Convex optimization problems appear naturally across the sciences in fields such as compressed sensing, statistics, machine learning, image processing, and inverse problems. This talk will discuss the theory, applications, and methods of proximal minimization: a wide-reaching subset of convex optimization.

Advantages: you do not need your optimization problem to be differentiable (e.g. minimization problems with l-1 regularization can be solved), these methods are proven to converge globally, and they are easy to implement.

My goal: by the end of this talk you will have the resources and algorithms necessary to solve your own convex minimization problem.