All-norm approximation algorithms
Yossi Azar, Leah Epstein, Yossi Richter, Gerhard J. Woeginger · Journal of Algorithms · 2004
A major drawback in optimization problems and in particular in scheduling problems is that for every measure there may be a different optimal solution. In many cases the various measures are different ℓ p norms. We address this problem by introducing the concept of an all-norm ρ -approximation algorithm , which supplies one solution that guarantees ρ -approximation to all ℓ p norms simultaneously. Specifically, we consider the problem of scheduling in the restricted assignment model, where there are m machines and n jobs, each job is associated with a subset of the machines and should be assigned to one of them. Previous work considered approximation algorithms for each norm separately. Lenstra et al. [Math. Program. 46 (1990) 259–271] showed a 2-approximation algorithm for the problem with respect to the ℓ ∞ norm. For any fixed ℓ p norm the previously known approximation algorithm has a performance of θ ( p ). We provide an all-norm 2-approximation polynomial algorithm for the restricted assignment problem. On the other hand, we show that for any given ℓ p norm ( p >1) there is no PTAS unless P=NP by showing an APX-hardness result. We also show for any given ℓ p norm a FPTAS for any fixed number of machines.