// 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/
//
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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.
// * Redistributions in binary form must reproduce the above copyright notice,
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//   and/or other materials provided with the distribution.
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//
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// Author: keir@google.com (Keir Mierle)

#ifndef CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_
#define CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_

#include <string>

#include "ceres/cost_function.h"

namespace ceres {
namespace internal {

class ProblemImpl;

// Creates a CostFunction that checks the jacobians that cost_function computes
// with finite differences. Bad results are logged; required precision is
// controlled by relative_precision and the numeric differentiation step size is
// controlled with relative_step_size. See solver.h for a better explanation of
// relative_step_size. Caller owns result.
//
// The condition enforced is that
//
//    (J_actual(i, j) - J_numeric(i, j))
//   ------------------------------------  <  relative_precision
//   max(J_actual(i, j), J_numeric(i, j))
//
// where J_actual(i, j) is the jacobian as computed by the supplied cost
// function (by the user) and J_numeric is the jacobian as computed by finite
// differences.
//
// Note: This is quite inefficient and is intended only for debugging.
CostFunction* CreateGradientCheckingCostFunction(
    const CostFunction* cost_function,
    double relative_step_size,
    double relative_precision,
    const string& extra_info);

// Create a new ProblemImpl object from the input problem_impl, where
// each CostFunctions in problem_impl are wrapped inside a
// GradientCheckingCostFunctions. This gives us a ProblemImpl object
// which checks its derivatives against estimates from numeric
// differentiation everytime a ResidualBlock is evaluated.
//
// 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. For more
// details see the documentation for
// CreateGradientCheckingCostFunction above.
ProblemImpl* CreateGradientCheckingProblemImpl(ProblemImpl* problem_impl,
                                               double relative_step_size,
                                               double relative_precision);

}  // namespace internal
}  // namespace ceres

#endif  // CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_