95 research outputs found
Maximum Matching in Turnstile Streams
We consider the unweighted bipartite maximum matching problem in the one-pass
turnstile streaming model where the input stream consists of edge insertions
and deletions. In the insertion-only model, a one-pass -approximation
streaming algorithm can be easily obtained with space , where
denotes the number of vertices of the input graph. We show that no such result
is possible if edge deletions are allowed, even if space is
granted, for every . Specifically, for every , we show that in the one-pass turnstile streaming model, in order to compute
a -approximation, space is
required for constant error randomized algorithms, and, up to logarithmic
factors, space is sufficient. Our lower bound result is
proved in the simultaneous message model of communication and may be of
independent interest
Hardness of Exact Distance Queries in Sparse Graphs Through Hub Labeling
A distance labeling scheme is an assignment of bit-labels to the vertices of
an undirected, unweighted graph such that the distance between any pair of
vertices can be decoded solely from their labels. An important class of
distance labeling schemes is that of hub labelings, where a node
stores its distance to the so-called hubs , chosen so that for
any there is belonging to some shortest
path. Notice that for most existing graph classes, the best distance labelling
constructions existing use at some point a hub labeling scheme at least as a
key building block. Our interest lies in hub labelings of sparse graphs, i.e.,
those with , for which we show a lowerbound of
for the average size of the hubsets.
Additionally, we show a hub-labeling construction for sparse graphs of average
size for some , where is the
so-called Ruzsa-Szemer{\'e}di function, linked to structure of induced
matchings in dense graphs. This implies that further improving the lower bound
on hub labeling size to would require a
breakthrough in the study of lower bounds on , which have resisted
substantial improvement in the last 70 years. For general distance labeling of
sparse graphs, we show a lowerbound of , where is the communication complexity of the
Sum-Index problem over . Our results suggest that the best achievable
hub-label size and distance-label size in sparse graphs may be
for some
Welfare Maximization with Limited Interaction
We continue the study of welfare maximization in unit-demand (matching)
markets, in a distributed information model where agent's valuations are
unknown to the central planner, and therefore communication is required to
determine an efficient allocation. Dobzinski, Nisan and Oren (STOC'14) showed
that if the market size is , then rounds of interaction (with
logarithmic bandwidth) suffice to obtain an -approximation to the
optimal social welfare. In particular, this implies that such markets converge
to a stable state (constant approximation) in time logarithmic in the market
size.
We obtain the first multi-round lower bound for this setup. We show that even
if the allowable per-round bandwidth of each agent is , the
approximation ratio of any -round (randomized) protocol is no better than
, implying an lower bound on the
rate of convergence of the market to equilibrium.
Our construction and technique may be of interest to round-communication
tradeoffs in the more general setting of combinatorial auctions, for which the
only known lower bound is for simultaneous () protocols [DNO14]
Submodular Maximization Meets Streaming: Matchings, Matroids, and More
We study the problem of finding a maximum matching in a graph given by an
input stream listing its edges in some arbitrary order, where the quantity to
be maximized is given by a monotone submodular function on subsets of edges.
This problem, which we call maximum submodular-function matching (MSM), is a
natural generalization of maximum weight matching (MWM), which is in turn a
generalization of maximum cardinality matching (MCM). We give two incomparable
algorithms for this problem with space usage falling in the semi-streaming
range---they store only edges, using working memory---that
achieve approximation ratios of in a single pass and in
passes respectively. The operations of these algorithms
mimic those of Zelke's and McGregor's respective algorithms for MWM; the
novelty lies in the analysis for the MSM setting. In fact we identify a general
framework for MWM algorithms that allows this kind of adaptation to the broader
setting of MSM.
In the sequel, we give generalizations of these results where the
maximization is over "independent sets" in a very general sense. This
generalization captures hypermatchings in hypergraphs as well as independence
in the intersection of multiple matroids.Comment: 18 page
Streaming Verification of Graph Computations via Graph Structure
We give new algorithms in the annotated data streaming setting - also known as verifiable data stream computation - for certain graph problems. This setting is meant to model outsourced computation, where a space-bounded verifier limited to sequential data access seeks to overcome its computational limitations by engaging a powerful prover, without needing to trust the prover. As is well established, several problems that admit no sublinear-space algorithms under traditional streaming do allow protocols using a sublinear amount of prover/verifier communication and sublinear-space verification. We give algorithms for many well-studied graph problems including triangle counting, its generalization to subgraph counting, maximum matching, problems about the existence (or not) of short paths, finding the shortest path between two vertices, and testing for an independent set. While some of these problems have been studied before, our results achieve new tradeoffs between space and communication costs that were hitherto unknown. In particular, two of our results disprove explicit conjectures of Thaler (ICALP, 2016) by giving triangle counting and maximum matching algorithms for n-vertex graphs, using o(n) space and o(n^2) communication
Sublinear Estimation of Weighted Matchings in Dynamic Data Streams
This paper presents an algorithm for estimating the weight of a maximum
weighted matching by augmenting any estimation routine for the size of an
unweighted matching. The algorithm is implementable in any streaming model
including dynamic graph streams. We also give the first constant estimation for
the maximum matching size in a dynamic graph stream for planar graphs (or any
graph with bounded arboricity) using space which also
extends to weighted matching. Using previous results by Kapralov, Khanna, and
Sudan (2014) we obtain a approximation for general graphs
using space in random order streams, respectively. In
addition, we give a space lower bound of for any
randomized algorithm estimating the size of a maximum matching up to a
factor for adversarial streams
Approximating Semi-Matchings in Streaming and in Two-Party Communication
We study the communication complexity and streaming complexity of
approximating unweighted semi-matchings. A semi-matching in a bipartite graph G
= (A, B, E), with n = |A|, is a subset of edges S that matches all A vertices
to B vertices with the goal usually being to do this as fairly as possible.
While the term 'semi-matching' was coined in 2003 by Harvey et al. [WADS 2003],
the problem had already previously been studied in the scheduling literature
under different names.
We present a deterministic one-pass streaming algorithm that for any 0 <=
\epsilon <= 1 uses space O(n^{1+\epsilon}) and computes an
O(n^{(1-\epsilon)/2})-approximation to the semi-matching problem. Furthermore,
with O(log n) passes it is possible to compute an O(log n)-approximation with
space O(n).
In the one-way two-party communication setting, we show that for every
\epsilon > 0, deterministic communication protocols for computing an
O(n^{1/((1+\epsilon)c + 1)})-approximation require a message of size more than
cn bits. We present two deterministic protocols communicating n and 2n edges
that compute an O(sqrt(n)) and an O(n^{1/3})-approximation respectively.
Finally, we improve on results of Harvey et al. [Journal of Algorithms 2006]
and prove new links between semi-matchings and matchings. While it was known
that an optimal semi-matching contains a maximum matching, we show that there
is a hierarchical decomposition of an optimal semi-matching into maximum
matchings. A similar result holds for semi-matchings that do not admit
length-two degree-minimizing paths.Comment: This is the long version including all proves of the ICALP 2013 pape
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