On the Complexity of Calculating Sensitivity Parameters in Boolean Programming Problems

This article shows that, for NP-hard problems, the calculation of even the stability ball of radius 1 for an optimal solution is computationally intensive (i.e., a suitable polynomial algorithm is absent when P ≠ NP). In using greedy algorithms for solving the set covering problem (knapsack problem)...

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Veröffentlicht in:Cybernetics and systems analysis Jg. 51; H. 5; S. 714 - 719
Hauptverfasser: Mikhailyuk, V. A., Lishchuk, N. V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.09.2015
Springer
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Boolean
Computer programming
Computer science
Control
Cybernetics
Exercise equipment
Integer programming
Knapsack problem
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Optimization
Parameter sensitivity
Polynomials
Processor Architectures
Sensitivity analysis
Software Engineering/Programming and Operating Systems
Stability
Studies
Systems Theory
complexity of sensitivity analysis
stability radius of a problem
stability ball of radius r for an ε-approximate problem solution
ISSN:1060-0396, 1573-8337
Online-Zugang:Volltext
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Zusammenfassung:This article shows that, for NP-hard problems, the calculation of even the stability ball of radius 1 for an optimal solution is computationally intensive (i.e., a suitable polynomial algorithm is absent when P ≠ NP). In using greedy algorithms for solving the set covering problem (knapsack problem) with the stability radius r = O(1) , there are polynomial algorithms for calculating the stability ball of radius r for an ln m-approximate solution (1-approximate solution).
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ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-015-9763-4