OpenCV
3.0.0dev
Open Source Computer Vision

This class is used to perform the nonlinear nonconstrained minimization of a function,. More...
#include "optim.hpp"
Public Member Functions  
virtual void  getInitStep (OutputArray step) const =0 
Returns the initial step that will be used in downhill simplex algorithm. More...  
virtual void  setInitStep (InputArray step)=0 
Sets the initial step that will be used in downhill simplex algorithm. More...  
Public Member Functions inherited from cv::MinProblemSolver  
virtual Ptr< Function >  getFunction () const =0 
Getter for the optimized function. More...  
virtual TermCriteria  getTermCriteria () const =0 
Getter for the previously set terminal criteria for this algorithm. More...  
virtual double  minimize (InputOutputArray x)=0 
actually runs the algorithm and performs the minimization. More...  
virtual void  setFunction (const Ptr< Function > &f)=0 
Setter for the optimized function. More...  
virtual void  setTermCriteria (const TermCriteria &termcrit)=0 
Set terminal criteria for solver. More...  
Public Member Functions inherited from cv::Algorithm  
Algorithm ()  
virtual  ~Algorithm () 
virtual void  clear () 
Clears the algorithm state. More...  
virtual bool  empty () const 
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...  
virtual String  getDefaultName () const 
virtual void  read (const FileNode &fn) 
Reads algorithm parameters from a file storage. More...  
virtual void  save (const String &filename) const 
virtual void  write (FileStorage &fs) const 
Stores algorithm parameters in a file storage. More...  
Static Public Member Functions  
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 readytouse DownhillSolver object. More...  
Static Public Member Functions inherited from cv::Algorithm  
template<typename _Tp >  
static Ptr< _Tp >  load (const String &filename, const String &objname=String()) 
Loads algorithm from the file. More...  
template<typename _Tp >  
static Ptr< _Tp >  loadFromString (const String &strModel, const String &objname=String()) 
Loads algorithm from a String. More...  
template<typename _Tp >  
static Ptr< _Tp >  read (const FileNode &fn) 
Reads algorithm from the file node. More...  
This class is used to perform the nonlinear nonconstrained minimization of a function,.
defined on an n
dimensional Euclidean space, using the NelderMead method, also known as downhill simplex method**. The basic idea about the method can be obtained from http://en.wikipedia.org/wiki/NelderMead_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 noninteger termcrit.epsilon.

static 
This function returns the reference to the readytouse 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).
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. 

pure virtual 
Returns the initial step that will be used in downhill simplex algorithm.
step  Initial step that will be used in algorithm. Note, that although corresponding setter accepts columnvectors as well as rowvectors, this method will return a rowvector. 

pure virtual 
Sets the initial step that will be used in downhill simplex algorithm.
Step, together with initial point (givin 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 \(s,x_0\in\mathbb{R}^n\) are the initial step and initial point respectively, the vertices of a simplex will be: \(v_0:=x_0\frac{1}{2} s\) and \(v_i:=x_0+s_i\) for \(i=1,2,\dots,n\) where \(s_i\) denotes projections of the initial step of nth coordinate (the result of projection is treated to be vector given by \(s_i:=e_i\cdot\left<e_i\cdot s\right>\), where \(e_i\) form canonical basis)
step  Initial step that will be used in algorithm. Roughly said, it determines the spread (size in each dimension) of an initial simplex. 