Classifying Identities: Subcubic Distributivity Checking and Hardness from Arithmetic Progression Detection
Bart{\l}omiej Dudek, Nick Fischer, Geri Gokaj, Ce Jin, Marvin K\"unnemann, Xiao Mao, Mirza Red\v{z}i\'c

TL;DR
This paper advances the understanding of algebraic identity verification by providing subcubic algorithms for distributivity, establishing hardness results linked to arithmetic progression detection, and classifying identities based on their computational complexity.
Contribution
It introduces a subcubic algorithm for distributivity verification, connects identity verification complexity to arithmetic progression detection, and classifies a subclass of identities by their computational difficulty.
Findings
Distributivity can be verified in strongly subcubic time $O(|S|^ ext{omega})$ with matching lower bounds.
Detecting 4-term arithmetic progressions is conditionally hard, impacting identity verification.
Classified a subclass of identities as either efficiently verifiable or conditionally hard.
Abstract
We revisit the complexity of verifying basic identities, such as associativity and distributivity, on a given finite algebraic structure. In particular, while Rajagopalan and Schulman (FOCS'96, SICOMP'00) gave a surprising randomized algorithm to verify associativity of an operation in optimal time , they left the open problem of finding any subcubic algorithm for verifying distributivity of given operations . Our results are as follows: * We resolve the open problem by Rajagopalan and Schulman by devising an algorithm verifying distributivity in strongly subcubic time , together with a matching conditional lower bound based on the Triangle Detection Hypothesis. * We propose arithmetic progression detection in small universes as a consequential algorithmic challenge: We show that unless we can detect…
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