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https://github.com/ceres-solver/ceres-solver.git
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Keir Mierle
92 lines
3.5 KiB
C++
92 lines
3.5 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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// Minimize 0.5 (10 - x)^2 using jacobian matrix computed using
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// numeric differentiation.
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#include <vector>
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#include "ceres/ceres.h"
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using ceres::NumericDiffCostFunction;
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using ceres::CENTRAL;
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using ceres::SizedCostFunction;
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using ceres::CostFunction;
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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 ResidualWithNoDerivative
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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 ~ResidualWithNoDerivative() {}
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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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(void) jacobians; // Ignored; filled in by numeric differentiation.
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// f(x) = 10 - x.
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residuals[0] = 10 - parameters[0][0];
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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 to solve for with its initial value.
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double initial_x = 5.0;
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double x = initial_x;
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// Set up the only cost function (also known as residual). This uses
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// numeric differentiation to obtain the derivative (jacobian).
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CostFunction* cost =
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new NumericDiffCostFunction<ResidualWithNoDerivative, CENTRAL, 1, 1> (
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new ResidualWithNoDerivative, ceres::TAKE_OWNERSHIP);
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// Build the problem.
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Problem problem;
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problem.AddResidualBlock(cost, 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 : " << initial_x
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<< " -> " << x << "\n";
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return 0;
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}
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