Fundamentals of Optimization in Signal Processing

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.