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/
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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
39using ceres::CostFunction;
40using ceres::SizedCostFunction;
41using ceres::Problem;
42using ceres::Solver;
43using ceres::Solve;
44
45// A CostFunction implementing analytically derivatives for the
46// function f(x) = 10 - x.
47class QuadraticCostFunction
48  : public SizedCostFunction<1 /* number of residuals */,
49                             1 /* size of first parameter */> {
50 public:
51  virtual ~QuadraticCostFunction() {}
52
53  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
81int 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