Subdifferential Stability and Subdifferential Sum Rules

Towards Supremum-Sum Subdifferential Calculus Free of Qualification Conditions

On subdifferential calculus ∗

The main purpose of these lectures is to familiarize the student with the basic ingredients of convex analysis, especially its subdifferential calculus. This is done while moving to a clearly discernible end-goal, the Karush-Kuhn-Tucker theorem, which is one of the main results of nonlinear programming. Of course, in the present lectures we have to limit ourselves most of the time to the Karush...

Subdifferential Calculus Rules for Supremum Functions in Convex Analysis

Extending and improving some recent results of Hantoute, López, and Zălinescu and others, we provide characterization conditions for subdifferential formulas to hold for the supremum function of a family of convex functions on a real locally convex space.

Subdifferential representations of risk measures

Measures of risk appear in two categories: Risk capital measures serve to determine the necessary amount of risk capital in order to avoid ruin if the outcomes of an economic activity are uncertain and their negative values may be interpreted as acceptability measures (safety measures). Pure risk measures (risk deviation measures) are natural generalizations of the standard deviation. While pur...

Elements of Quasiconvex Subdifferential Calculus

A number of rules for the calculus of subdifferentials of generalized convex functions are displayed. The subdifferentials we use are among the most significant for this class of functions, in particular for quasiconvex functions: we treat the Greenberg-Pierskalla’s subdifferential and its relatives and the Plastria’s lower subdifferential. We also deal with a recently introduced subdifferentia...