2,404 research outputs found
The zero-error randomized query complexity of the pointer function
The pointer function of G{\"{o}}{\"{o}}s, Pitassi and Watson
\cite{DBLP:journals/eccc/GoosP015a} and its variants have recently been used to
prove separation results among various measures of complexity such as
deterministic, randomized and quantum query complexities, exact and approximate
polynomial degrees, etc. In particular, the widest possible (quadratic)
separations between deterministic and zero-error randomized query complexity,
as well as between bounded-error and zero-error randomized query complexity,
have been obtained by considering {\em
variants}~\cite{DBLP:journals/corr/AmbainisBBL15} of this pointer function.
However, as was pointed out in \cite{DBLP:journals/corr/AmbainisBBL15}, the
precise zero-error complexity of the original pointer function was not known.
We show a lower bound of on the zero-error
randomized query complexity of the pointer function on bits;
since an upper bound is already known
\cite{DBLP:conf/fsttcs/MukhopadhyayS15}, our lower bound is optimal up to a
factor of \polylog\, n
Towards Better Separation between Deterministic and Randomized Query Complexity
We show that there exists a Boolean function which observes the following
separations among deterministic query complexity , randomized zero
error query complexity and randomized one-sided error query
complexity : and
. This refutes the conjecture made by Saks
and Wigderson that for any Boolean function ,
. This also shows widest separation between
and for any Boolean function. The function was defined by
G{\"{o}}{\"{o}}s, Pitassi and Watson who studied it for showing a separation
between deterministic decision tree complexity and unambiguous
non-deterministic decision tree complexity. Independently of us, Ambainis et al
proved that different variants of the function certify optimal (quadratic)
separation between and , and polynomial separation between
and . Viewed as separation results, our results are subsumed
by those of Ambainis et al. However, while the functions considerd in the work
of Ambainis et al are different variants of , we work with the original
function itself.Comment: Reference adde
Separations in Query Complexity Based on Pointer Functions
In 1986, Saks and Wigderson conjectured that the largest separation between
deterministic and zero-error randomized query complexity for a total boolean
function is given by the function on bits defined by a complete
binary tree of NAND gates of depth , which achieves . We show this is false by giving an example of a total
boolean function on bits whose deterministic query complexity is
while its zero-error randomized query complexity is . We further show that the quantum query complexity of the same
function is , giving the first example of a total function
with a super-quadratic gap between its quantum and deterministic query
complexities.
We also construct a total boolean function on variables that has
zero-error randomized query complexity and bounded-error
randomized query complexity . This is the first
super-linear separation between these two complexity measures. The exact
quantum query complexity of the same function is .
These two functions show that the relations and are optimal, up to poly-logarithmic factors. Further
variations of these functions give additional separations between other query
complexity measures: a cubic separation between and , a -power
separation between and , and a 4th power separation between
approximate degree and bounded-error randomized query complexity.
All of these examples are variants of a function recently introduced by
\goos, Pitassi, and Watson which they used to separate the unambiguous
1-certificate complexity from deterministic query complexity and to resolve the
famous Clique versus Independent Set problem in communication complexity.Comment: 25 pages, 6 figures. Version 3 improves separation between Q_E and
R_0 and updates reference
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
Separations between Combinatorial Measures for Transitive Functions
The role of symmetry in Boolean functions has been
extensively studied in complexity theory. For example, symmetric functions,
that is, functions that are invariant under the action of , is an
important class of functions in the study of Boolean functions. A function
is called transitive (or weakly-symmetric) if there
exists a transitive group of such that is invariant under the
action of - that is the function value remains unchanged even after the
bits of the input of are moved around according to some permutation . Understanding various complexity measures of transitive functions has
been a rich area of research for the past few decades.
In this work, we study transitive functions in light of several combinatorial
measures. We look at the maximum separation between various pairs of measures
for transitive functions. Such study for general Boolean functions has been
going on for past many years. The best-known results for general Boolean
functions have been nicely compiled by Aaronson et. al (STOC, 2021).
The separation between a pair of combinatorial measures is shown by
constructing interesting functions that demonstrate the separation. But many of
the celebrated separation results are via the construction of functions (like
"pointer functions" from Ambainis et al. (JACM, 2017) and "cheat-sheet
functions" Aaronson et al. (STOC, 2016)) that are not transitive. Hence, we
don't have such separation between the pairs of measures for transitive
functions.
In this paper we show how to modify some of these functions to construct
transitive functions that demonstrate similar separations between pairs of
combinatorial measures
The Skip Quadtree: A Simple Dynamic Data Structure for Multidimensional Data
We present a new multi-dimensional data structure, which we call the skip
quadtree (for point data in R^2) or the skip octree (for point data in R^d,
with constant d>2). Our data structure combines the best features of two
well-known data structures, in that it has the well-defined "box"-shaped
regions of region quadtrees and the logarithmic-height search and update
hierarchical structure of skip lists. Indeed, the bottom level of our structure
is exactly a region quadtree (or octree for higher dimensional data). We
describe efficient algorithms for inserting and deleting points in a skip
quadtree, as well as fast methods for performing point location and approximate
range queries.Comment: 12 pages, 3 figures. A preliminary version of this paper appeared in
the 21st ACM Symp. Comp. Geom., Pisa, 2005, pp. 296-30
Error-Correcting Data Structures
We study data structures in the presence of adversarial noise. We want to
encode a given object in a succinct data structure that enables us to
efficiently answer specific queries about the object, even if the data
structure has been corrupted by a constant fraction of errors. This new model
is the common generalization of (static) data structures and locally decodable
error-correcting codes. The main issue is the tradeoff between the space used
by the data structure and the time (number of probes) needed to answer a query
about the encoded object. We prove a number of upper and lower bounds on
various natural error-correcting data structure problems. In particular, we
show that the optimal length of error-correcting data structures for the
Membership problem (where we want to store subsets of size s from a universe of
size n) is closely related to the optimal length of locally decodable codes for
s-bit strings.Comment: 15 pages LaTeX; an abridged version will appear in the Proceedings of
the STACS 2009 conferenc
Optimal Separation and Strong Direct Sum for Randomized Query Complexity
We establish two results regarding the query complexity of bounded-error
randomized algorithms.
* Bounded-error separation theorem. There exists a total function whose -error randomized query complexity
satisfies .
* Strong direct sum theorem. For every function and every , the
randomized query complexity of computing instances of simultaneously
satisfies .
As a consequence of our two main results, we obtain an optimal superlinear
direct-sum-type theorem for randomized query complexity: there exists a
function for which . This answers an open question of Drucker (2012). Combining
this result with the query-to-communication complexity lifting theorem of
G\"o\"os, Pitassi, and Watson (2017), this also shows that there is a total
function whose public-coin randomized communication complexity satisfies
, answering a question of Feder, Kushilevitz,
Naor, and Nisan (1995).Comment: 15 pages, 2 figures, CCC 201
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