internal/ceres/linear_solver.h - ceres-solver - Git at Google

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2015 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)
 //
 // Abstract interface for objects solving linear systems of various
 // kinds.

 #ifndef CERES_INTERNAL_LINEAR_SOLVER_H_
 #define CERES_INTERNAL_LINEAR_SOLVER_H_

 #include <cstddef>
 #include <map>
 #include <string>
 #include <vector>
 #include "ceres/block_sparse_matrix.h"
 #include "ceres/casts.h"
 #include "ceres/compressed_row_sparse_matrix.h"
 #include "ceres/dense_sparse_matrix.h"
 #include "ceres/execution_summary.h"
 #include "ceres/triplet_sparse_matrix.h"
 #include "ceres/types.h"
 #include "glog/logging.h"

 namespace ceres {
 namespace internal {

 enum LinearSolverTerminationType {
   // Termination criterion was met.
   LINEAR_SOLVER_SUCCESS,

   // Solver ran for max_num_iterations and terminated before the
   // termination tolerance could be satisfied.
   LINEAR_SOLVER_NO_CONVERGENCE,

   // Solver was terminated due to numerical problems, generally due to
   // the linear system being poorly conditioned.
   LINEAR_SOLVER_FAILURE,

   // Solver failed with a fatal error that cannot be recovered from,
   // e.g. CHOLMOD ran out of memory when computing the symbolic or
   // numeric factorization or an underlying library was called with
   // the wrong arguments.
   LINEAR_SOLVER_FATAL_ERROR
 };

 // This enum controls the fill-reducing ordering a sparse linear
 // algebra library should use before computing a sparse factorization
 // (usually Cholesky).
 enum OrderingType {
   NATURAL, // Do not re-order the matrix. This is useful when the
            // matrix has been ordered using a fill-reducing ordering
            // already.
   AMD      // Use the Approximate Minimum Degree algorithm to re-order
            // the matrix.
 };

 class LinearOperator;

 // Abstract base class for objects that implement algorithms for
 // solving linear systems
 //
 //   Ax = b
 //
 // It is expected that a single instance of a LinearSolver object
 // maybe used multiple times for solving multiple linear systems with
 // the same sparsity structure. This allows them to cache and reuse
 // information across solves. This means that calling Solve on the
 // same LinearSolver instance with two different linear systems will
 // result in undefined behaviour.
 //
 // Subclasses of LinearSolver use two structs to configure themselves.
 // The Options struct configures the LinearSolver object for its
 // lifetime. The PerSolveOptions struct is used to specify options for
 // a particular Solve call.
 class LinearSolver {
  public:
   struct Options {
     Options()
         : type(SPARSE_NORMAL_CHOLESKY),
           preconditioner_type(JACOBI),
           visibility_clustering_type(CANONICAL_VIEWS),
           dense_linear_algebra_library_type(EIGEN),
           sparse_linear_algebra_library_type(SUITE_SPARSE),
           use_postordering(false),
           dynamic_sparsity(false),
           use_explicit_schur_complement(false),
           min_num_iterations(1),
           max_num_iterations(1),
           num_threads(1),
           residual_reset_period(10),
           row_block_size(Eigen::Dynamic),
           e_block_size(Eigen::Dynamic),
           f_block_size(Eigen::Dynamic) {
     }

     LinearSolverType type;
     PreconditionerType preconditioner_type;
     VisibilityClusteringType visibility_clustering_type;
     DenseLinearAlgebraLibraryType dense_linear_algebra_library_type;
     SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type;

     // See solver.h for information about these flags.
     bool use_postordering;
     bool dynamic_sparsity;
     bool use_explicit_schur_complement;

     // Number of internal iterations that the solver uses. This
     // parameter only makes sense for iterative solvers like CG.
     int min_num_iterations;
     int max_num_iterations;

     // If possible, how many threads can the solver use.
     int num_threads;

