## Stanford Engineering Everywhere EE364A - Convex Optimization I

author: Stephen P. Boyd,
Department of Electrical Engineering, Stanford University

released under terms of: Creative Commons Attribution Non-Commercial (CC-BY-NC)

released under terms of: Creative Commons Attribution Non-Commercial (CC-BY-NC)

Concentrates on recognizing and solving convex optimization problems that arise in engineering. Convex sets, functions, and optimization problems. Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Interiorpoint methods. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.

Prerequisites:

- Good knowledge of linear algebra.
- Exposure to numerical computing, optimization, and application fields helpful but not required; the engineering applications will be kept basic and simple.

**Course Homepage:** http://see.stanford.edu/see/courseinfo.aspx?coll=2db7ced4-39d1-4fdb-90e8-364129597c87

**Course features at Stanford Engineering Everywhere page:**

James Edward Smith, August 17, 2014 at 8:26 p.m.:Professor Boyd,

Please translate "Equilibrium of Heterogeneous Substances" by J. W. Gibbs into MODERN ENGLISH. Thermodynamics is the mother of all convex optimization problems and you are the only man I know of who could do the job. Videos would be a great medium for this unbelievably great intellectual milestone. Your lectures are the best I've ever seen, and I'm pushing 80. (And try to hurry on that account :)

Ed Smith