報告題目：An exact penalty approach for optimization with nonnegative orthogonality constraints
報 告 人：姜波 副教授 南京師範大學
報告時間：2021年01月04日 上午 9:40-10:20
報告地點：騰訊會議 ID：320 247 940
會議密碼：9999
校内聯系人：李欣欣 xinxinli@jlu.edu.cn
報告摘要：Optimization with nonnegative orthogonality constraints has wide applications in machine learning and data sciences. It is NP-hard due to some combinatorial properties of the constraints. In this talk, we shall discuss an exact penalty approach for solving the considered problems. The penalty model can recover the solution if the penalty parameter is sufficiently large other than going to infinity. We establish the convergence of the penalty method under some weak and standard assumptions Extensive numerical results on the orthogonal nonnegative matrix factorization problem and the K-indicators model show the effectiveness of our proposed approaches.
報告人簡介：姜波，南京師範大學數學科學學院副教授、碩士生導師、中國運籌學會數學規劃分會的青年理事。入選第三屆中國科協“青年人才托舉工程”。主要研究方向為非線性優化算法與理論，特别是帶有正交約束的優化問題及其應用，已在Mathematical Programming, SIAM Journal on Optimization, SIAM Journal on Scientific Computing, IEEE Transactions on Image Processing等優化和信息類頂級期刊發表論文7篇。目前主持國家自然科學基金面上項目1項，曾主持國家自然科學基金青年項目1項、江蘇省青年基金項目1項。
