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//
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6// modification, are permitted provided that the following conditions are met:
7//
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28//
29// Author: keir@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
39using ceres::AutoDiffCostFunction;
40using ceres::CostFunction;
41using ceres::Problem;
42using ceres::Solver;
43using 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.
49struct 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
56int 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