Approximate Evaluation of Quantitative Second Order Queries

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 a more expressive logic we call $\boxed\forall $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. 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 widely accepted complexity assumptions.The running time of our approximation scheme is polynomial in 1/ε, 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.

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Published
2025-06-23
DOI
https://doi.org/10.1109/lics65433.2025.00056
Primary Topic
Neural Networks and Applications
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article
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article

Approximate Evaluation of Quantitative Second Order Queries

Thekla Hamm, Jan Dreier, Robert Ganian
Neural Networks and Applications
article

Approximate Evaluation of Quantitative Second Order Queries

Thekla Hamm, Jan Dreier, Robert Ganian
article en

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 a more expressive logic we call $\boxed\forall $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. 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 widely accepted complexity assumptions.The running time of our approximation scheme is polynomial in 1/ε, 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.

TU Wien (AT), Eindhoven University of Technology (NL)
Openalex Percentile: Top 85%
Neural Networks and Applications
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