Convex Optimization: From embedded real-time to large-scale distributed

报告题目: Convex Optimization: From embedded real-time to large-scale distributed


报告人:  Stephen Boyd 教授,  Stanford University






摘要: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, which 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.

报告人简介Stephen P. Boyd is the Samsung Professor of Engineering, and Professor of Electrical Engineering in the Information Systems Laboratory at Stanford University. He also has a courtesy appointment in the Department of Management Science and Engineering, and is member of the Institute for Computational and Mathematical Engineering. His current research focus is on convex optimization applications in control, signal processing, and circuit design. He is the author of many papers and three books: Linear Controller Design: Limits of Performance, Linear Matrix Inequalities in System and Control Theory, and Convex Optimization. His group has produced several open source tools, including CVX, a widely used parser-solver for convex optimization.




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