Improved approximation algorithms for the Min-Max Selecting Items problem

被引：7
|
作者：
Doerr, Benjamin [1 ]
机构：
[1] Max Planck Inst Comp Sci, D-66123 Saarbrucken, Germany
来源：
INFORMATION PROCESSING LETTERS | 2013年 / 113卷 / 19-21期
关键词：
Approximation algorithm; Randomized rounding; Derandomization; Robust optimization; CONSTRAINTS; ROUNDINGS;
D O I：
10.1016/j.ipl.2013.07.011
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We give a simple deterministic O(log K/ log log K) approximation algorithm for the Min-Max Selecting Items problem, where K is the number of scenarios. While our main goal is simplicity, this result also improves over the previous best approximation ratio of O(log K) due to Kasperski, Kurpisz, and Zielinski (2013) [4]. Despite using the method of pessimistic estimators, the algorithm has a polynomial runtime also in the RAM model of computation. We also show that the LP formulation for this problem by Kasperski and Zielinski (2009) [6], which is the basis for the previous work and ours, has an integrality gap of at least Omega(log K / log log K). (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：747 / 749
页数：3
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