Struct opencv::core::DownhillSolver
source · pub struct DownhillSolver { /* private fields */ }Expand 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.
Note: term criteria should meet following condition:
termcrit.type == (TermCriteria::MAX_ITER + TermCriteria::EPS) && termcrit.epsilon > 0 && termcrit.maxCount > 0
Implementations§
source§impl DownhillSolver
impl DownhillSolver
sourcepub fn create(
f: &Ptr<MinProblemSolver_Function>,
init_step: &impl ToInputArray,
termcrit: TermCriteria
) -> Result<Ptr<DownhillSolver>>
pub fn create( f: &Ptr<MinProblemSolver_Function>, init_step: &impl ToInputArray, termcrit: TermCriteria ) -> Result<Ptr<DownhillSolver>>
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)
sourcepub fn create_def() -> Result<Ptr<DownhillSolver>>
pub fn create_def() -> Result<Ptr<DownhillSolver>>
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.
Note
This alternative version of [create] function uses the following default values for its arguments:
- f: PtrMinProblemSolver::Function()
- init_step: Mat_
(1,1,0.0) - termcrit: TermCriteria(TermCriteria::MAX_ITER+TermCriteria::EPS,5000,0.000001)