     // Hints about the order in which the parameter blocks should be
     // eliminated by the linear solver.
     //
     // For example if elimination_groups is a vector of size k, then
     // the linear solver is informed that it should eliminate the
     // parameter blocks 0 ... elimination_groups[0] - 1 first, and
     // then elimination_groups[0] ... elimination_groups[1] - 1 and so
     // on. Within each elimination group, the linear solver is free to
     // choose how the parameter blocks are ordered. Different linear
     // solvers have differing requirements on elimination_groups.
     //
     // The most common use is for Schur type solvers, where there
     // should be at least two elimination groups and the first
     // elimination group must form an independent set in the normal
     // equations. The first elimination group corresponds to the
     // num_eliminate_blocks in the Schur type solvers.
     std::vector<int> elimination_groups;

     // Iterative solvers, e.g. Preconditioned Conjugate Gradients
     // maintain a cheap estimate of the residual which may become
     // inaccurate over time. Thus for non-zero values of this
     // parameter, the solver can be told to recalculate the value of
     // the residual using a |b - Ax| evaluation.
     int residual_reset_period;

     // If the block sizes in a BlockSparseMatrix are fixed, then in
     // some cases the Schur complement based solvers can detect and
     // specialize on them.
     //
     // It is expected that these parameters are set programmatically
     // rather than manually.
     //
     // Please see schur_complement_solver.h and schur_eliminator.h for
     // more details.
     int row_block_size;
     int e_block_size;
     int f_block_size;
   };

   // Options for the Solve method.
   struct PerSolveOptions {
     PerSolveOptions()
         : D(NULL),
           preconditioner(NULL),
           r_tolerance(0.0),
           q_tolerance(0.0) {
     }

     // This option only makes sense for unsymmetric linear solvers
     // that can solve rectangular linear systems.
     //
     // Given a matrix A, an optional diagonal matrix D as a vector,
     // and a vector b, the linear solver will solve for
     //
     //   | A | x = | b |
     //   | D |     | 0 |
     //
     // If D is null, then it is treated as zero, and the solver returns
     // the solution to
     //
     //   A x = b
     //
     // In either case, x is the vector that solves the following
     // optimization problem.
     //
     //   arg min_x ||Ax - b||^2 + ||Dx||^2
     //
     // Here A is a matrix of size m x n, with full column rank. If A
     // does not have full column rank, the results returned by the
     // solver cannot be relied on. D, if it is not null is an array of
     // size n.  b is an array of size m and x is an array of size n.
     double * D;

     // This option only makes sense for iterative solvers.
     //
     // In general the performance of an iterative linear solver
     // depends on the condition number of the matrix A. For example
     // the convergence rate of the conjugate gradients algorithm
     // is proportional to the square root of the condition number.
     //
     // One particularly useful technique for improving the
     // conditioning of a linear system is to precondition it. In its
     // simplest form a preconditioner is a matrix M such that instead
     // of solving Ax = b, we solve the linear system AM^{-1} y = b
     // instead, where M is such that the condition number k(AM^{-1})
     // is smaller than the conditioner k(A). Given the solution to
     // this system, x = M^{-1} y. The iterative solver takes care of
     // the mechanics of solving the preconditioned system and
     // returning the corrected solution x. The user only needs to
     // supply a linear operator.
     //
     // A null preconditioner is equivalent to an identity matrix being
     // used a preconditioner.
     LinearOperator* preconditioner;


     // The following tolerance related options only makes sense for
     // iterative solvers. Direct solvers ignore them.

