Characterization Of Subdifferential Research Articles

Detecting profitable deviations

Rochet’s Theorem characterizes implementable allocations in a mechanism design environment in terms of cyclic monotonicity, a concept from convex analysis. In this paper, I offer an alternative approach to both the proof and interpretation of this result, based on linear duality. This duality reveals a formal relationship between incentives and detection, much like that between prices and quantities. Moreover, it allows me to extend Rochet’s Theorem and present a subdifferential characterization of implementing payments, revenue equivalence as differentiability of a value function, as well as budget-balanced implementation in terms of attributing innocence after unilateral deviations, together with other ancillary results.

Alternative Representations of the Normal Cone to the Domain of Supremum Functions and Subdifferential Calculus

The first part of the paper provides new characterizations of the normal cone to the effective domain of the supremum of an arbitrary family of convex functions. These results are applied in the second part to give new formulas for the subdifferential of the supremum function, which use both the active and nonactive functions at the reference point. Only the data functions are involved in these characterizations, the active ones from one side, together with the nonactive functions multiplied by some appropriate parameters. In contrast with previous works in the literature, the main feature of our subdifferential characterization is that the normal cone to the effective domain of the supremum (or to finite-dimensional sections of this domain) does not appear. A new type of optimality conditions for convex optimization is established at the end of the paper.

Subdifferential characterization of probability functions under Gaussian distribution

Probability functions figure prominently in optimization problems of engineering. They may be nonsmooth even if all input data are smooth. This fact motivates the consideration of subdifferentials for such typically just continuous functions. The aim of this paper is to provide subdifferential formulae of such functions in the case of Gaussian distributions for possibly infinite-dimensional decision variables and nonsmooth (locally Lipschitzian) input data. These formulae are based on the spheric-radial decomposition of Gaussian random vectors on the one hand and on a cone of directions of moderate growth on the other. By successively adding additional hypotheses, conditions are satisfied under which the probability function is locally Lipschitzian or even differentiable.

A subdifferential characterization of Motzkin decomposable functions

The paper provides a new subdifferential characterization for Motzkin decomposable (convex) functions. This characterization leads to diverse stability properties for such a decomposability for operations like addition and composition.

Subdifferential characterization of s-lower regular functions

As for Moreau envelopes of primal lower nice as well as prox-regular functions, Moreau s-envelopes of s-lower regular functions have been proved recently to have several remarkable differential properties and to have many important applications. Here, we provide a subdifferential characterization of extended real-valued s-lower regular functions on Banach spaces in terms of a hypomonotonicity-like property of the subdifferential.

Subdifferential characterization of approximate convexity: the lower semicontinuous case

It is known that a locally Lipschitz function is approximately convex if, and only if, its Clarke subdifferential is a submonotone operator. The main object of this work is to extend the above characterization to the class of lower semicontinuous functions. To this end, we establish a new approximate mean value inequality involving three points. We also show that an analogue of the Rockafellar maximal monotonicity theorem holds for this class of functions and we discuss the case of arbitrary subdifferentials.

Integrability of subdifferentials of directionally Lipschitz functions

Using a quantitative version of the subdifferential characterization of directionally Lipschitz functions, we study the integrability of subdifferentials of such functions over arbitrary Banach space.

Prox-regular functions in Hilbert spaces

This paper studies the prox-regularity concept for functions in the general context of Hilbert space. In particular, a subdifferential characterization is established as well as several other properties. It is also shown that the Moreau envelopes of such functions are continuously differentiable.

Prox-Regularity of Functions and Sets in Banach Spaces

In this paper, we define the prox-regularity for functions on Banach spaces by adapting the original definition in R n . In this context, we establish a subdifferential characterization and show that qualified convexly C 1,+-composite functions and primal lower nice functions belong to this class, as already known in the setting of Hilbert spaces. We also study, in a geometrical point of view, the epigraphs of prox-regular functions. The subdifferential characterization allows us to show that some Moreau-envelope-like regularizations of such functions are of class C 1 in the context of certain uniformly convex spaces.