ceres-solver / ceres-solver / e91995cce456d7edf404103bd3dc40794e13886e / . / include / ceres / types.h

// Ceres Solver - A fast non-linear least squares minimizer | |

// Copyright 2019 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 | |

// 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/disable_warnings.h" | |

#include "ceres/internal/export.h" | |

namespace ceres { | |

// 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. | |

CLUSTER_JACOBI, | |

CLUSTER_TRIDIAGONAL, | |

// Subset preconditioner is a general purpose preconditioner | |

// linear least squares problems. Given a set of residual blocks, | |

// it uses the corresponding subset of the rows of the Jacobian to | |

// construct a preconditioner. | |

// | |

// Suppose the Jacobian J has been horizontally partitioned as | |

// | |

// J = [P] | |

// [Q] | |

// | |

// Where, Q is the set of rows corresponding to the residual | |

// blocks in residual_blocks_for_subset_preconditioner. | |

// | |

// The preconditioner is the inverse of the matrix Q'Q. | |

// | |

// Obviously, the efficacy of the preconditioner depends on how | |

// well the matrix Q approximates J'J, or how well the chosen | |

// residual blocks approximate the non-linear least squares | |

// problem. | |

SUBSET, | |

}; | |

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 replacement 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, | |

// Apple's Accelerate framework sparse linear algebra routines. | |

ACCELERATE_SPARSE, | |

// No sparse linear solver should be used. This does not necessarily | |

// imply that Ceres was built without any sparse library, although that | |

// is the likely use case, merely that one should not be used. | |

NO_SPARSE | |

}; | |

enum DenseLinearAlgebraLibraryType { | |

EIGEN, | |

LAPACK, | |

CUDA, | |

}; | |

// 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 determine 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, | |

}; | |

// Nonlinear 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, | |

}; | |

// The differentiation method used to compute numerical derivatives in | |

// NumericDiffCostFunction and DynamicNumericDiffCostFunction. | |

enum NumericDiffMethodType { | |

// Compute central finite difference: f'(x) ~ (f(x+h) - f(x-h)) / 2h. | |

CENTRAL, | |

// Compute forward finite difference: f'(x) ~ (f(x+h) - f(x)) / h. | |

FORWARD, | |

// Adaptive numerical differentiation using Ridders' method. Provides more | |

// accurate and robust derivatives at the expense of additional cost | |

// function evaluations. | |

RIDDERS | |

}; | |

enum LineSearchInterpolationType { | |

BISECTION, | |

QUADRATIC, | |

CUBIC, | |

}; | |

enum CovarianceAlgorithmType { | |

DENSE_SVD, | |

SPARSE_QR, | |

}; | |

// It is a near impossibility that user code generates this exact | |

// value in normal operation, thus we will use it to fill arrays | |

// before passing them to user code. If on return an element of the | |

// array still contains this value, we will assume that the user code | |

// did not write to that memory location. | |

const double kImpossibleValue = 1e302; | |

CERES_EXPORT const char* LinearSolverTypeToString(LinearSolverType type); | |

CERES_EXPORT bool StringToLinearSolverType(std::string value, | |

LinearSolverType* type); | |

CERES_EXPORT const char* PreconditionerTypeToString(PreconditionerType type); | |

CERES_EXPORT bool StringToPreconditionerType(std::string value, | |

PreconditionerType* type); | |

CERES_EXPORT const char* VisibilityClusteringTypeToString( | |

VisibilityClusteringType type); | |

CERES_EXPORT bool StringToVisibilityClusteringType( | |

std::string value, VisibilityClusteringType* type); | |

CERES_EXPORT const char* SparseLinearAlgebraLibraryTypeToString( | |

SparseLinearAlgebraLibraryType type); | |

CERES_EXPORT bool StringToSparseLinearAlgebraLibraryType( | |

std::string value, SparseLinearAlgebraLibraryType* type); | |

CERES_EXPORT const char* DenseLinearAlgebraLibraryTypeToString( | |

DenseLinearAlgebraLibraryType type); | |

CERES_EXPORT bool StringToDenseLinearAlgebraLibraryType( | |

std::string value, DenseLinearAlgebraLibraryType* type); | |

CERES_EXPORT const char* TrustRegionStrategyTypeToString( | |

TrustRegionStrategyType type); | |

CERES_EXPORT bool StringToTrustRegionStrategyType( | |

std::string value, TrustRegionStrategyType* type); | |

CERES_EXPORT const char* DoglegTypeToString(DoglegType type); | |

CERES_EXPORT bool StringToDoglegType(std::string value, DoglegType* type); | |

CERES_EXPORT const char* MinimizerTypeToString(MinimizerType type); | |

CERES_EXPORT bool StringToMinimizerType(std::string value, MinimizerType* type); | |

CERES_EXPORT const char* LineSearchDirectionTypeToString( | |

LineSearchDirectionType type); | |

CERES_EXPORT bool StringToLineSearchDirectionType( | |

std::string value, LineSearchDirectionType* type); | |

CERES_EXPORT const char* LineSearchTypeToString(LineSearchType type); | |

CERES_EXPORT bool StringToLineSearchType(std::string value, | |

LineSearchType* type); | |

CERES_EXPORT const char* NonlinearConjugateGradientTypeToString( | |

NonlinearConjugateGradientType type); | |

CERES_EXPORT bool StringToNonlinearConjugateGradientType( | |

std::string value, NonlinearConjugateGradientType* type); | |

CERES_EXPORT const char* LineSearchInterpolationTypeToString( | |

LineSearchInterpolationType type); | |

CERES_EXPORT bool StringToLineSearchInterpolationType( | |

std::string value, LineSearchInterpolationType* type); | |

CERES_EXPORT const char* CovarianceAlgorithmTypeToString( | |

CovarianceAlgorithmType type); | |

CERES_EXPORT bool StringToCovarianceAlgorithmType( | |

std::string value, CovarianceAlgorithmType* type); | |

CERES_EXPORT const char* NumericDiffMethodTypeToString( | |

NumericDiffMethodType type); | |

CERES_EXPORT bool StringToNumericDiffMethodType(std::string value, | |

NumericDiffMethodType* type); | |

CERES_EXPORT const char* LoggingTypeToString(LoggingType type); | |

CERES_EXPORT bool StringtoLoggingType(std::string value, LoggingType* type); | |

CERES_EXPORT const char* DumpFormatTypeToString(DumpFormatType type); | |

CERES_EXPORT bool StringtoDumpFormatType(std::string value, | |

DumpFormatType* type); | |

CERES_EXPORT bool StringtoDumpFormatType(std::string value, LoggingType* 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_ |