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All-Norms and All-Lp-Norms Approximation Algorithms

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journal contribution
posted on 01.09.1996, 00:00 by Daniel Golovin, Anupam Gupta, Amit Kumar, Kanat Tangwongsan
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 optimized several norms simultaneously.


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