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- W2964180318 abstract "We consider the problem of developing automated techniques for solving recurrence relations to aid the expected-runtime analysis of programs. The motivation is that several classical textbook algorithms have quite efficient expected-runtime complexity, whereas the corresponding worst-case bounds are either inefficient (e.g., Quick-Sort), or completely ineffective (e.g., Coupon-Collector). Since the main focus of expected-runtime analysis is to obtain efficient bounds, we consider bounds that are either logarithmic, linear or almost-linear ( $$mathcal {O}(log {n})$$ , $$mathcal {O}(n)$$ , $$mathcal {O}(ncdot log {n})$$ , respectively, where n represents the input size). Our main contribution is an efficient (simple linear-time algorithm) sound approach for deriving such expected-runtime bounds for the analysis of recurrence relations induced by randomized algorithms. The experimental results show that our approach can efficiently derive asymptotically optimal expected-runtime bounds for recurrences of classical randomized algorithms, including Randomized-Search, Quick-Sort, Quick-Select, Coupon-Collector, where the worst-case bounds are either inefficient (such as linear as compared to logarithmic expected-runtime complexity, or quadratic as compared to linear or almost-linear expected-runtime complexity), or ineffective." @default.
- W2964180318 created "2019-07-30" @default.
- W2964180318 creator A5031020748 @default.
- W2964180318 creator A5049544227 @default.
- W2964180318 creator A5052607806 @default.
- W2964180318 date "2017-01-01" @default.
- W2964180318 modified "2023-10-17" @default.
- W2964180318 title "Automated Recurrence Analysis for Almost-Linear Expected-Runtime Bounds" @default.
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- W2964180318 doi "https://doi.org/10.1007/978-3-319-63387-9_6" @default.
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