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On the analysis of stochastic divide and conquer algorithms
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Analytic analysis of algorithms
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An improved master theorem for divide-and-conquer recurrences
inferredcites →https://doi.org/10.1007/bf00289467openalex-snapshot
The analysis of Quicksort programs
inferredcites →https://doi.org/10.1007/bf02679611openalex-snapshot
The contraction method for recursive algorithms
inferredcites →https://doi.org/10.1016/0304-4149(92)90035-oopenalex-snapshot
A fixed point theorem for distributions
inferredcites →https://doi.org/10.1017/s0001867800027142openalex-snapshot
Probability metrics and recursive algorithms
inferredcites →https://doi.org/10.1017/s000186780002735xopenalex-snapshot
Asymptotic distribution theory for Hoare's selection algorithm
inferredcites →https://doi.org/10.1017/s0963548399003855openalex-snapshot
Total Path Length for Random Recursive Trees
inferredcites →https://doi.org/10.1051/ita/1989230303171openalex-snapshot
Combinatorial analysis of quicksort algorithm
inferredcites →https://doi.org/10.1051/ita/1991250100851openalex-snapshot
A limit theorem for “quicksort”
inferredcites →https://doi.org/10.1145/362736.362753openalex-snapshot
Increasing the efficiency of quicksort
inferredcites →https://doi.org/10.1214/aos/1176345637openalex-snapshot
Some Asymptotic Theory for the Bootstrap
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Precise tail asymptotics of fixed points of the smoothing transform with general weights
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A Fuzzy-Based Fast Feature Selection Using Divide and Conquer Technique in Huge Dimension Dataset
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Analysis of the Multi-Pivot Quicksort Process
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Distributional Reinforcement Learning
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