Here is a short, well-written book that covers the material essential for learning LaTeX. It includes incisive examples that teach LaTeX in a powerful yet abbreviated fashion. This is the handbook to have if you don t want to wade through extraneous material. This manual includes the following crucial features: numerous examples of widely used mathematical expressions; complete documents illustrating the creation of articles, reports, presentations, and posters; troubleshooting tips to help you pinpoint an error; details of how to set up an index and a bibliography; and information about online LaTeX resources.
 Why do you need to learn LaTeX? LaTeX has become an extremely popular typesetting system and is widely used throughout the sciences. As a student, you may need to typeset reports and theses in LaTeX (particularly if you are a graduate student in any mathematics or computer science discipline). Or you may be someone who had planned to "eventually" get around to learning LaTeX, but you are still using older or less appropriate methods of typesetting. Procrastinate no more!
 This second edition of the well-regarded and highly successful book includes additional material on the American Mathematical Society packages for typesetting additional mathematical symbols and multi-line displays; the BiBTeX program for creating bibliographies; the Beamer package for creating presentations; and the a0poster class for creating posters.
 Audience: The text is aimed at advanced undergraduate and graduate students in computational engineering. It may also be useful to physicists, computational scientists, and developers of numerical simulation software.
 Contents: Chapter 1: Preamble; Chapter 2: Basic LaTeX; Chapter 3: Typesetting Mathematics; Chapter 4: Further Essential LaTeX; Chapter 5: More About LaTeX; Appendix A: A Sample Article; Appendix B: A Sample Report; Appendix C: A Sample Presentation; Appendix D: A Sample Poster; Appendix E: Internet Resources.

Learning LaTeX, Second Edition

Desmond J. Higham

David F. Griffiths

SIAM: Society for Industrial and Applied Mathematics

MATLAB is an interactive system for numerical computation that is widely used for teaching and research in industry and academia. It provides a modern programming language and problem solving environment, with powerful data structures, customizable graphics, and easy-to-use editing and debugging tools.
 This third edition of MATLAB Guide completely revises and updates the best-selling second edition and is more than 25 percent longer. The book remains a lively, concise introduction to the most popular and important features of MATLAB and the Symbolic Math Toolbox.
 Key features are a tutorial in Chapter 1 that gives a hands-on overview of MATLAB, a thorough treatment of MATLAB mathematics, including the linear algebra and numerical analysis functions and the differential equation solvers, and a web page that provides a link to example program files, updates, and links to MATLAB resources.
 The new edition contains color figures throughout, includes pithy discussions of related topics in new "Asides" boxes that augment the text, has new chapters on the Parallel Computing Toolbox, object-oriented programming, graphs, and large data sets, covers important new MATLAB data types such as categorical arrays, string arrays, tall arrays, tables, and timetables, contains more on MATLAB workflow, including the Live Editor and unit tests, and fully reflects major updates to the MATLAB graphics system.
 Audience: This book is suitable for both beginners and more experienced users, including students, researchers, and practitioners.
 Contents: List of Figures; List of Tables; List of Program Files; Preface; Chapter 1: A Brief Tutorial; Chapter 2: Basics; Chapter 3: Distinctive Features of MATLAB; Chapter 4: Arithmetic; Chapter 5: Matrices; Chapter 6: Operators and Flow Control; Chapter 7: Program Files; Chapter 8: Graphics; Chapter 9: Linear Algebra; Chapter 10: More on Functions; Chapter 11: Numerical Methods: Part I; Chapter 12: Numerical Methods: Part II; Chapter 13: Input and Output; Chapter 14: Troubleshooting; Chapter 15: Sparse Matrices; Chapter 16: More on Coding; Chapter 17: Advanced Graphics; Chapter 18: Other Data Types and Multidimensional Arrays; Chapter 19: Object-Oriented Programming; Chapter 20: The Symbolic Math Toolbox; Chapter 21: Graphs; Chapter 22: Large Data Sets; Chapter 23: Optimizing Codes; Chapter 24: Tricks and Tips; Chapter 25: The Parallel Computing Toolbox; Chapter 26: Case Studies; Appendix A: The Top 111 MATLAB Functions; Glossary; Bibliography; Index.

MATLAB Guide, Third Edition

Science & Math

Nicholas J. Higham

Mathematics

This book provides a lively and accessible introduction to the numerical solution of stochastic differential equations with the aim of making this subject available to the widest possible readership. It presents an outline of the underlying convergence and stability theory while avoiding technical details. Key ideas are illustrated with numerous computational examples and computer code is listed at the end of each chapter. The authors include 150 exercises, with solutions available online, and 40 programming tasks. Although introductory, the book covers a range of modern research topics, including Itô versus Stratonovich calculus, implicit methods, stability theory, nonconvergence on nonlinear problems, multilevel Monte Carlo, approximation of double stochastic integrals, and tau leaping for chemical and biochemical reaction networks. An Introduction to the Numerical Simulation of Stochastic Differential Equations is appropriate for undergraduates and postgraduates in mathematics, engineering, physics, chemistry, finance, and related disciplines, as well as researchers in these areas. The material assumes only a competence in algebra and calculus at the level reached by a typical first-year undergraduate mathematics class, and prerequisites are kept to a minimum. Some familiarity with basic concepts from numerical analysis and probability is also desirable but not necessary.

An Introduction to the Numerical Simulation of Stochastic Differential Equations

Peter E. Kloeden

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