17 research outputs found
Tight local approximation results for max-min linear programs
In a bipartite max-min LP, we are given a bipartite graph \myG = (V \cup I
\cup K, E), where each agent is adjacent to exactly one constraint
and exactly one objective . Each agent controls a
variable . For each we have a nonnegative linear constraint on
the variables of adjacent agents. For each we have a nonnegative
linear objective function of the variables of adjacent agents. The task is to
maximise the minimum of the objective functions. We study local algorithms
where each agent must choose based on input within its
constant-radius neighbourhood in \myG. We show that for every
there exists a local algorithm achieving the approximation ratio . We also show that this result is the best possible
-- no local algorithm can achieve the approximation ratio . Here is the maximum degree of a vertex , and
is the maximum degree of a vertex . As a methodological
contribution, we introduce the technique of graph unfolding for the design of
local approximation algorithms.Comment: 16 page
Fast Distributed Algorithms for LP-Type Problems of Bounded Dimension
In this paper we present various distributed algorithms for LP-type problems
in the well-known gossip model. LP-type problems include many important classes
of problems such as (integer) linear programming, geometric problems like
smallest enclosing ball and polytope distance, and set problems like hitting
set and set cover. In the gossip model, a node can only push information to or
pull information from nodes chosen uniformly at random. Protocols for the
gossip model are usually very practical due to their fast convergence, their
simplicity, and their stability under stress and disruptions. Our algorithms
are very efficient (logarithmic rounds or better with just polylogarithmic
communication work per node per round) whenever the combinatorial dimension of
the given LP-type problem is constant, even if the size of the given LP-type
problem is polynomially large in the number of nodes
Distributed Symmetry Breaking in Hypergraphs
Fundamental local symmetry breaking problems such as Maximal Independent Set
(MIS) and coloring have been recognized as important by the community, and
studied extensively in (standard) graphs. In particular, fast (i.e.,
logarithmic run time) randomized algorithms are well-established for MIS and
-coloring in both the LOCAL and CONGEST distributed computing
models. On the other hand, comparatively much less is known on the complexity
of distributed symmetry breaking in {\em hypergraphs}. In particular, a key
question is whether a fast (randomized) algorithm for MIS exists for
hypergraphs.
In this paper, we study the distributed complexity of symmetry breaking in
hypergraphs by presenting distributed randomized algorithms for a variety of
fundamental problems under a natural distributed computing model for
hypergraphs. We first show that MIS in hypergraphs (of arbitrary dimension) can
be solved in rounds ( is the number of nodes of the
hypergraph) in the LOCAL model. We then present a key result of this paper ---
an -round hypergraph MIS algorithm in
the CONGEST model where is the maximum node degree of the hypergraph
and is any arbitrarily small constant.
To demonstrate the usefulness of hypergraph MIS, we present applications of
our hypergraph algorithm to solving problems in (standard) graphs. In
particular, the hypergraph MIS yields fast distributed algorithms for the {\em
balanced minimal dominating set} problem (left open in Harris et al. [ICALP
2013]) and the {\em minimal connected dominating set problem}. We also present
distributed algorithms for coloring, maximal matching, and maximal clique in
hypergraphs.Comment: Changes from the previous version: More references adde
On the Complexity of Local Distributed Graph Problems
This paper is centered on the complexity of graph problems in the
well-studied LOCAL model of distributed computing, introduced by Linial [FOCS
'87]. It is widely known that for many of the classic distributed graph
problems (including maximal independent set (MIS) and -vertex
coloring), the randomized complexity is at most polylogarithmic in the size
of the network, while the best deterministic complexity is typically
. Understanding and narrowing down this exponential gap
is considered to be one of the central long-standing open questions in the area
of distributed graph algorithms. We investigate the problem by introducing a
complexity-theoretic framework that allows us to shed some light on the role of
randomness in the LOCAL model. We define the SLOCAL model as a sequential
version of the LOCAL model. Our framework allows us to prove completeness
results with respect to the class of problems which can be solved efficiently
in the SLOCAL model, implying that if any of the complete problems can be
solved deterministically in rounds in the LOCAL model, we can
deterministically solve all efficient SLOCAL-problems (including MIS and
-coloring) in rounds in the LOCAL model. We show
that a rather rudimentary looking graph coloring problem is complete in the
above sense: Color the nodes of a graph with colors red and blue such that each
node of sufficiently large polylogarithmic degree has at least one neighbor of
each color. The problem admits a trivial zero-round randomized solution. The
result can be viewed as showing that the only obstacle to getting efficient
determinstic algorithms in the LOCAL model is an efficient algorithm to
approximately round fractional values into integer values
On Derandomizing Local Distributed Algorithms
The gap between the known randomized and deterministic local distributed
algorithms underlies arguably the most fundamental and central open question in
distributed graph algorithms. In this paper, we develop a generic and clean
recipe for derandomizing LOCAL algorithms. We also exhibit how this simple
recipe leads to significant improvements on a number of problem. Two main
results are:
- An improved distributed hypergraph maximal matching algorithm, improving on
Fischer, Ghaffari, and Kuhn [FOCS'17], and giving improved algorithms for
edge-coloring, maximum matching approximation, and low out-degree edge
orientation. The first gives an improved algorithm for Open Problem 11.4 of the
book of Barenboim and Elkin, and the last gives the first positive resolution
of their Open Problem 11.10.
- An improved distributed algorithm for the Lov\'{a}sz Local Lemma, which
gets closer to a conjecture of Chang and Pettie [FOCS'17], and moreover leads
to improved distributed algorithms for problems such as defective coloring and
-SAT.Comment: 37 page
A Fast Distributed Stateless Algorithm for -Fair Packing Problems
Over the past two decades, fair resource allocation problems have received
considerable attention in a variety of application areas. However, little
progress has been made in the design of distributed algorithms with convergence
guarantees for general and commonly used -fair allocations. In this
paper, we study weighted -fair packing problems, that is, the problems
of maximizing the objective functions (i) when , and (ii) when , over linear constraints , ,
where are positive weights and and are non-negative. We consider
the distributed computation model that was used for packing linear programs and
network utility maximization problems. Under this model, we provide a
distributed algorithm for general that converges to an
approximate solution in time (number of distributed iterations)
that has an inverse polynomial dependence on the approximation parameter
and poly-logarithmic dependence on the problem size. This is the
first distributed algorithm for weighted fair packing with
poly-logarithmic convergence in the input size. The algorithm uses simple local
update rules and is stateless (namely, it allows asynchronous updates, is
self-stabilizing, and allows incremental and local adjustments). We also obtain
a number of structural results that characterize fair allocations as
the value of is varied. These results deepen our understanding of
fairness guarantees in fair packing allocations, and also provide
insight into the behavior of fair allocations in the asymptotic cases
, , and .Comment: Added structural results for asymptotic cases of \alpha-fairness
(\alpha approaching 0, 1, or infinity), improved presentation, and revised
throughou
Online set packing and competitive scheduling of multi-part tasks
We consider a scenario where large data frames are broken into a few packets and transmitted over the network. Our focus is on a bottleneck router: the model assumes that in each time step, a set of packets (a burst) arrives, from which only one packet can be served, and all other packets are lost. A data frame is considered useful only if none of its constituent packets is lost, and otherwise it is worthless. We abstract the problem as a new type of online set packing, present a randomized distributed algorithm and a matching lower bound on the competitive ratio for any randomized online algorithm. Our bounds are expressed in terms of the maximal burst size and the maximal number of packets per frame. We also present refined bounds that depend on the uniformity of these parameters