Online Learning With Inexact Proximal Online Gradient Descent Algorithms
Online Learning With Inexact Proximal Online Gradient Descent Algorithms
We consider non-differentiable dynamic optimization problems such as those arising in robotics and subspace tracking. Given the computational constraints and the time-varying nature of the problem, a low-complexity algorithm is desirable, while the accuracy of the solution may only increase slowly over time. We put forth the proximal online gradient …