In many problems in machine learning and operations research, we need to optimize a function whose input is a random variable or a probability density function, i.e. to solve optimization problems in an infinite dimensional space. On the other hand, online learning has the advantage of dealing with streaming examples, and better model a changing environment. In this paper, we extend the celebrated online gradient descent algorithm to Hilbert spaces (function spaces), and analyze the convergence guarantee of the algorithm. Finally, we demonstrate that our algorithms can be useful in several important problems.
In a similar framework, Zhu & Xu REF extend the celebrated online gradient descent algorithm to Hilbert spaces and analyze the convergence guarantee of the algorithm.
Online Gradient Descent in Function Space
ArXiv
ArXiv
Mathematics Computer Science