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Keir Mierle
88 lines
4.0 KiB
C++
88 lines
4.0 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: keir@google.com (Keir Mierle)
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//
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// Create CostFunctions as needed by the least squares framework with jacobians
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// computed via numeric differentiation.
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//
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// To get a numerically differentiated cost function, define a subclass of
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// CostFunction such that the Evaluate() function ignores the jacobian
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// parameter. The numeric differentiation wrapper will fill in the jacobian
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// parameter if nececssary by repeatedly calling the Evaluate() function with
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// small changes to the appropriate parameters, and computing the slope. This
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// implementation is not templated (hence the "Runtime" prefix), which is a bit
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// slower than but is more convenient than the templated version in
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// numeric_diff_cost_function.h
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//
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// The numerically differentiated version of a cost function for a cost function
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// can be constructed as follows:
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//
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// CostFunction* cost_function =
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// CreateRuntimeNumericDiffCostFunction(new MyCostFunction(...),
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// CENTRAL,
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// TAKE_OWNERSHIP);
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//
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// The central difference method is considerably more accurate; consider using
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// to start and only after that works, trying forward difference.
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//
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// TODO(keir): Characterize accuracy; mention pitfalls; provide alternatives.
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#ifndef CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_
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#define CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_
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#include "ceres/cost_function.h"
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namespace ceres {
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namespace internal {
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enum RuntimeNumericDiffMethod {
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CENTRAL,
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FORWARD,
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};
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// Create a cost function that evaluates the derivative with finite differences.
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// The base cost_function's implementation of Evaluate() only needs to fill in
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// the "residuals" argument and not the "jacobians". Any data written to the
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// jacobians by the base cost_function is overwritten.
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//
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// Forward difference or central difference is selected with CENTRAL or FORWARD.
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// The relative eps, which determines the step size for forward and central
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// differencing, is set with relative eps. Caller owns the resulting cost
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// function, and the resulting cost function does not own the base cost
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// function.
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CostFunction *CreateRuntimeNumericDiffCostFunction(
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const CostFunction *cost_function,
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RuntimeNumericDiffMethod method,
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double relative_eps);
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} // namespace internal
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} // namespace ceres
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#endif // CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_
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