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Allnorm Approximation Algorithms Yossi Azar # Leah Epstein + Yossi Richter #
 

Summary: All­norm Approximation Algorithms
Yossi Azar # Leah Epstein + Yossi Richter #
Gerhard J. Woeginger §
Abstract
A major drawback in optimization problems and in particular in scheduling prob­
lems is that for every measure there may be a di#erent optimal solution. In many cases
the various measures are di#erent # p norms. We address this problem by introducing
the concept of an All­norm #­approximation algorithm, which supplies one solution that
guarantees #­approximation to all # p norms simultaneously. Specifically, we consider the
problem of scheduling in the restricted assignment model, where there are m machines
and n jobs, each is associated with a subset of the machines and should be assigned to
one of them. Previous work considered approximation algorithms for each norm sep­
arately. Lenstra et al. [11] showed a 2­approximation algorithm for the problem with
respect to the # # norm. For any fixed # p norm the previously known approximation
algorithm has a performance of #(p). We provide an all­norm 2­approximation poly­
nomial algorithm for the restricted assignment problem. On the other hand, we show
that for any given # p norm (p > 1) there is no PTAS unless P=NP by showing an APX­
hardness result. We also show for any given # p norm a FPTAS for any fixed number of
machines.
1 Introduction

  

Source: Azar, Yossi - School of Computer Science, Tel Aviv University
Epstein, Leah - Department of Mathematics, University of Haifa

 

Collections: Computer Technologies and Information Sciences; Mathematics