Deterministic Extraction from Weak Random Sources / Monographs in Theoretical Computer Science. An EATCS Series (PDF)
(Sprache: Englisch)
A deterministic extractor is a function that extracts almost perfect random bits from a weak random source. In this research monograph the author constructs deterministic extractors for several types of sources. A basic theme in this work is a methodology...
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A deterministic extractor is a function that extracts almost perfect random bits from a weak random source. In this research monograph the author constructs deterministic extractors for several types of sources. A basic theme in this work is a methodology of recycling randomness which enables increasing the output length of deterministic extractors to near optimal length.
The author's main work examines deterministic extractors for bit-fixing sources, deterministic extractors for affine sources and polynomial sources over large fields, and increasing the output length of zero-error dispersers.
This work will be of interest to researchers and graduate students in combinatorics and theoretical computer science.
The author's main work examines deterministic extractors for bit-fixing sources, deterministic extractors for affine sources and polynomial sources over large fields, and increasing the output length of zero-error dispersers.
This work will be of interest to researchers and graduate students in combinatorics and theoretical computer science.
Bibliographische Angaben
- Autor: Ariel Gabizon
- 2010, 2011, 148 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3642149030
- ISBN-13: 9783642149030
- Erscheinungsdatum: 07.10.2010
Abhängig von Bildschirmgrösse und eingestellter Schriftgrösse kann die Seitenzahl auf Ihrem Lesegerät variieren.
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- Grösse: 1.87 MB
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Englisch
Pressezitat
From the reviews:“This monograph is in the European Association for Theoretical Computer Science (EATCS) monograph series. It is an edited version of the author’s PhD thesis. … the book presents probability arguments and methods quite clearly, and in a way that readers can study them separately. Finally, the book contains two very useful appendices, one on probability methods and the other on concepts from algebraic geometry.” (Bruce Litow, ACM Computing Reviews, November, 2011)
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