The content includes 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 t
Convex Optimization Stephen Boyd and Lieven Vandenberghe Cambridge University Press More material can be found at the web sites for EE364A (Stanford) or EE236B (UCLA), and our own web pages. Source code for almost all examples and figures in part 2
Prerequisites are linear algebra (preferably abstract) and real analysis (mathematical analysis). Proofs will matter ... but the rich geometry of the subject helps guide the mathematics. Applications: There are many and pervasive ... but do not expe
