Efficient Approximation Methods for Lexicographic Max-Min Optimization

Authors

DOI:

https://doi.org/10.26636/jtit.2024.1.1421

Keywords:

fairness, lexicographic max-min, lexicographic optimization, network dimensioning

Abstract

Lexicographic max-min (LMM) optimization is of considerable importance in many fairness-oriented applications. LMM problems can be reformulated in a way that allows to solve them by applying the standard lexicographic maximization algorithm. However, the reformulation introduces a large number of auxiliary variables and linear constraints, making the process computationally complex. In this paper, two approximation schemes for such a reformulation are presented, resulting in problem size reduction and significant performance gains. Their influence on the quality of the solution is shown in a series of computational experiments concerned with the fair network dimensioning and bandwidth allocation problem.

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Published

2024-02-13 — Updated on 2024-03-26

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How to Cite

Tomasz Śliwiński. (2024). Efficient Approximation Methods for Lexicographic Max-Min Optimization. Journal of Telecommunications and Information Technology, 1(1), 46-53. https://doi.org/10.26636/jtit.2024.1.1421