• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
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@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:
~QuadraticCostFunction()51   virtual ~QuadraticCostFunction() {}
52 
Evaluate(double const * const * parameters,double * residuals,double ** jacobians) const53   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 
main(int argc,char ** argv)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