Fine-grained reductions from approximate counting to decision
Holger Dell, John Lapinskas

TL;DR
This paper develops a framework for reducing approximate counting problems to decision problems in fine-grained complexity, enabling new insights into the complexity of problems like 3SUM, Orthogonal Vectors, and #SAT.
Contribution
It introduces a general method for fine-grained reductions from approximate counting to decision, extending foundational polynomial-time results to the fine-grained setting.
Findings
Reductions established for key problems like Orthogonal Vectors, 3SUM, and Negative-Weight Triangle.
Shows equivalence between ETH and the difficulty of approximating #3-SAT.
Provides a reduction from approximate #SAT to SAT under certain complexity assumptions.
Abstract
In this paper, we introduce a general framework for fine-grained reductions of approximate counting problems to their decision versions. (Thus we use an oracle that decides whether any witness exists to multiplicatively approximate the number of witnesses with minimal overhead.) This mirrors a foundational result of Sipser (STOC 1983) and Stockmeyer (SICOMP 1985) in the polynomial-time setting, and a similar result of M\"uller (IWPEC 2006) in the FPT setting. Using our framework, we obtain such reductions for some of the most important problems in fine-grained complexity: the Orthogonal Vectors problem, 3SUM, and the Negative-Weight Triangle problem (which is closely related to All-Pairs Shortest Path). We also provide a fine-grained reduction from approximate #SAT to SAT. Suppose the Strong Exponential Time Hypothesis (SETH) is false, so that for some and all there is an…
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