155 lines
5.5 KiB
C++
155 lines
5.5 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
|
|
// Copyright 2015 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: sameeragarwal@google.com (Sameer Agarwal)
|
|
//
|
|
// An example program that minimizes Powell's singular function.
|
|
//
|
|
// F = 1/2 (f1^2 + f2^2 + f3^2 + f4^2)
|
|
//
|
|
// f1 = x1 + 10*x2;
|
|
// f2 = sqrt(5) * (x3 - x4)
|
|
// f3 = (x2 - 2*x3)^2
|
|
// f4 = sqrt(10) * (x1 - x4)^2
|
|
//
|
|
// The starting values are x1 = 3, x2 = -1, x3 = 0, x4 = 1.
|
|
// The minimum is 0 at (x1, x2, x3, x4) = 0.
|
|
//
|
|
// From: Testing Unconstrained Optimization Software by Jorge J. More, Burton S.
|
|
// Garbow and Kenneth E. Hillstrom in ACM Transactions on Mathematical Software,
|
|
// Vol 7(1), March 1981.
|
|
|
|
#include <vector>
|
|
#include "ceres/ceres.h"
|
|
#include "gflags/gflags.h"
|
|
#include "glog/logging.h"
|
|
|
|
using ceres::AutoDiffCostFunction;
|
|
using ceres::CostFunction;
|
|
using ceres::Problem;
|
|
using ceres::Solver;
|
|
using ceres::Solve;
|
|
|
|
struct F1 {
|
|
template <typename T> bool operator()(const T* const x1,
|
|
const T* const x2,
|
|
T* residual) const {
|
|
// f1 = x1 + 10 * x2;
|
|
residual[0] = x1[0] + T(10.0) * x2[0];
|
|
return true;
|
|
}
|
|
};
|
|
|
|
struct F2 {
|
|
template <typename T> bool operator()(const T* const x3,
|
|
const T* const x4,
|
|
T* residual) const {
|
|
// f2 = sqrt(5) (x3 - x4)
|
|
residual[0] = T(sqrt(5.0)) * (x3[0] - x4[0]);
|
|
return true;
|
|
}
|
|
};
|
|
|
|
struct F3 {
|
|
template <typename T> bool operator()(const T* const x2,
|
|
const T* const x4,
|
|
T* residual) const {
|
|
// f3 = (x2 - 2 x3)^2
|
|
residual[0] = (x2[0] - T(2.0) * x4[0]) * (x2[0] - T(2.0) * x4[0]);
|
|
return true;
|
|
}
|
|
};
|
|
|
|
struct F4 {
|
|
template <typename T> bool operator()(const T* const x1,
|
|
const T* const x4,
|
|
T* residual) const {
|
|
// f4 = sqrt(10) (x1 - x4)^2
|
|
residual[0] = T(sqrt(10.0)) * (x1[0] - x4[0]) * (x1[0] - x4[0]);
|
|
return true;
|
|
}
|
|
};
|
|
|
|
DEFINE_string(minimizer, "trust_region",
|
|
"Minimizer type to use, choices are: line_search & trust_region");
|
|
|
|
int main(int argc, char** argv) {
|
|
CERES_GFLAGS_NAMESPACE::ParseCommandLineFlags(&argc, &argv, true);
|
|
google::InitGoogleLogging(argv[0]);
|
|
|
|
double x1 = 3.0;
|
|
double x2 = -1.0;
|
|
double x3 = 0.0;
|
|
double x4 = 1.0;
|
|
|
|
Problem problem;
|
|
// Add residual terms to the problem using the using the autodiff
|
|
// wrapper to get the derivatives automatically. The parameters, x1 through
|
|
// x4, are modified in place.
|
|
problem.AddResidualBlock(new AutoDiffCostFunction<F1, 1, 1, 1>(new F1),
|
|
NULL,
|
|
&x1, &x2);
|
|
problem.AddResidualBlock(new AutoDiffCostFunction<F2, 1, 1, 1>(new F2),
|
|
NULL,
|
|
&x3, &x4);
|
|
problem.AddResidualBlock(new AutoDiffCostFunction<F3, 1, 1, 1>(new F3),
|
|
NULL,
|
|
&x2, &x3);
|
|
problem.AddResidualBlock(new AutoDiffCostFunction<F4, 1, 1, 1>(new F4),
|
|
NULL,
|
|
&x1, &x4);
|
|
|
|
Solver::Options options;
|
|
LOG_IF(FATAL, !ceres::StringToMinimizerType(FLAGS_minimizer,
|
|
&options.minimizer_type))
|
|
<< "Invalid minimizer: " << FLAGS_minimizer
|
|
<< ", valid options are: trust_region and line_search.";
|
|
|
|
options.max_num_iterations = 100;
|
|
options.linear_solver_type = ceres::DENSE_QR;
|
|
options.minimizer_progress_to_stdout = true;
|
|
|
|
std::cout << "Initial x1 = " << x1
|
|
<< ", x2 = " << x2
|
|
<< ", x3 = " << x3
|
|
<< ", x4 = " << x4
|
|
<< "\n";
|
|
|
|
// Run the solver!
|
|
Solver::Summary summary;
|
|
Solve(options, &problem, &summary);
|
|
|
|
std::cout << summary.FullReport() << "\n";
|
|
std::cout << "Final x1 = " << x1
|
|
<< ", x2 = " << x2
|
|
<< ", x3 = " << x3
|
|
<< ", x4 = " << x4
|
|
<< "\n";
|
|
return 0;
|
|
}
|