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cv::DownhillSolver Class Referenceabstract

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

#include <opencv2/core/optim.hpp>

Inheritance diagram for cv::DownhillSolver:
Collaboration diagram for cv::DownhillSolver:

Public Member Functions

virtual void clear ()
 Clears the algorithm state. More...
 
virtual bool empty () const
 Returns true if the Algorithm is empty (e.g. More...
 
virtual String getDefaultName () const
 Returns the algorithm string identifier. More...
 
virtual Ptr< FunctiongetFunction () const =0
 Getter for the optimized function. More...
 
virtual void getInitStep (OutputArray step) const =0
 Returns the initial step that will be used in downhill simplex algorithm. 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 read (const FileNode &fn)
 Reads algorithm parameters from a file storage. More...
 
virtual void save (const String &filename) const
 Saves the algorithm to a file. More...
 
virtual void setFunction (const Ptr< Function > &f)=0
 Setter for the optimized function. More...
 
virtual void setInitStep (InputArray step)=0
 Sets the initial step that will be used in downhill simplex algorithm. More...
 
virtual void setTermCriteria (const TermCriteria &termcrit)=0
 Set terminal criteria for solver. More...
 
virtual void write (FileStorage &fs) const
 Stores algorithm parameters in a file storage. More...
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
 

Static Public Member Functions

static Ptr< DownhillSolvercreate (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. More...
 
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...
 

Protected Member Functions

void writeFormat (FileStorage &fs) const
 

Detailed Description

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.
term criteria should meet following condition:
termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0

Member Function Documentation

◆ clear()

virtual void cv::Algorithm::clear ( )
inlinevirtualinherited

Clears the algorithm state.

Reimplemented in cv::FlannBasedMatcher, and cv::DescriptorMatcher.

◆ create()

static Ptr<DownhillSolver> cv::DownhillSolver::create ( const Ptr< MinProblemSolver::Function > &  f = PtrMinProblemSolver::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).

Parameters
fPointer to the function that will be minimized, similarly to the one you submit via MinProblemSolver::setFunction.
initStepInitial step, that will be used to construct the initial simplex, similarly to the one you submit via MinProblemSolver::setInitStep.
termcritTerminal criteria to the algorithm, similarly to the one you submit via MinProblemSolver::setTermCriteria.

◆ empty()

virtual bool cv::Algorithm::empty ( ) const
inlinevirtualinherited

Returns true if the Algorithm is empty (e.g.

in the very beginning or after unsuccessful read

Reimplemented in cv::DescriptorMatcher, cv::ml::StatModel, cv::Feature2D, and cv::BaseCascadeClassifier.

◆ getDefaultName()

virtual String cv::Algorithm::getDefaultName ( ) const
virtualinherited

Returns the algorithm string identifier.

This string is used as top level xml/yml node tag when the object is saved to a file or string.

Reimplemented in cv::AKAZE, cv::KAZE, cv::SimpleBlobDetector, cv::GFTTDetector, cv::AgastFeatureDetector, cv::FastFeatureDetector, cv::MSER, cv::ORB, cv::BRISK, and cv::Feature2D.

◆ getFunction()

virtual Ptr<Function> cv::MinProblemSolver::getFunction ( ) const
pure virtualinherited

Getter for the optimized function.

The optimized function is represented by Function interface, which requires derivatives to implement the calc(double*) and getDim() methods to evaluate the function.

Returns
Smart-pointer to an object that implements Function interface - it represents the function that is being optimized. It can be empty, if no function was given so far.

◆ getInitStep()

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

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

Parameters
stepInitial 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

◆ getTermCriteria()

virtual TermCriteria cv::MinProblemSolver::getTermCriteria ( ) const
pure virtualinherited

Getter for the previously set terminal criteria for this algorithm.

Returns
Deep copy of the terminal criteria used at the moment.

◆ load()

template<typename _Tp >
static Ptr<_Tp> cv::Algorithm::load ( const String filename,
const String objname = String() 
)
inlinestaticinherited

Loads algorithm from the file.

Parameters
filenameName of the file to read.
objnameThe optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

References CV_Assert, cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::isOpened(), and cv::FileStorage::READ.

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◆ loadFromString()

template<typename _Tp >
static Ptr<_Tp> cv::Algorithm::loadFromString ( const String strModel,
const String objname = String() 
)
inlinestaticinherited

Loads algorithm from a String.

Parameters
strModelThe string variable containing the model you want to load.
objnameThe optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

References CV_WRAP, cv::FileNode::empty(), cv::FileStorage::getFirstTopLevelNode(), cv::FileStorage::MEMORY, and cv::FileStorage::READ.

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◆ minimize()

virtual double cv::MinProblemSolver::minimize ( InputOutputArray  x)
pure virtualinherited

actually runs the algorithm and performs the minimization.

The sole input parameter determines the centroid of the starting simplex (roughly, it tells where to start), all the others (terminal criteria, initial step, function to be minimized) are supposed to be set via the setters before the call to this method or the default values (not always sensible) will be used.

Parameters
xThe initial point, that will become a centroid of an initial simplex. After the algorithm will terminate, it will be set to the point where the algorithm stops, the point of possible minimum.
Returns
The value of a function at the point found.

◆ read() [1/2]

virtual void cv::Algorithm::read ( const FileNode fn)
inlinevirtualinherited

Reads algorithm parameters from a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.

◆ read() [2/2]

template<typename _Tp >
static Ptr<_Tp> cv::Algorithm::read ( const FileNode fn)
inlinestaticinherited

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

cv::FileStorage fsRead("example.xml", FileStorage::READ);
Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

◆ save()

virtual void cv::Algorithm::save ( const String filename) const
virtualinherited

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

◆ setFunction()

virtual void cv::MinProblemSolver::setFunction ( const Ptr< Function > &  f)
pure virtualinherited

Setter for the optimized function.

It should be called at least once before the call to* minimize(), as default value is not usable.

Parameters
fThe new function to optimize.

◆ 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 (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 n-th 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)

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

◆ setTermCriteria()

virtual void cv::MinProblemSolver::setTermCriteria ( const TermCriteria termcrit)
pure virtualinherited

Set terminal criteria for solver.

This method is not necessary to be called before the first call to minimize(), as the default value is sensible.

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.

Parameters
termcritTerminal criteria to be used, represented as cv::TermCriteria structure.

◆ write() [1/2]

virtual void cv::Algorithm::write ( FileStorage fs) const
inlinevirtualinherited

Stores algorithm parameters in a file storage.

Reimplemented in cv::FlannBasedMatcher, cv::DescriptorMatcher, and cv::Feature2D.

References CV_WRAP.

Referenced by cv::Feature2D::write(), and cv::DescriptorMatcher::write().

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◆ write() [2/2]

void cv::Algorithm::write ( const Ptr< FileStorage > &  fs,
const String name = String() 
) const
inherited

simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

◆ writeFormat()

void cv::Algorithm::writeFormat ( FileStorage fs) const
protectedinherited

The documentation for this class was generated from the following file: