• [CLRS] Introduction to Algorithms by Thomas H. Cormen, Chales E. Leiserson, Ronald R. Rivest, and Clifford Stein, MIT Press, 2009 (3rd edition). Standard textbook for algorithms' courses.

• [NRC] Numerical Recipes in C: The Art of Scientific Computing by William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery, Cambridge University Press. One of the more comprehensive references for computational algorithms and tricks. Chapters from the older version (2nd edition) can be downloaded for free.

• Introducing Monte Carlo methods with R by Christian P. Robert and George Casella, Springer, 2010. PDF chapters of this book is available online if accessed from Purdue computers. For a slightly more in-depth (and non R) version, consider Monte Carlo Statistical Methods by Christian P. Robert and George Casella, Springer.

• Non-Uniform Random Variate Generation by Luc Devroye, Springer-Verlag, 1986. Available for free download.

• Information Theory, Inference, and Learning Algorithms by David J. C. MacKay, Cambridge University Press, 2003. The book can be downloaded for free from the author's website. (Contains nice chapters on random variable generation and Monte Carlo approaches.)

• Convex Optimization by Stephen Boyd and Lieven Vandenberghe, Cambridge University Press, 2004. The book can be downloaded for free from the author's website.

• [NW06] Numerical Optimization by Jorge Nocedal and Stephen J. Wright, 2nd edition, Springer, 2006. The is available as an eBook (for free from Purdue) from Springer.

• Optimization -- Theeory and Practice by Wilhelm Forst and Dieter Hoffmann, Springer, 2010. The book is available as an eBook (for free from Purdue) from Springer.

• [She94] Not really a book, but a wonderful tutorial on the method of conjugate gradients by Jonathan Shewchuk.

In general, take a look at Use R! book series published by Springer. Purdue appears to have electronic access (in PDF) to chapters of these books. There are a few books in this series which can provide a sufficient background in R for this course.

• A Beginner's Guide to R by Alain F. Zuur, Elena N. Ieno, Erik H.W.G. Meesters, Springer, 2009. A very basic introduction to R. PDF chapters of this book is available online if accessed from Purdue computers.

• Introductory Statistics with R by Peter Dalgaard, 2nd edition, Springer, 2008. “Practical methods” in statistics heavily illustrated with the help of R.

• Data Manipulation with R by Phil Spector, Springer, 2008.