# General Unconstrained Minimization

## Modeling

### `FirstOrderFunction`

class FirstOrderFunction

  Instances of `FirstOrderFunction` implement the evaluation of a function and its gradient.

      class FirstOrderFunction {
        public:
         virtual ~FirstOrderFunction() {}
         virtual bool Evaluate(const double* const parameters,
                               double* cost,
                               double* gradient) const = 0;
         virtual int NumParameters() const = 0;
      };

bool FirstOrderFunction::Evaluate(const double *const parameters, double *cost, double *gradient) const

  Evaluate the cost/value of the function. If `gradient` is not `nullptr` then evaluate the gradient too. If evaluation is successful return, `true` else return `false`.

  `cost` guaranteed to be never `nullptr`, `gradient` can be `nullptr`.

int FirstOrderFunction::NumParameters() const

  Number of parameters in the domain of the function.

### `GradientProblem`

class GradientProblem

    class GradientProblem {
     public:
      explicit GradientProblem(FirstOrderFunction* function);
      GradientProblem(FirstOrderFunction* function,
                      Manifold* manifold);
      int NumParameters() const;
      int NumTangentParameters() const;
      bool Evaluate(const double* parameters, double* cost, double* gradient) const;
      bool Plus(const double* x, const double* delta, double* x_plus_delta) const;
    };

Instances of `GradientProblem` represent general non-linear optimization problems that must be solved using just the value of the objective function and its gradient. Unlike the `Problem` class, which can only be used to model non-linear least squares problems, instances of `GradientProblem` not restricted in the form of the objective function.

Structurally `GradientProblem` is a composition of a `FirstOrderFunction` and optionally a `Manifold`.

The `FirstOrderFunction` is responsible for evaluating the cost and gradient of the objective function.

The `Manifold` is responsible for going back and forth between the ambient space and the local tangent space. When a `Manifold` is not provided, then the tangent space is assumed to coincide with the ambient Euclidean space that the gradient vector lives in.

The constructor takes ownership of the `FirstOrderFunction` and `Manifold` objects passed to it.

void Solve(const GradientProblemSolver::Options &options, const GradientProblem &problem, double *parameters, GradientProblemSolver::Summary *summary)

  Solve the given `GradientProblem` using the values in `parameters` as the initial guess of the solution.

## Solving

### `GradientProblemSolver::Options`

class GradientProblemSolver::Options

  `GradientProblemSolver::Options` controls the overall behavior of the solver. We list the various settings and their default values below.

bool GradientProblemSolver::Options::IsValid(std::string *error) const

  Validate the values in the options struct and returns true on success. If there is a problem, the method returns false with `error` containing a textual description of the cause.

LineSearchDirectionType GradientProblemSolver::Options::line_search_direction_type

  Default: `LBFGS`

  Choices are `STEEPEST_DESCENT`, `NONLINEAR_CONJUGATE_GRADIENT`, `BFGS` and `LBFGS`.

LineSearchType GradientProblemSolver::Options::line_search_type

  Default: `WOLFE`

  Choices are `ARMIJO` and `WOLFE` (strong Wolfe conditions). Note that in order for the assumptions underlying the `BFGS` and `LBFGS` line search direction algorithms to be guaranteed to be satisfied, the `WOLFE` line search should be used.

NonlinearConjugateGradientType GradientProblemSolver::Options::nonlinear_conjugate_gradient_type

  Default: `FLETCHER_REEVES`

  Choices are `FLETCHER_REEVES`, `POLAK_RIBIERE` and `HESTENES_STIEFEL`.

int GradientProblemSolver::Options::max_lbfs_rank

  Default: 20

  The L-BFGS hessian approximation is a low rank approximation to the inverse of the Hessian matrix. The rank of the approximation determines (linearly) the space and time complexity of using the approximation. Higher the rank, the better is the quality of the approximation. The increase in quality is however is bounded for a number of reasons.

  > 1. The method only uses secant information and not actual derivatives.

  > 2. The Hessian approximation is constrained to be positive definite.

