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In convex analysis, a branch of mathematics, the effective domain extends of the domain of a function defined for functions that take values in the extended real number line [,]={±}.

In convex analysis and variational analysis, a point at which some given extended real-valued function is minimized is typically sought, where such a point is called a global minimum point. The effective domain of this function is defined to be the set of all points in this function's domain at which its value is not equal to +.Template:Sfn It is defined this way because it is only these points that have even a remote chance of being a global minimum point. Indeed, it is common practice in these fields to set a function equal to + at a point specifically to Template:Em that point from even being considered as a potential solution (to the minimization problem).Template:Sfn Points at which the function takes the value (if any) belong to the effective domain because such points are considered acceptable solutions to the minimization problem,Template:Sfn with the reasoning being that if such a point was not acceptable as a solution then the function would have already been set to + at that point instead.

When a minimum point (in X) of a function f:X[,] is to be found but f's domain X is a proper subset of some vector space V, then it often technically useful to extend f to all of V by setting f(x):=+ at every xVX.Template:Sfn By definition, no point of VX belongs to the effective domain of f, which is consistent with the desire to find a minimum point of the original function f:X[,] rather than of the newly defined extension to all of V.

If the problem is instead a maximization problem (which would be clearly indicated) then the effective domain instead consists of all points in the function's domain at which it is not equal to .

Definition

Suppose f:X[,] is a map valued in the extended real number line [,]={±} whose domain, which is denoted by domainf, is X (where X will be assumed to be a subset of some vector space whenever this assumption is necessary). Then the Template:Em of f is denoted by domf and typically defined to be the setTemplate:Sfn[1][2] domf={xX:f(x)<+} unless f is a concave function or the maximum (rather than the minimum) of f is being sought, in which case the Template:Em of f is instead the set[1] domf={xX:f(x)>}.

In convex analysis and variational analysis, domf is usually assumed to be domf={xX:f(x)<+} unless clearly indicated otherwise.

Characterizations

Let πX:X×X denote the canonical projection onto X, which is defined by (x,r)x. The effective domain of f:X[,] is equal to the image of f's epigraph epif under the canonical projection πX. That is

domf=πX(epif)={xX: there exists y such that (x,y)epif}.[3]

For a maximization problem (such as if the f is concave rather than convex), the effective domain is instead equal to the image under πX of f's hypograph.

Properties

If a function Template:Em takes the value +, such as if the function is real-valued, then its domain and effective domain are equal.

A function f:X[,] is a proper convex function if and only if f is convex, the effective domain of f is nonempty, and f(x)> for every xX.[3]

See also

References

Template:Reflist

Template:Convex analysis and variational analysis

Template:Mathanalysis-stub