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An Introduction to Online Computation [electronic resource] : Determinism, Randomization, Advice / by Dennis Komm.

By: Komm, Dennis [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Texts in Theoretical Computer Science. An EATCS Series: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XV, 349 p. 58 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319427492.Subject(s): Computer science | Computers | Algorithms | Computer Science | Theory of Computation | AlgorithmsAdditional physical formats: Printed edition:: No titleDDC classification: 004.0151 Online resources: Click here to access online
Contents:
Introduction -- Randomization -- Advice Complexity -- The k-Server Problem -- Job Shop Scheduling -- The Knapsack Problem -- The Bit Guessing Problem -- Problems on Graphs.
In: Springer eBooksSummary: This textbook explains online computation in different settings, with particular emphasis on randomization and advice complexity. These settings are analyzed for various online problems such as the paging problem, the k-server problem, job shop scheduling, the knapsack problem, the bit guessing problem, and problems on graphs. This book is appropriate for undergraduate and graduate students of computer science, assuming a basic knowledge in algorithmics and discrete mathematics. Also researchers will find this a valuable reference for the recent field of advice complexity.
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Introduction -- Randomization -- Advice Complexity -- The k-Server Problem -- Job Shop Scheduling -- The Knapsack Problem -- The Bit Guessing Problem -- Problems on Graphs.

This textbook explains online computation in different settings, with particular emphasis on randomization and advice complexity. These settings are analyzed for various online problems such as the paging problem, the k-server problem, job shop scheduling, the knapsack problem, the bit guessing problem, and problems on graphs. This book is appropriate for undergraduate and graduate students of computer science, assuming a basic knowledge in algorithmics and discrete mathematics. Also researchers will find this a valuable reference for the recent field of advice complexity.

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