6 research outputs found
Circuits with arbitrary gates for random operators
We consider boolean circuits computing n-operators f:{0,1}^n --> {0,1}^n. As
gates we allow arbitrary boolean functions; neither fanin nor fanout of gates
is restricted. An operator is linear if it computes n linear forms, that is,
computes a matrix-vector product y=Ax over GF(2). We prove the existence of
n-operators requiring about n^2 wires in any circuit, and linear n-operators
requiring about n^2/\log n wires in depth-2 circuits, if either all output
gates or all gates on the middle layer are linear.Comment: 7 page
Min-Rank Conjecture for Log-Depth Circuits
A completion of an m-by-n matrix A with entries in {0,1,*} is obtained by
setting all *-entries to constants 0 or 1. A system of semi-linear equations
over GF(2) has the form Mx=f(x), where M is a completion of A and f:{0,1}^n -->
{0,1}^m is an operator, the i-th coordinate of which can only depend on
variables corresponding to *-entries in the i-th row of A. We conjecture that
no such system can have more than 2^{n-c\cdot mr(A)} solutions, where c>0 is an
absolute constant and mr(A) is the smallest rank over GF(2) of a completion of
A. The conjecture is related to an old problem of proving super-linear lower
bounds on the size of log-depth boolean circuits computing linear operators x
--> Mx. The conjecture is also a generalization of a classical question about
how much larger can non-linear codes be than linear ones. We prove some special
cases of the conjecture and establish some structural properties of solution
sets.Comment: 22 pages, to appear in: J. Comput.Syst.Sci
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
Superconcentrators of Depth 2
It is shown that every n-superconcentrator of depth 2 has size μ(n log n); that there exist n-superconcentrators of depth 2 and size O(n(log n)2); and that there exist n-superconcentrators on which the pebble game can be played in space S and time [image], for a wide range of values of S