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Short course on Convex Optimization and Applications, 21, 22, 23, 27 March
Lecture 3: Code Generation for Embedded Convex Optimization

Optimization is typically used for problems that can be solved in a few minutes or seconds; often, with a human in the loop. However, using automatic code generation and specially modified algorithms, we can generate solvers can reliably find solutions in milliseconds or microseconds. Such solvers have potential application in many fields, including signal processing, automatic control, machine learning and finance. In this talk, we first describe the special requirements for and opportunities in embedded convex optimization. We describe CVXGEN, a convenient tool for generating library-free, flat C code ready for compilation into a high-speed solver. CVXGEN is just as simple to use as conventional parser-solvers like CVX or YALMIP, but creates solvers that are thousands of times faster, and suitable for embedding. The primary focus of the talk will be on the techniques used to make the solvers extremely reliable and fast.
Based on joint work by: Jacob Mattingley, Stephen Boyd.

Published recordings: On-demand video.
Type of Seminar:
IfA Seminar
Prof. Stephen Boyd
Electrical Engineering, Information Systems Laboratory, Stanford University
Mar 23, 2012   16:15

ETF E 1, Sternwartstr. 7
Contact Person:

John Lygeros
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Biographical Sketch:
See Lecture 1