| // 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 |
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| // |
| // 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_ |