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On Estimation Algorithms vs Approximation Algorithms

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Abstract

In a combinatorial optimization problem, when given an input instance, one seeks a feasible solution that optimizes the value of the objective function. Many combinatorial optimization problems are NP-hard. A way of coping with NP-hardness is by considering approximation algorithms. These algorithms run in polynomial time, and their performance is measured by their approximation ratio: the worst case ratio between the value of the solution produced and the value of the (unknown) optimal solution. In some cases the design of approximation algorithms includes a nonconstructive component. As a result, the algorithms become estimation algorithms rather than approximation algorithms: they allow one to estimate the value of the optimal solution, without actually producing a solution whose value is close to optimal. We shall present a few such examples, and discuss some open questions.

BibTeX - Entry

@InProceedings{feige:LIPIcs:2008:1767,
  author =	{Uriel Feige},
  title =	{{On Estimation Algorithms vs Approximation Algorithms}},
  booktitle =	{IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science},
  pages =	{357--363},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-08-8},
  ISSN =	{1868-8969},
  year =	{2008},
  volume =	{2},
  editor =	{Ramesh Hariharan and Madhavan Mukund and V Vinay},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2008/1767},
  URN =		{urn:nbn:de:0030-drops-17676},
  doi =		{http://dx.doi.org/10.4230/LIPIcs.FSTTCS.2008.1767},
  annote =	{Keywords: Estimation Algorithms, Approximation Algorithms, Combinatorial Optimization}
}

Keywords: Estimation Algorithms, Approximation Algorithms, Combinatorial Optimization
Seminar: IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science
Issue date: 2008
Date of publication: 2008


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