     // Solver terminates when
     //
     //   |Ax - b| <= r_tolerance * |b|.
     //
     // This is the most commonly used termination criterion for
     // iterative solvers.
     double r_tolerance;

     // For PSD matrices A, let
     //
     //   Q(x) = x'Ax - 2b'x
     //
     // be the cost of the quadratic function defined by A and b. Then,
     // the solver terminates at iteration i if
     //
     //   i * (Q(x_i) - Q(x_i-1)) / Q(x_i) < q_tolerance.
     //
     // This termination criterion is more useful when using CG to
     // solve the Newton step. This particular convergence test comes
     // from Stephen Nash's work on truncated Newton
     // methods. References:
     //
     //   1. Stephen G. Nash & Ariela Sofer, Assessing A Search
     //      Direction Within A Truncated Newton Method, Operation
     //      Research Letters 9(1990) 219-221.
     //
     //   2. Stephen G. Nash, A Survey of Truncated Newton Methods,
     //      Journal of Computational and Applied Mathematics,
     //      124(1-2), 45-59, 2000.
     //
     double q_tolerance;
   };

   // Summary of a call to the Solve method. We should move away from
   // the true/false method for determining solver success. We should
   // let the summary object do the talking.
   struct Summary {
     Summary()
         : residual_norm(0.0),
           num_iterations(-1),
           termination_type(LINEAR_SOLVER_FAILURE) {
     }

     double residual_norm;
     int num_iterations;
     LinearSolverTerminationType termination_type;
     std::string message;
   };

   // If the optimization problem is such that there are no remaining
   // e-blocks, a Schur type linear solver cannot be used. If the
   // linear solver is of Schur type, this function implements a policy
   // to select an alternate nearest linear solver to the one selected
   // by the user. The input linear_solver_type is returned otherwise.
   static LinearSolverType LinearSolverForZeroEBlocks(
       LinearSolverType linear_solver_type);

   virtual ~LinearSolver();

   // Solve Ax = b.
   virtual Summary Solve(LinearOperator* A,
                         const double* b,
                         const PerSolveOptions& per_solve_options,
                         double* x) = 0;

   // This method returns copies instead of references so that the base
   // class implementation does not have to worry about life time
   // issues. Further, this calls are not expected to be frequent or
   // performance sensitive.
   virtual std::map<std::string, CallStatistics> Statistics() const {
     return std::map<std::string, CallStatistics>();
   }

   // Factory
   static LinearSolver* Create(const Options& options);
 };

 // This templated subclass of LinearSolver serves as a base class for
 // other linear solvers that depend on the particular matrix layout of
 // the underlying linear operator. For example some linear solvers
 // need low level access to the TripletSparseMatrix implementing the
 // LinearOperator interface. This class hides those implementation
 // details behind a private virtual method, and has the Solve method
 // perform the necessary upcasting.
 template <typename MatrixType>
 class TypedLinearSolver : public LinearSolver {
  public:
   virtual ~TypedLinearSolver() {}
   virtual LinearSolver::Summary Solve(
       LinearOperator* A,
       const double* b,
       const LinearSolver::PerSolveOptions& per_solve_options,
       double* x) {
     ScopedExecutionTimer total_time("LinearSolver::Solve", &execution_summary_);
     CHECK_NOTNULL(A);
     CHECK_NOTNULL(b);
     CHECK_NOTNULL(x);
     return SolveImpl(down_cast<MatrixType*>(A), b, per_solve_options, x);
   }

   virtual std::map<std::string, CallStatistics> Statistics() const {
     return execution_summary_.statistics();
   }

  private:
   virtual LinearSolver::Summary SolveImpl(
       MatrixType* A,
       const double* b,
       const LinearSolver::PerSolveOptions& per_solve_options,
       double* x) = 0;

   ExecutionSummary execution_summary_;
 };

 // Linear solvers that depend on acccess to the low level structure of
 // a SparseMatrix.
 typedef TypedLinearSolver<BlockSparseMatrix>         BlockSparseMatrixSolver;          // NOLINT
 typedef TypedLinearSolver<CompressedRowSparseMatrix> CompressedRowSparseMatrixSolver;  // NOLINT
 typedef TypedLinearSolver<DenseSparseMatrix>         DenseSparseMatrixSolver;          // NOLINT
 typedef TypedLinearSolver<TripletSparseMatrix>       TripletSparseMatrixSolver;        // NOLINT

 }  // namespace internal
 }  // namespace ceres

 #endif  // CERES_INTERNAL_LINEAR_SOLVER_H_
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2015 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)
	//
	// Abstract interface for objects solving linear systems of various
	// kinds.

