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"Elephant in the Room: Non-Smooth Non-Convex Optimization"
Abstract: It is well-known that finding global minima of non-convex optimization problems is computationally hard in general. However, the problem of finding local minima-like points (at least in terms of gradient and Hessian properties) is tractable, and received much attention in recent years. The resulting literature has been largely motivated by the rising importance of non-convex problems such as deep learning, but in fact, does not quite address them: Nearly all positive results in this area require the objective function to be smooth, which is seldom satisfied in deep learning problems. This highlights the importance of understanding what we can do efficiently on non-convex, non-smooth optimization problems. In this talk, I'll describe some results, challenges, and possible approaches to tackle this fundamental question.
Contact crampersad@flatironinstitute.org for Zoom information