Online Convex Optimization in Changing Environments and its Application to Resource Allocation

TL;DR

This paper discusses the development of algorithms within the Online Convex Optimization framework to adapt to dynamic environments, enabling real-time data processing and resource allocation in various online applications.

Contribution

It introduces new algorithms for OCO that effectively adapt to changing environments, enhancing real-time data analysis and resource management capabilities.

Findings

Algorithms successfully adapt to environment changes

Improved performance in online resource allocation

Theoretical guarantees for algorithm convergence

Abstract

In the era of the big data, we create and collect lots of data from all different kinds of sources: the Internet, the sensors, the consumer market, and so on. Many of the data are coming sequentially, and would like to be processed and understood quickly. One classic way of analyzing data is based on batch processing, in which the data is stored and analyzed in an offline fashion. However, when the volume of the data is too large, it is much more difficult and time-consuming to do batch processing than sequential processing. What's more, sequential data is usually changing dynamically, and needs to be understood on-the-fly in order to capture the changes. Online Convex Optimization (OCO) is a popular framework that matches the above sequential data processing requirement. Applications using OCO include online routing, online auctions, online classification and regression, as well as online resource allocation. Due to the general applicability of OCO to the sequential data and the rigorous theoretical guarantee, it has attracted lots of researchers to develop useful algorithms to fulfill different needs. In this thesis, we show our contributions to OCO's development by designing algorithms to adapt to changing environments.