CSE 291, Fall 2017Convex OptimizationUniversity of California, San Diego

Instructor

- CK Cheng, ckcheng+291@ucsd.edu, 858 534-6184
Teaching Assistant

- Po-Ya Hsu, p8hsu@eng.ucsd.edu
- Office hours : M 14:30-15:30, F 10:30-11:30, in front of Room CSE2140
Schedule

- Lectures: 12:30-1:50PM TTH, Room CSE4140
References

- Convex Optimization, S. Boyd and L. Vandenberghe, Cambridge, 2004 (required textbook).
- Numerical Recipes: The Art of Scientific Computing, Third Edition, W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, Cambridge University Press, 2007.
- Funcions of Matrices: Theory and Computation, N.J. Higham, SIAM, 2008.
- Fall 2016, Convex Optimization by R. Tibshirani, http://www.stat.cmu.edu/~ryantibs/convexopt/
- EE364a: Convex Optimization I, S. Boyd, http://stanford.edu/class/ee364a/
PrerequisiteBasic knowledge of numerical methods or intention of conducting projects related to scientific computation.

Assignment and GradingGrading is based on class participation, 6 to 8 exercises and a project with final presentation.

ContentWe study the formulations and algorithms solving convex optimization problems. The topics include convex sets, functions, optimality conditions, duality concepts, gradient descent, conjugate gradient, interior-point methods, and applications. The objective of the course is to provide students the background and techniques for scientific computing and system optimization.

Extra Materials

- Conjugate Gradient Tutorial by CK Cheng.
- Nesterov Method: Differential Equation by Su, Boyd and Candes.
LecturesHomeworkProject