1 Elementary Optimization.- 2 The Seven C's of Analysis.- 3 Differentiation.- 4 Karush-Kuhn-Tucker Theory.- 5 Convexity.- 6 The MM Algorithm.- 7 The EM Algorithm.- 8 Newton's Method.- 9 Conjugate Gradient and Quasi-Newton.- 10 Analysis of Convergence.- 11 Convex Programming.- Appendix: The Normal Distribution.- A.1 Univariate Normal Random Variables.- A.2 Multivariate Normal Random Vectors.- References.

Lange is a Springer author of other successful books.

This is the first book that emphasizes the applications of optimization to statistics.

The emphasis on statistical applications will be especially appealing to graduate students of statistics and biostatistics.