Mathematics of Discrete Structures for Computer Science

The encyclopedia features about 600 cross-referenced entries - including more than 175 new to this volume - that reflect the new ways of using information technology and the important social issues t

The Encyclopedia of Computer Science is the definitive reference in computer science and technology. First published in 1976, it is still the only single volume to cover every major aspect of the fie

Outlines a program of R&D for the improvement of mathematics proficiency, and equity in proficiency, among all U.S. school students.

This anthology of essays from the inventor of literate programming is a survey of Donald Knuth's papers on computer science. Donald Knuth's influence in computer science ranges from the invention of

An introductory text for undergraduates majoring in mathematics, computer science, and related disciplines. The primary themes are the notions of proof, recursion, induction, modeling and algorithmic

This book presents a unified approach to developing accurate discrete-time models of communication and computer networks, using discrete-time queuing theory and approximation techniques to obtain solu

This book presents four mathematical essays that explore the foundations of mathematics and related topics ranging from philosophy and logic to modern computer mathematics. It details the state of the

This book presents some fundamental concepts behind the basic theories and tools of discrete element methods (DEM), its historical development, and its wide scope of applications in geology, geophysic

Using basic category theory, this Element describes all the central concepts and proves the main theorems of theoretical computer science. Category theory, which works with functions, processes, and structures, is uniquely qualified to present the fundamental results of theoretical computer science. In this Element, readers will meet some of the deepest ideas and theorems of modern computers and mathematics, such as Turing machines, unsolvable problems, the P=NP question, Kurt Gödel's incompleteness theorem, intractable problems, cryptographic protocols, Alan Turing's Halting problem, and much more. The concepts come alive with many examples and exercises.

This book explains exactly what human knowledge is. The key concepts in this book are structures and algorithms, i.e., what the readers “see” and how they make use of what they see. Thus in comparison

This volume details the mathematical background needed for geometric modeling in computer graphics. It begins with material from linear algebra and a discussion of the transformations in affine

Learn the essentials of computer scienceSchaum’s Outline of Principles of Computer Science provides aconcise overview of the theoretical foundation of computerscience. It also includes focused review

"Although they are less widely known than other models, combinatorial maps are very powerful data structures and can be useful in many applications, including computer graphics and image processing. T

A comprehensive look at General automata and how it can be used to establish the fundamentals for communication in human-computer systems Drawing on author Eldo C. Koenig's extensive expertise and cu

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overv

Category theory has become increasingly important and popular in computer science, and many universities now have introductions to category theory as part of their courses for undergraduate computer scientists. The author is a respected category theorist and has based this textbook on a course given over the last few years at the University of Sydney. The theory is developed in a straightforward way, and is enriched with many examples from computer science. Thus this book meets the needs of undergradute computer scientists, and yet retains a level of mathematical correctness that will broaden its appeal to include students of mathematics new to category theory.

Category theory has become increasingly important and popular in computer science, and many universities now have introductions to category theory as part of their courses for undergraduate computer scientists. The author is a respected category theorist and has based this textbook on a course given over the last few years at the University of Sydney. The theory is developed in a straightforward way, and is enriched with many examples from computer science. Thus this book meets the needs of undergradute computer scientists, and yet retains a level of mathematical correctness that will broaden its appeal to include students of mathematics new to category theory.

Solutions manual to accompany Logic and Discrete Mathematics: A Concise Introduction This book features a unique combination of comprehensive coverage of logic with a solid exposition of the most impo

A fascinating exploration of generalized crystallography, quasicrystals, icosahedral packing, other packing considerations, extended structures, data treatment and data mining is presented by luminari

This book discusses examples of discrete mathematics in school curricula, including in the areas of graph theory, recursion and discrete dynamical systems, combinatorics, logic, game theory, and the m