| // Copyright (c) 2022, ETH Zurich and UNC Chapel Hill. | |
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| // | |
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| // | |
| // Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de) | |
| namespace colmap { | |
| struct LeastAbsoluteDeviationsOptions { | |
| // Augmented Lagrangian parameter. | |
| double rho = 1.0; | |
| // Over-relaxation parameter, typical values are between 1.0 and 1.8. | |
| double alpha = 1.0; | |
| // Maximum solver iterations. | |
| int max_num_iterations = 1000; | |
| // Absolute and relative solution thresholds, as suggested by Boyd et al. | |
| double absolute_tolerance = 1e-4; | |
| double relative_tolerance = 1e-2; | |
| }; | |
| // Least absolute deviations (LAD) fitting via ADMM by solving the problem: | |
| // | |
| // min || A x - b ||_1 | |
| // | |
| // The solution is returned in the vector x and the iterative solver is | |
| // initialized with the given value. This implementation is based on the paper | |
| // "Distributed Optimization and Statistical Learning via the Alternating | |
| // Direction Method of Multipliers" by Boyd et al. and the Matlab implementation | |
| // at https://web.stanford.edu/~boyd/papers/admm/least_abs_deviations/lad.html | |
| bool SolveLeastAbsoluteDeviations(const LeastAbsoluteDeviationsOptions& options, | |
| const Eigen::SparseMatrix<double>& A, | |
| const Eigen::VectorXd& b, Eigen::VectorXd* x); | |
| } // namespace colmap | |