Online Optimization in Dynamic Environments

Understand

High-velocity streams of high-dimensional data pose significant "big data" analysis challenges across a range of applications and settings.

• Online learning and online convex programming play a significant role in the rapid recovery of important or anomalous information from these large datastreams.
• While recent advances in online learning have led to novel and rapidly converging algorithms, these methods are unable to adapt to nonstationary environments arising in real-world problems.
• This paper describes a dynamic mirror descent framework which addresses this challenge, yielding low theoretical regret bounds and accurate, adaptive, and computationally efficient algorithms which are applicable to broad classes of problems.