This book, a result of the authors' teaching and research experience in various universities and institutes over the past ten years, can be used as a textbook for an optimization course for graduates and senior undergraduates. It systematically describes optimization theory and several powerful methods, including recent results. For most methods, the authors discuss an idea's motivation, study the derivation, establish the global and local convergence, describe algorithmic steps, and discuss the numerical performance. The book deals with both theory and algorithms of optimization concurrently. It also contains an extensive bibliography with 366 references. Finally, apart from its use for teaching, Optimization Theory and Methods is also very beneficial for doing research.

"This book, a result of the author's teaching and research experience in various universities and institutes over the past ten years, can be used as a textbook for an optimization course for graduates and senior undergraduates. It systematically describes optimization theory and several powerful methods, including recent results. For most methods, the authors discuss an idea's motivation, study the derivation, establish the global and local convergence, describe algorithmic steps, and discuss the numerical performance. The book deals with both theory and algorithms of optimization concurrently. It also contains an extensive bibliography. Finally, apart from its use for teaching, Optimization Theory and Methods will be very beneficial as a research reference. TOC:Preface.- Introduction.- Line Search.- Newton's Methods.- Conjugate Gradient Method.- Quasi-Newton Methods.- Trust-Region and Conic Model Methods.- Nonlinear Least-Squares Problems.- Theory of Constrained Optimization.- Quadratic Programming.- Penalty Function Methods.- Feasible Direction Methods.- Sequential Quadratic Programming.- TR Methods for Constrained Problems.- Nonsmooth Optimization.- Appendix: Test Functions.- Bibliography.- Index."

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.