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+.. highlight:: c++
+
+.. default-domain:: cpp
+
+.. _chapter-gradient_tutorial:
+
+==================================
+General Unconstrained Minimization
+==================================
+
+While much of Ceres Solver is devoted to solving non-linear least
+squares problems, internally it contains a solver that can solve
+general unconstrained optimization problems using just their objective
+function value and gradients. The ``GradientProblem`` and
+``GradientProblemSolver`` objects give the user access to this solver.
+
+So without much further ado, let us look at how one goes about using
+them.
+
+Rosenbrock's Function
+=====================
+
+We consider the minimization of the famous `Rosenbrock's function
+<http://en.wikipedia.org/wiki/Rosenbrock_function>`_ [#f1]_.
+
+We begin by defining an instance of the ``FirstOrderFunction``
+interface. This is the object that is responsible for computing the
+objective function value and the gradient (if required). This is the
+analog of the :class:`CostFunction` when defining non-linear least
+squares problems in Ceres.
+
+.. code::
+
+ class Rosenbrock : public ceres::FirstOrderFunction {
+ public:
+ virtual bool Evaluate(const double* parameters,
+ double* cost,
+ double* gradient) const {
+ const double x = parameters[0];
+ const double y = parameters[1];
+
+ cost[0] = (1.0 - x) * (1.0 - x) + 100.0 * (y - x * x) * (y - x * x);
+ if (gradient != NULL) {
+ gradient[0] = -2.0 * (1.0 - x) - 200.0 * (y - x * x) * 2.0 * x;
+ gradient[1] = 200.0 * (y - x * x);
+ }
+ return true;
+ }
+
+ virtual int NumParameters() const { return 2; }
+ };
+
+
+Minimizing it then is a straightforward matter of constructing a
+:class:`GradientProblem` object and calling :func:`Solve` on it.
+
+.. code::
+
+ double parameters[2] = {-1.2, 1.0};
+
+ ceres::GradientProblem problem(new Rosenbrock());
+
+ ceres::GradientProblemSolver::Options options;
+ options.minimizer_progress_to_stdout = true;
+ ceres::GradientProblemSolver::Summary summary;
+ ceres::Solve(options, problem, parameters, &summary);
+
+ std::cout << summary.FullReport() << "\n";
+
+Executing this code results, solve the problem using limited memory
+`BFGS
+<http://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm>`_
+algorithm.
+
+.. code-block:: bash
+
+ 0: f: 2.420000e+01 d: 0.00e+00 g: 2.16e+02 h: 0.00e+00 s: 0.00e+00 e: 0 it: 2.00e-05 tt: 2.00e-05
+ 1: f: 4.280493e+00 d: 1.99e+01 g: 1.52e+01 h: 2.01e-01 s: 8.62e-04 e: 2 it: 7.32e-05 tt: 2.19e-04
+ 2: f: 3.571154e+00 d: 7.09e-01 g: 1.35e+01 h: 3.78e-01 s: 1.34e-01 e: 3 it: 2.50e-05 tt: 2.68e-04
+ 3: f: 3.440869e+00 d: 1.30e-01 g: 1.73e+01 h: 1.36e-01 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 2.92e-04
+ 4: f: 3.213597e+00 d: 2.27e-01 g: 1.55e+01 h: 1.06e-01 s: 4.59e-01 e: 1 it: 2.86e-06 tt: 3.14e-04
+ 5: f: 2.839723e+00 d: 3.74e-01 g: 1.05e+01 h: 1.34e-01 s: 5.24e-01 e: 1 it: 2.86e-06 tt: 3.36e-04
+ 6: f: 2.448490e+00 d: 3.91e-01 g: 1.29e+01 h: 3.04e-01 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 3.58e-04
+ 7: f: 1.943019e+00 d: 5.05e-01 g: 4.00e+00 h: 8.81e-02 s: 7.43e-01 e: 1 it: 4.05e-06 tt: 3.79e-04
+ 8: f: 1.731469e+00 d: 2.12e-01 g: 7.36e+00 h: 1.71e-01 s: 4.60e-01 e: 2 it: 9.06e-06 tt: 4.06e-04