  So increasing this rank to a large number will cost time and space complexity without the corresponding increase in solution quality. There are no hard and fast rules for choosing the maximum rank. The best choice usually requires some problem specific experimentation.

bool GradientProblemSolver::Options::use_approximate_eigenvalue_bfgs_scaling

  > Default: `false`

  > As part of the `BFGS` update step / `LBFGS` right-multiply step, the initial inverse Hessian approximation is taken to be the Identity. However, [Oren] showed that using instead \(I * \gamma\), where \(\gamma\) is a scalar chosen to approximate an eigenvalue of the true inverse Hessian can result in improved convergence in a wide variety of cases. Setting `use_approximate_eigenvalue_bfgs_scaling` to true enables this scaling in `BFGS` (before first iteration) and `LBFGS` (at each iteration).

  > Precisely, approximate eigenvalue scaling equates to

  > \[\gamma = \frac{y_k' s_k}{y_k' y_k}\]

  > With:

  \[y_k = \nabla f_{k+1} - \nabla f_k\]
  \[s_k = x_{k+1} - x_k\]

  Where \(f()\) is the line search objective and \(x\) the vector of parameter values [NocedalWright].

  It is important to note that approximate eigenvalue scaling does not always improve convergence, and that it can in fact significantly degrade performance for certain classes of problem, which is why it is disabled by default. In particular it can degrade performance when the sensitivity of the problem to different parameters varies significantly, as in this case a single scalar factor fails to capture this variation and detrimentally downscales parts of the Jacobian approximation which correspond to low-sensitivity parameters. It can also reduce the robustness of the solution to errors in the Jacobians.

LineSearchInterpolationType GradientProblemSolver::Options::line_search_interpolation_type

  Default: `CUBIC`

  Degree of the polynomial used to approximate the objective function. Valid values are `BISECTION`, `QUADRATIC` and `CUBIC`.

double GradientProblemSolver::Options::min_line_search_step_size

  The line search terminates if:

  \[\|\Delta x_k\|_\infty < \text{min_line_search_step_size}\]

  where \(\|\cdot\|_\infty\) refers to the max norm, and \(\Delta x_k\) is the step change in the parameter values at the \(k\)-th iteration.

double GradientProblemSolver::Options::line_search_sufficient_function_decrease

  Default: `1e-4`

  Solving the line search problem exactly is computationally prohibitive. Fortunately, line search based optimization algorithms can still guarantee convergence if instead of an exact solution, the line search algorithm returns a solution which decreases the value of the objective function sufficiently. More precisely, we are looking for a step size s.t.

  \[f(\text{step_size}) \le f(0) + \text{sufficient_decrease} * [f'(0) * \text{step_size}]\]

  This condition is known as the Armijo condition.

double GradientProblemSolver::Options::max_line_search_step_contraction

  Default: `1e-3`

  In each iteration of the line search,

  \[\text{new_step_size} \geq \text{max_line_search_step_contraction} * \text{step_size}\]

  Note that by definition, for contraction:

  \[0 < \text{max_step_contraction} < \text{min_step_contraction} < 1\]

double GradientProblemSolver::Options::min_line_search_step_contraction

  Default: `0.6`

  In each iteration of the line search,

  \[\text{new_step_size} \leq \text{min_line_search_step_contraction} * \text{step_size}\]

  Note that by definition, for contraction:

  \[0 < \text{max_step_contraction} < \text{min_step_contraction} < 1\]

int GradientProblemSolver::Options::max_num_line_search_step_size_iterations

  Default: `20`

  Maximum number of trial step size iterations during each line search, if a step size satisfying the search conditions cannot be found within this number of trials, the line search will stop.

