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An Improved Approximation Algorithm for Dynamic Minimum Linear Arrangement

Authors Marcin Bienkowski , Guy Even

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Author Details

Marcin Bienkowski
  • University of Wrocław, Poland
Guy Even
  • Tel Aviv University, Israel

Funding

Supported by Polish National Science Centre grant 2022/45/B/ST6/00559.

Cite AsGet BibTex

Marcin Bienkowski and Guy Even. An Improved Approximation Algorithm for Dynamic Minimum Linear Arrangement. In 41st International Symposium on Theoretical Aspects of Computer Science (STACS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 289, pp. 15:1-15:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.STACS.2024.15

Abstract

The dynamic offline linear arrangement problem deals with reordering n elements subject to a sequence of edge requests. The input consists of a sequence of m edges (i.e., unordered pairs of elements). The output is a sequence of permutations (i.e., bijective mapping of the elements to n equidistant points). In step t, the order of the elements is changed to the t-th permutation, and then the t-th request is served. The cost of the output consists of two parts per step: request cost and rearrangement cost. The former is the current distance between the endpoints of the request, while the latter is proportional to the number of adjacent element swaps required to move from one permutation to the consecutive permutation. The goal is to find a minimum cost solution. We present a deterministic O(log n log log n)-approximation algorithm for this problem, improving over a randomized O(log² n)-approximation by Olver et al. [Neil Olver et al., 2018]. Our algorithm is based on first solving spreading-metric LP relaxation on a time-expanded graph, applying a tree decomposition on the basis of the LP solution, and finally converting the tree decomposition to a sequence of permutations. The techniques we employ are general and have the potential to be useful for other dynamic graph optimization problems.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
Keywords
  • Minimum Linear Arrangement
  • dynamic Variant
  • Optimization Problems
  • Graph Problems
  • approximation Algorithms

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