教授
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黄文

职称:教授

职务:教师

学历:博士

电子邮件:wen.huang@xmu.edu.cn

联系电话:0592-2580070

办 公 室:数理大楼604

教育经历:

2014,美国佛罗里达州立大学,应用与计算数学,博士

2007,中国科学技术大学,信息与计算科学,学士

工作经历:

2020.11-至今 厦门大学,数学科学学院,教授

2018.09-2020.11 厦门大学,数学科学学院,副教授

2016-2018,美国莱斯大学,计算与应用数学,法伊佛讲师博士后

2014-2016,比利时新鲁汶大学,ICTEAM,博士后研究员

2014,美国佛罗里达州立大学,科学计算,博士后研究员

2017-2018,厦门吉比特网络技术有限公司,数值策划


研究方向:

数值优化方法,主要包括流形上的优化算法设计分析、相关软件设计开发及其应用。应用包括图像处理,信号复原,机器学习,图论计算等。

授课情况:


2022春,厦门大学,数值优化(本),数值优化(研)

2021春,厦门大学,数值优化、线性代数

2020春,厦门大学,数值优化、微积分II

2019春,厦门大学,微积分II

2018春,美国莱斯大学,Pedagogy for RLAs、Introduction to Engineering Computations


短课程:

2021,11月,四川大学(国家天元数学西南中心),流形上的优化第一期,数值优化基础

2021,1月,南京师范大学,数学科学学院,流形上的优化

2020,12月,广西大学,数学与信息科学学院,流形上的优化

2019,11月,复旦大学,大数据学院,流形上的优化

2019,12月,武汉大学(国家天元数学中部中心),流形上的优化


主持项目:

国家自然科学基金(青年),流形上非光滑优化问题的算法研究及应用,主持


获奖:

2021年国家级高层次人才青年项目入选者


部分论文:

• Wen Huang*, Ke Wei*, "An Extension of Fast Iterative Shrinkage-thresholding to Riemannian Optimization for Sparse Principal Component Analysis", Numerical Linear Algebra with Applications, 29(1), e2409, 2022.

• Melissa Marchand, Kyle Gallivan, Wen Huang, Paul Van Dooren*, "Analysis of the Neighborhood Pattern Similarity Measure for the Role Extraction Problem", SIAM Journal on Mathematics of Data Science, 3:2, pp. 736-757, 2021.

• Wen Huang*, Ke Wei*. "Riemannian Proximal Gradient Methods", Mathematical Programming, doi:10.1007/s10107-021-01632-3, 2021.

• Wen Huang*, Paul Hand, Reinhard Heckel, Vladislav Voroninski. "A Provably Convergent Scheme for Compressive Sensing under Random Generative Priors", Journal of Fourier Analysis and Applications, 27, doi:10.1007/s00041-021-09830-5, 2021.

• Chafik Samir*, Wen Huang*. "Coordinate Descent Optimization for One-to-One Correspondence with Applications to Supervised Classification of 3D Shapes", Applied Mathematics and Computation, Applied Mathematics and Computation, 388, 125539, 2021.

• Xinru Yuan, Wen Huang*, P.-A. Absil, K. A. Gallivan. "Computing the matrix geometric mean: Riemannian vs Euclidean conditioning, implementation techniques, and a Riemannian BFGS method", Numerical Linear Algebra with Applications, 27:5, 1-23, 2020.

• Sean Martin, Andrew M. Raim, Wen Huang, Kofi P. Adragni*. "ManifoldOptim: An R Interface to the ROPTLIB Library for Riemannian Manifold Optimization", Journal of Statistical Software, 93:1, pp. 1-32, 2020.

• Reinhard Heckel*, Wen Huang, Paul Hand, Vladislav Voroninski. "Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior", Information and Inference: A Journal of the IMA, 2020.

• Wen Huang*, Paul Hand. "Blind Deconvolution by a Steepest Descent Algorithm on a Quotient Manifold", SIAM Journal on Imaging Sciences, 11:4, pp. 2757-2785, 2018.

• Wen Huang*, P.-A. Absil, Kyle Gallivan, Paul Hand. "ROPTLIB: an object-oriented C++ library for optimization on Riemannian manifolds", ACM Transactions on Mathematical Software, 44:4, pp. 43:1-43:21, 2018.

• Somayeh Hosseini, Wen Huang*, Roholla Yousefpour. "Line Search Algorithms for Locally Lipschitz Functions on Riemannian Manifolds", SIAM Journal on Optimization, 28(1), pp. 596-619, 2018.

• Wen Huang*, P.-A. Absil, Kyle Gallivan. "A Riemannian BFGS Method without Differentiated Retraction for Nonconvex Optimization Problems", SIAM Journal on Optimization, 28:1, pp. 470-495, 2018.

• Wen Huang*, Kyle A. Gallivan, Xiangxiong Zhang. "Solving PhaseLift by low-rank Riemannian optimization methods for complex semidefinite constraints", SIAM Journal on Scientific Computing, 39:5, pp. B840-B859, 2017.

• Jim Wilgenbusch*, Wen Huang, Kyle A. Gallivan. "Visualizing Phylogenetic Tree Landscapes", BMC Bioinformatics, 18:85, DOI:10.1186/s12859-017-1479-1, 2017.

• Wen Huang*, P.-A. Absil, Kyle Gallivan. "Intrinsic Representation of Tangent Vectors and Vector Transport on Matrix Manifolds", Numerische Mathematik, 136:2, p.523-543, DOI:10.1007/s00211-016-0848-4, October, 2017.

• Wen Huang*, Guifang Zhou, Melissa Merchand, Jeremy Ash, Paul Van Dooren, Jeremy M. Brown, Kyle A. Gallivan, Jim Wilgenbush. "TreeScaper: visualizing and extracting phylogenetic signal from sets of trees", Molecular Biology and Evolution, 33(12):3314-3316 DOI:10.1093/molbev/msw196, 2016.

• Guifang Zhou, Wen Huang, Kyle Gallivan, Paul Van Dooren, P.-A. Absil*. "A Riemannian rank-adaptive method for low-rank optimization", Neurocomputing, 192, 72-80, June 2016.

• Wen Huang*, Kyle A. Gallivan, Anuj Srivastava, Pierre-Antoine Absil. "Riemannian Optimization for Registration of Curves in Elastic Shape Analysis", Journal of Mathematical Imaging and Vision, 54(3), 320-343, 2016.

• Wen Huang*, Kyle A. Gallivan, Pierre-Antoine Absil. "A Broyden Class of Quasi-Newton Methods for Riemannian Optimization", SIAM Journal on Optimization, 25:3, pp. 1660-1685, 2015.

• Wen Huang, Pierre-Antoine Absil*, Kyle A. Gallivan. "A Riemannian symmetric rank-one trust-region method", Mathematical Programming Series A, 150:2, pp. 179-216, 2015.

学生培养:

硕士

• 郭媛媛(厦门大学)

• 黄亦徽(厦门大学)

• 黄振威(厦门大学)

• 刘方玉(厦门大学)

• 秦婉璐(厦门大学)

• Florentin Goyens(比利时新鲁汶大学,已毕业)

博士

• 陈建恒(厦门大学)

• 司武涛(厦门大学)

• 魏萌(美国佛罗里达州立大学)

• 张束光(美国佛罗里达州立大学)