An exact penalty approach for optimization with nonnegative orthogonality constraints
报告人：姜波（南京师范大学）
时间：2022年5月19日（周四）上午10：00-11：00
地点： 腾讯会议：564-864-164，
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. Extensive numerical results on the orthogonal nonnegative matrix factorization problem and the K-indicators model show the effectiveness of our proposed approaches.
报告人简介：姜波，南京师范大学数学科学学院副教授、硕士生导师。主要研究兴趣为：非线性优化算法与理论，特别是带有正交约束的优化问题及其应用。目前主持国家自然科学基金面上项目1项。曾主持中国科协青年托举工程项目1项、国家自然科学基金青年项目1项和江苏省青年基金项目1项。现为中国运筹学会数学规划分会的青年理事。在Math. Program., SIAM J. Optim, SIAM J. Sci. Comput., IEEE T. Image Process.等杂志发表数篇学术论文。
