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Keir Mierle8ebb0732012-04-30 23:09:08 -07001// 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//
8// * Redistributions of source code must retain the above copyright notice,
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
19// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
20// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
21// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
22// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
23// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
24// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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26// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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28//
29// Author: keir@google.com (Keir Mierle)
30//
31// Create CostFunctions as needed by the least squares framework with jacobians
32// computed via numeric differentiation.
33//
34// To get a numerically differentiated cost function, define a subclass of
35// CostFunction such that the Evaluate() function ignores the jacobian
36// parameter. The numeric differentiation wrapper will fill in the jacobian
37// parameter if nececssary by repeatedly calling the Evaluate() function with
38// small changes to the appropriate parameters, and computing the slope. This
39// implementation is not templated (hence the "Runtime" prefix), which is a bit
40// slower than but is more convenient than the templated version in
41// numeric_diff_cost_function.h
42//
43// The numerically differentiated version of a cost function for a cost function
44// can be constructed as follows:
45//
46// CostFunction* cost_function =
47// CreateRuntimeNumericDiffCostFunction(new MyCostFunction(...),
48// CENTRAL,
49// TAKE_OWNERSHIP);
50//
51// The central difference method is considerably more accurate; consider using
52// to start and only after that works, trying forward difference.
53//
54// TODO(keir): Characterize accuracy; mention pitfalls; provide alternatives.
55
56#ifndef CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_
57#define CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_
58
59#include "ceres/cost_function.h"
60
61namespace ceres {
62namespace internal {
63
64enum RuntimeNumericDiffMethod {
65 CENTRAL,
66 FORWARD,
67};
68
69// Create a cost function that evaluates the derivative with finite differences.
70// The base cost_function's implementation of Evaluate() only needs to fill in
71// the "residuals" argument and not the "jacobians". Any data written to the
72// jacobians by the base cost_function is overwritten.
73//
74// Forward difference or central difference is selected with CENTRAL or FORWARD.
75// The relative eps, which determines the step size for forward and central
76// differencing, is set with relative eps. Caller owns the resulting cost
77// function, and the resulting cost function does not own the base cost
78// function.
79CostFunction *CreateRuntimeNumericDiffCostFunction(
80 const CostFunction *cost_function,
81 RuntimeNumericDiffMethod method,
82 double relative_eps);
83
84} // namespace internal
85} // namespace ceres
86
87#endif // CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_