// 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/ // // 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 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: keir@google.com (Keir Mierle) // // Create CostFunctions as needed by the least squares framework with jacobians // computed via numeric differentiation. // // To get a numerically differentiated cost function, define a subclass of // CostFunction such that the Evaluate() function ignores the jacobian // parameter. The numeric differentiation wrapper will fill in the jacobian // parameter if nececssary by repeatedly calling the Evaluate() function with // small changes to the appropriate parameters, and computing the slope. This // implementation is not templated (hence the "Runtime" prefix), which is a bit // slower than but is more convenient than the templated version in // numeric_diff_cost_function.h // // The numerically differentiated version of a cost function for a cost function // can be constructed as follows: // // CostFunction* cost_function = // CreateRuntimeNumericDiffCostFunction(new MyCostFunction(...), // CENTRAL, // TAKE_OWNERSHIP); // // The central difference method is considerably more accurate; consider using // to start and only after that works, trying forward difference. // // TODO(keir): Characterize accuracy; mention pitfalls; provide alternatives. #ifndef CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_ #define CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_ #include "ceres/cost_function.h" namespace ceres { namespace internal { enum RuntimeNumericDiffMethod { CENTRAL, FORWARD, }; // Create a cost function that evaluates the derivative with finite differences. // The base cost_function's implementation of Evaluate() only needs to fill in // the "residuals" argument and not the "jacobians". Any data written to the // jacobians by the base cost_function is overwritten. // // Forward difference or central difference is selected with CENTRAL or FORWARD. // The relative eps, which determines the step size for forward and central // differencing, is set with relative eps. Caller owns the resulting cost // function, and the resulting cost function does not own the base cost // function. CostFunction *CreateRuntimeNumericDiffCostFunction( const CostFunction *cost_function, RuntimeNumericDiffMethod method, double relative_eps); } // namespace internal } // namespace ceres #endif // CERES_INTERNAL_RUNTIME_NUMERIC_DIFF_COST_FUNCTION_H_