Stephen Boyd (Stanford University)

Convex Optimization

The course aims at giving a modern and thorough treatment of algorithms for solving convex, large-scale and nonsmooth optimization problems. Applications of convex optimization. Convex sets, functions and optimization problems. Optimality conditions. Basic algorithms for unconstrained optimization (gradient, fast gradient and Newton methods). Basic algorithms for constrained optimization (Interior point and active set methods). Subdifferential and conjugate of convex functions. Duality. Proximal mappings. Proximal minimization algorithm. Augmented Lagrangian Method.

Convex Optimization

The course covers the basics of convex optimization methods, with an emphasis on numerical algorithms that can solve a large variety of optimization problems arising in control engineering, machine learning, mechanical engineering, statistics, economics, and finance.

The materials for the course are available at http://www.stanford.edu/~boyd/papers/cvx_short_course.html