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Convex optimisation has emerged as useful tool for applications that include data analysis and model fitting, resource allocation, engineering design, network design and optimisation, finance, and control and signal processing.
After an overview of the mathematics, algorithms, and software frameworks for convex optimisation, we turn to common themes that arise across applications, such as sparsity and relaxation.
We describe recent work on real-time embedded convex optimisation, in which small problems are solved repeatedly in millisecond or microsecond time frames, and large-scale distributed convex optimisation, in which many solvers are coordinated to solve enormous problems.
Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory 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 optimisation applications in control, signal processing, machine learning, and finance. Read more.
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