Introduction to large-scale optimization - Part1

Опубликовано 21 июня 2016, 23:53

These lectures will cover both basics as well as cutting-edge topics in large-scale convex and nonconvex optimization (continuous case only). Examples include stochastic convex optimization, variance reduced stochastic gradient, coordinate descent methods, proximal-methods, operator splitting techniques, and more. The lectures will also cover relevant mathematical background, as well as some pointers to interesting directions of future research (time permitting).