double decrease_threshold_; - - Vector diagonal_; // sqrt(diag(J^T J)) - Vector lm_diagonal_; - - Vector gradient_; - Vector gauss_newton_step_; - - // cauchy_step = alpha * gradient - double alpha_; - double dogleg_step_norm_; - - // When, ComputeStep is called, reuse_ indicates whether the - // Gauss-Newton and Cauchy steps from the last call to ComputeStep - // can be reused or not. - // - // If the user called StepAccepted, then it is expected that the - // user has recomputed the Jacobian matrix and new Gauss-Newton - // solve is needed and reuse is set to false. - // - // If the user called StepRejected, then it is expected that the - // user wants to solve the trust region problem with the same matrix - // but a different trust region radius and the Gauss-Newton and - // Cauchy steps can be reused to compute the Dogleg, thus reuse is - // set to true. - // - // If the user called StepIsInvalid, then there was a numerical - // problem with the step computed in the last call to ComputeStep, - // and the regularization used to do the Gauss-Newton solve is - // increased and a new solve should be done when ComputeStep is - // called again, thus reuse is set to false. - bool reuse_; - - // The dogleg type determines how the minimum of the local - // quadratic model is found. - DoglegType dogleg_type_; - - // If the type is SUBSPACE_DOGLEG, the two-dimensional - // model 1/2 x^T B x + g^T x has to be computed and stored. - bool subspace_is_one_dimensional_; - Matrix subspace_basis_; - Vector2d subspace_g_; - Matrix2d subspace_B_; -}; - -} // namespace internal -} // namespace ceres - -#endif // CERES_INTERNAL_DOGLEG_STRATEGY_H_ |