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On the Sum-of-Squares algorithm for bin packing

Published: 01 January 2006 Publication History

Abstract

In this article we present a theoretical analysis of the online Sum-of-Squares algorithm (SS) for bin packing along with several new variants. SS is applicable to any instance of bin packing in which the bin capacity B and item sizes s(a) are integral (or can be scaled to be so), and runs in time O(nB). It performs remarkably well from an average case point of view: For any discrete distribution in which the optimal expected waste is sublinear, SS also has sublinear expected waste. For any discrete distribution where the optimal expected waste is bounded, SS has expected waste at most O(log n). We also discuss several interesting variants on SS, including a randomized O(nB log B)-time online algorithm SS* whose expected behavior is essentially optimal for all discrete distributions. Algorithm SS* depends on a new linear-programming-based pseudopolynomial-time algorithm for solving the NP-hard problem of determining, given a discrete distribution F, just what is the growth rate for the optimal expected waste.

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cover image Journal of the ACM
Journal of the ACM  Volume 53, Issue 1
January 2006
206 pages
ISSN:0004-5411
EISSN:1557-735X
DOI:10.1145/1120582
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 January 2006
Published in JACM Volume 53, Issue 1

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Author Tags

  1. Approximation algorithms
  2. average case analysis
  3. bin packing

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