Arithmetic Circuit Lower Bounds via MaxRank

Abstract

We introduce the polynomial coefficient matrix and identify maximum rank of this matrix under variable substitution as a complexity measure for multivariate polynomials. We use our techniques to prove super-polynomial lower bounds against several classes of non-multilinear arithmetic circuits. In particular, we obtain the following results :

• As our first main result, we prove that any homogeneous depth-3 circuit for computing the product of d matrices of dimension n ×n requires Ω(n ^d − 1/2^d) size. This improves the lower bounds in [9] for d = ω(1).

• As our second main result, we show that there is an explicit polynomial on n variables and degree at most $\frac{n}{2}$ for which any depth-3 circuit C of product dimension at most $\frac{n}{10}$ (dimension of the space of affine forms feeding into each product gate) requires size 2^Ω(n). This generalizes the lower bounds against diagonal circuits proved in [14]. Diagonal circuits are of product dimension 1.

• We prove a n ^Ω(logn) lower bound on the size of product-sparse formulas. By definition, any multilinear formula is a product-sparse formula. Thus, this result extends the known super-polynomial lower bounds on the size of multilinear formulas [11].

• We prove a 2^Ω(n) lower bound on the size of partitioned arithmetic branching programs. This result extends the known exponential lower bound on the size of ordered arithmetic branching programs [7].

The full version of the paper is available as a technical report at the ECCC. Technical report No. ECCC-TR-13-028. See http://eccc.hpi-web.de/report/2013/028/