scholarly journals Efficient algorithms for heavy-tail analysis under interval uncertainty

2011 ◽  
Vol 195 (1) ◽  
pp. 73-96 ◽  
Author(s):  
Vladik Kreinovich ◽  
Monchaya Chiangpradit ◽  
Wararit Panichkitkosolkul
Keyword(s):  
Heavy Tail ◽  
Efficient Algorithms ◽  
Interval Uncertainty ◽  
Tail Analysis

Computing Variance under Interval Uncertainty: Efficient Algorithms

2012 ◽  
pp. 95-117
Author(s):  
Hung T. Nguyen ◽  
Vladik Kreinovich ◽  
Berlin Wu ◽  
Gang Xiang
Keyword(s):  
Efficient Algorithms ◽  
Interval Uncertainty

Tidal Flow: A Fast and Teachable Maximum Flow Algorithm

2018 ◽  
Vol 12 ◽  
pp. 25-41
Author(s):  
Matthew C. FONTAINE
Keyword(s):  
Computer Science ◽  
Tidal Flow ◽  
Maximum Flow ◽  
Efficient Algorithms ◽  
Science Students ◽  
Flow Algorithm ◽  
Maximum Flows

Among the most interesting problems in competitive programming involve maximum flows. However, efficient algorithms for solving these problems are often difficult for students to understand at an intuitive level. One reason for this difficulty may be a lack of suitable metaphors relating these algorithms to concepts that the students already understand. This paper introduces a novel maximum flow algorithm, Tidal Flow, that is designed to be intuitive to undergraduate andpre-university computer science students.

Efficient Algorithms for the Partial Sum Dispersion Problem

2020 ◽  
Vol E103.A (10) ◽  
pp. 1206-1210
Author(s):  
Toshihiro AKAGI ◽  
Tetsuya ARAKI ◽  
Shin-ichi NAKANO
Keyword(s):  
Efficient Algorithms ◽  
Partial Sum ◽  
Dispersion Problem

Solving the Terminal Control Problem for a Linear Plant under Interval Uncertainty

Vestnik MEI ◽  
2018 ◽  
pp. 91-97
Author(s):  
Lyudmila L. Kosareva ◽  
◽  
Nikita V. Skibitskiy ◽  
Keyword(s):  
Control Problem ◽  
Interval Uncertainty ◽  
Terminal Control ◽  
Linear Plant

Division-by-q dichotomization for interval uncertainty reduction by cutting off equal parts from the left and right based on expert judgments under short-termed observations

2020 ◽  
Vol 45 (2) ◽  
pp. 125-155
Author(s):  
Vadim Romanuke
Keyword(s):  
Statistical Data ◽  
Uncertainty Reduction ◽  
Interval Uncertainty ◽  
Left And Right

AbstractA problem of reducing interval uncertainty is considered by an approach of cutting off equal parts from the left and right. The interval contains admissible values of an observed object’s parameter. The object’s parameter cannot be measured directly or deductively computed, so it is estimated by expert judgments. Terms of observations are short, and the object’s statistical data are poor. Thus an algorithm of flexibly reducing interval uncertainty is designed via adjusting the parameter by expert procedures and allowing to control cutting off. While the parameter is adjusted forward, the interval becomes progressively narrowed after every next expert procedure. The narrowing is performed via division-by-q dichotomization cutting off the q−1-th parts from the left and right. If the current parameter’s value falls outside of the interval, forward adjustment is canceled. Then backward adjustment is executed, where one of the endpoints is moved backwards. Adjustment is not executed when the current parameter’s value enclosed within the interval is simultaneously too close to both left and right endpoints. If the value is “trapped” like that for a definite number of times in succession, the early stop fires.

Efficient Algorithms for Mining Top-K High Utility Itemsets

2018 ◽  
Vol 6 (7) ◽  
pp. 1274-1280 ◽  
Author(s):  
Ameena Aiman ◽  
Raafiya Gulmeher
Keyword(s):  
Efficient Algorithms ◽  
High Utility ◽  
High Utility Itemsets
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