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 [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 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 (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 at that point instead.

When a minimum point (in ) of a function is to be found but 's domain is a proper subset of some vector space then it often technically useful to extend to all of by setting at every [1] By definition, no point of belongs to the effective domain of which is consistent with the desire to find a minimum point of the original function rather than of the newly defined extension to all of

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 edit

Suppose   is a map valued in the extended real number line   whose domain, which is denoted by   is   (where   will be assumed to be a subset of some vector space whenever this assumption is necessary). Then the effective domain of   is denoted by   and typically defined to be the set[1][2][3]

 
unless   is a concave function or the maximum (rather than the minimum) of   is being sought, in which case the effective domain of   is instead the set[2]
 

In convex analysis and variational analysis,   is usually assumed to be   unless clearly indicated otherwise.

Characterizations edit

Let   denote the canonical projection onto   which is defined by   The effective domain of   is equal to the image of  's epigraph   under the canonical projection   That is

 [4]

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

Properties edit

If a function never takes the value   such as if the function is real-valued, then its domain and effective domain are equal.

A function   is a proper convex function if and only if   is convex, the effective domain of   is nonempty, and   for every  [4]

See also edit

References edit

  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.