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- // Ceres Solver - A fast non-linear least squares minimizer
- // Copyright 2023 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)
- //
- #ifndef CERES_PUBLIC_NUMERIC_DIFF_OPTIONS_H_
- #define CERES_PUBLIC_NUMERIC_DIFF_OPTIONS_H_
- #include "ceres/internal/disable_warnings.h"
- #include "ceres/internal/export.h"
- 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
- #include "ceres/internal/reenable_warnings.h"
- #endif // CERES_PUBLIC_NUMERIC_DIFF_OPTIONS_H_
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