@Namespace(value="cv") @Properties(inherit=opencv_core.class) public class DownhillSolver extends MinProblemSolver
defined on an
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
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:
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
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).
termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
MinProblemSolver.FunctionPointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter| Constructor and Description |
|---|
DownhillSolver(Pointer p)
Pointer cast constructor.
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| Modifier and Type | Method and Description |
|---|---|
static DownhillSolver |
create() |
static DownhillSolver |
create(MinProblemSolver.Function f,
GpuMat initStep,
TermCriteria termcrit) |
static DownhillSolver |
create(MinProblemSolver.Function f,
Mat initStep,
TermCriteria termcrit)
\brief This function returns the reference to the ready-to-use DownhillSolver object.
|
static DownhillSolver |
create(MinProblemSolver.Function f,
UMat initStep,
TermCriteria termcrit) |
void |
getInitStep(GpuMat step) |
void |
getInitStep(Mat step)
\brief Returns the initial step that will be used in downhill simplex algorithm.
|
void |
getInitStep(UMat step) |
void |
setInitStep(GpuMat step) |
void |
setInitStep(Mat step)
\brief Sets the initial step that will be used in downhill simplex algorithm.
|
void |
setInitStep(UMat step) |
getFunction, getTermCriteria, minimize, minimize, minimize, setFunction, setTermCriteriaclear, empty, getDefaultName, position, read, save, save, write, write, writeaddress, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zeropublic DownhillSolver(Pointer p)
Pointer.Pointer(Pointer).public void getInitStep(@ByVal Mat step)
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.DownhillSolver::setInitSteppublic void setInitStep(@ByVal Mat step)
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 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)
step - Initial step that will be used in algorithm. Roughly said, it determines the spread
(size in each dimension) of an initial simplex.@opencv_core.Ptr public static DownhillSolver create(@opencv_core.Ptr MinProblemSolver.Function f, @ByVal(nullValue="cv::InputArray(cv::Mat_<double>(1,1,0.0))") Mat initStep, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::MAX_ITER+cv::TermCriteria::EPS,5000,0.000001)") TermCriteria termcrit)
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.@opencv_core.Ptr public static DownhillSolver create()
@opencv_core.Ptr public static DownhillSolver create(@opencv_core.Ptr MinProblemSolver.Function f, @ByVal(nullValue="cv::InputArray(cv::Mat_<double>(1,1,0.0))") UMat initStep, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::MAX_ITER+cv::TermCriteria::EPS,5000,0.000001)") TermCriteria termcrit)
@opencv_core.Ptr public static DownhillSolver create(@opencv_core.Ptr MinProblemSolver.Function f, @ByVal(nullValue="cv::InputArray(cv::Mat_<double>(1,1,0.0))") GpuMat initStep, @ByVal(nullValue="cv::TermCriteria(cv::TermCriteria::MAX_ITER+cv::TermCriteria::EPS,5000,0.000001)") TermCriteria termcrit)
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