814 research outputs found
Matroid and Knapsack Center Problems
In the classic -center problem, we are given a metric graph, and the
objective is to open nodes as centers such that the maximum distance from
any vertex to its closest center is minimized. In this paper, we consider two
important generalizations of -center, the matroid center problem and the
knapsack center problem. Both problems are motivated by recent content
distribution network applications. Our contributions can be summarized as
follows:
1. We consider the matroid center problem in which the centers are required
to form an independent set of a given matroid. We show this problem is NP-hard
even on a line. We present a 3-approximation algorithm for the problem on
general metrics. We also consider the outlier version of the problem where a
given number of vertices can be excluded as the outliers from the solution. We
present a 7-approximation for the outlier version.
2. We consider the (multi-)knapsack center problem in which the centers are
required to satisfy one (or more) knapsack constraint(s). It is known that the
knapsack center problem with a single knapsack constraint admits a
3-approximation. However, when there are at least two knapsack constraints, we
show this problem is not approximable at all. To complement the hardness
result, we present a polynomial time algorithm that gives a 3-approximate
solution such that one knapsack constraint is satisfied and the others may be
violated by at most a factor of . We also obtain a 3-approximation
for the outlier version that may violate the knapsack constraint by
.Comment: A preliminary version of this paper is accepted to IPCO 201
Constant Approximation for -Median and -Means with Outliers via Iterative Rounding
In this paper, we present a new iterative rounding framework for many
clustering problems. Using this, we obtain an -approximation algorithm for -median with outliers, greatly
improving upon the large implicit constant approximation ratio of Chen [Chen,
SODA 2018]. For -means with outliers, we give an -approximation, which is the first -approximation for
this problem. The iterative algorithm framework is very versatile; we show how
it can be used to give - and -approximation
algorithms for matroid and knapsack median problems respectively, improving
upon the previous best approximations ratios of [Swamy, ACM Trans.
Algorithms] and [Byrka et al, ESA 2015].
The natural LP relaxation for the -median/-means with outliers problem
has an unbounded integrality gap. In spite of this negative result, our
iterative rounding framework shows that we can round an LP solution to an
almost-integral solution of small cost, in which we have at most two
fractionally open facilities. Thus, the LP integrality gap arises due to the
gap between almost-integral and fully-integral solutions. Then, using a
pre-processing procedure, we show how to convert an almost-integral solution to
a fully-integral solution losing only a constant-factor in the approximation
ratio. By further using a sparsification technique, the additive factor loss
incurred by the conversion can be reduced to any
A New Framework for Distributed Submodular Maximization
A wide variety of problems in machine learning, including exemplar
clustering, document summarization, and sensor placement, can be cast as
constrained submodular maximization problems. A lot of recent effort has been
devoted to developing distributed algorithms for these problems. However, these
results suffer from high number of rounds, suboptimal approximation ratios, or
both. We develop a framework for bringing existing algorithms in the sequential
setting to the distributed setting, achieving near optimal approximation ratios
for many settings in only a constant number of MapReduce rounds. Our techniques
also give a fast sequential algorithm for non-monotone maximization subject to
a matroid constraint
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