	#ifndef CERES_INTERNAL_LINEAR_SOLVER_H_
	#define CERES_INTERNAL_LINEAR_SOLVER_H_

	#include <cstddef>
	#include <map>
	#include <string>
	#include <vector>
	#include "ceres/block_sparse_matrix.h"
	#include "ceres/casts.h"
	#include "ceres/compressed_row_sparse_matrix.h"
	#include "ceres/dense_sparse_matrix.h"
	#include "ceres/execution_summary.h"
	#include "ceres/triplet_sparse_matrix.h"
	#include "ceres/types.h"
	#include "glog/logging.h"

	namespace ceres {
	namespace internal {

	enum LinearSolverTerminationType {
	// Termination criterion was met.
	LINEAR_SOLVER_SUCCESS,

	// Solver ran for max_num_iterations and terminated before the
	// termination tolerance could be satisfied.
	LINEAR_SOLVER_NO_CONVERGENCE,

	// Solver was terminated due to numerical problems, generally due to
	// the linear system being poorly conditioned.
	LINEAR_SOLVER_FAILURE,

	// Solver failed with a fatal error that cannot be recovered from,
	// e.g. CHOLMOD ran out of memory when computing the symbolic or
	// numeric factorization or an underlying library was called with
	// the wrong arguments.
	LINEAR_SOLVER_FATAL_ERROR
	};

	// This enum controls the fill-reducing ordering a sparse linear
	// algebra library should use before computing a sparse factorization
	// (usually Cholesky).
	enum OrderingType {
	NATURAL, // Do not re-order the matrix. This is useful when the
	// matrix has been ordered using a fill-reducing ordering
	// already.
	AMD // Use the Approximate Minimum Degree algorithm to re-order
	// the matrix.
	};

	class LinearOperator;

	// Abstract base class for objects that implement algorithms for
	// solving linear systems
	//
	// Ax = b
	//
	// It is expected that a single instance of a LinearSolver object
	// maybe used multiple times for solving multiple linear systems with
	// the same sparsity structure. This allows them to cache and reuse
	// information across solves. This means that calling Solve on the
	// same LinearSolver instance with two different linear systems will
	// result in undefined behaviour.
	//
	// Subclasses of LinearSolver use two structs to configure themselves.
	// The Options struct configures the LinearSolver object for its
	// lifetime. The PerSolveOptions struct is used to specify options for
	// a particular Solve call.
	class LinearSolver {
	public:
	struct Options {
	Options()
	: type(SPARSE_NORMAL_CHOLESKY),
	preconditioner_type(JACOBI),
	visibility_clustering_type(CANONICAL_VIEWS),
	dense_linear_algebra_library_type(EIGEN),
	sparse_linear_algebra_library_type(SUITE_SPARSE),
	use_postordering(false),
	dynamic_sparsity(false),
	use_explicit_schur_complement(false),
	min_num_iterations(1),
	max_num_iterations(1),
	num_threads(1),
	residual_reset_period(10),
	row_block_size(Eigen::Dynamic),
	e_block_size(Eigen::Dynamic),
	f_block_size(Eigen::Dynamic) {
	}

	LinearSolverType type;
	PreconditionerType preconditioner_type;
	VisibilityClusteringType visibility_clustering_type;
	DenseLinearAlgebraLibraryType dense_linear_algebra_library_type;
	SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type;

	// See solver.h for information about these flags.
	bool use_postordering;
	bool dynamic_sparsity;
	bool use_explicit_schur_complement;

	// Number of internal iterations that the solver uses. This
	// parameter only makes sense for iterative solvers like CG.
	int min_num_iterations;
	int max_num_iterations;

	// If possible, how many threads can the solver use.
	int num_threads;

