Semismooth and Semiconvex Functions in Constrained Optimization

Mifflin, R. (1976). Semismooth and Semiconvex Functions in Constrained Optimization. IIASA Research Report. IIASA, Laxenburg, Austria: RR-76-021

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We introduce semismooth and semiconvex functions and discuss their properties with respect to nonsmooth nonconvex constrained optimization problems. These functions are locally Lipschitz, and hence have generalized gradients. The author has given an optimization algorithm that uses generalized gradients of the problem functions and converges to stationary points if the functions are semismooth. If the functions are semiconvex and a constraint qualification is satisfied, then we show that a stationary point is an optimal point.

We show that the pointwise maximum or minimum over a compact family of continuously differentiable functions is a semismooth function and that the pointwise maximum over a compact family of semiconvex functions is a semiconvex function. Furthermore, we show that a semismooth composition of semismooth functions is semismooth and gives a type of chain rule for generalized gradients.

Item Type: Monograph (IIASA Research Report)
Research Programs: System and Decision Sciences - Core (SDS)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 01:43
Last Modified: 27 Aug 2021 17:08

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