1 // Ceres Solver - A fast non-linear least squares minimizer 2 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. 3 // http://code.google.com/p/ceres-solver/ 4 // 5 // Redistribution and use in source and binary forms, with or without 6 // modification, are permitted provided that the following conditions are met: 7 // 8 // * Redistributions of source code must retain the above copyright notice, 9 // this list of conditions and the following disclaimer. 10 // * Redistributions in binary form must reproduce the above copyright notice, 11 // this list of conditions and the following disclaimer in the documentation 12 // and/or other materials provided with the distribution. 13 // * Neither the name of Google Inc. nor the names of its contributors may be 14 // used to endorse or promote products derived from this software without 15 // specific prior written permission. 16 // 17 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 18 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 19 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 20 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE 21 // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR 22 // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF 23 // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS 24 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 25 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) 26 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE 27 // POSSIBILITY OF SUCH DAMAGE. 28 // 29 // Author: keir (at) google.com (Keir Mierle) 30 // 31 // A simple example of using the Ceres minimizer. 32 // 33 // Minimize 0.5 (10 - x)^2 using jacobian matrix computed using 34 // automatic differentiation. 35 36 #include "ceres/ceres.h" 37 #include "glog/logging.h" 38 39 using ceres::AutoDiffCostFunction; 40 using ceres::CostFunction; 41 using ceres::Problem; 42 using ceres::Solver; 43 using ceres::Solve; 44 45 // A templated cost functor that implements the residual r = 10 - 46 // x. The method operator() is templated so that we can then use an 47 // automatic differentiation wrapper around it to generate its 48 // derivatives. 49 struct CostFunctor { 50 template <typename T> bool operator()(const T* const x, T* residual) const { 51 residual[0] = T(10.0) - x[0]; 52 return true; 53 } 54 }; 55 56 int main(int argc, char** argv) { 57 google::InitGoogleLogging(argv[0]); 58 59 // The variable to solve for with its initial value. It will be 60 // mutated in place by the solver. 61 double x = 0.5; 62 const double initial_x = x; 63 64 // Build the problem. 65 Problem problem; 66 67 // Set up the only cost function (also known as residual). This uses 68 // auto-differentiation to obtain the derivative (jacobian). 69 CostFunction* cost_function = 70 new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor); 71 problem.AddResidualBlock(cost_function, NULL, &x); 72 73 // Run the solver! 74 Solver::Options options; 75 options.minimizer_progress_to_stdout = true; 76 Solver::Summary summary; 77 Solve(options, &problem, &summary); 78 79 std::cout << summary.BriefReport() << "\n"; 80 std::cout << "x : " << initial_x 81 << " -> " << x << "\n"; 82 return 0; 83 } 84
