SDS Colloquium Series

Topic

Embedded Convex Optimization for Control

Speaker

Stephen P. BOYD, Professor of Electrical Engineering, Stanford University

Host

Zizhuo WANG, Professor & Associate Dean (Education), School of Data Science, CUHK-Shenzhen

Date

27  November (Wednesday), 2024

Time

11:00 AM - 12:00 PM, Beijing Time

Format

Onsite

Venue

103 Meeting Room, Dao Yuan Building

Language

English

Abstract

Control policies that involve the real-time solution of one or more convex optimization problems include model predictive (or receding horizon) control, approximate dynamic programming, and optimization based actuator allocation systems. They have been widely used in applications with slower dynamics, such as chemical process control, supply chain systems, and quantitative trading, and are now starting to appear in systems with faster dynamics. In this talk I will describe a number of advances over the last decade or so that make such policies easier to design, tune, and deploy. We describe solution algorithms that are extremely robust, even in some cases division free, and code generation systems that transform a problem description expressed in a high level domain specific language into source code for a real-time solver suitable for control. The recent development of systems for automatically differentiating through a convex optimization problem can be used to efficiently tune or design control policies that include embedded convex optimization.

Biography

Stephen P. Boyd is the Samsung Professor of Engineering, Professor of Electrical Engineering, and 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.

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. In 1985 he joined the faculty of Stanford's Electrical Engineering Department. He is the author of many research articles and four books in applied mathematics and engineering. His group has produced many open source tools, widely used parser-solvers for convex optimization. His group's CVXGEN software is used in many applications, including the SpaceX Falcon 9 landing system.

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. He received the 2012 Mathematical Optimization Society's Beale-Orchard-Hays Award, the 2013 IEEE Control Systems Award, and the 2023 AACC Richard E. Bellman Control Heritage Award. He is a Fellow of the IEEE, SIAM, INFORMS, and IFAC, a Distinguished Lecturer of the IEEE Control Systems Society, a member of the US National Academy of Engineering (NAE), a foreign member of the Chinese Academy of Engineering (CAE), and a foreign member of the National Academy of Engineering of Korea (NAEK). He has been invited to deliver more than 90 plenary and keynote lectures at major conferences in control, optimization, signal processing, and machine learning.

He has developed and taught many undergraduate and graduate courses, his graduate convex optimization course attracts 300 students from 25 departments. His website, makes available past papers, books, software, lecture notes, and selected lecture videos, is visited more than 1.6 million times per year.