// Ceres Solver - A fast non-linear least squares minimizer

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// http://ceres-solver.org/

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// Author: keir@google.com (Keir Mierle)

//

// A simple example of using the Ceres minimizer.

//

// Minimize 0.5 (10 - x)^2 using analytic jacobian matrix.

#include <vector>

#include "ceres/ceres.h"

#include "glog/logging.h"

using ceres::CostFunction;

using ceres::SizedCostFunction;

using ceres::Problem;

using ceres::Solver;

using ceres::Solve;

// A CostFunction implementing analytically derivatives for the

// function f(x) = 10 - x.

class QuadraticCostFunction

  : public SizedCostFunction<1 /* number of residuals */,

                             1 /* size of first parameter */> {

 public:

  virtual ~QuadraticCostFunction() {}

  virtual bool Evaluate(double const* const* parameters,

                        double* residuals,

                        double** jacobians) const {

    double x = parameters[0][0];

    // f(x) = 10 - x.

    residuals[0] = 10 - x;

    // f'(x) = -1. Since there's only 1 parameter and that parameter

    // has 1 dimension, there is only 1 element to fill in the

    // jacobians.

    //

    // Since the Evaluate function can be called with the jacobians

    // pointer equal to NULL, the Evaluate function must check to see

    // if jacobians need to be computed.

    //

    // For this simple problem it is overkill to check if jacobians[0]

    // is NULL, but in general when writing more complex

    // CostFunctions, it is possible that Ceres may only demand the

    // derivatives w.r.t. a subset of the parameter blocks.

    if (jacobians != NULL && jacobians[0] != NULL) {

      jacobians[0][0] = -1;

    }

    return true;

  }

};

int main(int argc, char** argv) {

  google::InitGoogleLogging(argv[0]);

  // The variable to solve for with its initial value. It will be

  // mutated in place by the solver.

  double x = 0.5;

  const double initial_x = x;

  // Build the problem.

  Problem problem;

  // Set up the only cost function (also known as residual).

  CostFunction* cost_function = new QuadraticCostFunction;

  problem.AddResidualBlock(cost_function, NULL, &x);

  // Run the solver!

  Solver::Options options;

  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;

}