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Author, Editor

Author(s):

Beier, Rene
Vöcking, Berthold

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Editor(s):





BibTeX cite key*:

Beier2003

Title, Booktitle

Title*:

Random Knapsack in Expected Polynomial Time

Booktitle*:

Proceedings of the 35th Annual ACM Symposium on Theory of Computing (STOC-03)

Event, URLs

URL of the conference:

http://www.egr.unlv.edu/~bein/stoc03.html

URL for downloading the paper:


Event Address*:

San Diego, USA

Language:

English

Event Date*
(no longer used):

June, 9-11

Organization:

Association of Computing Machinery (ACM)

Event Start Date:

16 January 2004

Event End Date:

16 January 2004

Publisher

Name*:

ACM

URL:


Address*:

New York, USA

Type:


Vol, No, Year, pp.

Series:


Volume:


Number:


Month:


Pages:

232-241

Year*:

2003

VG Wort Pages:

33

ISBN/ISSN:

1-58113-674-9

Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) Abstract:

In this paper, we present the first average-case analysis proving an expected
polynomial running time for an exact algorithm for the 0/1 knapsack problem.
In particular, we prove, for various input distributions, that the number of
{\em dominating solutions\/} (i.e., Pareto-optimal knapsack fillings)
to this problem is polynomially bounded in the number of available items.
An algorithm by Nemhauser and Ullmann can enumerate these solutions very
efficiently so that a polynomial upper bound on the number of dominating solutions
implies an algorithm with expected polynomial running time.

The random input model underlying our analysis is very general
and not restricted to a particular input distribution. We assume adversarial
weights and randomly drawn profits (or vice versa). Our analysis covers general
probability
distributions with finite mean, and, in its most general form, can even
handle different probability distributions for the profits of different items.
This feature enables us to study the effects of correlations between profits
and weights. Our analysis confirms and explains practical studies showing
that so-called {\em strongly correlated\/} instances are harder to solve than
{\em weakly correlated\/} ones.

Keywords:

Knapsack problem, Exact algorithm, Average Case Analysis



Download
Access Level:

Public

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Algorithms and Complexity Group

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{Beier2003,
AUTHOR = {Beier, Rene and V{\"o}cking, Berthold},
TITLE = {Random Knapsack in Expected Polynomial Time},
BOOKTITLE = {Proceedings of the 35th Annual ACM Symposium on Theory of Computing (STOC-03)},
PUBLISHER = {ACM},
YEAR = {2003},
ORGANIZATION = {Association of Computing Machinery (ACM)},
PAGES = {232--241},
ADDRESS = {San Diego, USA},
ISBN = {1-58113-674-9},
}


Entry last modified by Sabine Krott, 06/17/2004
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Editor(s)
Rene Beier
Created
05/07/2003 01:46:13 PM
Revisions
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Editor(s)
Sabine Krott
Sabine Krott
Christine Kiesel
Christine Kiesel
Christine Kiesel
Edit Dates
17.06.2004 10:41:38
17.06.2004 10:40:50
15.06.2004 15:18:00
11.06.2004 17:09:06
01/16/2004 03:41:27 PM
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