OpenCV  3.3.1-dev
Open Source Computer Vision
Classes | Enumerations | Functions
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_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)
 Solve given (non-integer) linear programming problem using the Simplex Algorithm (Simplex Method). More...
 

Detailed Description

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

Enumeration Type Documentation

§ SolveLPResult

return codes for cv::solveLP() function

Enumerator
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()

int cv::solveLP ( const Mat Func,
const Mat Constr,
Mat z 
)
Python:
retval=cv.solveLP(Func, Constr, z)

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
FuncThis 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 \(c^T\).
Constrm-by-n+1 matrix, whose rightmost column corresponds to \(b\) in formulation above and the remaining to \(A\). It should containt 32- or 64-bit floating point numbers.
zThe 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.
Returns
One of cv::SolveLPResult