	// Hints about the order in which the parameter blocks should be
	// eliminated by the linear solver.
	//
	// For example if elimination_groups is a vector of size k, then
	// the linear solver is informed that it should eliminate the
	// parameter blocks 0 ... elimination_groups[0] - 1 first, and
	// then elimination_groups[0] ... elimination_groups[1] - 1 and so
	// on. Within each elimination group, the linear solver is free to
	// choose how the parameter blocks are ordered. Different linear
	// solvers have differing requirements on elimination_groups.
	//
	// The most common use is for Schur type solvers, where there
	// should be at least two elimination groups and the first
	// elimination group must form an independent set in the normal
	// equations. The first elimination group corresponds to the
	// num_eliminate_blocks in the Schur type solvers.
	std::vector<int> elimination_groups;

	// Iterative solvers, e.g. Preconditioned Conjugate Gradients
	// maintain a cheap estimate of the residual which may become
	// inaccurate over time. Thus for non-zero values of this
	// parameter, the solver can be told to recalculate the value of
	// the residual using a \|b - Ax\| evaluation.
	int residual_reset_period;

	// If the block sizes in a BlockSparseMatrix are fixed, then in
	// some cases the Schur complement based solvers can detect and
	// specialize on them.
	//
	// It is expected that these parameters are set programmatically
	// rather than manually.
	//
	// Please see schur_complement_solver.h and schur_eliminator.h for
	// more details.
	int row_block_size;
	int e_block_size;
	int f_block_size;
	};

	// Options for the Solve method.
	struct PerSolveOptions {
	PerSolveOptions()
	: D(NULL),
	preconditioner(NULL),
	r_tolerance(0.0),
	q_tolerance(0.0) {
	}

	// This option only makes sense for unsymmetric linear solvers
	// that can solve rectangular linear systems.
	//
	// Given a matrix A, an optional diagonal matrix D as a vector,
	// and a vector b, the linear solver will solve for
	//
	// \| A \| x = \| b \|
	// \| D \| \| 0 \|
	//
	// If D is null, then it is treated as zero, and the solver returns
	// the solution to
	//
	// A x = b
	//
	// In either case, x is the vector that solves the following
	// optimization problem.
	//
	// arg min_x \|\|Ax - b\|\|^2 + \|\|Dx\|\|^2
	//
	// Here A is a matrix of size m x n, with full column rank. If A
	// does not have full column rank, the results returned by the
	// solver cannot be relied on. D, if it is not null is an array of
	// size n. b is an array of size m and x is an array of size n.
	double * D;

	// This option only makes sense for iterative solvers.
	//
	// In general the performance of an iterative linear solver
	// depends on the condition number of the matrix A. For example
	// the convergence rate of the conjugate gradients algorithm
	// is proportional to the square root of the condition number.
	//
	// One particularly useful technique for improving the
	// conditioning of a linear system is to precondition it. In its
	// simplest form a preconditioner is a matrix M such that instead
	// of solving Ax = b, we solve the linear system AM^{-1} y = b
	// instead, where M is such that the condition number k(AM^{-1})
	// is smaller than the conditioner k(A). Given the solution to
	// this system, x = M^{-1} y. The iterative solver takes care of
	// the mechanics of solving the preconditioned system and
	// returning the corrected solution x. The user only needs to
	// supply a linear operator.
	//
	// A null preconditioner is equivalent to an identity matrix being
	// used a preconditioner.
	LinearOperator* preconditioner;


	// The following tolerance related options only makes sense for
	// iterative solvers. Direct solvers ignore them.

