Finding 3-Swap-Optimal Independent Sets and Dominating Sets is Hard

Local search problems consist of a set of feasible solutions, an objective function, and a neighborhood relation which assigns to each feasible solution a set of neighbors. A solution S is called locally optimal if none of its neighbors has a better objective value than S . For PLS-complete local search problems, there is presumably no polynomial-time algorithm which finds a locally optimal solution, even though determining whether a solution is locally optimal and finding a better one if this is not the case can be done in polynomial time. We study local search for Weighted Independent Set and Weighted Dominating Set with the 3-swap neighborhood. The 3-swap neighborhood of a vertex set S in G is the collection of vertex sets which can be obtained from S by exchanging at most three vertices. On the negative side, the problem of finding a 3-swap-optimal independent set or dominating set is PLS-complete. Using the PLS-completeness for independent sets with the 3-swap-neighborhood, we also show PLS-completeness for a large class of maximum-weight subgraph problems. On the positive side, we show that locally optimal independent sets or dominating sets can be found in polynomial time when allowing all 3-swaps except (a) the swaps that remove two vertices from the current solution and add one vertex to the solution or (b) the swaps that remove one vertex from the current solution and add two vertices to the solution. This result is shown via general algorithms for subset-weight optimization problems with a restricted k -swap neighborhood.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Computation Theory
Published
2024-10-17
DOI
https://doi.org/10.1145/3700642
Citations
1
Primary Topic
Advanced Graph Theory Research
Type
article
Field-Weighted Citation Impact
0.36
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Finding 3-Swap-Optimal Independent Sets and Dominating Sets is Hard

Nils Morawietz, Christian Komusiewicz
1 citations
ACM Transactions on Computation Theory
Advanced Graph Theory Research
0.36
article

Finding 3-Swap-Optimal Independent Sets and Dominating Sets is Hard

Nils Morawietz, Christian Komusiewicz
article en
1 citations

Abstract

Local search problems consist of a set of feasible solutions, an objective function, and a neighborhood relation which assigns to each feasible solution a set of neighbors. A solution S is called locally optimal if none of its neighbors has a better objective value than S . For PLS-complete local search problems, there is presumably no polynomial-time algorithm which finds a locally optimal solution, even though determining whether a solution is locally optimal and finding a better one if this is not the case can be done in polynomial time. We study local search for Weighted Independent Set and Weighted Dominating Set with the 3-swap neighborhood. The 3-swap neighborhood of a vertex set S in G is the collection of vertex sets which can be obtained from S by exchanging at most three vertices. On the negative side, the problem of finding a 3-swap-optimal independent set or dominating set is PLS-complete. Using the PLS-completeness for independent sets with the 3-swap-neighborhood, we also show PLS-completeness for a large class of maximum-weight subgraph problems. On the positive side, we show that locally optimal independent sets or dominating sets can be found in polynomial time when allowing all 3-swaps except (a) the swaps that remove two vertices from the current solution and add one vertex to the solution or (b) the swaps that remove one vertex from the current solution and add two vertices to the solution. This result is shown via general algorithms for subset-weight optimization problems with a restricted k -swap neighborhood.

ACM Transactions on Computation TheoryVol. 17(1)
Friedrich Schiller University Jena (DE)
Sustainable cities and communities
Openalex Percentile: Top 35%
Advanced Graph Theory Research
0.36
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.