矩阵优化扰动性分析的若干进展

由于近年来实际问题特别是大数据应用的发展,矩阵优化问题越来越得到优化研究者,甚至是其他领域的研究者的高度关注,成为热点问题.优化问题的扰动性分析是优化理论研究的基础与核心,为包括算法设计在内的优化研究提供重要的理论基础.由于矩阵优化问题的非多面体性,使得相应扰动分析理论的研究本质上与经典的多面体优化问题(非线性规划)不同.结合文献1,2],简要介绍矩阵优化扰动性分析方面取得的若干最新进展.

Preemptive online algorithms for scheduling

Matrix optimization problems (MOPs) have been recognized in recent years to be a powerful tool to model many important applications arising from emerging fields such as data science {within and beyond the optimization community}. Perturbation analysis of optimization problems play a fundamental and crucial role in optimization, which provided important theoretical foundation for algorithm designing and others. Science MOPs are non-polyhedral, the corresponding analysis is totally different from that of the classical polyhedral case (e.g., the nonlinear programming). Basing on results obtained in 1,2], we summary the recent progress on perturbation analysis of MOPs.