报告题目：Optimal (0,1)-Matrix Completion with Majorization Ordered Objectives

报告人：莫焱芳

邀请人：倪雨青

报告时间：2025年6月14日星期六18:30-19:30

报告地点：一本道 C121

报告简介：We propose and examine two inherently symmetric (0,1)-matrix completion problems with majorization ordered objectives, which provides a unique perspective for electric vehicle charging, portfolio optimization, and multi-agent cooperation. Our work elevates the seminal study by Gale and Ryser from feasibility to optimality in partial order programming (POP), referring to optimization with partially ordered objectives. Solving such integer POP (iPOP) problems is challenging because of the integer requirements and the fact that two objective values may not be comparable. Nevertheless, we prove the essential uniqueness of all optimal objective values and identify two particular ones for each iPOP problem. Furthermore, for every optimal objective value of each iPOP problem, we respectively develop a “peak-shaving” and a “valley-filling” algorithm to construct an associated optimal (0,1)-matrix via a series of sorting processes. We show that the resulting algorithms have linear time complexities and numerically verify their efficiency compared to the commonly used order-preserving method for POP.

报告人简介：莫焱芳博士，香港岭南大学（LU）数据科学一本道助理教授。本科毕业于浙江大学自动化专业（卓越工程师计划），在香港科技大学（HKUST）电子及计算机工程学系获得博士学位，师从丘立教授。访问UC Berkeley期间，受Pravin Varaiya教授的指导。曾在香港城市大学，与秦泗钊教授，陈名华教授及李立帅教授合作进行博士后研究。在加入LU之前，于HKUST担任VPRDO研究助理教授。其成果发表在TAC, Automatica, LAA, TASE, TII, and TR_C等顶级期刊上。以第一作者身份荣获INFOCOM 2021 Best Poster Award，并担任IEEE INFOCOM Poster 2022 TPC成员。