Stephen Boyd
Stanford University
Convex Optimization: From embedded real-time to large-scale distributed
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.
Stephen Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University, with courtesy appointments in Computer Science and Management Science and Engineering.
He received the A.B. degree in Mathematics from Harvard University in 1980, and the Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley, in 1985, and then joined the faculty at Stanford.
His current research focus is on convex optimization applications in control, signal processing, machine learning, finance, and circuit design.
