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Approximation algorithms for geometric median problems

Jyh-Han Lin, +1 more
- 21 Dec 1992 - 
- Vol. 44, Iss: 5, pp 245-249
TLDR
This paper presents approximation algorithms for median problems in metric spaces and fixed-dimensional Euclidean space that use a new method for transforming an optimal solution of the linear program relaxation of the s-median problem into a provably good integral solution.
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This article is published in Information Processing Letters.The article was published on 1992-12-21 and is currently open access. It has received 191 citations till now. The article focuses on the topics: Approximation algorithm & Randomized rounding.

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Citations
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Journal ArticleDOI

Clustering data streams: Theory and practice

TL;DR: This work describes a streaming algorithm that effectively clusters large data streams and provides empirical evidence of the algorithm's performance on synthetic and real data streams.
Journal ArticleDOI

Facility location models for distribution system design

TL;DR: In this article, the authors present a review of the state-of-the-art in continuous location models and network location models, mixed-integer programming models, and applications for distribution system design.
Journal ArticleDOI

Approximation algorithms for metric facility location and k-Median problems using the primal-dual schema and Lagrangian relaxation

TL;DR: A new extension of the primal-dual schema and the use of Lagrangian relaxation to derive approximation algorithms for the metric uncapacitated facility location problem and the metric k-median problem achieving guarantees of 3 and 6 respectively.
Proceedings ArticleDOI

Clustering data streams

TL;DR: This work gives constant-factor approximation algorithms for the k-median problem in the data stream model of computation in a single pass, and shows negative results implying that these algorithms cannot be improved in a certain sense.
Proceedings ArticleDOI

Greedy strikes back: improved facility location algorithms

TL;DR: It is shown that a simple greedy heuristic combined with the algorithm by Shmoys, Tardos, and Aardal, can be used to obtain an approximation guarantee of 2.408, and a lower bound of 1.463 is proved on the best possible approximation ratio.
References
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Journal ArticleDOI

A new polynomial-time algorithm for linear programming

TL;DR: It is proved that given a polytopeP and a strictly interior point a εP, there is a projective transformation of the space that mapsP, a toP′, a′ having the following property: the ratio of the radius of the smallest sphere with center a′, containingP′ to theradius of the largest sphere withCenter a′ contained inP′ isO(n).
Journal ArticleDOI

Clustering to minimize the maximum intercluster distance

TL;DR: An O(kn) approximation algorithm that guarantees solutions with an objective function value within two times the optimal solution value is presented and it is shown that this approximation algorithm succeeds as long as the set of points satisfies the triangular inequality.
Journal ArticleDOI

Randomized rounding: a technique for provably good algorithms and algorithmic proofs

TL;DR: In this paper, the relation between a class of 0-1 integer linear programs and their rational relaxations was studied and a randomized algorithm for transforming an optimal solution of a relaxed problem into a provably good solution for the 0 -1 problem was given.
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Q1. What contributions have the authors mentioned in the paper "Approximation algorithms for geometric median problems" ?

In this paper the authors present approximation algorithms for median problems in metric spaces and xed-dimensional Euclidean space. This transformation technique is fundamentally di erent from the methods of randomized and deterministic rounding [ Rag, RaT ] and the methods proposed in [ LiV ] in the following way: Previous techniques never set variables with zero values in the fractional solution to 1. Support was provided in part by an National Science Foundation Presidential Young Investigator Award CCR { 9047466 with matching funds from IBM, by NSF research grant CCR { 9007851, by Army Research O ce grant DAAL03 { 91 { G { 0035, and by the O ce of Naval Research and the Defense Advanced Research Projects Agency under contract N00014 { 91 { J { 4052, ARPA order 8225. The research was conducted while the author was at the Department of Computer Science, Brown University. 

Given a bound D > 0, the goal of the (discrete) median problem is to choose vertices as medians so that the sum of distances from each vertex to its nearest median is no more than D and the number of medians chosen is minimized. 

There exists a deterministic approximation algorithm for the median problem in metric spaces that, given any > 0, outputs a median set U satisfyingX j2V min i2U cij 2(1 + 

Since Vi; is bounded by a ball of diameter 2 (1+1= ) bCi in d-dimensional Euclideanspace, there exists a median set Ui of size at most (2 1) d such that for all j 2 Vi; the authors havemin `2Ui cj` (1 + 1= ) bCi: For each j 2 Vi Vi; , there exists j0 2 Vi; such that cjj0 (1 + 1= ) bCj. 

Let vertex i be a median selected by Algorithm M and let Vi; Vi be a subset of vertices such that a vertex j 2 Vi is in Vi; if and only if bCj bCi. 

For each i 2 V and > 0, the authors haveX j2Vi byj X j2Vi bxij > 1 + ;where Vi is the neighborhood of vertex i. Proof : Suppose Pj2Vi bxij =(1 + ). 

Let us consider a complete (directed or undirected) graph G = (V;E) on n vertices, with vertex set V = f1; . . . ; ng, edge set E V V , and nonnegative distance cij associated with edges. 

Without loss of generality, the authors assume Vi is selected before Vj. Since cij0 (1 + 1= ) bCi and cjj0 (1 + 1= ) bCj, by (R2) the authors must have j 2 Vi. Hence, Algorithm M will delete 

The median problem can be formulated as a 0-1 integer linear program of minimizingnX j=1 yj (3)subject tonX i=1 nX j=1 cijxij D (4)nX j=1 xij = 1; i = 1; . . . ; n; (5)xij yj; i; j = 1; . . . ; n; (6)xij; yj 2 f0; 1g; i; j = 1; . . . ; n; (7)where yj = 1 if and only if j is chosen as a median, xij = 1 if and only if yj = 1 and i is assigned to j, and D > 0 is a given bound on the total distance. 

by Lemma 3 the number of sets (medians) selected is less than s1=(1 + ) = (1 + )s:By Lemma 5 the authors haveX j2V min i2U cij 2(1 + 1= ) X j2V bCj 2(1 + 1= )D:In this section the authors give the proof of Theorem 2. 

1= )D (1)andjU j < (1 + )s; (2)where s is the optimal size of median sets with a total distance at most D.The greedy approximation algorithm in [LiV] gives a better cost bound using more medians; the right-hand sides of 1 and 2 are replaced by (1+1= )D and (1 + )s(lnn+ 1), respectively. 

By symmetry and the triangle inequality, the authors havecji(j) cjj0 + cj0i(j) (1 + 1= ) bCj + (1 + 1= ) bCi(j) 2(1 + 1= ) bCj:The last inequality follows from Lemma 4.2Lemma 6