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Ukrainian Folk Ballet of the Twin Cities video
Algorithms for SDP. Interior-point, bundle, and first order methods.
Introduction to optimization. Quadratic forms. Gradient, Hessian. Convex sets and convex functions. Least squares.
Optimization methods: First order methods: basic gradient and extensions (accelerated, stochastic, subgradient, and projection).
Polynomials and sum of squares (SOS). Nonconvex quadratic optimization and semidefinite relaxations. Generalizations via SOS.
Sparsity and rank: Applications to estimation, machine learning, and system identification.
Hogwild for Machine Learning on Multicore
Interview with Michael Rechtman
Protected Defect Modes in a Photonic Topological Insulator
On Protocols for the Automatic Discovery of Elementary Geometry Theorems