[−][src]Trait opencv::prelude::DownhillSolver

pub trait DownhillSolver: MinProblemSolver {
    pub fn as_raw_DownhillSolver(&self) -> *const c_void;

    pub fn as_raw_mut_DownhillSolver(&mut self) -> *mut c_void;

    pub fn get_init_step(&self, step: &mut dyn ToOutputArray) -> Result<()> { ... }

    pub fn set_init_step(&mut self, step: &dyn ToInputArray) -> Result<()> { ... }
}

This class is used to perform the non-linear non-constrained minimization of a function,

defined on an n-dimensional Euclidean space, using the Nelder-Mead method, also known as downhill simplex method. The basic idea about the method can be obtained from http://en.wikipedia.org/wiki/Nelder-Mead_method.

It should be noted, that this method, although deterministic, is rather a heuristic and therefore may converge to a local minima, not necessary a global one. It is iterative optimization technique, which at each step uses an information about the values of a function evaluated only at n+1 points, arranged as a simplex in n-dimensional space (hence the second name of the method). At each step new point is chosen to evaluate function at, obtained value is compared with previous ones and based on this information simplex changes it's shape , slowly moving to the local minimum. Thus this method is using only function values to make decision, on contrary to, say, Nonlinear Conjugate Gradient method (which is also implemented in optim).

Algorithm stops when the number of function evaluations done exceeds termcrit.maxCount, when the function values at the vertices of simplex are within termcrit.epsilon range or simplex becomes so small that it can enclosed in a box with termcrit.epsilon sides, whatever comes first, for some defined by user positive integer termcrit.maxCount and positive non-integer termcrit.epsilon.

Note: DownhillSolver is a derivative of the abstract interface cv::MinProblemSolver, which in turn is derived from the Algorithm interface and is used to encapsulate the functionality, common to all non-linear optimization algorithms in the optim module.

Note: term criteria should meet following condition:

ⓘ
   termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0

Required methods

pub fn as_raw_DownhillSolver(&self) -> *const c_void[src]

pub fn as_raw_mut_DownhillSolver(&mut self) -> *mut c_void[src]

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Provided methods

pub fn get_init_step(&self, step: &mut dyn ToOutputArray) -> Result<()>[src]

Returns the initial step that will be used in downhill simplex algorithm.

Parameters

  • step: Initial step that will be used in algorithm. Note, that although corresponding setter accepts column-vectors as well as row-vectors, this method will return a row-vector.

See also

DownhillSolver::setInitStep

pub fn set_init_step(&mut self, step: &dyn ToInputArray) -> Result<()>[src]

Sets the initial step that will be used in downhill simplex algorithm.

Step, together with initial point (given in DownhillSolver::minimize) are two n-dimensional vectors that are used to determine the shape of initial simplex. Roughly said, initial point determines the position of a simplex (it will become simplex's centroid), while step determines the spread (size in each dimension) of a simplex. To be more precise, if inline formula are the initial step and initial point respectively, the vertices of a simplex will be: inline formula and inline formula for inline formula where inline formula denotes projections of the initial step of n-th coordinate (the result of projection is treated to be vector given by inline formula, where inline formula form canonical basis)

Parameters

  • step: Initial step that will be used in algorithm. Roughly said, it determines the spread (size in each dimension) of an initial simplex.
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Implementations

impl<'_> dyn DownhillSolver + '_[src]

pub fn create(
    f: &Ptr<dyn MinProblemSolver_Function>,
    init_step: &dyn ToInputArray,
    termcrit: TermCriteria
) -> Result<Ptr<dyn DownhillSolver>>[src]

This function returns the reference to the ready-to-use DownhillSolver object.

All the parameters are optional, so this procedure can be called even without parameters at all. In this case, the default values will be used. As default value for terminal criteria are the only sensible ones, MinProblemSolver::setFunction() and DownhillSolver::setInitStep() should be called upon the obtained object, if the respective parameters were not given to create(). Otherwise, the two ways (give parameters to createDownhillSolver() or miss them out and call the MinProblemSolver::setFunction() and DownhillSolver::setInitStep()) are absolutely equivalent (and will drop the same errors in the same way, should invalid input be detected).

Parameters

  • f: Pointer to the function that will be minimized, similarly to the one you submit via MinProblemSolver::setFunction.
  • initStep: Initial step, that will be used to construct the initial simplex, similarly to the one you submit via MinProblemSolver::setInitStep.
  • termcrit: Terminal criteria to the algorithm, similarly to the one you submit via MinProblemSolver::setTermCriteria.

C++ default parameters

  • f: PtrMinProblemSolver::Function()
  • init_step: Mat_(1,1,0.0)
  • termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)

Implementors

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