cv::DownhillSolver クラス

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

#include <optim.hpp>

cv::MinProblemSolverを継承しています。

公開メンバ関数
virtual void	getInitStep (OutputArray step) const =0
	Returns the initial step that will be used in downhill simplex algorithm. [詳解]
virtual void	setInitStep (InputArray step)=0
	Sets the initial step that will be used in downhill simplex algorithm. [詳解]
基底クラス cv::MinProblemSolver に属する継承公開メンバ関数
virtual Ptr< Function >	getFunction () const =0
	Getter for the optimized function. [詳解]
virtual void	setFunction (const Ptr< Function > &f)=0
	Setter for the optimized function. [詳解]
virtual TermCriteria	getTermCriteria () const =0
	Getter for the previously set terminal criteria for this algorithm. [詳解]
virtual void	setTermCriteria (const TermCriteria &termcrit)=0
	Set terminal criteria for solver. [詳解]
virtual double	minimize (InputOutputArray x)=0
	actually runs the algorithm and performs the minimization. [詳解]
基底クラス cv::Algorithm に属する継承公開メンバ関数
virtual CV_WRAP void	clear ()
	Clears the algorithm state [詳解]
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage [詳解]
CV_WRAP void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。
virtual CV_WRAP void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage [詳解]
virtual CV_WRAP bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read [詳解]
virtual CV_WRAP void	save (const String &filename) const
virtual CV_WRAP String	getDefaultName () const

静的公開メンバ関数
static Ptr< DownhillSolver >	create (const Ptr< MinProblemSolver::Function > &f=Ptr< MinProblemSolver::Function >(), InputArray initStep=Mat_< double >(1, 1, 0.0), TermCriteria termcrit=TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5000, 0.000001))
	This function returns the reference to the ready-to-use DownhillSolver object. [詳解]
基底クラス cv::Algorithm に属する継承静的公開メンバ関数
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node [詳解]
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file [詳解]
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String [詳解]

その他の継承メンバ
基底クラス cv::Algorithm に属する継承限定公開メンバ関数
void	writeFormat (FileStorage &fs) const

詳解

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.

覚え書き

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.

term criteria should meet following condition:

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

関数詳解

create()

static Ptr< DownhillSolver > cv::DownhillSolver::create ( const Ptr< MinProblemSolver::Function > &  f = Ptr< MinProblemSolver::Function >(), InputArray  initStep = Mat_< double >(1, 1, 0.0), TermCriteria  termcrit = TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS, 5000, 0.000001)  )

static

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).

引数

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.

getInitStep()

virtual void cv::DownhillSolver::getInitStep ( OutputArray  step ) const

pure virtual

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

引数

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.

参照

DownhillSolver::setInitStep

setInitStep()

virtual void cv::DownhillSolver::setInitStep ( InputArray  step )

pure virtual

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 are the initial step and initial point respectively, the vertices of a simplex will be: and for where denotes projections of the initial step of n-th coordinate (the result of projection is treated to be vector given by , where form canonical basis)

引数

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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このクラス詳解は次のファイルから抽出されました:

• optim.hpp