Title: "Convex Optimization: From Real-Time Embedded to Large-Scale Distributed"
Speaker: Prof. Stephen P. Boyd, Stanford University
Abstract
Convex optimization has emerged as useful tool for applications that include data analysis and model fitting, resource allocation, engineering design, network design and optimization, finance, and control and signal processing. After an overview, the talk will focus on two extremes: real-time embedded convex optimization, and distributed convex optimization. Code generation can be used to generate extremely efficient and reliable solvers for small problems, that can execute in milliseconds or microseconds, and are ideal for embedding in real-time systems. At the other extreme, we describe methods for large-scale distributed optimization, which coordinate many solvers to solve enormous problems.
Biosketch. Stephen P. Boyd is the Samsung Professor of Engineering, in the Information Systems Laboratory, Electrical Engineering Department, Stanford University. He is a member of the Institute for Mathematical and Computational and Engineering, and holds courtesy appointments in the department of Computer Science and the department of Management Science and Engineering. His current interests include convex programming applications in control, signal processing, and circuit design. He 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. He holds an honorary PhD from Royal Institute of Technology, Stockholm. He is the author of 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). He received an ONR Young Investigator Award, a Presidential Young Investigator Award, the 1992 AACC Donald P. Eckman Award, and the 2013 IEEE Technical Field Award in Control Systems. In 2012, he and former student Michael Grant were awarded the Mathematical Optimization Society Beale-Orchard-Hays award for computational optimization. He has received the Perrin Award for Outstanding Undergraduate Teaching in the School of Engineering, and an ASSU Graduate Teaching Award. In 2003, he received the AACC Ragazzini Education award. He is a fellow of the IEEE, and a Distinguished Lecturer of the IEEE Control Systems Society.
