Applications of Monotone Rank to Complexity Theory
Yang D. Li

TL;DR
This paper explores the applications of monotone tensor rank in complexity theory, demonstrating super-exponential separations, implications for quantum correlations, and limitations of the log-rank conjecture in high-dimensional settings.
Contribution
It provides new unconditional bounds on monotone rank, shows a super-exponential separation in algebraic complexity, and extends the log-rank conjecture analysis to multiparty communication complexity.
Findings
Super-exponential separation between monotone and non-monotone ABP complexity.
Monotone rank bounds imply limitations on local hidden variable theories.
High-dimensional communication tensors violate the log-rank conjecture.
Abstract
Raz's recent result \cite{Raz2010} has rekindled people's interest in the study of \emph{tensor rank}, the generalization of matrix rank to high dimensions, by showing its connections to arithmetic formulas. In this paper, we follow Raz's work and show that \emph{monotone rank}, the monotone variant of tensor rank and matrix rank, has applications in algebraic complexity, quantum computing and communication complexity. This paper differs from Raz's paper in that it leverages existing results to show unconditional bounds while Raz's result relies on some assumptions. We show a super-exponential separation between monotone and non-monotone computation in the non-commutative model, and thus provide a strong solution to Nisan's question \cite{Nis1991} in algebraic complexity. More specifically, we exhibit that there exists a homogeneous algebraic function of degree ( even) on…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Markov Chains and Monte Carlo Methods · Quantum Computing Algorithms and Architecture
