// Ceres Solver - A fast non-linear least squares minimizer // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. // http://code.google.com/p/ceres-solver/ // // 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: keir@google.com (Keir Mierle) // // Minimize 0.5 (10 - x)^2 using jacobian matrix computed using // numeric differentiation. #include "ceres/ceres.h" #include "glog/logging.h" using ceres::NumericDiffCostFunction; using ceres::CENTRAL; using ceres::CostFunction; using ceres::Problem; using ceres::Solver; using ceres::Solve; // A cost functor that implements the residual r = 10 - x. struct CostFunctor { bool operator()(const double* const x, double* residual) const { residual[0] = 10.0 - x[0]; return true; } }; int main(int argc, char** argv) { google::InitGoogleLogging(argv[0]); // The variable to solve for with its initial value. It will be // mutated in place by the solver. double x = 0.5; const double initial_x = x; // Build the problem. Problem problem; // Set up the only cost function (also known as residual). This uses // numeric differentiation to obtain the derivative (jacobian). CostFunction* cost_function = new NumericDiffCostFunction<CostFunctor, CENTRAL, 1, 1> (new CostFunctor); problem.AddResidualBlock(cost_function, NULL, &x); // Run the solver! Solver::Options options; options.minimizer_progress_to_stdout = true; Solver::Summary summary; Solve(options, &problem, &summary); std::cout << summary.BriefReport() << "\n"; std::cout << "x : " << initial_x << " -> " << x << "\n"; return 0; }