Convex Projection and Convex Vector Optimization

Kovacova, Gabriela ORCID: and Rudloff, Birgit ORCID: (2021) Convex Projection and Convex Vector Optimization. Journal of Global Optimization. ISSN 1573-2916

Available under License Creative Commons: Attribution 4.0 International (CC BY 4.0).

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In this paper we consider a problem, called convex projection, of projecting a convex set onto a subspace. We will show that to a convex projection one can assign a particular multiobjective convex optimization problem, such that the solution to that problem also solves the convex projection (and vice versa), which is analogous to the result in the polyhedral convex case considered in Löhne and Weißing (Math Methods Oper Res 84(2):411–426, 2016). In practice, however, one can only compute approximate solutions in the (bounded or selfbounded) convex case, which solve the problem up to a given error tolerance. We will show that for approximate solutions a similar connection can be proven, but the tolerance level needs to be adjusted. That is, an approximate solution of the convex projection solves the multiobjective problem only with an increased error. Similarly, an approximate solution of the multi-objective problem solves the convex projection with an increased error. In both cases the tolerance is increased proportionally to amultiplier. Thesemultipliers are deduced and shown to be sharp. These results allow to compute approximate solutions to a convex projection problem by computing approximate solutions to the corresponding multi-objective convex optimization problem, for which algorithms exist in the bounded case. For completeness, we will also investigate the potential generalization of the following result to the convex case. In Löhne and Weißing (Math Methods Oper Res 84(2):411–426, 2016), it has been shown for the polyhedral case, how to construct a polyhedral projection associated to any given vector linear program and how to relate their solutions. This in turn yields an equivalence between polyhedral projection, multi-objective linear programming and vector linear programming. We will show that only some parts of this result can be generalized to the convex case, and discuss the limitations.

Item Type: Article
Additional Information: Open access funding provided by Vienna University of Economics and Business (WU).
Keywords: Convex projection, Convex vector optimization, Convex multi-objective optimization
Classification Codes: Mathematics Subject Classification 52A20, 90C29, 90C25
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Depositing User: Gertraud Novotny
Date Deposited: 24 Nov 2021 13:20
Last Modified: 25 Nov 2021 09:58
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