Approximate Evaluation of Quantitative Second Order Queries

TL;DR

This paper generalizes Courcelle's theorem to develop efficient approximation schemes for a broad class of problems, including those not fixed-parameter tractable, by using an expanded logic called Blocked CMSO that handles weight comparisons.

Contribution

It introduces Blocked CMSO, a logic enabling approximation algorithms for problems beyond Courcelle's theorem's scope, with a polynomial-time scheme depending on the approximation parameter.

Findings

Provides a unified framework for approximating graph problems parameterized by treewidth and cliquewidth.

Shows the logic's restrictions are tight under complexity assumptions.

Applies to classical problems like Subset Sum and Knapsack.

Abstract

Courcelle's theorem and its adaptations to cliquewidth have shaped the field of exact parameterized algorithms and are widely considered the archetype of algorithmic meta-theorems. In the past decade, there has been growing interest in developing parameterized approximation algorithms for problems which are not captured by Courcelle's theorem and, in particular, are considered not fixed-parameter tractable under the associated widths. We develop a generalization of Courcelle's theorem that yields efficient approximation schemes for any problem that can be captured by an expanded logic we call Blocked CMSO, capable of making logical statements about the sizes of set variables via so-called weight comparisons. The logic controls weight comparisons via the quantifier-alternation depth of the involved variables, allowing full comparisons for zero-alternation variables and limited comparisons for one-alternation variables. We show that the developed framework threads the very needle of tractability: on one hand it can describe a broad range of approximable problems, while on the other hand we show that the restrictions of our logic cannot be relaxed under well-established complexity assumptions. The running time of our approximation scheme is polynomial in $1/\varepsilon$, allowing us to fully interpolate between faster approximate algorithms and slower exact algorithms. This provides a unified framework to explain the tractability landscape of graph problems parameterized by treewidth and cliquewidth, as well as classical non-graph problems such as Subset Sum and Knapsack.