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// All rights reserved.
//
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// 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
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// documentation and/or other materials provided with the distribution.
//
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// Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
#ifndef COLMAP_SRC_OPTIM_LEAST_ABSOLUTE_DEVIATIONS_H_
#define COLMAP_SRC_OPTIM_LEAST_ABSOLUTE_DEVIATIONS_H_
#include <Eigen/Core>
#include <Eigen/SparseCore>
#include "util/logging.h"
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
#endif // COLMAP_SRC_OPTIM_LEAST_ABSOLUTE_DEVIATIONS_H_
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