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.
These
are lecture notes from the University of Toronto, giving a very brief
introduction to some of the basic ideas in the theory of computation. We start
with some basic topics: induction and recursion; the correctness of programs,
that must be understood if more advanced computational theories are to be
enlightened. Then we go on to develop the topics of regular languages and finite
automata, giving the basic models and techniques used in analysing and
recognising regular languages. The coverage is designed to provide students with
a reasonably solid grounding in the basic ideas of the theory of computation and
to render a clear and thorough exposition of the fundamental concepts underlying
more advanced topics.
Authored by Margaret Fleck and Sariel Har Peled, this
is a wide set of lecture notes on the theory of computation. These start with
the very basic objects such as strings and deterministic finite automata (DFAs)
before moving up to regular expressions and nondeterministic automata. This
course covers formal language theory, including some advanced topics such as
Turing machines, decidability, and several language-related problems. It is
intended that these notes afford a comprehensively broad yet deep exploration of
the formal languages, automata, and computability material with an excellent
bibliography that creates interest among students and researchers.
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.
This
is an all-inclusive course on computational theory provided in this online
resource by Wikiversity. It begins with Finite State Machines–their
definitions, operations, and minimization techniques. The notes also cover
closure and nondeterminism—how these properties may affect computational
models. Their discussion greatly involves the Pumping Lemma, proving language
property. The book also surveys Context-Free Languages and their connection to
Compilers and introduces Pushdown Machines emphatically and focuses on their
importance in parsing. It contains important material on the CYK algorithm for
parsing and the more basic problems of Undecidability. It also surveys Turing
Machines, the Halting Problem, and more general areas of Complexity Theory,
including Quantified Boolean Formulae, Savitch's Theorem, and Space Hierarchy.
The notes end with the Recursion Theorem, and it can be considered as a
landmark in the theoretical study of the science of computers.
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.
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.