Title: Algorithms for solving optimization problems arising from deep neural net models: nonsmooth problems,
Abstract: Machine Learning models incorporating multiple layered learning networks have
been seen to provide effective models for various classification problems. The
resulting optimization problem to solve for the optimal vector minimizing the
empirical risk is, however, highly nonconvex. This alone presents a challenge
to application and development of appropriate optimization algorithms for
solving the problem. However, in addition, there are a number of interesting
problems for which the objective function is non- smooth and nonseparable. In
this paper, we summarize the primary challenges involved, the state of the art,
and present some numerical results on an interesting and representative class
of problems.