Optimization for and by Machine Learning

Abstract

In machine learning, tasks like making predictions using a model and learning model parameters can often be formulated as optimization problems. The feasibility of using a machine learning model depends on the efficiency with which the corresponding optimization problems can be solved. As such, the area of machine learning throws up many challenges and interesting problems for research in the field of optimization. While in some cases, it is possible to directly apply off-the-shelf optimization methods for problems in machine learning, in many other cases, it becomes necessary to develop optimization algorithms that are tailor made for specific problems. On the other hand, developing optimization algorithms for specific problem domains can itself be helped by machine learning techniques. Learning optimization algorithms from data can help relieve tedious effort required to develop optimization methods for new problem domains. The challenge here is to appropriately parameterize the space the algorithms for different optimization problems. In this context, we explore the interplay between the areas of optimization and machine learning and make contributions in specific problems of interest that lie in the overlap of these fields. newline