Logo

Introduction to Complexity Theory

Introduction to Complexity Theory
by


Number of pages: 375

Description:
Complexity Theory is a central field of Theoretical Computer Science, with a remarkable list of celebrated achievements as well as a very vibrant present research activity. The field is concerned with the study of the intrinsic complexity of computational tasks, and this study tend to aim at generality: It focuses on natural computational resources, and the effect of limiting those on the class of problems that can be solved. These lecture notes were taken by students attending my year-long introductory course on Complexity Theory, given in 1998-99 at the Weizmann Institute of Science. The course was aimed at exposing the students to the basic results and research directions in the field. The focus was on concepts and ideas, and complex technical proofs were avoided. It was assumed that students have taken a course in computability, and hence are familiar with Turing Machines.

Home page url

Download or read it online for free here:
Download link
(2.3MB, PDF)

Similar books

Book cover: Introduction to Computational ComplexityIntroduction to Computational Complexity
by
Lecture notes for a graduate course on computational complexity taught at the University of Washington. Alternating Turing machines are introduced very early, and deterministic and nondeterministic Turing machines treated as special cases.
(5040 views)
Book cover: Computational Complexity: A Conceptual PerspectiveComputational Complexity: A Conceptual Perspective
by - Cambridge University Press
This book offers a comprehensive perspective to modern topics in complexity theory. It can be used as an introduction as either a textbook or for self-study, or to experts, since it provides expositions of the various sub-areas of complexity theory.
(6223 views)
Book cover: Complexity Theory: A Modern ApproachComplexity Theory: A Modern Approach
by - Cambridge University Press
The book provides an introduction to basic complexity classes, lower bounds on resources required to solve tasks on concrete models such as decision trees or circuits, derandomization and pseudorandomness, proof complexity, quantum computing, etc.
(11130 views)
Book cover: Communication Complexity (for Algorithm Designers)Communication Complexity (for Algorithm Designers)
by - 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.
(710 views)