+ 9: f: 1.503267e+00 d: 2.28e-01 g: 6.47e+00 h: 8.66e-02 s: 1.00e+00 e: 1 it: 3.81e-06 tt: 4.33e-04
+ 10: f: 1.228331e+00 d: 2.75e-01 g: 2.00e+00 h: 7.70e-02 s: 7.90e-01 e: 1 it: 3.81e-06 tt: 4.54e-04
+ 11: f: 1.016523e+00 d: 2.12e-01 g: 5.15e+00 h: 1.39e-01 s: 3.76e-01 e: 2 it: 1.00e-05 tt: 4.82e-04
+ 12: f: 9.145773e-01 d: 1.02e-01 g: 6.74e+00 h: 7.98e-02 s: 1.00e+00 e: 1 it: 3.10e-06 tt: 5.03e-04
+ 13: f: 7.508302e-01 d: 1.64e-01 g: 3.88e+00 h: 5.76e-02 s: 4.93e-01 e: 1 it: 2.86e-06 tt: 5.25e-04
+ 14: f: 5.832378e-01 d: 1.68e-01 g: 5.56e+00 h: 1.42e-01 s: 1.00e+00 e: 1 it: 3.81e-06 tt: 5.47e-04
+ 15: f: 3.969581e-01 d: 1.86e-01 g: 1.64e+00 h: 1.17e-01 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 5.68e-04
+ 16: f: 3.171557e-01 d: 7.98e-02 g: 3.84e+00 h: 1.18e-01 s: 3.97e-01 e: 2 it: 9.06e-06 tt: 5.94e-04
+ 17: f: 2.641257e-01 d: 5.30e-02 g: 3.27e+00 h: 6.14e-02 s: 1.00e+00 e: 1 it: 3.10e-06 tt: 6.16e-04
+ 18: f: 1.909730e-01 d: 7.32e-02 g: 5.29e-01 h: 8.55e-02 s: 6.82e-01 e: 1 it: 4.05e-06 tt: 6.42e-04
+ 19: f: 1.472012e-01 d: 4.38e-02 g: 3.11e+00 h: 1.20e-01 s: 3.47e-01 e: 2 it: 1.00e-05 tt: 6.69e-04
+ 20: f: 1.093558e-01 d: 3.78e-02 g: 2.97e+00 h: 8.43e-02 s: 1.00e+00 e: 1 it: 3.81e-06 tt: 6.91e-04
+ 21: f: 6.710346e-02 d: 4.23e-02 g: 1.42e+00 h: 9.64e-02 s: 8.85e-01 e: 1 it: 3.81e-06 tt: 7.12e-04
+ 22: f: 3.993377e-02 d: 2.72e-02 g: 2.30e+00 h: 1.29e-01 s: 4.63e-01 e: 2 it: 9.06e-06 tt: 7.39e-04
+ 23: f: 2.911794e-02 d: 1.08e-02 g: 2.55e+00 h: 6.55e-02 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 7.62e-04
+ 24: f: 1.457683e-02 d: 1.45e-02 g: 2.77e-01 h: 6.37e-02 s: 6.14e-01 e: 1 it: 3.81e-06 tt: 7.84e-04
+ 25: f: 8.577515e-03 d: 6.00e-03 g: 2.86e+00 h: 1.40e-01 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 8.05e-04
+ 26: f: 3.486574e-03 d: 5.09e-03 g: 1.76e-01 h: 1.23e-02 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 8.27e-04
+ 27: f: 1.257570e-03 d: 2.23e-03 g: 1.39e-01 h: 5.08e-02 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 8.48e-04
+ 28: f: 2.783568e-04 d: 9.79e-04 g: 6.20e-01 h: 6.47e-02 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 8.69e-04
+ 29: f: 2.533399e-05 d: 2.53e-04 g: 1.68e-02 h: 1.98e-03 s: 1.00e+00 e: 1 it: 3.81e-06 tt: 8.91e-04
+ 30: f: 7.591572e-07 d: 2.46e-05 g: 5.40e-03 h: 9.27e-03 s: 1.00e+00 e: 1 it: 3.81e-06 tt: 9.12e-04
+ 31: f: 1.902460e-09 d: 7.57e-07 g: 1.62e-03 h: 1.89e-03 s: 1.00e+00 e: 1 it: 2.86e-06 tt: 9.33e-04
+ 32: f: 1.003030e-12 d: 1.90e-09 g: 3.50e-05 h: 3.52e-05 s: 1.00e+00 e: 1 it: 3.10e-06 tt: 9.54e-04
+ 33: f: 4.835994e-17 d: 1.00e-12 g: 1.05e-07 h: 1.13e-06 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 9.81e-04
+ 34: f: 1.885250e-22 d: 4.84e-17 g: 2.69e-10 h: 1.45e-08 s: 1.00e+00 e: 1 it: 4.05e-06 tt: 1.00e-03
+
+ Solver Summary (v 1.12.0-lapack-suitesparse-cxsparse-no_openmp)
+
+ Parameters 2
+ Line search direction LBFGS (20)
+ Line search type CUBIC WOLFE
+
+
+ Cost:
+ Initial 2.420000e+01
+ Final 1.885250e-22
+ Change 2.420000e+01
+
+ Minimizer iterations 35
+
+ Time (in seconds):
+
+ Cost evaluation 0.000
+ Gradient evaluation 0.000
+ Total 0.003
+
+ Termination: CONVERGENCE (Gradient tolerance reached. Gradient max norm: 9.032775e-13 <= 1.000000e-10)
+
+.. rubric:: Footnotes
+
+.. [#f1] `examples/rosenbrock.cc
+ <https://ceres-solver.googlesource.com/ceres-solver/+/master/examples/rosenbrock.cc>`_