报告主题：Parallel Secure Outsourcing of Large-scale Nonlinearly Constrained Nonlinear Programming Problems
Nonlinearly constrained nonlinear programming (NLC-NLP) problems arise in various real-world decision-making fields, such as financial engineering, urban planning, supply chain management, and power system control. They are usually large-scale because of having to consider massive variables and constraints. Solving NLC-NLP problems by employing common algorithms (e.g., gradient projection method (GPM)) is usually computationally-expensive, which challenges common organizations in solving large-scale NLC-NLP problems. To address this issue, an option is to adopt cloud computing for help. However, this raises security concerns since real-world NLC-NLP problems may carry sensitive information. Although previous secure outsourcing algorithms try to protect sensitive information, they still let cloud service tenants bear heavy computation burden. In this talk, I focus on how to develop a practical secure outsourcing algorithm for using the GPM to solve large-scale NLC-NLP problems. Particularly, to accelerate computations and avoid possible memory overflowing, we focus on how to parallelize the developed algorithm.
Bio：Dr. Changqing Luo is an Assistant Professor with the Department of Computer Science at Virginia Commonwealth University. He received his Ph. D. degree from Case Western Reserve University in 2018. He received his B.E. and M.E. degree in Telecommunication Engineering and Communication and Information Systems from Chongqing University of Posts and Telecommunications, Chongqing, China, in 2004 and 2007, respectively, and his previous Ph. D. degree in Communication and Information Systems from Beijing University of Posts and Telecommunications, Beijing, China, in 2011.