Abstract: We show that a single composition rule, along with a small library of basic convex and concave functions, is enough to describe almost all convex optimization problems that arise in practice. Checking the rule can be automated, as can the transformation of a problem to standard form when the problem description conforms to the rule.  All current packages for convex optimization rely on this one rule.

Bio: Stephen P. Boyd is the Samsung Professor of Engineering, Professor of Electrical Engineering in the Information Systems Laboratory, and chair of the Electrical Engineering Department at Stanford University. He has courtesy appointments in the Department of Management Science and Engineering and the Department of Computer Science, and is a member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, machine learning, and finance. 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 the AACC Donald P. Eckman Award. In 2013, he received the IEEE Control Systems Award, given for outstanding contributions to control systems engineering, science, or technology. In 2012, Michael Grant and he were given the Mathematical Optimization Society's Beale-Orchard-Hays Award, given every three years for excellence in computational mathematical programming. He is a Fellow of the IEEE, SIAM, and INFORMS, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering, and a foreign member of the Chinese Academy of Engineering. He has been invited to deliver more than 90 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.