YU Gaohang - 喻高航

Author：Date：2020-07-07Views：286



ProfessorGaohang YU is now thedirector of the Center of machine intelligence and data ****ysis atHangzhou Dianzi 


University. He is also the Coordinating Editor of theinternational journal of Statastics,Optimization & Information 

Computing(eISSN2310-5070, ISSN 2311-004X)



ResearchInterests: Adaptivecontrol and Optimization in pattern recognition and intelligentsystem/machine learning, 

mainly engaged in tensor data ****ysis,large-scale optimization and its applications in machine learning,visual 

computing and medical imaging.

MainGrants:

  • “Deeplearning / Statistical learning for tensor data: optimizationmethods and applications”, theNatural Science Foundation of Zhejiang Province, (No. LD19A010002),2019-2022, totalgrant:RMB450,000.PI

  • “Studyon computing eigenvalue of structured tensor with applications intensor-data ****ysis”,theNationalNaturalScienceFoundationofChina,(No.11661007),2017-2020,totalgrant:RMB400,000.PI

  • “SpectralTheoryofHighOrderTensor,VisualizationofTensorFieldsandItsApplicationsinMRI”,theNationalNaturalScienceFoundationofChina,(No.61262026),2013-2016,totalgrant:RMB470,000.PI

  • “FastAlgorithmsforImageProcessingandHighOrderDiffusionTensorMedicalImaging”,theNationalNaturalScienceFoundationofChina,(No.11001060),2011-2013,totalgrant:RMB180,000. PI

  • “HighOrderTensor:Analysis, Computationand Applications”,theNewCentury Excellent TalentProjectofMinistryofEducationofChina,(No.NCET 13-0738),January2014-December2016,totalgrant:RMB500,000. PI

  •  “AdaptiveGradientAlgorithmsforImageRestorationandLarge-scaleNonlinearEqua-tions”,theNationalNaturalScienceFoundationofChina,(No.10926029),January2010-December2010,totalgrant:RMB30,000. PI

  •  “Self-scalingConjugateGradientMethodsforNonlinearEquationsandLarge-scaleOptimization”,thePh.D.ProgramsFoundationofMinistryofEducationofChina(No.200805581022),2009-2011,totalgrant:RMB36,000. PI

ListofPublications:

  • G.Yu, Y. Song, Y. Xu,Z. Yu, Spectral projected gradient methods for generalized tensoreigenvalue complementarity problems. NumericalAlgorithms 80(4):1181-1201 (2019)

  • J.Huang, G. Zhou, G. Yu,Orthogonal tensor dictionary learning for accelerated dynamic MRI.Med. Biol.Engineering and Computing57(9): 1933-1946 (2019)

  • G.Shao,W Xue,G Yu,X Zheng, Improved SVRG for finite sum structure optimization withapplication to binary classification,Journal of Industrial & Management Optimization,141-148, 2019

  • S.Niu, Z. Bian, D. Zeng, G.Yu, J. Ma, J. Wang,Total image constrained diffusion tensor for spectral computedtomography reconstruction, AppliedMathematical Modelling,Volume 68, April 2019, Pages 487-508.

  • W.Hu, S. Li, W. Zheng, Y. Lu, G.Yu*,Robust sequential subspace clustering via ℓ1-norm temporal graph,Neurocomputing,383, 380-395, 2020.

  • S.Niu, Y. Zhang, Y. Zhong, G. Liu, S. Lu, X. Zhang, S. Hu, T.Wang, G.Yu, J. Wang,Iterative reconstruction for photon-counting CT using prior imageconstrained total generalized variation. Computersin biology and medicine,103: 167-182 (2018)

  • W.Hu, Z. Wang, S. Liu, X. Yang, G.Yu, J. Zhang, MotionCapture Data Completion via Truncated Nuclear Norm Regularization.IEEE SignalProcess. Lett. 25(2):258-262 (2018)

  • S.Niu, G.Yu,J.Ma, J.Wang, Nonlocal low-rank and sparse matrix decomposition forspectral CT reconstruction, InverseProblems，vol.34, pp. 024003, 2018

  • Y.Sun, G. Yu,On strong controllability for planar affine nonlinear systems,International Journal of Robust and Nonlinear Control,28 (2018)2668-2677.

  • W.Xue, W. Zhang, G. Yu*,Least absolute deviations learning of multiple tasks, Journalof Industrialand Management Optimization, April 2018, 14(2): 719-729.

