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Faculty of Mathematics

 

Career

  • 2017-date: Postdoc Research Associate, DAMTP, University of Cambridge
  • 2013-2016: PhD in Applied Mathematics, ENSICAEN, France
  • 2010-2013: Master in Applied Mathematics, Shanghai Jiao Tong University, China

Research

Jingwei Liang is a postdoc research associate of the Cambridge Image Analysis group of Dr Carola-Bibiane Schönlieb. With his work on convergence rate of first-order operator splitting methods he obtained his doctoral degree at ENSICAEN, France, under supervision of Prof. Jalal M. Fadili (ENSICAEN) and Prof. Gabriel Peyré (ENS, Paris). His current research focuses on non-smooth optimization, image processing and machine learning.

Selected Publications

Journal publications

  • JL, J. Fadili and G, Peyré, Local Linear Convergence Analysis of Primal-Dual Splitting Methods, to appear in Optimization, 2018. arXiv
  • JL, J. Fadili and G, Peyré, Activity Identification and Local Linear Convergence of Forward–Backward- type Methods, SIAM Journal on Optimization, 27 (1), 408-437, 2017. arXiv
  • JL, J. Fadili and G. Peyré, Local Convergence Properties of Douglas–Rachford and Alternating Direction Method of Multipliers, Journal of Optimization Theory and Applications, 72 (3), 874- 913, 2017. arXiv
  • JL, J. Fadili and G. Peyré, Convergence Rates with Inexact Non-expansive Operators, Mathematical Programming ser. A, 159 (1), 403-434, 2016. arXiv
  • JL, X. Zhang, Retinex by Higher Order Total Variation L1 Decomposition, Journal of Mathematical Imaging and Vision, 52(3):345-355, 2015. link

Conference proceedings

  • JL, J. Fadili and G. Peyré, A Multi-step Inertial Forward–Backward Splitting Method for Non-convex Optimization, Advances in Neural Information Processing Systems (NIPS), 2016. arXiv

  • JL, J. Fadili, G. Peyré and R. Luke, Activity Identification and Local Linear Convergence of Douglas– Rachford/ADMM under Partial Smoothness, 5th Int. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM), 2015. arXiv
  • JL, J. Fadili and G. Peyré, Locally Linear Convergence of Forward–Backward under Partial Smoothness, Advances in Neural Information Processing Systems (NIPS), 2014. arXiv

 

 

 

Publications

Convergence rates of Forward--Douglas--Rachford splitting method
C Molinari, J Liang, J Fadili
– Journal of Optimization Theory and Applications
(2019)
182,
606
Faster fista
J Liang, CB Schönlieb
– European Signal Processing Conference
(2018)
2018-September,
1
Local convergence properties of SAGA/Prox-SVRG and acceleration
C Poon, J Liang, CB Schönlieb
– 35th International Conference on Machine Learning, ICML 2018
(2018)
9,
6585
Supplementary material for "local convergence properties of SAGA/Prox-SVRG and acceleration"
C Poon, J Liang, CB Schönlieb
– 35th International Conference on Machine Learning, ICML 2018
(2018)
9,
6594
Local linear convergence analysis of Primal–Dual splitting methods
J Liang, J Fadili, G Peyré
– Optimization
(2018)
67,
1
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration.
C Poon, J Liang, C-B Schönlieb
– ICML
(2018)
80,
4121
Local Linear Convergence of Forward-Backward under Partial Smoothness
J Liang, J Fadili, G Peyré
Iteration-Complexity of a Generalized Forward Backward Splitting Algorithm
J Liang, JM Fadili, G Peyré
Improving "Fast Iterative Shrinkage-Thresholding Algorithm": Faster, Smarter and Greedier
J Liang, T Luo, C-B Schönlieb
Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration
C Poon, J Liang
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Research Group

Cambridge Image Analysis