Introduction to Theoretical Computer Science or Theory of Computation

Theory of Computation by Kyle Burke

This book surveys some of the most relevant theoretical concepts with computational models. The limits of computation, undecidability of the Halting Problem, several automata models, including both deterministic and nondeterministic finite-state automata, pushdown automata, and Turing machines, are introduced. The ending is dedicated to computational complexity, with NP-Completeness, approximation algorithms, and hardness of approximation.

Author(s): Kyle Burke

114 Pages

Models of Computation by John E. Savage

This book, written for graduates, covers general subjects on computational models, logic circuits, and memory machines, with advanced subjects being parallel computation, circuit complexity, and space-time trade-offs; therefore, it's a very thorough course on computational models and their complexities.

Author(s): John E. Savage, Brown University

698 Pages

Lecture Notes Theory of Computation

This lecture note from S R Engineering College offers a detailed introduction to key concepts in the Theory of Computation. It begins with Properties of Binary Operations, exploring fundamental mathematical operations and their essential properties like associativity and commutativity. The section on Concatenation Properties covers how strings can be joined and the characteristics of such operations, including associativity and the identity element. Finite Automata are thoroughly discussed, explaining both deterministic and nondeterministic (NFA) models, and their role in recognizing regular languages. The notes also cover Formal Languages, categorizing them into regular, context-free, context-sensitive, and recursively enumerable types based on complexity. Finally, the Pumping Lemma is introduced as a critical tool for proving the non-regularity and non-context-freeness of languages by demonstrating how strings in these languages can be decomposed and manipulated.

Author(s): S R Engineering College

155 Pages

Introduction to Computational Theory Lecture Notes

These broad-ranging notes introduce some of the fundamental concepts in the theory of computation. The set starts with a brief introduction to formal languages and their classification, including regular languages and sets. In these notes, finite automata are introduced, discussing their structure and role in recognizing regular languages. This is followed by Context-Free Grammars and Pushdown Automata, focusing on the role in defining and recognizing context-free languages. This will cover Turing Machines, the original model of computation; a review of the Chomsky Hierarchy from a perspective on the various levels of languages about their power of generation. The conclusion deals with an overview of Complexity Theory, mainly dealing with the P and NP problems. It gives insight into the computational complexity in general and into the famous P vs NP questions.

Author(s): BYU Computer Science Department

NA Pages

Introduction to Theory of Computation Lecture Notes

These lecture notes give an introduction to the more fundamental parts of the theory of computation and begin by presenting finite automata: starting with deterministic and nondeterministic finite automata, their equivalence, and practical implications of these concepts. The lecture notes include sections on regular expressions and their relationship to finite automata, non-regular languages, and the Pumping Lemma to prove non-regularity. Myhill-Nerode Theorem: For understanding recognition of languages. The notes go further to present context-free languages, including their ambiguity and properties of closure. The pumping lemma for context-free languages is also discussed, while decidable and recognizable languages are informed by a deep underpinning in computational theory.

Author(s): Mahesh Viswanathan

NA Pages

Advanced Theory in Computation

This is an advanced set of notes on the analysis of algorithms and their complexity. Of interest in these notes are the topics on string matching algorithms, such as Knuth-Morris-Pratt and Boyer-Moore. Suffix trees and dictionary techniques are also part of the discussion here. Among the methods to be shown in a way of analyzing algorithm efficiency are amortized analysis and randomized algorithms. It also treats the pairing technique, Ziv-Lempel coding; further topics on statistical adversaries, portfolio selection, and reservation-price policies that are objects of other techniques discussed herein.

Author(s): Seoul National University

NA Pages