18 research outputs found
The Partition Bound for Classical Communication Complexity and Query Complexity
We describe new lower bounds for randomized communication complexity and
query complexity which we call the partition bounds. They are expressed as the
optimum value of linear programs. For communication complexity we show that the
partition bound is stronger than both the rectangle/corruption bound and the
\gamma_2/generalized discrepancy bounds. In the model of query complexity we
show that the partition bound is stronger than the approximate polynomial
degree and classical adversary bounds. We also exhibit an example where the
partition bound is quadratically larger than polynomial degree and classical
adversary bounds.Comment: 28 pages, ver. 2, added conten
Partition bound is quadratically tight for product distributions
Let be a 2-party
function. For every product distribution on ,
we show that
where is the distributional communication
complexity of with error at most under the distribution
and is the {\em partition bound} of , as defined by
Jain and Klauck [{\em Proc. 25th CCC}, 2010]. We also prove a similar bound in
terms of , the {\em information complexity} of ,
namely, The latter bound was recently and
independently established by Kol [{\em Proc. 48th STOC}, 2016] using a
different technique.
We show a similar result for query complexity under product distributions.
Let be a function. For every bit-wise
product distribution on , we show that
where
is the distributional query complexity of
with error at most under the distribution and
is the {\em query partition bound} of the function
.
Partition bounds were introduced (in both communication complexity and query
complexity models) to provide LP-based lower bounds for randomized
communication complexity and randomized query complexity. Our results
demonstrate that these lower bounds are polynomially tight for {\em product}
distributions.Comment: The previous version of the paper erroneously stated the main result
in terms of relaxed partition number instead of partition numbe
A concentration inequality for the overlap of a vector on a large set, with application to the communication complexity of the Gap-Hamming-Distance problem
Given two sets A, B ⊆ R_n, a measure of their correlation is given by the expected squared inner product between random x ϵ A and y ϵ B. We prove an inequality showing that no two sets of large enough Gaussian measure (at least e^(-δn) for some constant δ > 0) can have correlation substantially lower than would two random sets of the same size. Our proof is based on a concentration inequality for the overlap of a random Gaussian vector on a large set.
As an application, we show how our result can be combined with the partition bound of Jain and Klauck to give a simpler proof of a recent linear lower bound on the randomized communication complexity of the Gap-Hamming-Distance problem due to Chakrabarti and Regev
On The Communication Complexity of Linear Algebraic Problems in the Message Passing Model
We study the communication complexity of linear algebraic problems over
finite fields in the multi-player message passing model, proving a number of
tight lower bounds. Specifically, for a matrix which is distributed among a
number of players, we consider the problem of determining its rank, of
computing entries in its inverse, and of solving linear equations. We also
consider related problems such as computing the generalized inner product of
vectors held on different servers. We give a general framework for reducing
these multi-player problems to their two-player counterparts, showing that the
randomized -player communication complexity of these problems is at least
times the randomized two-player communication complexity. Provided the
problem has a certain amount of algebraic symmetry, which we formally define,
we can show the hardest input distribution is a symmetric distribution, and
therefore apply a recent multi-player lower bound technique of Phillips et al.
Further, we give new two-player lower bounds for a number of these problems. In
particular, our optimal lower bound for the two-player version of the matrix
rank problem resolves an open question of Sun and Wang.
A common feature of our lower bounds is that they apply even to the special
"threshold promise" versions of these problems, wherein the underlying
quantity, e.g., rank, is promised to be one of just two values, one on each
side of some critical threshold. These kinds of promise problems are
commonplace in the literature on data streaming as sources of hardness for
reductions giving space lower bounds
Separating decision tree complexity from subcube partition complexity
The subcube partition model of computation is at least as powerful as
decision trees but no separation between these models was known. We show that
there exists a function whose deterministic subcube partition complexity is
asymptotically smaller than its randomized decision tree complexity, resolving
an open problem of Friedgut, Kahn, and Wigderson (2002). Our lower bound is
based on the information-theoretic techniques first introduced to lower bound
the randomized decision tree complexity of the recursive majority function.
We also show that the public-coin partition bound, the best known lower bound
method for randomized decision tree complexity subsuming other general
techniques such as block sensitivity, approximate degree, randomized
certificate complexity, and the classical adversary bound, also lower bounds
randomized subcube partition complexity. This shows that all these lower bound
techniques cannot prove optimal lower bounds for randomized decision tree
complexity, which answers an open question of Jain and Klauck (2010) and Jain,
Lee, and Vishnoi (2014).Comment: 16 pages, 1 figur
An Optimal Lower Bound on the Communication Complexity of Gap-Hamming-Distance
We prove an optimal lower bound on the randomized communication
complexity of the much-studied Gap-Hamming-Distance problem. As a consequence,
we obtain essentially optimal multi-pass space lower bounds in the data stream
model for a number of fundamental problems, including the estimation of
frequency moments.
The Gap-Hamming-Distance problem is a communication problem, wherein Alice
and Bob receive -bit strings and , respectively. They are promised
that the Hamming distance between and is either at least
or at most , and their goal is to decide which of these is the
case. Since the formal presentation of the problem by Indyk and Woodruff (FOCS,
2003), it had been conjectured that the naive protocol, which uses bits of
communication, is asymptotically optimal. The conjecture was shown to be true
in several special cases, e.g., when the communication is deterministic, or
when the number of rounds of communication is limited.
The proof of our aforementioned result, which settles this conjecture fully,
is based on a new geometric statement regarding correlations in Gaussian space,
related to a result of C. Borell (1985). To prove this geometric statement, we
show that random projections of not-too-small sets in Gaussian space are close
to a mixture of translated normal variables