350 research outputs found
Inapproximability of Combinatorial Optimization Problems
We survey results on the hardness of approximating combinatorial optimization
problems
The Complexity of Computing Optimal Assignments of Generalized Propositional Formulae
We consider the problems of finding the lexicographically minimal (or
maximal) satisfying assignment of propositional formulae for different
restricted formula classes. It turns out that for each class from our
framework, the above problem is either polynomial time solvable or complete for
OptP. We also consider the problem of deciding if in the optimal assignment the
largest variable gets value 1. We show that this problem is either in P or P^NP
complete.Comment: 17 pages, 1 figur
On parallel versus sequential approximation
In this paper we deal with the class NCX of NP Optimization problems that are approximable within constant ratio in NC. This class is the parallel counterpart of the class APX. Our main motivation here is to reduce the study of sequential and parallel approximability to the same framework. To this aim, we first introduce a new kind of NC-reduction that preserves the relative error of the approximate solutions and show that the class NCX has {em complete} problems under this reducibility.
An important subset of NCX is the class MAXSNP, we show that MAXSNP-complete problems have a threshold on the parallel approximation ratio that is, there are positive constants , such that although the problem can be approximated in P within it cannot be approximated in NC within epsilon_2$, unless P=NC. This result is attained by showing that the problem of approximating the value obtained through a non-oblivious local search algorithm is P-complete, for some values of the approximation ratio. Finally, we show that approximating through non-oblivious local search is in average NC.Postprint (published version
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