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Geometric median in nearly linear time

Published: 19 June 2016 Publication History
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  • Abstract

    In this paper we provide faster algorithms for solving the geometric median problem: given n points in d compute a point that minimizes the sum of Euclidean distances to the points. This is one of the oldest non-trivial problems in computational geometry yet despite a long history of research the previous fastest running times for computing a (1+є)-approximate geometric median were O(d· n4/3є−8/3) by Chin et. al, Õ(dexpє−4logє−1) by Badoiu et. al, O(nd+poly(d−1)) by Feldman and Langberg, and the polynomial running time of O((nd)O(1)log1/є) by Parrilo and Sturmfels and Xue and Ye.
    In this paper we show how to compute such an approximate geometric median in time O(ndlog3n/є) and O(dє−2). While our O(dє−2) is a fairly straightforward application of stochastic subgradient descent, our O(ndlog3n/є) time algorithm is a novel long step interior point method. We start with a simple O((nd)O(1)log1/є) time interior point method and show how to improve it, ultimately building an algorithm that is quite non-standard from the perspective of interior point literature. Our result is one of few cases of outperforming standard interior point theory. Furthermore, it is the only case we know of where interior point methods yield a nearly linear time algorithm for a canonical optimization problem that traditionally requires superlinear time.

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    cover image ACM Conferences
    STOC '16: Proceedings of the forty-eighth annual ACM symposium on Theory of Computing
    June 2016
    1141 pages
    ISBN:9781450341325
    DOI:10.1145/2897518
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 19 June 2016

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    Author Tags

    1. geometric median
    2. interior point methods
    3. stochastic gradient descent

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    STOC '16: Symposium on Theory of Computing
    June 19 - 21, 2016
    MA, Cambridge, USA

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