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diff --git a/extern/libmv/third_party/ceres/include/ceres/types.h b/extern/libmv/third_party/ceres/include/ceres/types.h deleted file mode 100644 index a07c8933e64..00000000000 --- a/extern/libmv/third_party/ceres/include/ceres/types.h +++ /dev/null @@ -1,487 +0,0 @@ -// Ceres Solver - A fast non-linear least squares minimizer -// Copyright 2010, 2011, 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) -// -// Enums and other top level class definitions. -// -// Note: internal/types.cc defines stringification routines for some -// of these enums. Please update those routines if you extend or -// remove enums from here. - -#ifndef CERES_PUBLIC_TYPES_H_ -#define CERES_PUBLIC_TYPES_H_ - -#include <string> - -#include "ceres/internal/port.h" -#include "ceres/internal/disable_warnings.h" - -namespace ceres { - -// Basic integer types. These typedefs are in the Ceres namespace to avoid -// conflicts with other packages having similar typedefs. -typedef int int32; - -// Argument type used in interfaces that can optionally take ownership -// of a passed in argument. If TAKE_OWNERSHIP is passed, the called -// object takes ownership of the pointer argument, and will call -// delete on it upon completion. -enum Ownership { - DO_NOT_TAKE_OWNERSHIP, - TAKE_OWNERSHIP -}; - -// TODO(keir): Considerably expand the explanations of each solver type. -enum LinearSolverType { - // These solvers are for general rectangular systems formed from the - // normal equations A'A x = A'b. They are direct solvers and do not - // assume any special problem structure. - - // Solve the normal equations using a dense Cholesky solver; based - // on Eigen. - DENSE_NORMAL_CHOLESKY, - - // Solve the normal equations using a dense QR solver; based on - // Eigen. - DENSE_QR, - - // Solve the normal equations using a sparse cholesky solver; requires - // SuiteSparse or CXSparse. - SPARSE_NORMAL_CHOLESKY, - - // Specialized solvers, specific to problems with a generalized - // bi-partitite structure. - - // Solves the reduced linear system using a dense Cholesky solver; - // based on Eigen. - DENSE_SCHUR, - - // Solves the reduced linear system using a sparse Cholesky solver; - // based on CHOLMOD. - SPARSE_SCHUR, - - // Solves the reduced linear system using Conjugate Gradients, based - // on a new Ceres implementation. Suitable for large scale - // problems. - ITERATIVE_SCHUR, - - // Conjugate gradients on the normal equations. - CGNR -}; - -enum PreconditionerType { - // Trivial preconditioner - the identity matrix. - IDENTITY, - - // Block diagonal of the Gauss-Newton Hessian. - JACOBI, - - // Note: The following three preconditioners can only be used with - // the ITERATIVE_SCHUR solver. They are well suited for Structure - // from Motion problems. - - // Block diagonal of the Schur complement. This preconditioner may - // only be used with the ITERATIVE_SCHUR solver. - SCHUR_JACOBI, - - // Visibility clustering based preconditioners. - // - // The following two preconditioners use the visibility structure of - // the scene to determine the sparsity structure of the - // preconditioner. This is done using a clustering algorithm. The - // available visibility clustering algorithms are described below. - // - // Note: Requires SuiteSparse. - CLUSTER_JACOBI, - CLUSTER_TRIDIAGONAL -}; - -enum VisibilityClusteringType { - // Canonical views algorithm as described in - // - // "Scene Summarization for Online Image Collections", Ian Simon, Noah - // Snavely, Steven M. Seitz, ICCV 2007. - // - // This clustering algorithm can be quite slow, but gives high - // quality clusters. The original visibility based clustering paper - // used this algorithm. - CANONICAL_VIEWS, - - // The classic single linkage algorithm. It is extremely fast as - // compared to CANONICAL_VIEWS, but can give slightly poorer - // results. For problems with large number of cameras though, this - // is generally a pretty good option. - // - // If you are using SCHUR_JACOBI preconditioner and have SuiteSparse - // available, CLUSTER_JACOBI and CLUSTER_TRIDIAGONAL in combination - // with the SINGLE_LINKAGE algorithm will generally give better - // results. - SINGLE_LINKAGE -}; - -enum