Initial commit of Ceres Solver.
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+// 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"
+
+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;
+}
