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 //
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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.
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14 // used to endorse or promote products derived from this software without
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16 //
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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
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 {
operator ()CostFunctor50 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
main(int argc,char ** argv)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