Probabilistic approximation of some NP optimization problems by finite-state machines

Dawei Hong, Jean Camille Birget

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We introduce a subclass of NP optimization problems which contains, e.g., Bin Covering and Bin Packing. For each problem in this subclass we prove that with probability tending to 1 as the number of input items tends to infinity, the problem is approximable up to any given constant factor ε > 0 by a finite-state machine. More precisely, let II be a problem in our subclass of NP optimization problems, and let I be an input represented by a sequence of n independent identically distributed random variables with a fixed distribution. Then for any ε > 0 there exists a finite-state machine which does the following: On a random input I the finite-state machine produces a feasible solution whose objective value M(I) satisfies (Formula Presented)when n is large enough. Here K and h are positive constants.

Original languageEnglish (US)
Title of host publicationRandomization and Approximation Techniques in Computer Science - International Workshop, RANDOM 1997, Proceedings
EditorsJosé Rolim
PublisherSpringer Verlag
Pages151-164
Number of pages14
ISBN (Print)3540632484, 9783540632481
DOIs
StatePublished - 1997
Externally publishedYes
EventInternational Workshop on Randomization and Approximation Techniques in Computer Science, RANDOM 1997 - Bologna, Italy
Duration: Jul 11 1997Jul 12 1997

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1269

Other

OtherInternational Workshop on Randomization and Approximation Techniques in Computer Science, RANDOM 1997
Country/TerritoryItaly
CityBologna
Period7/11/977/12/97

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Approximation
  • Finite-state machines
  • NP- optimization problems
  • Probabilistic algorithms

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