Probabilistic approximation of metric spaces and its algorithmic applications Export

@proceedings{citeulike:1505080, abstract = {This paper provides a novel technique for the analysis of randomized algorithms for optimization problems on metric spaces, by relating the randomized performance ratio for any, metric space to the randomized performance ratio for a set of \“simple\” metric spaces. We define a notion of a set of metric spaces that probabilistically-approximates another metric space. We prove that any metric space can be probabilistically-approximated by hierarchically well-separated trees (HST) with a polylogarithmic distortion. These metric spaces are \“simple\” as being: (1) tree metrics; (2) natural for applying a divide-and-conquer algorithmic approach. The technique presented is of particular interest in the context of on-line computation. A large number of on-line algorithmic problems, including metrical task systems, server problems, distributed paging, and dynamic storage rearrangement are defined in terms of some metric space. Typically for these problems, there are linear lower bounds on the competitive ratio of deterministic algorithms. Although randomization against an oblivious adversary has the potential of overcoming these high ratios, very little progress has been made in the analysis. We demonstrate the use of our technique by obtaining substantially improved results for two different on-line problems}, author = {Bartal, Y.}, citeulike-article-id = {1505080}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=548477}, journal = {Foundations of Computer Science, 1996. Proceedings., 37th Annual Symposium on}, keywords = {metrics, probabilistic}, pages = {184--193}, posted-at = {2007-07-26 18:34:45}, priority = {2}, title = {Probabilistic approximation of metric spaces and its algorithmic applications}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=548477}, year = {1996} }

TY - CONF ID - citeulike:1505080 L3 - citeulike-article-id:1505080 N2 - This paper provides a novel technique for the analysis of randomized algorithms for optimization problems on metric spaces, by relating the randomized performance ratio for any, metric space to the randomized performance ratio for a set of “simple” metric spaces. We define a notion of a set of metric spaces that probabilistically-approximates another metric space. We prove that any metric space can be probabilistically-approximated by hierarchically well-separated trees (HST) with a polylogarithmic distortion. These metric spaces are “simple” as being: (1) tree metrics; (2) natural for applying a divide-and-conquer algorithmic approach. The technique presented is of particular interest in the context of on-line computation. A large number of on-line algorithmic problems, including metrical task systems, server problems, distributed paging, and dynamic storage rearrangement are defined in terms of some metric space. Typically for these problems, there are linear lower bounds on the competitive ratio of deterministic algorithms. Although randomization against an oblivious adversary has the potential of overcoming these high ratios, very little progress has been made in the analysis. We demonstrate the use of our technique by obtaining substantially improved results for two different on-line problems JF - Foundations of Computer Science, 1996. Proceedings., 37th Annual Symposium on EP - 193 TI - Probabilistic approximation of metric spaces and its algorithmic applications SP - 184 KW - metrics KW - probabilistic AU - Bartal, Y PY - 1996/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=548477 ER -