Generalized outer linearizations and extremal properties of rotational epi-symmetrizations
Steven Hoehner, Fabian Mussnig

TL;DR
This paper extends extremal principles for convex functions using generalized outer linearizations, enabling new geometric and functional approximation results, including a functional Urysohn's inequality.
Contribution
It introduces a novel functional extension of extremal principles with generalized outer linearizations, applicable to coercive convex functions, and derives new extremal and approximation results.
Findings
Rotational epi-symmetrization maximizes best approximations under outer linearizations.
Derived a functional version of Urysohn's inequality.
Proved an extremal inequality related to piecewise affine approximation.
Abstract
We develop a functional extension of an extremal principle by Schneider (Monatsh. Math., 1967) by introducing generalized outer linearizations of convex functions. Given a coercive convex function on , a generalized outer linearization is defined as a convex minorant represented by a general but function-dependent set of slopes, thereby extending classical outer representations of convex bodies by supporting halfspaces. This representation converts geometric outer approximations by supporting halfspaces into functional approximations by supporting affine functions, and replaces outer normal data by a dual sampling problem in the domain of the Legendre--Fenchel transform. On a standard class of coercive convex functions, we derive a general extremal principle, showing that the rotational epi-symmetrization maximizes best approximations under outer linearizations of any…
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