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Introduction to Computing and Algorithms prepares students for the world of computing by giving them a solid foundation in the study of computer science--algorithms. By taking an algorithm-based approach to the subject, this new introductory text helps students grasp overall concepts, rather than getting them bogged down with specific syntax details of a programming language that can become obsolete. Students work with algorithms from the start and apply these ideas to real problems that computers can help solve. The benefit of this approach is that students will understand the power of computers as problem-solving tools, learn to think like programmers and gain an appreication of the computer science discipline.Features Introduces basic computing ideas, including essential algorithm constructs, software engineering, and certain foundational aspects of theory, tailored to students at a beginning level. Using a "Java-like" pseudo-code, this text prepares students for learning how to program with an object-oriented language. Provides a comprehensive lab manaual that introduces students to various software applications that they will use throughout their careers. Reinforces key concepts with pedagogical features like "Food for Thought" sections and boxed definitions. 0201314517B04062001
the design and analysis of algorithms, including an exhaustive array of algorithms and their complexity analyses. Baase emphasizes the development of algorithms through a step-by-step process, rather than merely presenting the end result. Three chapters on modern topics are new to this edition: adversary arguments and selection, dynamic programming, and parallel algorithms.
Parallel algorithms Made Easy The complexity of today's applications coupled with the widespread use of parallel computing has made the design and analysis of parallel algorithms topics of growing interest. This volume fills a need in the field for an introductory treatment of parallel algorithms-appropriate even at the undergraduate level, where no other textbooks on the subject exist. It features a systematic approach to the latest design techniques, providing analysis and implementation details for each parallel algorithm described in the book. Introduction to Parallel Algorithms covers foundations of parallel computing; parallel algorithms for trees and graphs; parallel algorithms for sorting, searching, and merging; and numerical algorithms. This remarkable book: * Presents basic concepts in clear and simple terms * Incorporates numerous examples to enhance students' understanding * Shows how to develop parallel algorithms for all classical problems in computer science, mathematics, and engineering * Employs extensive illustrations of new design techniques * Discusses parallel algorithms in the context of PRAM model * Includes end-of-chapter exercises and detailed references on parallel computing. This book enables universities to offer parallel algorithm courses at the senior undergraduate level in computer science and engineering. It is also an invaluable text/reference for graduate students, scientists, and engineers in computer science, mathematics, and engineering.
Author is an alumnus of Evanston Township High School, class of 1956.
This work is a comprehensive study of the field. It provides an entry point to the novice willing to move in the research field reconfigurable computing, FPGA and system on programmable chip design. The book can also be used as teaching reference for a graduate course in computer engineering, or as reference to advance electrical and computer engineers. It provides a very strong theoretical and practical background to the field, from the early Estrin’s machine to the very modern architecture such as embedded logic devices.
The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
An Introduction to Distributed Algorithms takes up some of the main concepts and algorithms, ranging from basic to advanced techniques and applications, that underlie the programming of distributed-memory systems such as computer networks, networks of workstations, and multiprocessors. Written from the broad perspective of distributed-memory systems in general it includes topics such as algorithms for maximum flow, program debugging, and simulation that do not appear in more orthodox texts on distributed algorithms. Moving from fundamentals to advances and applications, ten chapters—with exercises and bibliographic notes—cover a variety of topics. These include models of distributed computation, information propagation, leader election, distributed snapshots, network synchronization, self- stability, termination detection, deadlock detection, graph algorithms, mutual exclusion, program debugging, and simulation. All of the algorithms are presented in a clear, template- based format for the description of message-passing computations among the nodes of a connected graph. Such a generic setting allows the treatment of problems originating from many different application areas. The main ideas and algorithms are described in a way that balances intuition and formal rigor—most are preceded by a general intuitive discussion and followed by formal statements as to correctness complexity or other properties.

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