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This course will focus on formulating and solving convex optimization problems arising in engineering and science. Topics include: convex analysis, linear and quadratic programming, semidefinite programming, optimality conditions, duality theory, interior-point methods, subgradient methods, convex relaxation.
Modeling and application
** http://www.ics.uci.edu/~xhx/courses/ConvexOpt/projects/PatrickFlynn.pdf Conjugate gradient method]
* Final exam: Mar 15, 4:00-6:00pm (Bring one examination blue book!) * Final project due: Mar 18, 5pm, hard copy in Bren Hall 4058
* Convex sets: 2.1, 2.9, 2.12, 2.15, 2.23, 2.24, 2.33 (from the textbook)
* Convex functions: 3.2, 3.15, 3.16, 3.36, 3.42
* Convex problems; 4.1, 4.65
* Duality: 5.1, 5.13, 5.38, 5.42