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# Depth-4 Lower Bounds, Determinantal Complexity: A Unified Approach

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LIPIcs.STACS.2014.239.pdf
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## Cite As

Suryajith Chillara and Partha Mukhopadhyay. Depth-4 Lower Bounds, Determinantal Complexity: A Unified Approach. In 31st International Symposium on Theoretical Aspects of Computer Science (STACS 2014). Leibniz International Proceedings in Informatics (LIPIcs), Volume 25, pp. 239-250, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)
https://doi.org/10.4230/LIPIcs.STACS.2014.239

## Abstract

Tavenas has recently proved that any n^{O(1)}-variate and degree n polynomial in VP can be computed by a depth-4 SigmaPi^[O(sqrt{n})]SigmaPi^{[sqrt{n}]} circuit of size 2^{O(n^{1/2}.log(n))} [Tavenas, 2013]. So, to prove that VP is not equal to VNP it is sufficient to show that an explicit polynomial in VNP of degree n requires 2^{omega(n^{1/2}.log(n))} size depth-4 circuits. Soon after Tavenas' result, for two different explicit polynomials, depth-4 circuit size lower bounds of 2^{Omega(n^{1/2}.log(n))} have been proved (see [Kayal, Saha, and Saptharishi, 2013] and [Fournier et al., 2013]). In particular, using combinatorial design [Kayal et al., 2013] construct an explicit polynomial in VNP that requires depth-4 circuits of size 2^{Omega(n^{1/2}.log(n))} and [Fournier et al., 2013] show that the iterated matrix multiplication polynomial (which is in VP) also requires 2^{Omega(n^{1/2}.log(n))} size depth-4 circuits. In this paper, we identify a simple combinatorial property such that any polynomial f that satisfies this property would achieve a similar depth-4 circuit size lower bound. In particular, it does not matter whether f is in VP or in VNP. As a result, we get a simple unified lower bound analysis for the above mentioned polynomials. Another goal of this paper is to compare our current knowledge of the depth-4 circuit size lower bounds and the determinantal complexity lower bounds. Currently the best known determinantal complexity lower bound is Omega(n^2) for Permanent of a nxn matrix (which is a n^2-variate and degree n polynomial) [Cai, Chen, and Li, 2008]. We prove that the determinantal complexity of the iterated matrix multiplication polynomial is Omega(dn) where d is the number of matrices and n is the dimension of the matrices. So for d=n, we get that the iterated matrix multiplication polynomial achieves the current best known lower bounds in both fronts: depth-4 circuit size and determinantal complexity. Our result also settles the determinantal complexity of the iterated matrix multiplication polynomial to Theta(dn). To the best of our knowledge, a Theta(n) bound for the determinantal complexity for the iterated matrix multiplication polynomial was known only for any constant d>1 [Jansen, 2011].
##### Keywords
• Arithmetic Circuits
• Determinantal Complexity
• Depth-4 Lower Bounds

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