Scientific Computation

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Overview

Using real-life applications, this graduate-level textbook introduces different mathematical methods of scientific computation to solve minimization problems using examples ranging from locating an aircraft, finding the best time to replace a computer, analyzing developments on the stock market, and constructing phylogenetic trees. The textbook focuses on several methods, including nonlinear least squares with confidence analysis, singular value decomposition, best basis, dynamic programming, linear programming, and various optimization procedures. Each chapter solves several realistic problems, introducing the modeling optimization techniques and simulation as required. This allows readers to see how the methods are put to use, making it easier to grasp the basic ideas. There are also worked examples, practical notes, and background materials to help the reader understand the topics covered. Interactive exercises are available at www.cambridge.org/9780521849890.

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Editorial Reviews

From the Publisher
"I do agree that this book will "help students understand methods in some of their appropriate contexts," in a relatively easy, fresh, and concise way."
E. Vavalis, reviews.com

"I recommend this text for instructors who are interested in problem-based or other interactive learning styles."
D.E. Stevenson, Physics Today

"This book fills an important gap in terms of both problems and methods, and thus could well be an important component of a range of materials for teaching scientific computing."
Martin Berzins for IEEE Computing in Science & Engineering

"This book contains clear descriptions of important mathematical techniques. It is organized around some practical problems that might spur students' interest in deepening their understanding of those techniques."
Fernando Berzal, Computing Reviews

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Product Details

  • ISBN-13: 9780521849890
  • Publisher: Cambridge University Press
  • Publication date: 12/31/2009
  • Pages: 236
  • Product dimensions: 7.50 (w) x 9.70 (h) x 0.60 (d)

Meet the Author

Gaston H. Gonnet is a Professor in the Department of Informatik at ETH, Zurich. His research interests lie in symbolic and algebraic computation, system development, limit and series computation and heuristic algorithms, computational biochemistry algorithms, and the design and analysis of algorithms.

Ralf Scholl teaches mathematics, physics, and science and technology at the Geschwister-Scholl-Gymnasium in Stuttgart. His interests lie in the didactics of teaching science and mathematics at high school to graduate level.

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Table of Contents

Preface; 1. Determination of the accurate location of an aircraft; 2. When to replace equipment; 3. SSP using LS and SVD; 4. SSP using least squares and best basis; 5. SSP learning methods (nearest neighbours); 6. SSP with linear programming (LP); 7. Stock market prediction; 8. Phylogenetic tree construction; Appendixes; Index.

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