**Complexity Theory: A Modern Approach**

by Sanjeev Arora, Boaz Barak

**Publisher**: Cambridge University Press 2008**ISBN/ASIN**: 0521424267**ISBN-13**: 9780521424264**Number of pages**: 489

**Description**:

This book aims to describe such recent achievements of complexity theory in the context of the classical results. It is intended to be a text and as well as a reference for self-study. This means it must simultaneously cater to many audiences, and it is carefully designed with that goal. The book will explain the context in which a certain notion is useful, and why things are defined in a certain way. Examples and solved exercises accompany key definitions. This book assumes essentially no computational background (though a slight exposure to computing may help) and very little mathematical background apart from the ability to understand proofs and some elementary probability on finite sample spaces. A typical undergraduate course on "Discrete Math" taught in many math and CS departments should suffice (together with the Appendix).

Download or read it online for free here:

**Download link**

(4.4MB, PDF)

## Similar books

**Measure-Preserving Systems**

by

**Karl Petersen**-

**University of North Carolina**

These notes provide an introduction to the subject of measure-preserving dynamical systems, discussing the dynamical viewpoint; how and from where measure-preserving systems arise; the construction of measures and invariant measures; etc.

(

**5969**views)

**Computability and Complexity from a Programming Perspective**

by

**Neil D. Jones**-

**The MIT Press**

The author builds a bridge between computability and complexity theory and other areas of computer science. Jones uses concepts familiar from programming languages to make computability and complexity more accessible to computer scientists.

(

**6449**views)

**Around Kolmogorov Complexity: Basic Notions and Results**

by

**Alexander Shen**-

**arXiv.org**

Algorithmic information theory studies description complexity and randomness. This text covers the basic notions of algorithmic information theory: Kolmogorov complexity, Solomonoff universal a priori probability, effective Hausdorff dimension, etc.

(

**606**views)

**Communication Complexity (for Algorithm Designers)**

by

**Tim Roughgarden**-

**Stanford University**

The two biggest goals of the course are: 1. Learn several canonical problems that have proved the most useful for proving lower bounds; 2. Learn how to reduce lower bounds for fundamental algorithmic problems to communication complexity lower bounds.

(

**721**views)