Randomness Efficient Testing of Sparse Black Box Identities of Unbounded Degree over the Reals

We construct a hitting set generator for sparse multivariate polynomials over the reals. The seed length of our generator is O(log^2 (mn/epsilon)) where m is the number of monomials, n is number of variables, and 1 - epsilon is the hitting probability. The generator can be evaluated in time polynomial in log m, n, and log 1/epsilon. This is the first hitting set generator whose seed length is independent of the degree of the polynomial. The seed length of the best generator so far by Klivans and Spielman [STOC 2001] depends logarithmically on the degree. From this, we get a randomized algorithm for testing sparse black box polynomial identities over the reals using O(log^2 (mn/epsilon)) random bits with running time polynomial in log m, n, and log(1/epsilon). We also design a deterministic test with running time ~O(m^3 n^3). Here, the ~O-notation suppresses polylogarithmic factors. The previously best deterministic test by Lipton and Vishnoi [SODA 2003] has a running time that depends polynomially on log delta, where $delta$ is the degree of the black box polynomial

Greedy Strikes Again: A Deterministic PTAS for Commutative Rank of Matrix Spaces

We consider the problem of commutative rank computation of a given matrix space. A matrix space is a (linear) subspace of the (linear) space of n x n matrices over a given field. The problem is fundamental, as it generalizes several computational problems from algebra and combinatorics. For instance, checking if the commutative rank of the space is n, subsumes problems such as testing perfect matching in graphs and identity testing of algebraic branching programs. An efficient deterministic computation of the commutative rank is a major open problem, although there is a simple and efficient randomized algorithm for it. Recently, there has been a series of results on computing the non-commutative rank of matrix spaces in deterministic polynomial time. Since the non-commutative rank of any matrix space is at most twice the commutative rank, one immediately gets a deterministic 1/2-approximation algorithm for the computation of the commutative rank. This leads to a natural question of whether this approximation ratio can be improved. In this paper, we answer this question affirmatively. We present a deterministic Polynomial-time approximation scheme (PTAS) for computing the commutative rank of a given matrix space B. More specifically, given a matrix space and a rational number e > 0, we give an algorithm, that runs in time O(n^(4 + 3/e)) and computes a matrix A in the given matrix space B such that the rank of A is at least (1-e) times the commutative rank of B. The algorithm is the natural greedy algorithm. It always takes the first set of k matrices that will increase the rank of the matrix constructed so far until it does not find any improvement, where the size of the set k depends on e