SparseLinearAlgebraLibraryType { - // High performance sparse Cholesky factorization and approximate - // minimum degree ordering. - SUITE_SPARSE, - - // A lightweight replacment for SuiteSparse, which does not require - // a LAPACK/BLAS implementation. Consequently, its performance is - // also a bit lower than SuiteSparse. - CX_SPARSE, - - // Eigen's sparse linear algebra routines. In particular Ceres uses - // the Simplicial LDLT routines. - EIGEN_SPARSE -}; - -enum DenseLinearAlgebraLibraryType { - EIGEN, - LAPACK -}; - -// Logging options -// The options get progressively noisier. -enum LoggingType { - SILENT, - PER_MINIMIZER_ITERATION -}; - -enum MinimizerType { - LINE_SEARCH, - TRUST_REGION -}; - -enum LineSearchDirectionType { - // Negative of the gradient. - STEEPEST_DESCENT, - - // A generalization of the Conjugate Gradient method to non-linear - // functions. The generalization can be performed in a number of - // different ways, resulting in a variety of search directions. The - // precise choice of the non-linear conjugate gradient algorithm - // used is determined by NonlinerConjuateGradientType. - NONLINEAR_CONJUGATE_GRADIENT, - - // BFGS, and it's limited memory approximation L-BFGS, are quasi-Newton - // algorithms that approximate the Hessian matrix by iteratively refining - // an initial estimate with rank-one updates using the gradient at each - // iteration. They are a generalisation of the Secant method and satisfy - // the Secant equation. The Secant equation has an infinium of solutions - // in multiple dimensions, as there are N*(N+1)/2 degrees of freedom in a - // symmetric matrix but only N conditions are specified by the Secant - // equation. The requirement that the Hessian approximation be positive - // definite imposes another N additional constraints, but that still leaves - // remaining degrees-of-freedom. (L)BFGS methods uniquely deteremine the - // approximate Hessian by imposing the additional constraints that the - // approximation at the next iteration must be the 'closest' to the current - // approximation (the nature of how this proximity is measured is actually - // the defining difference between a family of quasi-Newton methods including - // (L)BFGS & DFP). (L)BFGS is currently regarded as being the best known - // general quasi-Newton method. - // - // The principal difference between BFGS and L-BFGS is that whilst BFGS - // maintains a full, dense approximation to the (inverse) Hessian, L-BFGS - // maintains only a window of the last M observations of the parameters and - // gradients. Using this observation history, the calculation of the next - // search direction can be computed without requiring the construction of the - // full dense inverse Hessian approximation. This is particularly important - // for problems with a large number of parameters, where storage of an N-by-N - // matrix in memory would be prohibitive. - // - // For more details on BFGS see: - // - // Broyden, C.G., "The Convergence of a Class of Double-rank Minimization - // Algorithms,"; J. Inst. Maths. Applics., Vol. 6, pp 76–90, 1970. - // - // Fletcher, R., "A New Approach to Variable Metric Algorithms," - // Computer Journal, Vol. 13, pp 317–322, 1970. - // - // Goldfarb, D., "A Family of Variable Metric Updates Derived by Variational - // Means," Mathematics of Computing, Vol. 24, pp 23–26, 1970. - // - // Shanno, D.F., "Conditioning of Quasi-Newton Methods for Function - // Minimization," Mathematics of Computing, Vol. 24, pp 647–656, 1970. - // - // For more details on L-BFGS see: - // - // Nocedal, J. (1980). "Updating Quasi-Newton Matrices with Limited - // Storage". Mathematics of Computation 35 (151): 773–782. - // - // Byrd, R. H.; Nocedal, J.; Schnabel, R. B. (1994). - // "Representations of Quasi-Newton Matrices and their use in - // Limited Memory Methods". Mathematical Programming 63 (4): - // 129–156. - // - // A general reference for both methods: - // - // Nocedal J., Wright S., Numerical Optimization, 2nd Ed. Springer, 1999. - LBFGS, - BFGS, -}; - -// Nonliner conjugate gradient methods are a generalization of the -// method of Conjugate Gradients for linear systems. The -// generalization can be carried out in a number of