Convex Optimization: Algorithms and Complexity

I am thrilled to announce that my short introduction to convex optimization has just came out in the Foundations and Trends in Machine Learning series (free version on arxiv). This project started on this blog in 2013 with the lecture notes “The complexities of optimization”, it then morphed a year later into a first draft titled “Theory of Convex Optimization for Machine Learning”, and finally after one more iteration it has found the more appropriate name: “Convex Optimization: Algorithms and Complexity”. Notable additions since the last version include: a short treatment of conjugate gradient, an almost self-contained analysis of Vaidya’s 1989 cutting plane method (which attains the best of both center of gravity and ellipsoid method in terms of oracle complexity and computational complexity), and finally an algorithm with a simple geometric intuition which attains the rate of convergence of Nesterov’s accelerated gradient descent.

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