scholarly journals Analysis of threat based algorithm using different performance measures

2020 ◽  
Author(s):  
Iftikhar Ahmad ◽  
Marcus Pirron ◽  
Günter Schmidt
Keyword(s):  
Performance Measures ◽  
Interval Analysis ◽  
Competitive Analysis ◽  
Case Analysis ◽  
Theoretical Computer Science ◽  
Search Problem ◽  
Online Search ◽  
Average Case Analysis ◽  
Average Case ◽  
Theoretical Computer

Since its introduction in $1985$, competitive analysis is a widely used tool for the performance measurement of online algorithms. Despite its simplicity and popularity, competitive analysis has its own set of drawbacks which lead to the development of other performance measures. However, these measures were seldom applied to problems in other domains. Recently Boyar et al. (A comparison of performance measures via online search, \textit{Theoretical Computer Science}, 2014) studied the online search problem using various performance analysis measures for non-preemptive algorithms. We extend the work by considering preemptive \textit{threat-based} algorithms and evaluate it using competitive analysis, bijective analysis, average case and relative interval analysis. For competitive analysis, and average case analysis, our findings are in contrast with that of Boyar et al., whereas for bijective and relative interval analysis our findings complement that of Boyar et al.

Average-Case Analysis of Approximate Trie Search

Algorithmica ◽  
2006 ◽  
Vol 46 (3-4) ◽  
pp. 469-491 ◽  
Author(s):  
Moritz G. Maass
Keyword(s):  
Case Analysis ◽  
Average Case Analysis ◽  
Average Case

scholarly journals Average Case Analysis of Multichannel Thresholding

2007 ◽  
Author(s):  
Remi Gribonval ◽  
Boris Mailhe ◽  
Holger Rauhut ◽  
Karin Schnass ◽  
Pierre Vandergheynst
Keyword(s):  
Case Analysis ◽  
Average Case Analysis ◽  
Average Case

scholarly journals Average-Case Approximation Ratio of Scheduling without Payments

Algorithmica ◽  
2021 ◽  
Author(s):  
Jie Zhang
Keyword(s):  
Mechanism Design ◽  
Case Analysis ◽  
Theoretical Computer Science ◽  
Approximation Ratio ◽  
Approximation Schemes ◽  
Worst Case Analysis ◽  
Algorithmic Mechanism Design ◽  
Worst Case ◽  
Average Case ◽  
Optimal Mechanism

AbstractApart from the principles and methodologies inherited from Economics and Game Theory, the studies in Algorithmic Mechanism Design typically employ the worst-case analysis and design of approximation schemes of Theoretical Computer Science. For instance, the approximation ratio, which is the canonical measure of evaluating how well an incentive-compatible mechanism approximately optimizes the objective, is defined in the worst-case sense. It compares the performance of the optimal mechanism against the performance of a truthful mechanism, for all possible inputs. In this paper, we take the average-case analysis approach, and tackle one of the primary motivating problems in Algorithmic Mechanism Design—the scheduling problem (Nisan and Ronen, in: Proceedings of the 31st annual ACM symposium on theory of computing (STOC), 1999). One version of this problem, which includes a verification component, is studied by Koutsoupias (Theory Comput Syst 54(3):375–387, 2014). It was shown that the problem has a tight approximation ratio bound of $$(n+1)/2$$ ( n + 1 ) / 2 for the single-task setting, where n is the number of machines. We show, however, when the costs of the machines to executing the task follow any independent and identical distribution, the average-case approximation ratio of the mechanism given by Koutsoupias (Theory Comput Syst 54(3):375–387, 2014) is upper bounded by a constant. This positive result asymptotically separates the average-case ratio from the worst-case ratio. It indicates that the optimal mechanism devised for a worst-case guarantee works well on average.

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