| 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192 | // Ceres Solver - A fast non-linear least squares minimizer// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.// http://code.google.com/p/ceres-solver///// Redistribution and use in source and binary forms, with or without// modification, are permitted provided that the following conditions are met://// * Redistributions of source code must retain the above copyright notice,//   this list of conditions and the following disclaimer.// * Redistributions in binary form must reproduce the above copyright notice,//   this list of conditions and the following disclaimer in the documentation//   and/or other materials provided with the distribution.// * Neither the name of Google Inc. nor the names of its contributors may be//   used to endorse or promote products derived from this software without//   specific prior written permission.//// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE// POSSIBILITY OF SUCH DAMAGE.//// Author: keir@google.com (Keir Mierle)//// Minimize 0.5 (10 - x)^2 using jacobian matrix computed using// numeric differentiation.#include <vector>#include "ceres/ceres.h"#include "gflags/gflags.h"#include "glog/logging.h"using ceres::NumericDiffCostFunction;using ceres::CENTRAL;using ceres::SizedCostFunction;using ceres::CostFunction;using ceres::Problem;using ceres::Solver;using ceres::Solve;class ResidualWithNoDerivative  : public SizedCostFunction<1 /* number of residuals */,                             1 /* size of first parameter */> { public:  virtual ~ResidualWithNoDerivative() {}  virtual bool Evaluate(double const* const* parameters,                        double* residuals,                        double** jacobians) const {    (void) jacobians;  // Ignored; filled in by numeric differentiation.    // f(x) = 10 - x.    residuals[0] = 10 - parameters[0][0];    return true;  }};int main(int argc, char** argv) {  google::ParseCommandLineFlags(&argc, &argv, true);  google::InitGoogleLogging(argv[0]);  // The variable to solve for with its initial value.  double initial_x = 5.0;  double x = initial_x;  // Set up the only cost function (also known as residual). This uses  // numeric differentiation to obtain the derivative (jacobian).  CostFunction* cost =      new NumericDiffCostFunction<ResidualWithNoDerivative, CENTRAL, 1, 1> (          new ResidualWithNoDerivative, ceres::TAKE_OWNERSHIP);  // Build the problem.  Problem problem;  problem.AddResidualBlock(cost, NULL, &x);  // Run the solver!  Solver::Options options;  options.max_num_iterations = 10;  options.linear_solver_type = ceres::DENSE_QR;  options.minimizer_progress_to_stdout = true;  Solver::Summary summary;  Solve(options, &problem, &summary);  std::cout << summary.BriefReport() << "\n";  std::cout << "x : " << initial_x            << " -> " << x << "\n";  return 0;}
 |