On the complexity of isomorphism problems for tensors, groups, and polynomials IV: linear-length reductions and their applications
Joshua A. Grochow, Youming Qiao

TL;DR
This paper introduces linear-length tensor reductions to improve the complexity of isomorphism problems for tensors, groups, and polynomials, enabling faster algorithms and extending existing results to broader classes.
Contribution
It presents a new tensor gadget that replaces quadratic-length reductions with linear-length ones, leading to improved algorithms for isomorphism testing.
Findings
Graph isomorphism in P implies faster tests for cubic forms and algebra isomorphism.
Extended isomorphism algorithms for p-groups of higher class and exponent.
Polynomial-time reduction for p-groups of class 2 and exponent p with Cayley table input.
Abstract
Many isomorphism problems for tensors, groups, algebras, and polynomials were recently shown to be equivalent to one another under polynomial-time reductions, prompting the introduction of the complexity class TI (Grochow & Qiao, ITCS '21; SIAM J. Comp., '23). Using the tensorial viewpoint, Grochow & Qiao (CCC '21) then gave moderately exponential-time search- and counting-to-decision reductions for a class of -groups. A significant issue was that the reductions usually incurred a quadratic increase in the length of the tensors involved. When the tensors represent -groups, this corresponds to an increase in the order of the group of the form , negating any asymptotic gains in the Cayley table model. In this paper, we present a new kind of tensor gadget that allows us to replace those quadratic-length reductions with linear-length ones, yielding the…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Machine Learning and Algorithms · Algorithms and Data Compression
