helloworld_numeric_diff.cc revision 1d2624a10e2c559f8ba9ef89eaa30832c0a83a96 
1// Ceres Solver - A fast non-linear least squares minimizer
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3// http://code.google.com/p/ceres-solver/
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28//
29// Author: keir@google.com (Keir Mierle)
30//
31// Minimize 0.5 (10 - x)^2 using jacobian matrix computed using
32// numeric differentiation.
33
34#include "ceres/ceres.h"
35#include "glog/logging.h"
36
37using ceres::NumericDiffCostFunction;
38using ceres::CENTRAL;
39using ceres::CostFunction;
40using ceres::Problem;
41using ceres::Solver;
42using ceres::Solve;
43
44// A cost functor that implements the residual r = 10 - x.
45struct CostFunctor {
46  bool operator()(const double* const x, double* residual) const {
47    residual[0] = 10.0 - x[0];
48    return true;
49  }
50};
51
52int main(int argc, char** argv) {
53  google::InitGoogleLogging(argv[0]);
54
55  // The variable to solve for with its initial value. It will be
56  // mutated in place by the solver.
57  double x = 0.5;
58  const double initial_x = x;
59
60  // Build the problem.
61  Problem problem;
62
63  // Set up the only cost function (also known as residual). This uses
64  // numeric differentiation to obtain the derivative (jacobian).
65  CostFunction* cost_function =
66      new NumericDiffCostFunction<CostFunctor, CENTRAL, 1, 1> (new CostFunctor);
67  problem.AddResidualBlock(cost_function, NULL, &x);
68
69  // Run the solver!
70  Solver::Options options;
71  options.minimizer_progress_to_stdout = true;
72  Solver::Summary summary;
73  Solve(options, &problem, &summary);
74
75  std::cout << summary.BriefReport() << "\n";
76  std::cout << "x : " << initial_x
77            << " -> " << x << "\n";
78  return 0;
79}
80