Effective domain

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 [ , ] = R { ± } . {\displaystyle [-\infty ,\infty ]=\mathbb {R} \cup \{\pm \infty \}.}

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 + . {\displaystyle +\infty .} [1] 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 + {\displaystyle +\infty } at a point specifically to exclude that point from even being considered as a potential solution (to the minimization problem).[1] Points at which the function takes the value {\displaystyle -\infty } (if any) belong to the effective domain because such points are considered acceptable solutions to the minimization problem,[1] with the reasoning being that if such a point was not acceptable as a solution then the function would have already been set to + {\displaystyle +\infty } at that point instead.

When a minimum point (in X {\displaystyle X} ) of a function f : X [ , ] {\displaystyle f:X\to [-\infty ,\infty ]} is to be found but f {\displaystyle f} 's domain X {\displaystyle X} is a proper subset of some vector space V , {\displaystyle V,} then it often technically useful to extend f {\displaystyle f} to all of V {\displaystyle V} by setting f ( x ) := + {\displaystyle f(x):=+\infty } at every x V X . {\displaystyle x\in V\setminus X.} [1] By definition, no point of V X {\displaystyle V\setminus X} belongs to the effective domain of f , {\displaystyle f,} which is consistent with the desire to find a minimum point of the original function f : X [ , ] {\displaystyle f:X\to [-\infty ,\infty ]} rather than of the newly defined extension to all of V . {\displaystyle 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 . {\displaystyle -\infty .}

Definition

Suppose f : X [ , ] {\displaystyle f:X\to [-\infty ,\infty ]} is a map valued in the extended real number line [ , ] = R { ± } {\displaystyle [-\infty ,\infty ]=\mathbb {R} \cup \{\pm \infty \}} whose domain, which is denoted by domain f , {\displaystyle \operatorname {domain} f,} is X {\displaystyle X} (where X {\displaystyle X} will be assumed to be a subset of some vector space whenever this assumption is necessary). Then the effective domain of f {\displaystyle f} is denoted by dom f {\displaystyle \operatorname {dom} f} and typically defined to be the set[1][2][3]

dom f = { x X   :   f ( x ) < + } {\displaystyle \operatorname {dom} f=\{x\in X~:~f(x)<+\infty \}}
unless f {\displaystyle f} is a concave function or the maximum (rather than the minimum) of f {\displaystyle f} is being sought, in which case the effective domain of f {\displaystyle f} is instead the set[2]
dom f = { x X   :   f ( x ) > } . {\displaystyle \operatorname {dom} f=\{x\in X~:~f(x)>-\infty \}.}

In convex analysis and variational analysis, dom f {\displaystyle \operatorname {dom} f} is usually assumed to be dom f = { x X   :   f ( x ) < + } {\displaystyle \operatorname {dom} f=\{x\in X~:~f(x)<+\infty \}} unless clearly indicated otherwise.

Characterizations

Let π X : X × R X {\displaystyle \pi _{X}:X\times \mathbb {R} \to X} denote the canonical projection onto X , {\displaystyle X,} which is defined by ( x , r ) x . {\displaystyle (x,r)\mapsto x.} The effective domain of f : X [ , ] {\displaystyle f:X\to [-\infty ,\infty ]} is equal to the image of f {\displaystyle f} 's epigraph epi f {\displaystyle \operatorname {epi} f} under the canonical projection π X . {\displaystyle \pi _{X}.} That is

dom f = π X ( epi f ) = { x X   :    there exists  y R  such that  ( x , y ) epi f } . {\displaystyle \operatorname {dom} f=\pi _{X}\left(\operatorname {epi} f\right)=\left\{x\in X~:~{\text{ there exists }}y\in \mathbb {R} {\text{ such that }}(x,y)\in \operatorname {epi} f\right\}.} [4]

For a maximization problem (such as if the f {\displaystyle f} is concave rather than convex), the effective domain is instead equal to the image under π X {\displaystyle \pi _{X}} of f {\displaystyle f} 's hypograph.

Properties

If a function never takes the value + , {\displaystyle +\infty ,} such as if the function is real-valued, then its domain and effective domain are equal.

A function f : X [ , ] {\displaystyle f:X\to [-\infty ,\infty ]} is a proper convex function if and only if f {\displaystyle f} is convex, the effective domain of f {\displaystyle f} is nonempty, and f ( x ) > {\displaystyle f(x)>-\infty } for every x X . {\displaystyle x\in X.} [4]

See also

  • Proper convex function
  • Epigraph (mathematics) – the set of points lying on or above the graph of a functionPages displaying wikidata descriptions as a fallback
  • Hypograph (mathematics) – Mathematical analysis termPages displaying wikidata descriptions as a fallback

References

  1. ^ a b c d e Rockafellar & Wets 2009, pp. 1–28.
  2. ^ a b Aliprantis, C.D.; Border, K.C. (2007). Infinite Dimensional Analysis: A Hitchhiker's Guide (3 ed.). Springer. p. 254. doi:10.1007/3-540-29587-9. ISBN 978-3-540-32696-0.
  3. ^ Föllmer, Hans; Schied, Alexander (2004). Stochastic finance: an introduction in discrete time (2 ed.). Walter de Gruyter. p. 400. ISBN 978-3-11-018346-7.
  4. ^ a b Rockafellar, R. Tyrrell (1997) [1970]. Convex Analysis. Princeton, NJ: Princeton University Press. p. 23. ISBN 978-0-691-01586-6.
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