xref: /external/ceres-solver/include/ceres/problem.h

// 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)
//         keir@google.com (Keir Mierle)
//
// The Problem object is used to build and hold least squares problems.

#ifndef CERES_PUBLIC_PROBLEM_H_
#define CERES_PUBLIC_PROBLEM_H_

#include <cstddef>
#include <map>
#include <set>
#include <vector>

#include "ceres/internal/macros.h"
#include "ceres/internal/port.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/types.h"
#include "glog/logging.h"


namespace ceres {

class CostFunction;
class LossFunction;
class LocalParameterization;
class Solver;
struct CRSMatrix;

namespace internal {
class Preprocessor;
class ProblemImpl;
class ParameterBlock;
class ResidualBlock;
}  // namespace internal

// A ResidualBlockId is an opaque handle clients can use to remove residual
// blocks from a Problem after adding them.
typedef internal::ResidualBlock* ResidualBlockId;

// A class to represent non-linear least squares problems. Such
// problems have a cost function that is a sum of error terms (known
// as "residuals"), where each residual is a function of some subset
// of the parameters. The cost function takes the form
//
//    N    1
//   SUM  --- loss( || r_i1, r_i2,..., r_ik ||^2  ),
//   i=1   2
//
// where
//
//   r_ij     is residual number i, component j; the residual is a
//            function of some subset of the parameters x1...xk. For
//            example, in a structure from motion problem a residual
//            might be the difference between a measured point in an
//            image and the reprojected position for the matching
//            camera, point pair. The residual would have two
//            components, error in x and error in y.
//
//   loss(y)  is the loss function; for example, squared error or
//            Huber L1 loss. If loss(y) = y, then the cost function is
//            non-robustified least squares.
//
// This class is specifically designed to address the important subset
// of "sparse" least squares problems, where each component of the
// residual depends only on a small number number of parameters, even
// though the total number of residuals and parameters may be very
// large. This property affords tremendous gains in scale, allowing
// efficient solving of large problems that are otherwise
// inaccessible.
//
// The canonical example of a sparse least squares problem is
// "structure-from-motion" (SFM), where the parameters are points and
// cameras, and residuals are reprojection errors. Typically a single
// residual will depend only on 9 parameters (3 for the point, 6 for
// the camera).
//
// To create a least squares problem, use the AddResidualBlock() and
// AddParameterBlock() methods, documented below. Here is an example least
// squares problem containing 3 parameter blocks of sizes 3, 4 and 5
// respectively and two residual terms of size 2 and 6:
//
//   double x1[] = { 1.0, 2.0, 3.0 };
//   double x2[] = { 1.0, 2.0, 3.0, 5.0 };
//   double x3[] = { 1.0, 2.0, 3.0, 6.0, 7.0 };
//
//   Problem problem;
//
//   problem.AddResidualBlock(new MyUnaryCostFunction(...), x1);
//   problem.AddResidualBlock(new MyBinaryCostFunction(...), x2, x3);
//
// Please see cost_function.h for details of the CostFunction object.
class Problem {
 public:
  struct Options {
    Options()
        : cost_function_ownership(TAKE_OWNERSHIP),
          loss_function_ownership(TAKE_OWNERSHIP),
          local_parameterization_ownership(TAKE_OWNERSHIP),
          enable_fast_parameter_block_removal(false),
          disable_all_safety_checks(false) {}

    // These flags control whether the Problem object owns the cost
    // functions, loss functions, and parameterizations passed into
    // the Problem. If set to TAKE_OWNERSHIP, then the problem object
    // will delete the corresponding cost or loss functions on
    // destruction. The destructor is careful to delete the pointers
    // only once, since sharing cost/loss/parameterizations is
    // allowed.
    Ownership cost_function_ownership;
    Ownership loss_function_ownership;
    Ownership local_parameterization_ownership;

    // If true, trades memory for a faster RemoveParameterBlock() operation.
    //
    // RemoveParameterBlock() takes time proportional to the size of the entire
    // Problem. If you only remove parameter blocks from the Problem
    // occassionaly, this may be acceptable. However, if you are modifying the
    // Problem frequently, and have memory to spare, then flip this switch to
    // make RemoveParameterBlock() take time proportional to the number of
    // residual blocks that depend on it.  The increase in memory usage is an
    // additonal hash set per parameter block containing all the residuals that
    // depend on the parameter block.
    bool enable_fast_parameter_block_removal;

    // By default, Ceres performs a variety of safety checks when constructing
    // the problem. There is a small but measurable performance penalty to
    // these checks, typically around 5% of construction time. If you are sure
    // your problem construction is correct, and 5% of the problem construction
    // time is truly an overhead you want to avoid, then you can set
    // disable_all_safety_checks to true.
    //
    // WARNING: Do not set this to true, unless you are absolutely sure of what
    // you are doing.
    bool disable_all_safety_checks;
  };

