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/
5
// Redistribution and use in source and binary forms, with or without
6
// modification, are permitted provided that the following conditions are met:
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.
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.
29
// Author: keir@google.com (Keir Mierle)
31
// A simple example of using the Ceres minimizer.
33
// Minimize 0.5 (10 - x)^2 using jacobian matrix computed using
34
// automatic differentiation.
38
#include "ceres/ceres.h"
40
using ceres::AutoDiffCostFunction;
41
using ceres::CostFunction;
46
// A templated cost function that implements the residual r = 10 - x. The method
47
// Map is templated so that we can then use an automatic differentiation wrapper
48
// around it to generate its derivatives.
49
class QuadraticCostFunction {
51
template <typename T> bool operator()(const T* const x, T* residual) const {
52
residual[0] = T(10.0) - x[0];
57
int main(int argc, char** argv) {
58
google::ParseCommandLineFlags(&argc, &argv, true);
59
google::InitGoogleLogging(argv[0]);
61
// The variable to solve for with its initial value.
62
double initial_x = 5.0;
68
// Set up the only cost function (also known as residual). This uses
69
// auto-differentiation to obtain the derivative (jacobian).
70
problem.AddResidualBlock(
71
new AutoDiffCostFunction<QuadraticCostFunction, 1, 1>(
72
new QuadraticCostFunction),
77
Solver::Options options;
78
options.max_num_iterations = 10;
79
options.linear_solver_type = ceres::DENSE_QR;
80
options.minimizer_progress_to_stdout = true;
81
Solver::Summary summary;
82
Solve(options, &problem, &summary);
83
std::cout << summary.BriefReport() << "\n";
84
std::cout << "x : " << initial_x
85
<< " -> " << x << "\n";
added
removed
examples/quadratic_auto_diff.cc
