| // Ceres Solver - A fast non-linear least squares minimizer | |
| // Copyright 2019 Google Inc. All rights reserved. | |
| // http://ceres-solver.org/ | |
| // | |
| // 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: tbennun@gmail.com (Tal Ben-Nun) | |
| // | |
| namespace ceres { | |
| // Options pertaining to numeric differentiation (e.g., convergence criteria, | |
| // step sizes). | |
| struct CERES_EXPORT NumericDiffOptions { | |
| // Numeric differentiation step size (multiplied by parameter block's | |
| // order of magnitude). If parameters are close to zero, the step size | |
| // is set to sqrt(machine_epsilon). | |
| double relative_step_size = 1e-6; | |
| // Initial step size for Ridders adaptive numeric differentiation (multiplied | |
| // by parameter block's order of magnitude). | |
| // If parameters are close to zero, Ridders' method sets the step size | |
| // directly to this value. This parameter is separate from | |
| // "relative_step_size" in order to set a different default value. | |
| // | |
| // Note: For Ridders' method to converge, the step size should be initialized | |
| // to a value that is large enough to produce a significant change in the | |
| // function. As the derivative is estimated, the step size decreases. | |
| double ridders_relative_initial_step_size = 1e-2; | |
| // Maximal number of adaptive extrapolations (sampling) in Ridders' method. | |
| int max_num_ridders_extrapolations = 10; | |
| // Convergence criterion on extrapolation error for Ridders adaptive | |
| // differentiation. The available error estimation methods are defined in | |
| // NumericDiffErrorType and set in the "ridders_error_method" field. | |
| double ridders_epsilon = 1e-12; | |
| // The factor in which to shrink the step size with each extrapolation in | |
| // Ridders' method. | |
| double ridders_step_shrink_factor = 2.0; | |
| }; | |
| } // namespace ceres | |