This textbook provides a hands-on treatment of the subject of optimization. A comprehensive set of problems and exercises makes it suitable for use in one or two semesters of an advanced undergraduate course or a first-year graduate course. Each half of the book contains a full semester's worth of complementary yet stand-alone material. The practical orientation of the topics chosen and a wealth of useful examples also make the book suitable as a reference work for practitioners in the field. In this second edition the authors have added sections on recent innovations, techniques, and methodologies.

Advancements in the efficiency of digital computers and the evolution of reliable software have led to rapid growth in the theory, methods, and algorithms of numerical optimization Applications include engineering, business, and science Suitable for advanced undergraduate courses and graduate courses

Autorentext

Prof. Andreas Antoniou received his Ph.D. in Electrical Engineering from the University of London in 1966 and is a Fellow of the IET and IEEE. He served as the founding Chair of the Dept. of Electrical and Computer Engineering at the University of Victoria, Canada, and is now Professor Emeritus in the same department. He is the author of Digital Filters: Analysis, Design, and Applications (McGraw-Hill, 1993) and Digital Signal Processing: Signals, Systems, and Filters (McGraw-Hill, 2005). He served as Associate Editor/Editor of IEEE Trans. on Circuits and Systems from June 1983 to May 1987, as a Distinguished Lecturer of the IEEE Signal Processing Society in 2003, as General Chair of the 2004 Intl. Symp. on Circuits and Systems, and is currently serving as a Distinguished Lecturer of the IEEE Circuits and Systems Society. He received the Ambrose Fleming Premium for 1964 from the IEEE (best paper award), the CAS Golden Jubilee Medal from the IEEE Circuits and Systems Society,the B.C. Science Council Chairman's Award for Career Achievement for 2000, a Doctor Honoris Causa from the Metsovio National Technical University of Athens, Greece, n 2002, and the IEEE Circuits and Systems Society 2005 Technical Achievement Award. Prof. Wu-Sheng Lu received his B.S. degree in Mathematics from Fudan University, Shanghai, China in 1964, an M.E. degree in Automation from the East China Normal University, Shanghai in 1981, and an M.S. degree in Electrical Engineering and his Ph.D. in Control Science from the University of Minnesota, Minneapolis, n 1983 and 1984, respectively. He was a postdoctoral fellow at the University of Victoria, Canada in 1985 and a visiting Asst. Professor with the University of Minnesota in 1986. Since 1987 he has been with the University of Victoria where he is a full professor. His current teaching and research interests are digital signal processing and the application of optimization methods. He is the coauthor with Prof. Antoniouof Two-Dimensional Digital Filters (Marcel Dekker, 1992). He served as an Associate Editor of the Canadian Journal of Electrical and Computer Engineering in 1989, and Editor of the same journal from 1990 to 1992. He served as an Associate Editor for the IEEE Trans. on Circuits and Systems, Part II, from 1993 to 1995 and for Part I of the same journal from 1999 to 2001 and from 2004 to 2005. Presently he is serving as Associate Editor for the Intl. J. of Multidimensional Systems and Signal Processing. He is a Fellow of the Engineering Institute of Canada and the IEEE.

Zusammenfassung
The practical orientation of the topics chosen and a wealth of useful examples also make the book suitable as a reference work for practitioners in the field.In this second edition the authors have added sections on recent innovations, techniques, and methodologies.

Inhalt

The Optimization Problem.- Basic Principles.- General Properties of Algorithms.- One-Dimensional Optimization.- Basic Multidimensional Gradient Methods.- Conjugate-Direction Methods.- Quasi-Newton Methods.- Minimax Methods.- Applications of Unconstrained Optimization.- Fundamentals of Constrained Optimization.- Linear Programming Part I: The Simplex Method.- Linear Programming Part II: Interior-Point Methods.- Quadratic and Convex Programming.- Semidefinite and Second-Order Cone Programming.- General Nonlinear Optimization Problems.- Applications of Constrained Optimization.