Relationships Between Convexificators and Greenberg–Pierskalla Subdifferentials for Quasiconvex Functions

In this paper, the relationship between convexificators and Greenberg–Pierskalla-based (GP-based) subdifferentials for quasiconvex functions is proved. The established results lead to a mean value theorem, a chain rule, and the closedness property for GP-based subdifferentials. Furthermore, the connection between Clarke generalized gradient and Mordukhovich subdifferential with GP-based subdifferentials is highlighted.     

Chain rules for coderivatives of multivalued mappings in Banach spaces

Our basic object in this paper is to establish calculus rules for coderivatives of multivalued mappings between Banach spaces. We consider the coderivative which is associated to some geometrical approximate subdifferential for functions.

     

Existence and comparison results for quasilinear parrabolic inclusions woth state-dependent subdifferentials ∗

In this paper we consider initial boundary value problems for quasilinear parabolic differential inclusions governed by nonmonotone operators of Leray-Lions type and state-dependent subdifferentials. Our main goal is to prove the existence of solutions lying in a sector formed by appropriately defined upper and lower solutions. The main tools used in the proof are abstract results on nonlinear evolution equations, regularization, comparison and truncation techniques as well as special test function techniques     

On generalized weak subdifferentials and some properties

In this article, some characterizations for gw-subdifferentiability of functions from ? ⁿ to ? ^m are stated. Some criteria for gw-subdifferentiability of generalized lower locally Lipschitz functions and positively homogeneous functions are given. Furthermore, it is proved that every Lipschitz function is gw-subdifferentiable at any point in its domain. Finally, the relationship between directional derivative and gw-subdifferential is given and a convexity criteria for Fréchet differentiable function is given by using gw-subdifferential.     

Optimality conditions under relaxed quasiconvexity assumptions using star and adjusted subdifferentials

A set-constrained optimization problem and a mathematical programming problem are considered. We assume that the sublevel sets of the involving functions are convex only at the point under question and hence these functions are not assumed quasiconvex. Using the two star subdifferentials and the adjusted subdifferential, we establish optimality conditions for usual minima and strict minima. Our results contain and improve some recent ones in the literature. Examples are provided to explain the advantages of each of our results.     

Multipliers and Generalized Derivatives of Performance Functions

It is known that multipliers in mathematical programming have to do with the interpretation of generalized derivatives for the performance function of some perturbed problem. In economics, this fact is known under the term of shadow price. Here, we point out a precise relationship with the subdifferential of the performance function in the contingent sense and in the Fréchet sense, which are the simplest notions of nonsmooth analysis.     

Convergence analysis of gradient descent stochastic algorithms

This paper proves convergence of a sample-path based stochastic gradient-descent algorithm for optimizing expected-value performance measures in discrete event systems. The algorithm uses increasing precision at successive iterations, and it moves against the direction of a generalized gradient of the computed sample performance function. Two convergence results are established: one, for the case where the expected-value function is continuously differentiable; and the other, when that function is nondifferentiable but the sample performance functions are convex. The proofs are based on a version of the uniform law of large numbers which is provable for many discrete event systems where infinitesimal perturbation analysis is known to be strongly consistent.     

局部Lipschitz连续的伪线性函数的性质

本文使用Clarke次微分分析了定义在Banach空间的局部Lipschitz连续的伪线性函数的性质,并且考虑了伪线性规划解集的性质.     

Optimality Conditions for C 1,1 Constrained Multiobjective Problems

The present paper is concerned with the study of the optimality conditions for constrained multiobjective programming problems in which the data have locally Lipschitz Jacobian maps. Second-order necessary and sufficient conditions for efficient solutions are established in terms of second-order subdifferentials of vector functions.     

Exact calculus of Fréchet subdifferentials for Hadamard directionally differentiable functions

We continue the study of the calculus of the generalized subdifferentials started in [V.F. Demyanov, V. Roshchina, Exhausters and subdifferentials in nonsmooth analysis, Optimization (2006) (in press)] and [V. Roshchina, Relationships between upper exhausters and the basic subdifferential in Variational Analysis, Journal of Mathematical Analysis and Applications 334 (2007) 261–272] and provide some basic calculus rules for the Fréchet subdifferentials via collections of compact convex sets associated with Hadamard directional derivative. The main result of this paper is the sum rule for the Fréchet subdifferential in the form of an equality, which holds for Hadamard directionally differentiable functions, and is of significant interest from the points of view of both theory and applications.