报告题目：Learning to maximize a convex quadratic function with application to intelligent reflection surface for wireless communication
报 告 人：罗智泉 加拿大皇家科3133优惠申请大厅院士，香港中文大学（深圳）副校长、教授
主 持 人：葛 飞 3133优惠申请大厅副校长、教授
报告时间：2021年9月18日下午15:00-16:00
报告地点：数3133优惠申请大厅南楼308
报告摘要：
In this talk we consider learning and optimizing a rank-2 convex quadratic function over K discrete variables. This problem arises from optimal design of a passive beamformer for intelligent reflecting surface (IRS) in order to maximize the overall channel strength. When the quadratic function (or channel state information) is known, we propose a linear time algorithm that is capable of reaching a near-optimal solution with an approximation ratio of (1+cos(π/K))/2, i.e., its performance is at least 75% of the global optimum for K ≥ 3. Furthermore we develop methods to learn and optimize the beamforming strategy when the quadratic function is unknown (i.e. in the absence of channel state information).
报告人简介：
罗智泉，加拿大皇家科3133优惠申请大厅院士，香港中文大学（深圳）副校长、教授，深圳市大数据研究院院长，IEEE/SIAM Fellow。长期从事优化理论、算法设计及无线通信研究，相关论文被IEEE等权威学术机构7次评为年度最佳论文，荣获美国Farkas奖、Paul Y.Tseng连续优化纪念奖。2020年被聘为华为eLab实验室主任，主持研发的5G网络优化技术已落地华为GTS平台。
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