Finding Small Satisfying Assignments Faster Than Brute Force: A Fine-grained Perspective into Boolean Constraint Satisfaction
Marvin K\"unnemann, D\'aniel Marx

TL;DR
This paper investigates the complexity of finding small satisfying assignments in Boolean constraint satisfaction problems, providing a detailed classification of the optimal algorithms' running times based on the size constraint k.
Contribution
It offers a fine-grained complexity classification for Boolean CSPs with size constraints, extending beyond the traditional FPT/W[1]-hardness dichotomy.
Findings
Characterizes four regimes of algorithmic complexity based on the parameter k.
Provides an almost tight characterization of the exponent g(k) in the running time.
Introduces a novel algorithm for SubsetSum with precedence constraints, generalizing the Frobenius coin problem.
Abstract
To study the question under which circumstances small solutions can be found faster than by exhaustive search (and by how much), we study the fine-grained complexity of Boolean constraint satisfaction with size constraint exactly . More precisely, we aim to determine, for any finite constraint family, the optimal running time required to find satisfying assignments that set precisely of the variables to . Under central hardness assumptions on detecting cliques in graphs and 3-uniform hypergraphs, we give an almost tight characterization of into four regimes: (1) Brute force is essentially best-possible, i.e., , (2) the best algorithms are as fast as current -clique algorithms, i.e., , (3) the exponent has sublinear dependence on with , or (4) the…
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