ceres-solver/internal/ceres/evaluator.h

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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//   this list of conditions and the following disclaimer.
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//   this list of conditions and the following disclaimer in the documentation
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//
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// Author: sameeragarwal@google.com (Sameer Agarwal)
//         keir@google.com (Keir Mierle)

#ifndef CERES_INTERNAL_EVALUATOR_H_
#define CERES_INTERNAL_EVALUATOR_H_

#include <map>
#include <memory>
#include <string>
#include <vector>

#include "ceres/context_impl.h"
#include "ceres/execution_summary.h"
#include "ceres/internal/disable_warnings.h"
#include "ceres/internal/export.h"
#include "ceres/types.h"

namespace ceres {

struct CRSMatrix;
class EvaluationCallback;

namespace internal {

class Program;
class SparseMatrix;

// The Evaluator interface offers a way to interact with a least squares cost
// function that is useful for an optimizer that wants to minimize the least
// squares objective. This insulates the optimizer from issues like Jacobian
// storage, manifolds, etc.
class CERES_NO_EXPORT Evaluator {
 public:
  virtual ~Evaluator();

  struct Options {
    int num_threads = 1;
    int num_eliminate_blocks = -1;
    LinearSolverType linear_solver_type = DENSE_QR;
    SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type =
        NO_SPARSE;
    bool dynamic_sparsity = false;
    ContextImpl* context = nullptr;
    EvaluationCallback* evaluation_callback = nullptr;
  };

  static std::unique_ptr<Evaluator> Create(const Options& options,
                                           Program* program,
                                           std::string* error);

  // Build and return a sparse matrix for storing and working with the Jacobian
  // of the objective function. The jacobian has dimensions
  // NumEffectiveParameters() by NumParameters(), and is typically extremely
  // sparse. Since the sparsity pattern of the Jacobian remains constant over
  // the lifetime of the optimization problem, this method is used to
  // instantiate a SparseMatrix object with the appropriate sparsity structure
  // (which can be an expensive operation) and then reused by the optimization
  // algorithm and the various linear solvers.
  //
  // It is expected that the classes implementing this interface will be aware
  // of their client's requirements for the kind of sparse matrix storage and
  // layout that is needed for an efficient implementation. For example
  // CompressedRowOptimizationProblem creates a compressed row representation of
  // the jacobian for use with CHOLMOD, where as BlockOptimizationProblem
  // creates a BlockSparseMatrix representation of the jacobian for use in the
  // Schur complement based methods.
  virtual std::unique_ptr<SparseMatrix> CreateJacobian() const = 0;

  // Options struct to control Evaluator::Evaluate;
  struct EvaluateOptions {
    // If false, the loss function correction is not applied to the
    // residual blocks.
    bool apply_loss_function = true;

    // If false, this evaluation point is the same as the last one.
    bool new_evaluation_point = true;
  };

  // Evaluate the cost function for the given state. Returns the cost,
  // residuals, and jacobian in the corresponding arguments. Both residuals and
  // jacobian are optional; to avoid computing them, pass nullptr.
  //
  // If non-nullptr, the Jacobian must have a suitable sparsity pattern; only
  // the values array of the jacobian is modified.
  //
  // state is an array of size NumParameters(), cost is a pointer to a single
  // double, and residuals is an array of doubles of size NumResiduals().
  virtual bool Evaluate(const EvaluateOptions& evaluate_options,
                        const double* state,
                        double* cost,
                        double* residuals,
                        double* gradient,
                        SparseMatrix* jacobian) = 0;

  // Variant of Evaluator::Evaluate where the user wishes to use the
  // default EvaluateOptions struct. This is mostly here as a
  // convenience method.
  bool Evaluate(const double* state,
                double* cost,
                double* residuals,
                double* gradient,
                SparseMatrix* jacobian) {
    return Evaluate(
        EvaluateOptions(), state, cost, residuals, gradient, jacobian);
  }

  // Make a change delta (of size NumEffectiveParameters()) to state (of size
  // NumParameters()) and store the result in state_plus_delta.
  //
  // In the case that there are no manifolds used, this is equivalent to
  //
  //   state_plus_delta[i] = state[i] + delta[i] ;
  //
  // however, the mapping is more complicated in the case of manifolds
  // like quaternions. This is the same as the "Plus()" operation in
  // manifold.h, but operating over the entire state vector for a
  // problem.
  virtual bool Plus(const double* state,
                    const double* delta,
                    double* state_plus_delta) const = 0;

  // The number of parameters in the optimization problem.
  virtual int NumParameters() const = 0;

  // This is the effective number of parameters that the optimizer may adjust.
  // This applies when there are manifolds on some of the parameters.
  virtual int NumEffectiveParameters() const = 0;

  // The number of residuals in the optimization problem.
  virtual int NumResiduals() const = 0;

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

}  // namespace internal
}  // namespace ceres

#include "ceres/internal/reenable_warnings.h"

#endif  // CERES_INTERNAL_EVALUATOR_H_