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https://github.com/ceres-solver/ceres-solver.git
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Keir Mierle
90 lines
3.3 KiB
C++
90 lines
3.3 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: keir@google.com (Keir Mierle)
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//
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// A simple example of using the Ceres minimizer.
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//
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// Minimize 0.5 (10 - x)^2 using analytic jacobian matrix.
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#include <vector>
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#include "ceres/ceres.h"
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using ceres::SizedCostFunction;
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using ceres::Problem;
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using ceres::Solver;
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using ceres::Solve;
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class SimpleCostFunction
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: public SizedCostFunction<1 /* number of residuals */,
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1 /* size of first parameter */> {
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public:
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virtual ~SimpleCostFunction() {}
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virtual bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const {
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double x = parameters[0][0];
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// f(x) = 10 - x.
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residuals[0] = 10 - x;
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// f'(x) = -1. Since there's only 1 parameter and that parameter
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// has 1 dimension, there is only 1 element to fill in the
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// jacobians.
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if (jacobians != NULL && jacobians[0] != NULL) {
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jacobians[0][0] = -1;
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}
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return true;
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}
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};
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int main(int argc, char** argv) {
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google::ParseCommandLineFlags(&argc, &argv, true);
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google::InitGoogleLogging(argv[0]);
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// The variable with its initial value that we will be solving for.
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double x = 5.0;
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// Build the problem.
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Problem problem;
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// Set up the only cost function (also known as residual).
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problem.AddResidualBlock(new SimpleCostFunction, NULL, &x);
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// Run the solver!
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Solver::Options options;
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options.max_num_iterations = 10;
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options.linear_solver_type = ceres::DENSE_QR;
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options.minimizer_progress_to_stdout = true;
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Solver::Summary summary;
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Solve(options, &problem, &summary);
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std::cout << summary.BriefReport() << "\n";
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std::cout << "x : 5.0 -> " << x << "\n";
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return 0;
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}
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