7 research outputs found
Lower Bounds for Matrix Factorization
We study the problem of constructing explicit families of matrices which
cannot be expressed as a product of a few sparse matrices. In addition to being
a natural mathematical question on its own, this problem appears in various
incarnations in computer science; the most significant being in the context of
lower bounds for algebraic circuits which compute linear transformations,
matrix rigidity and data structure lower bounds.
We first show, for every constant , a deterministic construction in
subexponential time of a family of matrices which cannot
be expressed as a product where the total sparsity of
is less than . In other words, any depth-
linear circuit computing the linear transformation has size at
least . This improves upon the prior best lower bounds for
this problem, which are barely super-linear, and were obtained by a long line
of research based on the study of super-concentrators (albeit at the cost of a
blow up in the time required to construct these matrices).
We then outline an approach for proving improved lower bounds through a
certain derandomization problem, and use this approach to prove asymptotically
optimal quadratic lower bounds for natural special cases, which generalize many
of the common matrix decompositions
Polynomial Identity Testing for Low Degree Polynomials with Optimal Randomness
We give a randomized polynomial time algorithm for polynomial identity testing for the class of n-variate poynomials of degree bounded by d over a field ?, in the blackbox setting.
Our algorithm works for every field ? with | ? | ? d+1, and uses only d log n + log (1/ ?) + O(d log log n) random bits to achieve a success probability 1 - ? for some ? > 0. In the low degree regime that is d ? n, it hits the information theoretic lower bound and differs from it only in the lower order terms. Previous best known algorithms achieve the number of random bits (Guruswami-Xing, CCC\u2714 and Bshouty, ITCS\u2714) that are constant factor away from our bound. Like Bshouty, we use Sidon sets for our algorithm. However, we use a new construction of Sidon sets to achieve the improved bound.
We also collect two simple constructions of hitting sets with information theoretically optimal size against the class of n-variate, degree d polynomials. Our contribution is that we give new, very simple proofs for both the constructions
Variety Membership Testing in Algebraic Complexity Theory
In this thesis, we study some of the central problems in algebraic complexity theory through the lens of the variety membership testing problem. In the first part, we investigate whether separations between algebraic complexity classes can be phrased as instances of the variety membership testing problem. For this, we compare some complexity classes with their closures. We show that monotone commutative single-(source, sink) ABPs are closed. Further, we prove that multi-(source, sink) ABPs are not closed in both the monotone commutative and the noncommutative settings. However, the corresponding complexity classes are closed in all these settings. Next, we observe a separation between the complexity class VQP and the closure of VNP. In the second part, we cover the blackbox polynomial identity testing (PIT) problem, and the rank computation problem of symbolic matrices, both phrasable as instances of the variety membership testing problem. For the blackbox PIT, we give a randomized polynomial time algorithm that uses the number of random bits that matches the information-theoretic lower bound, differing from it only in the lower order terms. For the rank computation problem, we give a deterministic polynomial time approximation scheme (PTAS) when the degrees of the entries of the matrices are bounded by a constant. Finally, we show NP-hardness of two problems on 3-tensors, both of which are instances of the variety membership testing problem. The first problem is the orbit closure containment problem for the action of GLk x GLm x GLn on 3-tensors, while the second problem is to decide whether the slice rank of a given 3-tensor is at most r