  • W.Hu, L. Lu, C. Yin andG. Yu*,A**oothing Newton method for tensor eigenvalue complementarityproblems, PacificJournal of Optimization,13(2017)243-253.

  • G.Yu, Z. Yu, Y. Xu, Y.Song, Y. Zhou, An adaptive gradient method for computing generalizedtensor eigenpairs. Comp.Opt. and Appl. 65(3):781-797 (2016)

  • S.Niu, S.Zhang, J.Huang, Z. Bian, W. Chen, G.Yu*, Z. Liang, J. Ma,Low-dose cerebral perfusion computed tomography image restorationvia low-rank and total variation regularizations. Neurocomputing197: 143-160 (2016)

  • Y.Song, G. Yu,Properties of Solution Set of Tensor Complementarity Problem.J. Optimization Theory and Applications 170(1):85-96 (2016)

  • G.Yu, W. Xue, Y. Zhou,A nonmonotone adaptive projected gradient method for primal-dualtotal variation image restoration. SignalProcessing 103:242-249 (2014)

  • SNiu, Y Gao, Z Bian, J Huang, W Chen, GYu, Z Liang, J Ma,Sparse-view x-ray CT reconstruction via total generalized variationregularization, Physicsin Medicine & Biology59 (2014) 2997.

  • G.Li, L. Qi, G. Yu,Semi**oothness of the maximum eigenvalue function of a symmetrictensor and its application, LinearAlgebra and its Applications438 (2013) 813-833.

  • L.Qi, G. Yu*,Y. Xu, Nonnegative Diffusion Orientation Distribution Function.Journal ofMathematical Imaging and Vision45(2): 103-113 (2013)

  • G.Li, L. Qi, G. Yu,The Z-eigenvalues of a symmetric tensor and its application tospectral hypergraph theory. NumericalLin. Alg. with Applic.20(6): 1001-1029 (2013)

  • G.Yu, S. Niu, J. Ma,Multivariate spectral gradient projection method for nonlinearmonotone equations with convex constraints, Journalof Industrial & Management Optimization2013, 9(1): 117-129

  • YXu, G Yu,L Guan, Tri-cubic polynomial natural spline interpolation forscattered data,Calcolo, 49(2012) 127-148.

  • G.Yu, Nonmonotonespectral gradient-type methods for large-scale unconstrainedoptimization and nonlinear systems of equations, PacificJournal of Optimization,7 (2011), 387-404.

  • G.Yu, J. Huang, Y.Zhou, A descent spectral conjugate gradient method for impulse noiseremoval. Appl.Math. Lett. 23(5):555-560 (2010)

  • L.Qi, G. Yu,Ed X. Wu, Higher Order Positive Semidefinite Diffusion TensorImaging. SIAM J.Imaging Sciences3(3): 416-433 (2010)

  • G.Yu, L. Qi, Y. Sun, Y.Zhou, Impulse noise removal by a nonmonotone adaptive gradientmethod. SignalProcessing90(10): 2891-2897 (2010)

  • G.Yu, L. Qi, Y. Dai, OnNonmonotone Chambolle Gradient Projection Algorithms for TotalVariation Image Restoration.Journal of Mathematical Imaging and Vision35(2): 143-154 (2009)

  • L.Han, G. Yu,L. Guan, Multivariate spectral gradient method for unconstrainedoptimization. AppliedMathematics and Computation201(1-2): 621-630 (2008)

  • G.Yu, Lutai Guan, WufanChen, Spectral conjugate gradient methods with sufficient descentproperty for large-scale unconstrained optimization.Optimization Methods and Software 23(2):275-293 (2008)

  • G.Yu, Lutai Guan,Guoyin Li, Global convergence of modified Polak-Ribière-Polyakconjugate gradient methods with sufficient descent property, Journalof Industrial & Management Optimization2008, 4(3): 565-579

  • Z.Wei, G. Yu,G. Yuan, Z. Lian, The Superlinear Convergence of a ModifiedBFGS-Type Method for Unconstrained Optimization. Comp.Opt. and Appl.29(3): 315-332 (2004)

  • W.Hu, S. Li, J. Huang, T. Wang, G.Yu*,Computing the nearest polynomial to multiple given polynomials witha given zero via l2, q-norm minimization, TheoreticalComputer Science,809: 94-406, 2020.

  • W.Hu, Z. Tu, G.Yu*,The nearest polynomial to multiple given polynomials: a unifiedoptimization approach, AppliedMathematics Letters,to appear, 2020.