different ways -// leading to number of different rules for computing the search -// direction. Ceres provides a number of different variants. For more -// details see Numerical Optimization by Nocedal & Wright. -enum NonlinearConjugateGradientType { - FLETCHER_REEVES, - POLAK_RIBIERE, - HESTENES_STIEFEL, -}; - -enum LineSearchType { - // Backtracking line search with polynomial interpolation or - // bisection. - ARMIJO, - WOLFE, -}; - -// Ceres supports different strategies for computing the trust region -// step. -enum TrustRegionStrategyType { - // The default trust region strategy is to use the step computation - // used in the Levenberg-Marquardt algorithm. For more details see - // levenberg_marquardt_strategy.h - LEVENBERG_MARQUARDT, - - // Powell's dogleg algorithm interpolates between the Cauchy point - // and the Gauss-Newton step. It is particularly useful if the - // LEVENBERG_MARQUARDT algorithm is making a large number of - // unsuccessful steps. For more details see dogleg_strategy.h. - // - // NOTES: - // - // 1. This strategy has not been experimented with or tested as - // extensively as LEVENBERG_MARQUARDT, and therefore it should be - // considered EXPERIMENTAL for now. - // - // 2. For now this strategy should only be used with exact - // factorization based linear solvers, i.e., SPARSE_SCHUR, - // DENSE_SCHUR, DENSE_QR and SPARSE_NORMAL_CHOLESKY. - DOGLEG -}; - -// Ceres supports two different dogleg strategies. -// The "traditional" dogleg method by Powell and the -// "subspace" method described in -// R. H. Byrd, R. B. Schnabel, and G. A. Shultz, -// "Approximate solution of the trust region problem by minimization -// over two-dimensional subspaces", Mathematical Programming, -// 40 (1988), pp. 247--263 -enum DoglegType { - // The traditional approach constructs a dogleg path - // consisting of two line segments and finds the furthest - // point on that path that is still inside the trust region. - TRADITIONAL_DOGLEG, - - // The subspace approach finds the exact minimum of the model - // constrained to the subspace spanned by the dogleg path. - SUBSPACE_DOGLEG -}; - -enum TerminationType { - // Minimizer terminated because one of the convergence criterion set - // by the user was satisfied. - // - // 1. (new_cost - old_cost) < function_tolerance * old_cost; - // 2. max_i |gradient_i| < gradient_tolerance - // 3. |step|_2 <= parameter_tolerance * ( |x|_2 + parameter_tolerance) - // - // The user's parameter blocks will be updated with the solution. - CONVERGENCE, - - // The solver ran for maximum number of iterations or maximum amount - // of time specified by the user, but none of the convergence - // criterion specified by the user were met. The user's parameter - // blocks will be updated with the solution found so far. - NO_CONVERGENCE, - - // The minimizer terminated because of an error. The user's - // parameter blocks will not be updated. - FAILURE, - - // Using an IterationCallback object, user code can control the - // minimizer. The following enums indicate that the user code was - // responsible for termination. - // - // Minimizer terminated successfully because a user - // IterationCallback returned SOLVER_TERMINATE_SUCCESSFULLY. - // - // The user's parameter blocks will be updated with the solution. - USER_SUCCESS, - - // Minimizer terminated because because a user IterationCallback - // returned SOLVER_ABORT. - // - // The user's parameter blocks will not be updated. - USER_FAILURE -}; - -// Enums used by the IterationCallback instances to indicate to the -// solver whether it should continue solving, the user detected an -// error or the solution is good enough and the solver should -// terminate. -enum CallbackReturnType { - // Continue solving to next iteration. - SOLVER_CONTINUE, - - // Terminate solver, and do not update the parameter blocks upon - // return. Unless the user has set - // Solver:Options:::update_state_every_iteration, in which case the - // state would have been updated every iteration - // anyways. Solver::Summary::termination_type is set to USER_ABORT. - SOLVER_ABORT, - - // Terminate solver, update state and - // return. Solver::Summary::termination_type is set to USER_SUCCESS. - SOLVER_TERMINATE_SUCCESSFULLY -}; - -// The format in which linear least squares problems should be logged -// when Solver::Options::lsqp_iterations_to_dump is non-empty. -enum DumpFormatType { - // Print the linear least squares problem in a human readable format - // to stderr. The Jacobian is printed as a dense matrix. The vectors - // D, x and f are printed as dense vectors. This should only be used - // for small problems. - CONSOLE, - - // Write out the linear least squares problem to the directory - // pointed to by Solver::Options::lsqp_dump_directory as text files - // which can be read into MATLAB/Octave. The Jacobian is dumped as a - // text file containing (i,j,s) triplets, the vectors D, x and f are - // dumped as text files containing a list of their values. - // - // A MATLAB/octave script called lm_iteration_???.m is also output, - // which can be used to parse and load the problem into memory. - TEXTFILE -}; - -// For SizedCostFunction and AutoDiffCostFunction, DYNAMIC can be -// specified for the number of residuals. If specified, then the -// number of residuas for that cost function can vary at runtime. -enum DimensionType { - DYNAMIC = -1 -}; - -enum NumericDiffMethod { - CENTRAL, - FORWARD -}; - -enum LineSearchInterpolationType { - BISECTION, - QUADRATIC, - CUBIC -}; - -enum CovarianceAlgorithmType { - DENSE_SVD, - SUITE_SPARSE_QR, - EIGEN_SPARSE_QR -}; - -CERES_EXPORT const char* LinearSolverTypeToString( - LinearSolverType type); -CERES_EXPORT bool StringToLinearSolverType(string value, - LinearSolverType* type); - -CERES_EXPORT const char* PreconditionerTypeToString(PreconditionerType type); -CERES_EXPORT bool StringToPreconditionerType(string value, - PreconditionerType* type); - -CERES_EXPORT const char* VisibilityClusteringTypeToString( - VisibilityClusteringType type); -CERES_EXPORT bool StringToVisibilityClusteringType(string value, - VisibilityClusteringType* type); - -CERES_EXPORT const char* SparseLinearAlgebraLibraryTypeToString( - SparseLinearAlgebraLibraryType type); -CERES_EXPORT bool StringToSparseLinearAlgebraLibraryType( - string value, - SparseLinearAlgebraLibraryType* type); - -CERES_EXPORT const char* DenseLinearAlgebraLibraryTypeToString( - DenseLinearAlgebraLibraryType type); -CERES_EXPORT bool StringToDenseLinearAlgebraLibraryType( - string value, - DenseLinearAlgebraLibraryType* type); - -CERES_EXPORT const char* TrustRegionStrategyTypeToString( - TrustRegionStrategyType type); -CERES_EXPORT bool StringToTrustRegionStrategyType(string value, - TrustRegionStrategyType* type); - -CERES_EXPORT const char* DoglegTypeToString(DoglegType type); -CERES_EXPORT bool StringToDoglegType(string value, DoglegType* type); - -CERES_EXPORT const char* MinimizerTypeToString(MinimizerType type); -CERES_EXPORT bool StringToMinimizerType(string value, MinimizerType* type); - -CERES_EXPORT const char* LineSearchDirectionTypeToString( - LineSearchDirectionType type); -CERES_EXPORT bool StringToLineSearchDirectionType(string value, - LineSearchDirectionType* type); - -CERES_EXPORT const char* LineSearchTypeToString(LineSearchType type); -CERES_EXPORT bool StringToLineSearchType(string value, LineSearchType* type); - -CERES_EXPORT const char* NonlinearConjugateGradientTypeToString( - NonlinearConjugateGradientType type); -CERES_EXPORT bool StringToNonlinearConjugateGradientType( - string value, - NonlinearConjugateGradientType* type); - -CERES_EXPORT const char* LineSearchInterpolationTypeToString( - LineSearchInterpolationType type); -CERES_EXPORT bool StringToLineSearchInterpolationType( - string value, - LineSearchInterpolationType* type); - -CERES_EXPORT const char* CovarianceAlgorithmTypeToString( - CovarianceAlgorithmType type); -CERES_EXPORT bool StringToCovarianceAlgorithmType( - string value, - CovarianceAlgorithmType* type); - -CERES_EXPORT const char* TerminationTypeToString(TerminationType type); - -CERES_EXPORT bool IsSchurType(LinearSolverType type); -CERES_EXPORT bool IsSparseLinearAlgebraLibraryTypeAvailable( - SparseLinearAlgebraLibraryType type); -CERES_EXPORT bool IsDenseLinearAlgebraLibraryTypeAvailable( - DenseLinearAlgebraLibraryType type); - -} // namespace ceres - -#include "ceres/internal/reenable_warnings.h" - -#endif // CERES_PUBLIC_TYPES_H_ |