Optimization Algorithms

Classes
class	cv::ConjGradSolver
	This class is used to perform the non-linear non-constrained minimization of a function with known gradient,. More...
class	cv::DownhillSolver
	This class is used to perform the non-linear non-constrained minimization of a function,. More...
class	cv::MinProblemSolver
	Basic interface for all solvers. More...

Enumerations
enum	cv::SolveLPResult {   cv::SOLVELP_LOST = -3,   cv::SOLVELP_UNBOUNDED = -2,   cv::SOLVELP_UNFEASIBLE = -1,   cv::SOLVELP_SINGLE = 0,   cv::SOLVELP_MULTI = 1 }
	return codes for cv::solveLP() function More...

Functions
int	cv::solveLP (const Mat &Func, const Mat &Constr, Mat &z, double constr_eps)
	Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). More...
int	cv::solveLP (const Mat &Func, const Mat &Constr, Mat &z)

Detailed Description

The algorithms in this section minimize or maximize function value within specified constraints or without any constraints.

Enumeration Type Documentation

SolveLPResult

enum cv::SolveLPResult

#include <opencv2/core/optim.hpp>

return codes for cv::solveLP() function

Enumerator
SOLVELP_LOST  Python: cv.SOLVELP_LOST	problem is feasible, but solver lost solution due to floating-point arithmetic errors
SOLVELP_UNBOUNDED  Python: cv.SOLVELP_UNBOUNDED	problem is unbounded (target function can achieve arbitrary high values)
SOLVELP_UNFEASIBLE  Python: cv.SOLVELP_UNFEASIBLE	problem is unfeasible (there are no points that satisfy all the constraints imposed)
SOLVELP_SINGLE  Python: cv.SOLVELP_SINGLE	there is only one maximum for target function
SOLVELP_MULTI  Python: cv.SOLVELP_MULTI	there are multiple maxima for target function - the arbitrary one is returned

Function Documentation

solveLP() [1/2]

int cv::solveLP	(	const Mat &	Func,
		const Mat &	Constr,
		Mat &	z,
		double	constr_eps
	)

Python:
	cv.solveLP(	Func, Constr, z, constr_eps	) ->	retval

#include <opencv2/core/optim.hpp>

Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method).

What we mean here by "linear programming problem" (or LP problem, for short) can be formulated as:

\[\mbox{Maximize } c\cdot x\\ \mbox{Subject to:}\\ Ax\leq b\\ x\geq 0\]

Where \(c\) is fixed 1-by-n row-vector, \(A\) is fixed m-by-n matrix, \(b\) is fixed m-by-1 column vector and \(x\) is an arbitrary n-by-1 column vector, which satisfies the constraints.

Simplex algorithm is one of many algorithms that are designed to handle this sort of problems efficiently. Although it is not optimal in theoretical sense (there exist algorithms that can solve any problem written as above in polynomial time, while simplex method degenerates to exponential time for some special cases), it is well-studied, easy to implement and is shown to work well for real-life purposes.

The particular implementation is taken almost verbatim from Introduction to Algorithms, third edition by T. H. Cormen, C. E. Leiserson, R. L. Rivest and Clifford Stein. In particular, the Bland's rule http://en.wikipedia.org/wiki/Bland%27s_rule is used to prevent cycling.

Parameters

Func	This row-vector corresponds to \(c\) in the LP problem formulation (see above). It should contain 32- or 64-bit floating point numbers. As a convenience, column-vector may be also submitted, in the latter case it is understood to correspond to \(c^T\).
Constr	m-by-n+1 matrix, whose rightmost column corresponds to \(b\) in formulation above and the remaining to \(A\). It should contain 32- or 64-bit floating point numbers.
z	The solution will be returned here as a column-vector - it corresponds to \(c\) in the formulation above. It will contain 64-bit floating point numbers.
constr_eps	allowed numeric disparity for constraints

Returns

One of cv::SolveLPResult

solveLP() [2/2]

int cv::solveLP	(	const Mat &	Func,
		const Mat &	Constr,
		Mat &	z
	)

Python:
	cv.solveLP(	Func, Constr, z, constr_eps	) ->	retval

#include <opencv2/core/optim.hpp>

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