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Recent Advances in Convex Optimization (Information & Communication Mini-Symposium)

Convex optimization is now widely used in control, signal processing, networking, communications, machine learning, finance, combinatorial optimization, and other fields. For many problem classes reliable general purpose solvers are now available, with development of new algorithms and implementations continuing at a rapid pace. In this talk I will given an overview of some recent advances. The first is the development of specification and modeling languages specifically for convex optimization. These languages allow very rapid development of applications based on convex optimization, and enhance learning and teaching of the methods. The second is the development of methods for extremely large convex problems, with millions (or more) of variables and constraints, for specific families of problems arising in applications. Truncated Newton interior-point methods, with well-chosen pre-conditioner, can solve far larger problems than generic methods. The third advance is in the area of algorithms for fast solution of convex optimization problems, for use in real-time and embedded applications. Joint work with Michael Grant, Kwangmoo Koh, Seung-Jean Kim, Yang Wang
Type of Seminar:
Public Seminar
Prof. Stephen P. Boyd
Stanford University, USA
May 19, 2008   17:00

Contact Person:

Prof. Manfred Morari
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Biographical Sketch:
Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University. His current research focus is on convex optimization applications in control, signal processing, and circuit design. Professor Boyd received an AB degree in Mathematics, summa cum laude, from Harvard University in 1980, and a PhD in EECS from U. C. Berkeley in 1985. In 1985 he joined the faculty of Stanford's Electrical Engineering Department. He has held visiting Professor positions at Katholieke University (Leuven), McGill University (Montreal), Ecole Polytechnique Federale (Lausanne), Qinghua University (Beijing), Universite Paul Sabatier (Toulouse), Royal Institute of Technology (Stockholm), Kyoto University, and Harbin Institute of Technology. He holds an honorary doctorate from Royal Institute of Technology (KTH), Stockholm. Professor Boyd is the author of many research articles and three books: Linear Controller Design: Limits of Performance (with Craig Barratt, 1991), Linear Matrix Inequalities in System and Control Theory (with L. El Ghaoui, E. Feron, and V. Balakrishnan, 1994), and Convex Optimization (with Lieven Vandenberghe, 2004). Professor Boyd has received many awards and honors for his research in control systems engineering and optimization, including an ONR Young Investigator Award, a Presidential Young Investigator Award, and an IBM faculty development award. In 1992 he received the AACC Donald P. Eckman Award, which is given annually for the greatest contribution to the field of control engineering by someone under the age of 35. In 1993 he was elected Distinguished Lecturer of the IEEE Control Systems Society, and in 1999, he was elected Fellow of the IEEE, with citation: "For contributions to the design and analysis of control systems using convex optimization based CAD tools." He has been invited to deliver more than 30 plenary and keynote lectures at major conferences in both control and optimization. In addition to teaching large graduate courses on Linear Dynamical Systems, Nonlinear Feedback Systems, and Convex Optimization, Professor Boyd has regularly taught introductory undergraduate Electrical Engineering courses on Circuits, Signals and Systems, Digital Signal Processing, and Automatic Control. In 1994 he received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering, and in 1991, an ASSU Graduate Teaching Award. In 2003, he received the AACC Ragazzini Education award, for contributions to control education, with citation: "For excellence in classroom teaching, textbook and monograph preparation, and undergraduate and graduate mentoring of students in the area of systems, control, and optimization."