// 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

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//

// Authors: keir@google.com (Keir Mierle),

//          dgossow@google.com (David Gossow)

#ifndef CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_

#define CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_

#include <string>

#include "ceres/cost_function.h"

#include "ceres/iteration_callback.h"

#include "ceres/local_parameterization.h"

#include "ceres/mutex.h"

namespace ceres {

namespace internal {

class ProblemImpl;

// Callback that collects information about gradient checking errors, and

// will abort the solve as soon as an error occurs.

class GradientCheckingIterationCallback : public IterationCallback {

 public:

  GradientCheckingIterationCallback();

  // Will return SOLVER_CONTINUE until a gradient error has been detected,

  // then return SOLVER_ABORT.

  virtual CallbackReturnType operator()(const IterationSummary& summary);

  // Notify this that a gradient error has occurred (thread safe).

  void SetGradientErrorDetected(std::string& error_log);

  // Retrieve error status (not thread safe).

  bool gradient_error_detected() const { return gradient_error_detected_; }

  const std::string& error_log() const { return error_log_; }

 private:

  bool gradient_error_detected_;

  std::string error_log_;

  // Mutex protecting member variables.

  ceres::internal::Mutex mutex_;

};

// Creates a CostFunction that checks the Jacobians that cost_function computes

// with finite differences. This API is only intended for unit tests that intend

// to  check the functionality of the GradientCheckingCostFunction

// implementation directly.

CostFunction* CreateGradientCheckingCostFunction(

    const CostFunction* cost_function,

    const std::vector<const LocalParameterization*>* local_parameterizations,

    double relative_step_size,

    double relative_precision,

    const std::string& extra_info,

    GradientCheckingIterationCallback* callback);

// Create a new ProblemImpl object from the input problem_impl, where all

// cost functions are wrapped so that each time their Evaluate method is called,

// an additional check is performed that compares the Jacobians computed by

// the original cost function with alternative Jacobians computed using

// numerical differentiation. If local parameterizations are given for any

// parameters, the Jacobians will be compared in the local space instead of the

// ambient space. For details on the gradient checking procedure, see the

// documentation of the GradientChecker class. If an error is detected in any

// iteration, the respective cost function will notify the

// GradientCheckingIterationCallback.

//

// The caller owns the returned ProblemImpl object.

//

// Note: This is quite inefficient and is intended only for debugging.

//

// relative_step_size and relative_precision are parameters to control

// the numeric differentiation and the relative tolerance between the

// jacobian computed by the CostFunctions in problem_impl and

// jacobians obtained by numerically differentiating them. See the

// documentation of 'numeric_derivative_relative_step_size' in solver.h for a

// better explanation.

ProblemImpl* CreateGradientCheckingProblemImpl(

    ProblemImpl* problem_impl,

    double relative_step_size,

    double relative_precision,

    GradientCheckingIterationCallback* callback);

}  // namespace internal

}  // namespace ceres

#endif  // CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_