Optimization Algorithms for Solving Problems Arising from Large Scale Machine Learning
In the contemporary “big data” age, the use of Machine Learning models for analyzing large volumes of data has been instrumental in a lot of current technological development. These models necessitate solving very large scale optimization problems, presenting challenges in terms of developing appropriate solvers. In addition, especially for problems arising from Deep Neural Network architectures, the resulting problems are often nonconvex, and sometimes nonsmooth, giving additional difficulty.
In this talk I present the standard structural elements of this class of problems, and how these structures can be handled with appropriate parallel architectures. I discuss the state of the art in terms of optimization algorithms for this setting and summarize the prognosis for ongoing and future research.