
Algorithmic and Analysis Techniques in Property Testing
Dana Ron(Author)
now publishers Inc
1st Edition
Published on 8. February 2010
Book
Paperback/Softback
152 pages
978-1-60198-318-3 (ISBN)
Description
Property testing algorithms are ultra"-efficient algorithms that decide whether a given object (e.g., a graph) has a certain property (e.g., bipartiteness), or is significantly different from any object that has the property. To this end property testing algorithms are given the ability to perform (local) queries to the input, though the decisions they need to make usually concern properties with a global nature. In the last two decades, property testing algorithms have been designed for many types of objects and properties, amongst them, graph properties, algebraic properties, geometric properties, and more. In this article we survey results in property testing, where our emphasis is on common analysis and algorithmic techniques. Among the techniques surveyed are the following:
a) The self-correcting approach, which was mainly applied in the study of property testing of algebraic properties;
b) The enforce and test approach, which was applied quite extensively in the analysis of algorithms for testing graph properties (in the dense-graphs model), as well as in other contexts;
c) Szemeredi's Regularity Lemma, which plays a very important role in the analysis of algorithms for testing graph properties (in the dense-graphs model);
d) The approach of Testing by implicit learning, which implies efficient testability of membership in many functions classes.
e) Algorithmic techniques for testing properties of sparse graphs, which include local search and random walks.
a) The self-correcting approach, which was mainly applied in the study of property testing of algebraic properties;
b) The enforce and test approach, which was applied quite extensively in the analysis of algorithms for testing graph properties (in the dense-graphs model), as well as in other contexts;
c) Szemeredi's Regularity Lemma, which plays a very important role in the analysis of algorithms for testing graph properties (in the dense-graphs model);
d) The approach of Testing by implicit learning, which implies efficient testability of membership in many functions classes.
e) Algorithmic techniques for testing properties of sparse graphs, which include local search and random walks.
More details
Series
Language
English
Place of publication
Hanover
United States
Target group
Professional and scholarly
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 8 mm
Weight
224 gr
ISBN-13
978-1-60198-318-3 (9781601983183)
DOI
10.1561/0400000029
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Content
1: Introduction 2: Preliminaries 3: The Self-Correcting Approach 4: The Enforce-and-Test Approach 5: Testing by Implicit Learning 6: The Regularity Lemma 7: Local-Search Algorithms 8: Random Walks Algorithms 9: Lower Bounds 10: Other Results 11: Extensions, Generalizations, and Related Problems. Acknowledgements. References.