Golovin, Daniel ; Gupta, Anupam ; Kumar, Amit ; Tangwongsan, Kanat (2008) All-Norms and All-Lp-Norms Approximation Algorithms In: Foundations of Software Technology and Theoretical Computer Science (Bangalore) 2008, 2008.
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Official URL: https://www.fsttcs.org.in/archives/2008/
Abstract
In many optimization problems, a solution can be viewed as ascribing a “cost” to each client, and the goal is to optimize some aggregation of the per-client costs. We often optimize some Lp-norm (or some other symmetric convex function or norm) of the vector of costs—though different applications may suggest different norms to use. Ideally, we could obtain a solution that optimizes several norms simultaneously. In this paper, we examine approximation algorithms that simultaneously perform well on all norms, or on all Lp norms.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to author(s). |
| ID Code: | 123539 |
| Deposited On: | 30 Sep 2021 09:48 |
| Last Modified: | 30 Sep 2021 09:48 |
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