Publisher Summary This chapter discusses the nondifferentiable optimization (NDO).
Nondifferentiable optimization or nonsmooth optimization (NSO) deals with the situations in …

… Then we can compute at least one subgradient at each point of nondifferentiability; see
definition (1.4). Consequently the general assumption for the following seems to be rather natural…

… "Quadratic extremal problems and nondifferentiable optimization" - Kiev, … optimization
from the date of edition of this book. The variety of applications of nondifferentiable optimization …

… about numerical methods for nondifferentiable optimization. Nondifferentiable optimization,
also … In this section we describe general properties of nondifferentiable optimization problems …

Necessary Lagrangian conditions are obtained for nonsmooth optimization problems, involving
locally Lipschitz functions and arbitrary convex cones.The approach is by a sequence of …

… concepts in the local theory of nonsmooth nondifferentiable optimization. (3) We intend to …
; furthermore, there remain many optimization problems with locally Lipschitz continuous …

… nondifferentiable optimization algorithms (such as [Lem. ], [Lem.2], [Lem.3], [Mif. ], [Kiw. ], …
In this paper we present an axiomatic approach to a class of nondifferentiable optimization …

… these fields by helping to solve optimization problems arising in … programming and for
nondifferentiable optimization. In the … meeting on nondifferentiable optimization, a field whose …

… Nonetheless, the idea of using randomization in the context of deterministic nondifferentiable
optimization is original and much of our analysis, particularly the part that relates to the …

Composite functions ϕ(x)=f(x)+h(c(x)), where f and c are smooth and h is convex, encompass
many nondifferentiable optimization problems of interest including exact penalty functions …