Learning LaTeX

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.

David F. Griffiths, now retired, originally trained as an applied mathematician and, after a brief spell at New York University, spent his academic career at the University of Dundee specializing in the numerical solution of differential equations. He has published 3 books and over 50 scientific articles on differential equations. David was joint organizing secretary of the Dundee Biennial Conferences on Numerical Analysis from 1983 to 2005.

Desmond J. Higham is a numerical analyst at the University of Strathclyde in Glasgow. He has research interests in stochastic computation, network science and city analytics. He is a SIAM Dahlquist Prize winner, a SIAM Fellow and a Fellow of the Royal Society of Edinburgh.