  As this is an ‘artificial’ constraint (one imposed by the user, not the underlying math), if `WOLFE` line search is being used, and points satisfying the Armijo sufficient (function) decrease condition have been found during the current search (in \(\leq\) `max_num_line_search_step_size_iterations`). Then, the step size with the lowest function value which satisfies the Armijo condition will be returned as the new valid step, even though it does not satisfy the strong Wolfe conditions. This behaviour protects against early termination of the optimizer at a sub-optimal point.

int GradientProblemSolver::Options::max_num_line_search_direction_restarts

  Default: `5`

  Maximum number of restarts of the line search direction algorithm before terminating the optimization. Restarts of the line search direction algorithm occur when the current algorithm fails to produce a new descent direction. This typically indicates a numerical failure, or a breakdown in the validity of the approximations used.

double GradientProblemSolver::Options::line_search_sufficient_curvature_decrease

  Default: `0.9`

  The strong Wolfe conditions consist of the Armijo sufficient decrease condition, and an additional requirement that the step size be chosen s.t. the magnitude (‘strong’ Wolfe conditions) of the gradient along the search direction decreases sufficiently. Precisely, this second condition is that we seek a step size s.t.

  \[\|f'(\text{step_size})\| \leq \text{sufficient_curvature_decrease} * \|f'(0)\|\]

  Where \(f()\) is the line search objective and \(f'()\) is the derivative of \(f\) with respect to the step size: \(\frac{d f}{d~\text{step size}}\).

double GradientProblemSolver::Options::max_line_search_step_expansion

  Default: `10.0`

  During the bracketing phase of a Wolfe line search, the step size is increased until either a point satisfying the Wolfe conditions is found, or an upper bound for a bracket containing a point satisfying the conditions is found. Precisely, at each iteration of the expansion:

  \[\text{new_step_size} \leq \text{max_step_expansion} * \text{step_size}\]

  By definition for expansion

  \[\text{max_step_expansion} > 1.0\]

int GradientProblemSolver::Options::max_num_iterations

  Default: `50`

  Maximum number of iterations for which the solver should run.

double GradientProblemSolver::Options::max_solver_time_in_seconds

  Default: `1e6` Maximum amount of time for which the solver should run.

double GradientProblemSolver::Options::function_tolerance

  Default: `1e-6`

  Solver terminates if

  \[\frac{|\Delta \text{cost}|}{\text{cost}} \leq \text{function_tolerance}\]

  where, \(\Delta \text{cost}\) is the change in objective function value (up or down) in the current iteration of the line search.

double GradientProblemSolver::Options::gradient_tolerance

  Default: `1e-10`

  Solver terminates if

  \[\|x - \Pi \boxplus(x, -g(x))\|_\infty \leq \text{gradient_tolerance}\]

  where \(\|\cdot\|_\infty\) refers to the max norm, \(\Pi\) is projection onto the bounds constraints and \(\boxplus\) is Plus operation for the manifold associated with the parameter vector.

double GradientProblemSolver::Options::parameter_tolerance

  Default: `1e-8`

  Solver terminates if

  \[\|\Delta x\| \leq (\|x\| + \text{parameter_tolerance}) * \text{parameter_tolerance}\]

  where \(\Delta x\) is the step computed by the linear solver in the current iteration of the line search.

LoggingType GradientProblemSolver::Options::logging_type

  Default: `PER_MINIMIZER_ITERATION`

bool GradientProblemSolver::Options::minimizer_progress_to_stdout

  Default: `false`

  By default the `Minimizer` progress is logged to `STDERR` depending on the `vlog` level. If this flag is set to true, and `GradientProblemSolver::Options::logging_type` is not `SILENT`, the logging output is sent to `STDOUT`.

  The progress display looks like

      0: f: 2.317806e+05 d: 0.00e+00 g: 3.19e-01 h: 0.00e+00 s: 0.00e+00 e:  0 it: 2.98e-02 tt: 8.50e-02
      1: f: 2.312019e+05 d: 5.79e+02 g: 3.18e-01 h: 2.41e+01 s: 1.00e+00 e:  1 it: 4.54e-02 tt: 1.31e-01
      2: f: 2.300462e+05 d: 1.16e+03 g: 3.17e-01 h: 4.90e+01 s: 2.54e-03 e:  1 it: 4.96e-02 tt: 1.81e-01

  Here

  1. `f` is the value of the objective function.

