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The Minrank of Random Graph

Speaker: Sasha
Title: The Minrank of Random Graph
Date: 22 Oct 2018 5:30pm-7:00pm
Location: Maxwell-Dworkin 123
Food: Unknown

Abstract: The minrank of a directed graph $G$ is the minimum rank of a matrix $M$ that can be obtained from the adjacency matrix of $G$ by switching some ones to zeros (i.e., deleting edges) and then setting all diagonal entries to one. This quantity is closely related to the fundamental information-theoretic problems of (linear) index coding (Bar-Yossef et al., FOCS’06), network coding and distributed storage, and to Valiant’s approach for proving superlinear circuit lower bounds (Valiant, Boolean Function Complexity ‘92).

We prove tight bounds on the minrank of directed Erdös-Rényi random graphs $G(n,p)$ for all regimes of $p\in[0,1]$. In particular, for any constant $p$, we show that $\mathsf{minrk}(G) = \Theta(n/\log n)$ with high probability, where $G$ is chosen from $G(n,p)$. This bound gives a near quadratic improvement over the previous best lower bound of $\Omega(\sqrt{n})$ (Haviv and Langberg, ISIT’12), and partially settles an open problem raised by Lubetzky and Stav (FOCS ‘07). Our lower bound matches the well-known upper bound obtained by the “clique covering” solution, and settles the linear index coding problem for random graphs.

Finally, our result suggests a new avenue of attack, via derandomization, on Valiant’s approach for proving superlinear lower bounds for logarithmic-depth semilinear circuits.