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Property Testing

Current Research and Surveys

  • Oded Goldreich

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6390)

Table of contents

  1. Front Matter
  2. Editor’s Introduction

    1. Oded Goldreich
      Pages 1-5
    2. Oded Goldreich
      Pages 6-12
  3. Surveys

    1. Eric Blais
      Pages 32-40
    2. Artur Czumaj, Christian Sohler
      Pages 41-64
    3. Oded Goldreich
      Pages 105-141
    4. Krzysztof Onak
      Pages 158-166
    5. Sofya Raskhodnikova
      Pages 167-196
    6. Rocco A. Servedio
      Pages 197-210
    7. Madhu Sudan
      Pages 211-227
  4. Extended Abstracts

    1. Michał Adamaszek, Artur Czumaj, Christian Sohler
      Pages 228-233
    2. Alexandr Andoni, Piotr Indyk, Krzysztof Onak, Ronitt Rubinfeld
      Pages 240-243
    3. Alexandr Andoni, Robert Krauthgamer, Krzysztof Onak
      Pages 244-252
    4. Ido Ben-Eliezer, Tali Kaufman, Michael Krivelevich, Dana Ron
      Pages 253-259
    5. Arnab Bhattacharyya, Victor Chen, Madhu Sudan, Ning Xie
      Pages 260-268
    6. Arnab Bhattacharyya, Swastik Kopparty, Grant Schoenebeck, Madhu Sudan, David Zuckerman
      Pages 269-275
    7. Irit Dinur, Prahladh Harsha
      Pages 280-288
    8. Oded Goldreich, Michael Krivelevich, Ilan Newman, Eyal Rozenberg
      Pages 289-294
    9. Frank Hellweg, Melanie Schmidt, Christian Sohler
      Pages 306-311
    10. Tali Kaufman, Avi Wigderson
      Pages 312-319
    11. Swastik Kopparty, Shubhangi Saraf
      Pages 320-333
    12. Kevin Matulef, Ryan O’Donnell, Ronitt Rubinfeld, Rocco Servedio
      Pages 334-340
    13. Krzysztof Onak, Ronitt Rubinfeld
      Pages 341-345
    14. Michael Saks, C. Seshadhri
      Pages 346-354
  5. Back Matter

About this book

Introduction

Property Testing is the study of super-fast (randomized) algorithms for approximate decision making. These algorithms are given direct access to items of a huge data set, and determine, whether this data set has some predetermined (global) property or is far from having this property. Remarkably, this approximate decision is made by accessing a small portion of the data set. This state-of-the-art survey presents a collection of extended abstracts and surveys of leading researchers in property testing and related areas; it reflects the program of a mini-workshop on property testing that took place in January 2010 at the Institute for Computer Science (ITCS), Tsinghua University, Beijing, China. The volume contains two editor's introductions, 10 survey papers and 18 extended abstracts.

Keywords

VLDB algorithms clustering coding theory combinatorics complexity computational geometry computational learning theory decision making graph theory huge data set learning statistics vertex cover

Editors and affiliations

  • Oded Goldreich
    • 1
  1. 1.Faculty of Mathematics and Computer ScienceWeizmann Institute of ScienceRehovotIsrael

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-16367-8
  • Copyright Information Springer Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-642-16366-1
  • Online ISBN 978-3-642-16367-8
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site