Rutgers University 3 credits 544
This course covers convex sets and functions, unconstrained and constrained optimization, applications to matrix completion, matrix factorization, least squares, logistic regression, and principal component analysis, Gradient Descent methods, Franke-Wolfe algorithm, momentum methods, Polyak and Nesterov accelerations, AdaGrad and Adam, Lagranian duality and Karush-Kuhn-Ticker conditions, Stochastic Gradient Descent, acceleration methods for stochastic gradient approach, coordinate descent methods, quasi-Newton methods, applications to neural networks, and deep learning.
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