helloworld.cc

// A simple example of using the Ceres minimizer.
//
// Minimize 0.5 (10 - x)^2 using jacobian matrix computed using
// automatic differentiation.

#include "ceres/ceres.h"
#include "glog/logging.h"

using ceres::AutoDiffCostFunction;
using ceres::CostFunction;
using ceres::Problem;
using ceres::Solver;
using ceres::Solve;

// A templated cost functor that implements the residual r = 10 -
// x. The method operator() is templated so that we can then use an
// automatic differentiation wrapper around it to generate its
// derivatives.
struct CostFunctor {
    template<typename T>
    bool operator()(const T *const x, T *residual) const {
        residual[0] = 10.0 - x[0];
        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). This uses
    // auto-differentiation to obtain the derivative (jacobian).
    CostFunction *cost_function =
                         new AutoDiffCostFunction<CostFunctor, 1, 1>(new CostFunctor);
    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;
}