English

Finding Diverse Solutions Parameterized by Cliquewidth

Data Structures and Algorithms 2026-03-30 v2 Discrete Mathematics

Abstract

Finding a few solutions for a given problem that are diverse, as opposed to finding a single best solution to solve the problem, has recently become a notable topic in theoretical computer science. Recently, Baste, Fellows, Jaffke, Masa\v{r}\'ik, Oliveira, Philip, and Rosamond showed that under a standard structural parameterization by treewidth, one can find a set of diverse solutions for many problems with only a very small additional cost [Artificial Intelligence 2022]. In this paper, we investigate a much stronger graph parameter, the cliquewidth, which can additionally describe some dense graph classes. Broadly speaking, it describes graphs that can be recursively constructed by a few operations defined on graphs whose vertices are divided into a bounded number of groups while each such group behaves uniformly with respect to any operation. We show that for any vertex problem, if we are given a dynamic program solving that problem on cliquewidth decomposition, we can modify it to produce a few solutions that are as diverse as possible with as little overhead as in the above-mentioned treewidth paper. As a consequence, we prove that a diverse version of any MSO1_1 expressible problem can be solved in linear FPT time parameterized by the cliquewidth, the number of sought solutions, and the number of quantifiers in the formula, which was a natural missing piece in the complexity landscape of structural graph parameters and logic for the diverse problems. We prove our results allowing for a more general natural collection of diversity functions compared to only two mostly studied diversity functions previously. That might be of independent interest as a larger pool of different diversity functions can highlight various aspects of different solutions to a problem.

Keywords

Cite

@article{arxiv.2405.20931,
  title  = {Finding Diverse Solutions Parameterized by Cliquewidth},
  author = {Karolina Drabik and Tomáš Masařík},
  journal= {arXiv preprint arXiv:2405.20931},
  year   = {2026}
}

Comments

Accepted at AAAI 2026: the 40th Annual AAAI Conference on Artificial Intelligence, 30 pages, 3 figure

R2 v1 2026-06-28T16:48:36.097Z