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
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20 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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24 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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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
37 using ceres::NumericDiffCostFunction;
38 using ceres::CENTRAL;
39 using ceres::CostFunction;
40 using ceres::Problem;
41 using ceres::Solver;
42 using ceres::Solve;
43
44 // A cost functor that implements the residual r = 10 - x.
45 struct CostFunctor {
operator ()CostFunctor46 bool operator()(const double* const x, double* residual) const {
47 residual[0] = 10.0 - x[0];
48 return true;
49 }
50 };
51
main(int argc,char ** argv)52 int 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