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学术沙龙系列报告(第四期):Total Least Squares Regression in Input Sparsity Time

2021-06-09    点击:[]

报告题目:Total Least Squares Regression in Input Sparsity Time

报告人:刁怀安 副教授

报告地点:格物楼3306

报告时间:2021610 19:30-20:30

报告摘要:The total least squares problem (TLS) is a generalization of the linear least squares problem and has many applications in linear system theory, computer vision, image reconstruction, system identification, speech and audio processing, modal and spectral analysis, etc. Algorithms for TLS have been studied extensively in the past decades. In this talk, I shall report our recent progresses on randomized algorithms for TLS.

报告人简介:刁怀安,副教授,博士生导师,2007年于香港城市大学获得数学博士学位,研究兴趣为数值代数、随机化算法、微分算子谱理论、波散射问题,在Journal de Mathématiques Pures et Appliquées

Calculus of Variations and Partial Differential EquationsCommunications in Partial Differential EquationsMathematics of ComputationSIAM Journal on Mathematics AnalysisInverse ProblemsNumer. Linear Algebra Appl. BITLinear Algebra and its Applications以及机器学习领域顶级会议NeurIPS 2019等国际主流期刊发表科研论文40余篇。出版学术专著一本。主持并完成国家自然科学基金青年基金、数学天元基金与教育部博士点基金新教师基金项目各一项。曾多次受邀访问国内外高校进行合作研究与学术交流。