  // The default constructor is equivalent to the
  // invocation Problem(Problem::Options()).
  Problem();
  explicit Problem(const Options& options);

  ~Problem();

  // Add a residual block to the overall cost function. The cost
  // function carries with it information about the sizes of the
  // parameter blocks it expects. The function checks that these match
  // the sizes of the parameter blocks listed in parameter_blocks. The
  // program aborts if a mismatch is detected. loss_function can be
  // NULL, in which case the cost of the term is just the squared norm
  // of the residuals.
  //
  // The user has the option of explicitly adding the parameter blocks
  // using AddParameterBlock. This causes additional correctness
  // checking; however, AddResidualBlock implicitly adds the parameter
  // blocks if they are not present, so calling AddParameterBlock
  // explicitly is not required.
  //
  // The Problem object by default takes ownership of the
  // cost_function and loss_function pointers. These objects remain
  // live for the life of the Problem object. If the user wishes to
  // keep control over the destruction of these objects, then they can
  // do this by setting the corresponding enums in the Options struct.
  //
  // Note: Even though the Problem takes ownership of cost_function
  // and loss_function, it does not preclude the user from re-using
  // them in another residual block. The destructor takes care to call
  // delete on each cost_function or loss_function pointer only once,
  // regardless of how many residual blocks refer to them.
  //
  // Example usage:
  //
  //   double x1[] = {1.0, 2.0, 3.0};
  //   double x2[] = {1.0, 2.0, 5.0, 6.0};
  //   double x3[] = {3.0, 6.0, 2.0, 5.0, 1.0};
  //
  //   Problem problem;
  //
  //   problem.AddResidualBlock(new MyUnaryCostFunction(...), NULL, x1);
  //   problem.AddResidualBlock(new MyBinaryCostFunction(...), NULL, x2, x1);
  //
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   const vector<double*>& parameter_blocks);

  // Convenience methods for adding residuals with a small number of
  // parameters. This is the common case. Instead of specifying the
  // parameter block arguments as a vector, list them as pointers.
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1, double* x2);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1, double* x2,
                                   double* x3);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1, double* x2,
                                   double* x3, double* x4);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1, double* x2,
                                   double* x3, double* x4, double* x5);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1, double* x2,
                                   double* x3, double* x4, double* x5,
                                   double* x6);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1, double* x2,
                                   double* x3, double* x4, double* x5,
                                   double* x6, double* x7);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1, double* x2,
                                   double* x3, double* x4, double* x5,
                                   double* x6, double* x7, double* x8);
  ResidualBlockId AddResidualBlock(CostFunction* cost_function,
                                   LossFunction* loss_function,
                                   double* x0, double* x1, double* x2,
                                   double* x3, double* x4, double* x5,
                                   double* x6, double* x7, double* x8,
                                   double* x9);

  // Add a parameter block with appropriate size to the problem.
  // Repeated calls with the same arguments are ignored. Repeated
  // calls with the same double pointer but a different size results
  // in undefined behaviour.
  void AddParameterBlock(double* values, int size);

  // Add a parameter block with appropriate size and parameterization
  // to the problem. Repeated calls with the same arguments are
  // ignored. Repeated calls with the same double pointer but a
  // different size results in undefined behaviour.
  void AddParameterBlock(double* values,
                         int size,
                         LocalParameterization* local_parameterization);

  // Remove a parameter block from the problem. The parameterization of the
  // parameter block, if it exists, will persist until the deletion of the
  // problem (similar to cost/loss functions in residual block removal). Any
  // residual blocks that depend on the parameter are also removed, as
  // described above in RemoveResidualBlock().
  //
  // If Problem::Options::enable_fast_parameter_block_removal is true, then the
  // removal is fast (almost constant time). Otherwise, removing a parameter
  // block will incur a scan of the entire Problem object.
  //
  // WARNING: Removing a residual or parameter block will destroy the implicit
  // ordering, rendering the jacobian or residuals returned from the solver
  // uninterpretable. If you depend on the evaluated jacobian, do not use
  // remove! This may change in a future release.
  void RemoveParameterBlock(double* values);

  // Remove a residual block from the problem. Any parameters that the residual
  // block depends on are not removed. The cost and loss functions for the
  // residual block will not get deleted immediately; won't happen until the
  // problem itself is deleted.
  //
  // WARNING: Removing a residual or parameter block will destroy the implicit
  // ordering, rendering the jacobian or residuals returned from the solver
  // uninterpretable. If you depend on the evaluated jacobian, do not use
  // remove! This may change in a future release.
  void RemoveResidualBlock(ResidualBlockId residual_block);

  // Hold the indicated parameter block constant during optimization.
  void SetParameterBlockConstant(double* values);

  // Allow the indicated parameter to vary during optimization.
  void SetParameterBlockVariable(double* values);

