1// Ceres Solver - A fast non-linear least squares minimizer
2// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
3// http://code.google.com/p/ceres-solver/
4//
5// Redistribution and use in source and binary forms, with or without
6// modification, are permitted provided that the following conditions are met:
7//
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9//   this list of conditions and the following disclaimer.
10// * Redistributions in binary form must reproduce the above copyright notice,
11//   this list of conditions and the following disclaimer in the documentation
12//   and/or other materials provided with the distribution.
13// * Neither the name of Google Inc. nor the names of its contributors may be
14//   used to endorse or promote products derived from this software without
15//   specific prior written permission.
16//
17// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
18// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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28//
29// Author: keir@google.com (Keir Mierle)
30
31#ifndef CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_
32#define CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_
33
34#include <string>
35
36#include "ceres/cost_function.h"
37
38namespace ceres {
39namespace internal {
40
41class ProblemImpl;
42
43// Creates a CostFunction that checks the jacobians that cost_function computes
44// with finite differences. Bad results are logged; required precision is
45// controlled by relative_precision and the numeric differentiation step size is
46// controlled with relative_step_size. See solver.h for a better explanation of
47// relative_step_size. Caller owns result.
48//
49// The condition enforced is that
50//
51//    (J_actual(i, j) - J_numeric(i, j))
52//   ------------------------------------  <  relative_precision
53//   max(J_actual(i, j), J_numeric(i, j))
54//
55// where J_actual(i, j) is the jacobian as computed by the supplied cost
56// function (by the user) and J_numeric is the jacobian as computed by finite
57// differences.
58//
59// Note: This is quite inefficient and is intended only for debugging.
60CostFunction* CreateGradientCheckingCostFunction(
61    const CostFunction* cost_function,
62    double relative_step_size,
63    double relative_precision,
64    const string& extra_info);
65
66// Create a new ProblemImpl object from the input problem_impl, where
67// each CostFunctions in problem_impl are wrapped inside a
68// GradientCheckingCostFunctions. This gives us a ProblemImpl object
69// which checks its derivatives against estimates from numeric
70// differentiation everytime a ResidualBlock is evaluated.
71//
72// relative_step_size and relative_precision are parameters to control
73// the numeric differentiation and the relative tolerance between the
74// jacobian computed by the CostFunctions in problem_impl and
75// jacobians obtained by numerically differentiating them. For more
76// details see the documentation for
77// CreateGradientCheckingCostFunction above.
78ProblemImpl* CreateGradientCheckingProblemImpl(ProblemImpl* problem_impl,
79                                               double relative_step_size,
80                                               double relative_precision);
81
82}  // namespace internal
83}  // namespace ceres
84
85#endif  // CERES_INTERNAL_GRADIENT_CHECKING_COST_FUNCTION_H_
86