Fundamentals of Optimization in Signal Processing

Explore the essential role of optimization formulations and algorithms in solving signal processing problems through this comprehensive lecture. Delve into key topics including first-order methods, regularized optimization, forward-backward methods, stochastic gradient methods, coordinate descent methods, conditional gradient / Frank-Wolfe methods, asynchronous parallel implementations, matrix optimization (including matrix completion), and Augmented Lagrangian methods / ADMM. Gain valuable insights from Stephen Wright's expertise in this 1-hour 17-minute presentation, offered by the Hausdorff Center for Mathematics, as part of a series on the fundamentals of optimization in signal processing.