论文标题
在不断变化的环境中的在线凸优化及其在资源分配中的应用
Online Convex Optimization in Changing Environments and its Application to Resource Allocation
论文作者
论文摘要
在大数据时代，我们从各种来源创建并收集了许多数据：互联网，传感器，消费者市场等。许多数据正在顺序进行，并希望快速处理和理解。分析数据的一种经典方法是基于批处理处理，在该处理中，数据以离线方式存储和分析。但是，当数据的体积太大时，进行批处理处理要比顺序处理要困难得多且耗时。更重要的是，顺序数据通常是动态变化的，需要直接理解以捕获更改。在线凸优化（OCO）是一个流行的框架，与上述顺序数据处理要求匹配。使用OCO的应用程序包括在线路由，在线拍卖，在线分类和回归以及在线资源分配。由于OCO对顺序数据和严格的理论保证的一般适用性，它吸引了许多研究人员来开发有用的算法以满足不同的需求。在本文中，我们通过设计算法以适应不断变化的环境来展示对OCO开发的贡献。
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.
