Randomized Communication and Implicit Representations for Matrices and Graphs of Small Sign-Rank
Nathaniel Harms, Viktor Zamaraev

TL;DR
This paper characterizes when matrices of sign-rank 3 and certain graphs allow constant-cost randomized communication protocols and implicit representations, linking structural properties to computational complexity.
Contribution
It provides a precise characterization of sign-rank 3 matrices and UDGs that admit constant randomized communication complexity and implicit representations based on forbidden substructures.
Findings
Sign-rank 3 matrices have constant randomized communication complexity if they avoid large half-graphs.
UDGs have constant complexity under the same conditions, extending the result.
The results connect structural graph properties with communication complexity and implicit representations.
Abstract
We prove a characterization of the structural conditions on matrices of sign-rank 3 and unit disk graphs (UDGs) which permit constant-cost public-coin randomized communication protocols. Therefore, under these conditions, these graphs also admit implicit representations. The sign-rank of a matrix is the smallest rank of a matrix such that for all ; equivalently, it is the smallest dimension in which can be represented as a point-halfspace incidence matrix with halfspaces through the origin, and it is essentially equivalent to the unbounded-error communication complexity. Matrices of sign-rank 3 can achieve the maximum possible bounded-error randomized communication complexity , and meanwhile the existence of implicit representations for graphs of bounded sign-rank (including…
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Taxonomy
TopicsDNA and Biological Computing · Topological and Geometric Data Analysis
