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 analytic jacobian matrix. 34 35 #include <vector> 36 #include "ceres/ceres.h" 37 #include "glog/logging.h" 38 39 using ceres::CostFunction; 40 using ceres::SizedCostFunction; 41 using ceres::Problem; 42 using ceres::Solver; 43 using ceres::Solve; 44 45 // A CostFunction implementing analytically derivatives for the 46 // function f(x) = 10 - x. 47 class QuadraticCostFunction 48 : public SizedCostFunction<1 /* number of residuals */, 49 1 /* size of first parameter */> { 50 public: 51 virtual ~QuadraticCostFunction() {} 52 53 virtual bool Evaluate(double const* const* parameters, 54 double* residuals, 55 double** jacobians) const { 56 double x = parameters[0][0]; 57 58 // f(x) = 10 - x. 59 residuals[0] = 10 - x; 60 61 // f'(x) = -1. Since there's only 1 parameter and that parameter 62 // has 1 dimension, there is only 1 element to fill in the 63 // jacobians. 64 // 65 // Since the Evaluate function can be called with the jacobians 66 // pointer equal to NULL, the Evaluate function must check to see 67 // if jacobians need to be computed. 68 // 69 // For this simple problem it is overkill to check if jacobians[0] 70 // is NULL, but in general when writing more complex 71 // CostFunctions, it is possible that Ceres may only demand the 72 // derivatives w.r.t. a subset of the parameter blocks. 73 if (jacobians != NULL && jacobians[0] != NULL) { 74 jacobians[0][0] = -1; 75 } 76 77 return true; 78 } 79 }; 80 81 int main(int argc, char** argv) { 82 google::InitGoogleLogging(argv[0]); 83 84 // The variable to solve for with its initial value. It will be 85 // mutated in place by the solver. 86 double x = 0.5; 87 const double initial_x = x; 88 89 // Build the problem. 90 Problem problem; 91 92 // Set up the only cost function (also known as residual). 93 CostFunction* cost_function = new QuadraticCostFunction; 94 problem.AddResidualBlock(cost_function, NULL, &x); 95 96 // Run the solver! 97 Solver::Options options; 98 options.minimizer_progress_to_stdout = true; 99 Solver::Summary summary; 100 Solve(options, &problem, &summary); 101 102 std::cout << summary.BriefReport() << "\n"; 103 std::cout << "x : " << initial_x 104 << " -> " << x << "\n"; 105 106 return 0; 107 } 108
