{"id":154565,"date":"2004-01-01T00:00:00","date_gmt":"2004-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/bypassing-the-embedding-algorithms-for-low-dimensional-metrics\/"},"modified":"2018-10-16T21:06:33","modified_gmt":"2018-10-17T04:06:33","slug":"bypassing-the-embedding-algorithms-for-low-dimensional-metrics","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/bypassing-the-embedding-algorithms-for-low-dimensional-metrics\/","title":{"rendered":"Bypassing the embedding: algorithms for low dimensional metrics"},"content":{"rendered":"<p>The doubling dimension of a metric is the smallest k such that any ball of radius 2r can be covered using 2k balls of radius r. This concept for abstract metrics has been proposed as a natural analog to the dimension of a Euclidean space. If we could embed metrics with low doubling dimension into low dimensional Euclidean spaces, they would inherit several algorithmic and structural properties of the Euclidean spaces. Unfortunately however, such a restriction on dimension does not su\ufb03ce to guarantee embeddibility in a normed space. In this paper we explore the option of bypassing the embedding. In particular we show the following for low dimensional metrics:<br \/>\n\u2022 Quasi-polynomial time (1+\u000f)-approximation algorithm for various optimization problems such as TSP, k-median and facility location.<br \/>\n\u2022 (1+\u000f)-approximate distance labeling scheme with optimal label length.<br \/>\n\u2022 (1+\u000f)-stretch polylogarithmic storage routing scheme.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The doubling dimension of a metric is the smallest k such that any ball of radius 2r can be covered using 2k balls of radius r. This concept for abstract metrics has been proposed as a natural analog to the dimension of a Euclidean space. If we could embed metrics with low doubling dimension into [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"kunal"}],"msr_publishername":"Association for Computing Machinery, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"STOC '04: Proceedings of the thirty-sixth annual ACM symposium on Theory of computing","msr_editors":"","msr_how_published":"","msr_isbn":"1-58113-852-0","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"281\u2013290","msr_page_range_start":"281","msr_page_range_end":"290","msr_series":"","msr_volume":"","msr_copyright":"Copyright \u00a9 2007 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all 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, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and\/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org. The definitive version of this paper can be found at ACM's Digital Library --http:\/\/www.acm.org\/dl\/.","msr_conference_name":"STOC '04: Proceedings of the thirty-sixth annual ACM symposium on Theory of 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