Sequential And Parallel Algorithms And Data Structures

This textbook is a concise introduction to the basic toolbox of structures that allow efficient organization and retrieval of data, key algorithms for problems on graphs, and generic techniques for modeling, understanding, and solving algorithmic problems. The authors aim for a balance between simplicity and efficiency, between theory and practice, and between classical results and the forefront of research. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, optimization, collective communication and computation, and load balancing. The authors also discuss important issues such as algorithm engineering, memory hierarchies, algorithm libraries, and certifying algorithms. Moving beyond the sequential algorithms and data structures of the earlier related title, this book takes into account the paradigm shift towards the parallel processing required to solve modern performance-critical applications and how this impacts on the teaching of algorithms. The book is suitable for undergraduate and graduate students and professionals familiar with programming and basic mathematical language. Most chapters have the same basic structure: the authors discuss a problem as it occurs in a real-life situation, they illustrate the most important applications, and then they introduce simple solutions as informally as possible and as formally as necessary so the reader really understands the issues at hand. As they move to more advanced and optional issues, their approach gradually leads to a more mathematical treatment, including theorems and proofs. The book includes many examples, pictures, informal explanations, and exercises, and the implementation notes introduce clean, efficient implementations in languages such as C++ and Java.

This work aims to provide an understanding of the analysis and applications of algorithmic paradigms, to both the traditional sequential model of computing and to a variety of parallel models. Concepts are applied to a broad range of subject areas, including matrix operations.

With multi-core processors replacing traditional processors and the movement to multiprocessor workstations and servers, parallel computing has moved from a specialty area to the core of computer science. In order to provide efficient and cost-effective solutions to problems, algorithms must be designed for multiprocessor systems. Algorithms: Sequential and Parallel provides a state-of-the-art approach to an algorithms course. The book considers algorithms, paradigms, and the analysis of solutions to critical problems for sequential and parallel models of computation in a unified fashion. This gives practicing engineers and scientists, undergraduates, and beginning graduate students a background in algorithms for sequential and parallel algorithms within one text. Prerequisites include fundamentals of data structures, discrete mathematics, and calculus.

Introduction fro ancient to modern times; Elementary data structures; Design analysis of sequential algorithms; Sequential sortin algorithms and their analysis; Introduction to parallel algorithms and architectures; parallel sorting; Expanding the design and analysis of the algorithms toolkit; Introduction, correctness proofs, and recurrence relations;Graphs, digraphs, and sets; Probability and average complexity of agorithms; Introduction to Lower bound theory; Parallel prefix, matix multiplication, and pointer jumping; Major design strategies; The Greedy method; Divide conquer; Dynamic programming; Backtracking and branch-and-bound; Special topics; Heuristic search: A- search, game trees; The dictionary problem: hashing and balanced trees; Probabilistic algorithms; graph algorithms; NP- complete problems and the class NC; The classes NC and P-complete; Closing remarks.

This proceedings contains contributions on topics such as the models of computation, analysis and design of sequential and parallel algorithms, data structures and their applications, approximating algorithms and probabilistic analysis, and computational complexity. Contents:Analysis and Implementation of Parallel Uniform Hashing (F Luccio et al)On PVchunk Operations and Matrogenic Graphs (S De Agostino & R Petreschi)Combinatorial Optimization through Algorithms for Exact Problems and Scaling (P M Camerini et al)Optimization, Average Cost and Complexity Classes (M Bonamico et al)Ranking and Formal Power Series (A Bertoni et al)PAC and Mistake Bounded Learning: a Comparison and Applications (P Campadelli & A Morpurgo)and other papers Readership: Computer scientists and mathematicians.

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.

This book gives a compact yet comprehensive survey of major results in the computational complexity of sequential algorithms.