Approximate minimization algorithms for the 0/1 Knapsack and Subset-Sum Problem

Authors:
Michael M. GüNtzer;Dieter Jungnickel
Affiliations:
Lehrstuhl für Diskrete Mathematik, Optimierung und Operations Research, Universität Augsburg, Universitätsstrasse 14, D-86135 Augsburg, Germany;Lehrstuhl für Diskrete Mathematik, Optimierung und Operations Research, Universität Augsburg, Universitätsstrasse 14, D-86135 Augsburg, Germany
Venue:
Operations Research Letters
Year:
2000

Citing 3
Cited 10

Knapsack problems: algorithms and computer implementations

Knapsack problems: algorithms and computer implementations
Approximation algorithms for the capacitated plant allocation problem

Operations Research Letters
Worst-case analysis of greedy algorithms for the unbounded knapsack, subset-sum and partition problems

Operations Research Letters

Algorithms for solving the single-sink fixed-charge transportation problem

Computers and Operations Research
Analysis of some greedy algorithms for the single-sink fixed-charge transportation problem

Journal of Heuristics
Online Knapsack Problems with Limited Cuts

ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Single-machine scheduling under the job rejection constraint

Theoretical Computer Science
Online removable knapsack with limited cuts

Theoretical Computer Science
Bandwidth constrained multi-interface networks

SOFSEM'11 Proceedings of the 37th international conference on Current trends in theory and practice of computer science
Greedy algorithms for the minimization knapsack problem: Average behavior

Journal of Computer and Systems Sciences International
2D knapsack: packing squares

FAW-AAIM'11 Proceedings of the 5th joint international frontiers in algorithmics, and 7th international conference on Algorithmic aspects in information and management
Online minimization knapsack problem

WAOA'09 Proceedings of the 7th international conference on Approximation and Online Algorithms
Efficient top-k similarity join processing over multi-valued objects

World Wide Web

Quantified Score

Hi-index	0.00

Visualization

Abstract

The well-studied 0/1 Knapsack and Subset-Sum Problem are maximization problems that have an equivalent minimization version. While exact algorithms for one of these two versions also yield an exact solution for the other version, this does not apply to @e-approximate algorithms. We present several @e-approximate Greedy Algorithms for the minimization version of the 0/1 Knapsack and the Subset-Sum Problem, that are also @e-approximate for the respective maximization version.