	// Solver terminates when
	//
	// \|Ax - b\| <= r_tolerance * \|b\|.
	//
	// This is the most commonly used termination criterion for
	// iterative solvers.
	double r_tolerance;

	// For PSD matrices A, let
	//
	// Q(x) = x'Ax - 2b'x
	//
	// be the cost of the quadratic function defined by A and b. Then,
	// the solver terminates at iteration i if
	//
	// i * (Q(x_i) - Q(x_i-1)) / Q(x_i) < q_tolerance.
	//
	// This termination criterion is more useful when using CG to
	// solve the Newton step. This particular convergence test comes
	// from Stephen Nash's work on truncated Newton
	// methods. References:
	//
	// 1. Stephen G. Nash & Ariela Sofer, Assessing A Search
	// Direction Within A Truncated Newton Method, Operation
	// Research Letters 9(1990) 219-221.
	//
	// 2. Stephen G. Nash, A Survey of Truncated Newton Methods,
	// Journal of Computational and Applied Mathematics,
	// 124(1-2), 45-59, 2000.
	//
	double q_tolerance;
	};

	// Summary of a call to the Solve method. We should move away from
	// the true/false method for determining solver success. We should
	// let the summary object do the talking.
	struct Summary {
	Summary()
	: residual_norm(0.0),
	num_iterations(-1),
	termination_type(LINEAR_SOLVER_FAILURE) {
	}

	double residual_norm;
	int num_iterations;
	LinearSolverTerminationType termination_type;
	std::string message;
	};

	// If the optimization problem is such that there are no remaining
	// e-blocks, a Schur type linear solver cannot be used. If the
	// linear solver is of Schur type, this function implements a policy
	// to select an alternate nearest linear solver to the one selected
	// by the user. The input linear_solver_type is returned otherwise.
	static LinearSolverType LinearSolverForZeroEBlocks(
	LinearSolverType linear_solver_type);

	virtual ~LinearSolver();

	// Solve Ax = b.
	virtual Summary Solve(LinearOperator* A,
	const double* b,
	const PerSolveOptions& per_solve_options,
	double* x) = 0;

	// This method returns copies instead of references so that the base
	// class implementation does not have to worry about life time
	// issues. Further, this calls are not expected to be frequent or
	// performance sensitive.
	virtual std::map<std::string, CallStatistics> Statistics() const {
	return std::map<std::string, CallStatistics>();
	}

	// Factory
	static LinearSolver* Create(const Options& options);
	};

	// This templated subclass of LinearSolver serves as a base class for
	// other linear solvers that depend on the particular matrix layout of
	// the underlying linear operator. For example some linear solvers
	// need low level access to the TripletSparseMatrix implementing the
	// LinearOperator interface. This class hides those implementation
	// details behind a private virtual method, and has the Solve method
	// perform the necessary upcasting.
	template <typename MatrixType>
	class TypedLinearSolver : public LinearSolver {
	public:
	virtual ~TypedLinearSolver() {}
	virtual LinearSolver::Summary Solve(
	LinearOperator* A,
	const double* b,
	const LinearSolver::PerSolveOptions& per_solve_options,
	double* x) {
	ScopedExecutionTimer total_time("LinearSolver::Solve", &execution_summary_);
	CHECK_NOTNULL(A);
	CHECK_NOTNULL(b);
	CHECK_NOTNULL(x);
	return SolveImpl(down_cast<MatrixType*>(A), b, per_solve_options, x);
	}

	virtual std::map<std::string, CallStatistics> Statistics() const {
	return execution_summary_.statistics();
	}

	private:
	virtual LinearSolver::Summary SolveImpl(
	MatrixType* A,
	const double* b,
	const LinearSolver::PerSolveOptions& per_solve_options,
	double* x) = 0;

	ExecutionSummary execution_summary_;
	};

	// Linear solvers that depend on acccess to the low level structure of
	// a SparseMatrix.
	typedef TypedLinearSolver<BlockSparseMatrix> BlockSparseMatrixSolver; // NOLINT
	typedef TypedLinearSolver<CompressedRowSparseMatrix> CompressedRowSparseMatrixSolver; // NOLINT
	typedef TypedLinearSolver<DenseSparseMatrix> DenseSparseMatrixSolver; // NOLINT
	typedef TypedLinearSolver<TripletSparseMatrix> TripletSparseMatrixSolver; // NOLINT

	} // namespace internal
	} // namespace ceres

	#endif // CERES_INTERNAL_LINEAR_SOLVER_H_