On some efficiency conditions for vector optimization problems with uncertain cone constraints: a robust approach

TL;DR

This paper develops robust efficiency conditions for vector optimization problems with uncertain cone constraints, using nonsmooth analysis and set-valued inclusion approaches without relying on stochastic data assumptions.

Contribution

It introduces novel necessary efficiency conditions for uncertain vector optimization problems via a robust, nonsmooth analysis framework, extending classical methods.

Findings

Established necessary conditions for weak efficiency using penalization and Euler-Lagrange methods.

Utilized recent error bounds and tangential approximations for solution set analysis.

Formulated conditions with nonsmooth analysis and metric increase property as a constraint qualification.

Abstract

In the present paper, several types of efficiency conditions are established for vector optimization problems with cone constraints affected by uncertainty, but with no information of stochastic nature about the uncertain data. Following a robust optimization approach, data uncertainty is faced by handling set-valued inclusion problems. The employment of recent results about error bounds and tangential approximations of the solution set to the latter enables one to achieve necessary conditions for weak efficiency via a penalization method as well as via the modern revisitation of the Euler-Lagrange method, with or without generalized convexity assumptions. The presented conditions are formulated in terms of various nonsmooth analysis constructions, expressing first-order approximations of mappings and sets, while the metric increase property plays the role of a constraint qualification.