  // Set the local parameterization for one of the parameter blocks.
  // The local_parameterization is owned by the Problem by default. It
  // is acceptable to set the same parameterization for multiple
  // parameters; the destructor is careful to delete local
  // parameterizations only once. The local parameterization can only
  // be set once per parameter, and cannot be changed once set.
  void SetParameterization(double* values,
                           LocalParameterization* local_parameterization);

  // Number of parameter blocks in the problem. Always equals
  // parameter_blocks().size() and parameter_block_sizes().size().
  int NumParameterBlocks() const;

  // The size of the parameter vector obtained by summing over the
  // sizes of all the parameter blocks.
  int NumParameters() const;

  // Number of residual blocks in the problem. Always equals
  // residual_blocks().size().
  int NumResidualBlocks() const;

  // The size of the residual vector obtained by summing over the
  // sizes of all of the residual blocks.
  int NumResiduals() const;

  // The size of the parameter block.
  int ParameterBlockSize(const double* values) const;

  // The size of local parameterization for the parameter block. If
  // there is no local parameterization associated with this parameter
  // block, then ParameterBlockLocalSize = ParameterBlockSize.
  int ParameterBlockLocalSize(const double* values) const;

  // Fills the passed parameter_blocks vector with pointers to the
  // parameter blocks currently in the problem. After this call,
  // parameter_block.size() == NumParameterBlocks.
  void GetParameterBlocks(vector<double*>* parameter_blocks) const;

  // Options struct to control Problem::Evaluate.
  struct EvaluateOptions {
    EvaluateOptions()
        : apply_loss_function(true),
          num_threads(1) {
    }

    // The set of parameter blocks for which evaluation should be
    // performed. This vector determines the order that parameter
    // blocks occur in the gradient vector and in the columns of the
    // jacobian matrix. If parameter_blocks is empty, then it is
    // assumed to be equal to vector containing ALL the parameter
    // blocks.  Generally speaking the parameter blocks will occur in
    // the order in which they were added to the problem. But, this
    // may change if the user removes any parameter blocks from the
    // problem.
    //
    // NOTE: This vector should contain the same pointers as the ones
    // used to add parameter blocks to the Problem. These parameter
    // block should NOT point to new memory locations. Bad things will
    // happen otherwise.
    vector<double*> parameter_blocks;

    // The set of residual blocks to evaluate. This vector determines
    // the order in which the residuals occur, and how the rows of the
    // jacobian are ordered. If residual_blocks is empty, then it is
    // assumed to be equal to the vector containing all the residual
    // blocks. If this vector is empty, then it is assumed to be equal
    // to a vector containing ALL the residual blocks. Generally
    // speaking the residual blocks will occur in the order in which
    // they were added to the problem. But, this may change if the
    // user removes any residual blocks from the problem.
    vector<ResidualBlockId> residual_blocks;

    // Even though the residual blocks in the problem may contain loss
    // functions, setting apply_loss_function to false will turn off
    // the application of the loss function to the output of the cost
    // function. This is of use for example if the user wishes to
    // analyse the solution quality by studying the distribution of
    // residuals before and after the solve.
    bool apply_loss_function;

    int num_threads;
  };

  // Evaluate Problem. Any of the output pointers can be NULL. Which
  // residual blocks and parameter blocks are used is controlled by
  // the EvaluateOptions struct above.
  //
  // Note 1: The evaluation will use the values stored in the memory
  // locations pointed to by the parameter block pointers used at the
  // time of the construction of the problem. i.e.,
  //
  //   Problem problem;
  //   double x = 1;
  //   problem.AddResidualBlock(new MyCostFunction, NULL, &x);
  //
  //   double cost = 0.0;
  //   problem.Evaluate(Problem::EvaluateOptions(), &cost, NULL, NULL, NULL);
  //
  // The cost is evaluated at x = 1. If you wish to evaluate the
  // problem at x = 2, then
  //
  //    x = 2;
  //    problem.Evaluate(Problem::EvaluateOptions(), &cost, NULL, NULL, NULL);
  //
  // is the way to do so.
  //
  // Note 2: If no local parameterizations are used, then the size of
  // the gradient vector (and the number of columns in the jacobian)
  // is the sum of the sizes of all the parameter blocks. If a
  // parameter block has a local parameterization, then it contributes
  // "LocalSize" entries to the gradient vector (and the number of
  // columns in the jacobian).
  bool Evaluate(const EvaluateOptions& options,
                double* cost,
                vector<double>* residuals,
                vector<double>* gradient,
                CRSMatrix* jacobian);

 private:
  friend class Solver;
  friend class Covariance;
  internal::scoped_ptr<internal::ProblemImpl> problem_impl_;
  CERES_DISALLOW_COPY_AND_ASSIGN(Problem);
};

}  // namespace ceres

#endif  // CERES_PUBLIC_PROBLEM_H_