Fast Algorithms for Parameterized Problems with Relaxed Disjointness Constraints
Ariel Gabizon, Daniel Lokshtanov, Michal Pilipczuk

TL;DR
This paper develops a derandomization technique for parameterized problems with relaxed disjointness constraints, enabling deterministic algorithms for problems like $(r,k)$-Monomial Detection and others exhibiting complexity reduction with relaxation.
Contribution
It introduces a derandomization tool applicable to a broad class of circuits, providing deterministic algorithms for problems with relaxation phenomena, and introduces the concept of representative sets for multisets.
Findings
Deterministic algorithm for $(r,k)$-Monomial Detection with complexity $O^*(2^{O(k rac{ ext{log} r}{r})})$.
Applicable to derandomize algorithms for $r$-Simple $k$-Path.
Identifies relaxation phenomena in problems like Set Packing and Degree Bounded Spanning Tree.
Abstract
In parameterized complexity, it is a natural idea to consider different generalizations of classic problems. Usually, such generalization are obtained by introducing a "relaxation" variable, where the original problem corresponds to setting this variable to a constant value. For instance, the problem of packing sets of size at most into a given universe generalizes the Maximum Matching problem, which is recovered by taking . Most often, the complexity of the problem increases with the relaxation variable, but very recently Abasi et al. have given a surprising example of a problem --- -Simple -Path --- that can be solved by a randomized algorithm with running time . That is, the complexity of the problem decreases with . In this paper we pursue further the direction sketched by Abasi et al. Our main contribution is a derandomization tool…
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Taxonomy
TopicsAdvanced Graph Theory Research · Complexity and Algorithms in Graphs · Limits and Structures in Graph Theory
