Computer Science

For a one-semester, junior/senior-level course in Algorithms.

Attuned to the rapidly changing landscape in computer technology, this unique and very progressive text helps students understand the application and analysis of algorithmic paradigms to both the traditional sequential model of computing and to a variety of parallel models—offering a unified, fully integrated coverage of both model types so that students can learn to recognize how solution strategies may be shared among computer paradigms and architectures.

• Unified approach—Relates/contrasts sequential and parallel algorithms where appropriate (i.e., covers a paradigm—such as divide-and-conquer—and then considers design, analysis, and implementation issues for both the sequential and parallel models).

  • Helps students learn how abstract solution strategies apply in a variety of environments. Ex.___

• Early chapter development of mathematical tools—Covers asymptotic analysis, induction and recursion, master method, and more.

  • Prepares students for the work ahead with a practical review of mathematical concepts and terms, and provides them with a handy reference to draw upon as they progress through more advanced chapter material. Ex.___

• Broad application areas—Applies concepts in matrix operations, computational geometry, image processing, graph algorithms, and numerical problems.

  • Shows students how to take techniques beyond their theoretical basis. Ex.___

• Richly illustrated—Adds strong visual appeal and supports narrative with numerous figures and illustrations throughout.

  • Enables students to understand difficult concepts better. Ex.___

• Chapter exercises & examples—Includes an extensive number of in-chapter examples and end-of-chapter exercises at varying levels of difficulty (typically 10 to 30 worked exercises and 10 to 25 exercises per chapter); in many examples and exercises, the problem is discussed for both sequential and parallel implementations.

  • Gives students ample opportunity to grasp and review chapter material— consistently supporting and challenging their learning endeavor as they progress through the book; and provides instructors with a wealth of material for homework assignments. Ex.___

• Variety of architectures—Discusses RAM, PRAM, simple algorithms, fundamental terminology, interconnection networks, processor organizations, linear array, ring, mesh, tree, pyramid, hypercube, and more.

  • Provides students with a complete overview of the many different models of computation used in the industry today, and gives them a thorough introduction to parallel computers. Ex.___

 1. Asymptotic Analysis.


 2. Induction and Recursion.


 3. The Master Method.


 4. Combinational Circuits.


 5. Models of Computation.


 6. Matrix Operations.


 7. Parallel Prefix.


 8. Pointer Jumping.


 9. Divide-and-Conquer.


10. Computational Geometry.


11. Image Processing.


12. Graph Algorithms.


13. Numerical Problems.


Bibliography.


Index.

Algorithms (Computer Science)
Parallel Algorithms (Computer Science)

RUSS MILLER is Director of the Center for Computational Research, Professor of Computer Science and Engineering, and Adjunct Professor of Electrical Engineering at the State University of New York at Buffalo (SUNY-Buffalo). He is also Senior Research Scientist at the Hauptman-Woodward Medical Research Institute. Dr. Miller sits on the editorial board of Parallel Processing Letters, on the Advisory Board of the IEEE Technical Committee on Parallel Processing, has co-authored another book, and has authored or co-authored over 100 journal and conference proceedings papers.

LAURENCE BOXER is a faculty member at Niagara University in the Department of Computer and Information Sciences, which he chairs. He is also an Adjunct Professor at SUNY-Buffalo. Dr. Boxer sits on the editorial board of the Journal of Computing and Information and has authored or co-authored over 30 journal and conference proceedings papers.

Algorithms: Sequential and Parallel takes an innovative approach to a traditional algorithms-based course of study. This new approach addresses the changing challenges of computer scientists in the fields of computational science and engineering. With the onset of parallel computing becoming more mainstream, the authors address this dynamic by merging the application and analysis of algorithmic paradigms to both the (traditional) sequential model of computing and to a variety of parallel models. This book offers the student and the scientist an integrated coverage of a fundamental course in algorithms. The book was successfully class tested at both the undergraduate and graduate levels at the State University of New York at Buffalo.

• The text offers a unified approach that relates sequential and parallel algorithms where appropriate and contrasts where appropriate
.
• Mathematical tools are developed in early chapters
.
• A variety of examples are worked out in great detail with multiple methods of solutions
.
• Sequential and parallel examples and exercises are featured
.
• Supplemental material is available for instructors
.
• A Prentice Hall Companion Website with additional material is available at http://www.prenhall.com/millerboxer.

View a Sample Chapter PDF:

Pearson Higher Education offers special pricing when you choose to package your text with other student resources. If you're interested in creating a cost-saving package for your students, contact your Pearson Higher Education representative for pricing and ordering information.

Pearson Higher Education offers special pricing when you choose to package your text with other student resources. If you're interested in creating a cost-saving package for your students contact your Pearson Higher Education representative.