  2. `d` is the change in the value of the objective function if the step computed in this iteration is accepted.

  3. `g` is the max norm of the gradient.

  4. `h` is the change in the parameter vector.

  5. `s` is the optimal step length computed by the line search.

  6. `it` is the time take by the current iteration.

  7. `tt` is the total time taken by the minimizer.

std::vector<IterationCallback> GradientProblemSolver::Options::callbacks

  Callbacks that are executed at the end of each iteration of the `Minimizer`. They are executed in the order that they are specified in this vector. By default, parameter blocks are updated only at the end of the optimization, i.e., when the `Minimizer` terminates. This behavior is controlled by `GradientProblemSolver::Options::update_state_every_iteration`. If the user wishes to have access to the update parameter blocks when his/her callbacks are executed, then set `GradientProblemSolver::Options::update_state_every_iteration` to true.

  The solver does NOT take ownership of these pointers.

bool GradientProblemSolver::Options::update_state_every_iteration

  Default: `false`

  Normally the parameter vector is only updated when the solver terminates. Setting this to true updates it every iteration. This setting is useful when building an interactive application using Ceres and using an `IterationCallback`.

### `GradientProblemSolver::Summary`

class GradientProblemSolver::Summary

  Summary of the various stages of the solver after termination.

std::string GradientProblemSolver::Summary::BriefReport() const

  A brief one line description of the state of the solver after termination.

std::string GradientProblemSolver::Summary::FullReport() const

  A full multiline description of the state of the solver after termination.

bool GradientProblemSolver::Summary::IsSolutionUsable() const

  Whether the solution returned by the optimization algorithm can be relied on to be numerically sane. This will be the case if GradientProblemSolver::Summary:termination_type is set to CONVERGENCE, USER_SUCCESS or NO_CONVERGENCE, i.e., either the solver converged by meeting one of the convergence tolerances or because the user indicated that it had converged or it ran to the maximum number of iterations or time.

TerminationType GradientProblemSolver::Summary::termination_type

  The cause of the minimizer terminating.

std::string GradientProblemSolver::Summary::message

  Reason why the solver terminated.

double GradientProblemSolver::Summary::initial_cost

  Cost of the problem (value of the objective function) before the optimization.

double GradientProblemSolver::Summary::final_cost

  Cost of the problem (value of the objective function) after the optimization.

std::vector<IterationSummary> GradientProblemSolver::Summary::iterations

  `IterationSummary` for each minimizer iteration in order.

int num_cost_evaluations

  Number of times the cost (and not the gradient) was evaluated.

int num_gradient_evaluations

  Number of times the gradient (and the cost) were evaluated.

double GradientProblemSolver::Summary::total_time_in_seconds

  Time (in seconds) spent in the solver.

double GradientProblemSolver::Summary::cost_evaluation_time_in_seconds

  Time (in seconds) spent evaluating the cost vector.

double GradientProblemSolver::Summary::gradient_evaluation_time_in_seconds

  Time (in seconds) spent evaluating the gradient vector.

int GradientProblemSolver::Summary::num_parameters

  Number of parameters in the problem.

int GradientProblemSolver::Summary::num_tangent_parameters

  Dimension of the tangent space of the problem. This is different from `GradientProblemSolver::Summary::num_parameters` if a `Manifold` object is used.

LineSearchDirectionType GradientProblemSolver::Summary::line_search_direction_type

  Type of line search direction used.

LineSearchType GradientProblemSolver::Summary::line_search_type

  Type of the line search algorithm used.

LineSearchInterpolationType GradientProblemSolver::Summary::line_search_interpolation_type

  When performing line search, the degree of the polynomial used to approximate the objective function.

NonlinearConjugateGradientType GradientProblemSolver::Summary::nonlinear_conjugate_gradient_type

  If the line search direction is NONLINEAR_CONJUGATE_GRADIENT, then this indicates the particular variant of non-linear conjugate gradient used.

int GradientProblemSolver::Summary::max_lbfgs_rank

  If the type of the line search direction is LBFGS, then this indicates the rank of the Hessian approximation.