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Communication Complexity: and Applications [Kõva köide]

(Technion - Israel Institute of Technology, Haifa), (University of Washington)
  • Formaat: Hardback, 266 pages, kõrgus x laius x paksus: 259x181x19 mm, kaal: 660 g, Worked examples or Exercises; 10 Halftones, black and white; 74 Line drawings, black and white
  • Ilmumisaeg: 20-Feb-2020
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1108497985
  • ISBN-13: 9781108497985
Teised raamatud teemal:
  • Formaat: Hardback, 266 pages, kõrgus x laius x paksus: 259x181x19 mm, kaal: 660 g, Worked examples or Exercises; 10 Halftones, black and white; 74 Line drawings, black and white
  • Ilmumisaeg: 20-Feb-2020
  • Kirjastus: Cambridge University Press
  • ISBN-10: 1108497985
  • ISBN-13: 9781108497985
Teised raamatud teemal:
Communication complexity is the mathematical study of scenarios where several parties need to communicate to achieve a common goal, a situation that naturally appears during computation. This introduction presents the most recent developments in an accessible form, providing the language to unify several disjoint research subareas. Written as a guide for a graduate course on communication complexity, it will interest a broad audience in computer science, from advanced undergraduates to researchers in areas ranging from theory to algorithm design to distributed computing. The first part presents basic theory in a clear and illustrative way, offering beginners an entry into the field. The second part describes applications including circuit complexity, proof complexity, streaming algorithms, extension complexity of polytopes, and distributed computing. Proofs throughout the text use ideas from a wide range of mathematics, including geometry, algebra, and probability. Each chapter contains numerous examples, figures, and exercises to aid understanding.

Arvustused

'This looks like an essential resource for any student who wants to understand deterministic and randomized communication complexity deeply.' Scott Aaronson, University of Texas 'Communication complexity is not only a beautiful and important area of the theory of computing, it is also vibrant and ever-changing. Two of the leading researchers in this area take us through a fascinating journey into the theory and applications of communication complexity and through old and new jams. I feel inspired to teach a course based on this book and help spread the word.' Omer Reingold, Stanford University, California 'This book is a much-needed introductory text on communication complexity. It will bring the reader up to speed on both classical and more recent lower bound techniques, and on key application areas. An invaluable resource for anyone interested in complexity theory.' Mark Braverman, Princeton University, New Jersey ' a great book relevant to advanced undergrads and graduate students alike, while the more advanced topics will also be of interest to researchers ' Michael Cadilhac, SIGACT News Book review column ' must-have reference for students but will be welcomed by researchers as well because it is so well-written and aptly organized Highly recommended.' A. Misseldine, CHOICE

Muu info

Presents basic theory for graduate students and researchers with applications in circuit and proof complexity, streaming algorithms and distributed computing.
Preface xi
Conventions and Preliminaries xiii
Introduction 1(8)
Part I Communication
1 Deterministic Protocols
9(24)
Rectangles
11(2)
Balancing Protocols
13(1)
From Rectangles to Protocols
14(1)
Lower Bounds
15(11)
Rectangle Covers
26(2)
Direct-Sums in Communication Complexity
28(5)
2 Rank
33(13)
Communication Complexity and Rank
34(1)
Properties of Rank
35(1)
Lower Bounds Based on Rank
36(2)
Nonnegative Rank
38(2)
Better Upper Bounds Using Rank
40(6)
3 Randomized Protocols
46(11)
Some Protocols
46(4)
Randomized Communication Complexity
50(3)
Public Coins versus Private Coins
53(1)
Nearly Monochromatic Rectangles
54(3)
4 Numbers on Foreheads
57(10)
Some Protocols
57(4)
Defining Protocols in the Number-on-Forehead Model
61(1)
Cylinder Intersections
61(1)
Lower Bounds from Ramsey Theory
62(5)
5 Discrepancy
67(26)
Definitions
67(1)
Discrepancy and Communication
68(1)
Convexity in Combinatorics
69(2)
Lower Bounds for Inner-Product
71(3)
Disjointness and Discrepancy
74(6)
Concentration of Measure
80(13)
6 Information
93(28)
Entropy
93(3)
Chain Rule and Conditional Entropy
96(9)
Divergence and Mutual Information
105(2)
Lower Bound for Indexing
107(4)
The Power of Interaction
111(4)
Randomized Complexity of Disjointness
115(6)
7 Compressing Communication
121(23)
Simulations
123(1)
Compressing Protocols with No Private Randomness
124(2)
Correlated Sampling
126(2)
Compressing a Single Round
128(8)
Internal Compression of Protocols
136(3)
Direct Sums in Randomized Communication Complexity
139(2)
Other Methods to Compress Protocols
141(3)
8 Lifting
144(13)
Decision Trees
144(1)
The Lifting Theorem
145(6)
Separating Rank and Communication
151(6)
Part II Applications
9 Circuits and Proofs
157(18)
Boolean Circuits
157(1)
Karchmer-Wigderson Games
158(1)
Monotone Circuit-Depth Lower Bounds
159(2)
Monotone Circuit-Depth Hierarchy
161(1)
Boolean Formulas
162(3)
Boolean Depth Conjecture
165(1)
Proof Systems
166(1)
Resolution Refutations
166(4)
Cutting Planes
170(5)
10 Memory Size
175(12)
Lower Bounds for Streaming Algorithms
178(5)
Lower Bounds for Branching Programs
183(4)
11 Data Structures
187(23)
Dictionaries
187(1)
Ordered Sets
188(6)
Lower Bounds on Static Data Structures
194(5)
Lower Bounds on Dynamic Data Structures
199(4)
Graph Connectivity
203(7)
12 Extension Complexity of Polytopes
210(29)
Transformations of Polytopes
212(4)
Algorithms from Polytopes
216(2)
Extension Complexity
218(7)
Slack Matrices
225(4)
Lower Bounds on Extension Complexity
229(10)
13 Distributed Computing
239(5)
Some Protocols
239(1)
Lower Bounds
240(2)
Computing the Girth
242(2)
Bibliography 244(6)
Index 250
Anup Rao is an Associate Professor at the School of Computer Science, University of Washington. He received his Ph.D. in Computer Science from the University of Texas, Austin, and was a researcher at the Institute for Advanced Study, Princeton. His research interests are primarily in theoretical computer science. Amir Yehudayoff is Associate Professor of Mathematics at Technion Israel Institute of Technology, Haifa. He is interested in mathematical questions that are motivated by theoretical computer science and machine learning. He was a member of the Institute for Advanced Study in Princeton, and served as the secretary of the Israel Mathematical Union. He has won several prizes, including the Cooper Prize and the Krill Prize for excellence in scientific research, and the Kurt Mahler Prize for excellence in mathematics.