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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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
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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 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