Learning Minimum Linear Arrangement of Cliques and Lines

TL;DR

This paper introduces an online learning algorithm for the Minimum Linear Arrangement problem on graphs revealed piece-by-piece, achieving near-optimal competitive ratios for specific graph classes like cliques and lines.

Contribution

It presents the first online randomized algorithm with provable guarantees for MinLA on restricted graph classes, along with matching lower bounds.

Findings

Achieves an $8 \, \ln n$-competitive ratio for cliques and lines.

Provides an asymptotically matching lower bound of $\Omega(\ln n)$.

Demonstrates effectiveness of online algorithms in dynamic graph arrangement problems.

Abstract

In the well-known Minimum Linear Arrangement problem (MinLA), the goal is to arrange the nodes of an undirected graph into a permutation so that the total stretch of the edges is minimized. This paper studies an online (learning) variant of MinLA where the graph is not given at the beginning, but rather revealed piece-by-piece. The algorithm starts in a fixed initial permutation, and after a piece of the graph is revealed, the algorithm must update its current permutation to be a MinLA of the subgraph revealed so far. The objective is to minimize the total number of swaps of adjacent nodes as the algorithm updates the permutation. The main result of this paper is an online randomized algorithm that solves this online variant for the restricted cases where the revealed graph is either a collection of cliques or a collection of lines. We show that the algorithm is $8 \ln n$-competitive, where $n$ is the number of nodes of the graph. We complement this result by constructing an asymptotically matching lower bound of $\Omega(\ln n)$.