Optimization Theory and Methods can be used as a textbook for an optimization course for graduates and senior undergraduates. It is the result of the author's teaching and research over the past decade. It describes optimization theory and several powerful methods. For most methods, the book discusses an ideäs motivation, studies the derivation, establishes the global and local convergence, describes algorithmic steps, and discusses the numerical performance.

Line Search.- Newton's Methods.- Conjugate Gradient Method.- Quasi-Newton Methods.- Trust-Region Methods and Conic Model Methods.- Solving Nonlinear Least-Squares Problems.- Theory of Constrained Optimization.- Quadratic Programming.- Penalty Function Methods.- Feasible Direction Methods.- Sequential Quadratic Programming.- Trust-Region Methods for Constrained Problems.- Nonsmooth Optimization.