| Extragradient algorithms extended to equilibrium problems¶ Q Tran-Dinh, M Le Dung, VH Nguyen Optimization 57 (6), 749-776, 2008 | 433* | 2008 |
| Learning with tensors: a framework based on convex optimization and spectral regularization M Signoretto, Q Tran-Dinh, L De Lathauwer, JAK Suykens Machine Learning 94, 303-351, 2014 | 268 | 2014 |
| Combining convex–concave decompositions and linearization approaches for solving BMIs, with application to static output feedback Q Tran-Dinh, S Gumussoy, W Michiels, M Diehl IEEE Transactions on Automatic Control 57 (6), 1377-1390, 2011 | 239 | 2011 |
| WASP: Scalable Bayes via barycenters of subset posteriors S Srivastava, V Cevher, Q Tran-Dinh, D Dunson Artificial Intelligence and Statistics, 912-920, 2015 | 182 | 2015 |
| Local convergence of sequential convex programming for nonconvex optimization Q Tran-Dinh, M Diehl Recent Advances in Optimization and its Applications in Engineering: The …, 2010 | 159 | 2010 |
| Dual extragradient algorithms extended to equilibrium problems Q Tran-Dinh, PN Anh, LD Muu Journal of Global Optimization 52 (1), 139-159, 2012 | 151 | 2012 |
| ProxSARAH: An efficient algorithmic framework for stochastic composite nonconvex optimization NH Pham, LM Nguyen, DT Phan, Q Tran-Dinh Journal of Machine Learning Research 21 (110), 1-48, 2020 | 141 | 2020 |
| Regularization algorithms for solving monotone Ky Fan inequalities with application to a Nash-Cournot equilibrium model M Le Dung, Q Tran-Dinh Journal of optimization theory and applications 142 (1), 185-204, 2009 | 114 | 2009 |
| A hybrid stochastic optimization framework for composite nonconvex optimization Q Tran-Dinh, NH Pham, DT Phan, LM Nguyen Mathematical Programming, 1-67, 2021 | 108* | 2021 |
| Computational complexity of inexact gradient augmented Lagrangian methods: application to constrained MPC V Nedelcu, I Necoara, Q Tran-Dinh SIAM Journal on Control and Optimization 52 (5), 3109-3134, 2014 | 107 | 2014 |
| Composite self-concordant minimization. Q Tran-Dinh, A Kyrillidis, V Cevher J. Mach. Learn. Res. 16 (1), 371-416, 2015 | 102 | 2015 |
| A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization Q Tran-Dinh, O Fercoq, V Cevher SIAM Journal on Optimization, 28(1), 96–134 (2018), 2015 | 101 | 2015 |
| Time-optimal path following for robots with convex–concave constraints using sequential convex programming F Debrouwere, W Van Loock, G Pipeleers, Q Tran-Dinh, M Diehl, ... IEEE Transactions on Robotics 29 (6), 1485-1495, 2013 | 93 | 2013 |
| Generalized self-concordant functions: a recipe for newton-type methods T Sun, Q Tran-Dinh Mathematical Programming 178 (1), 145-213, 2019 | 73 | 2019 |
| Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization Q Tran-Dinh, C Savorgnan, M Diehl SIAM Journal on Optimization 22 (4), 1258-1284, 2012 | 73 | 2012 |
| A universal primal-dual convex optimization framework A Yurtsever, Q Tran-Dinh, V Cevher Advances in Neural Information Processing Systems 28, 2015 | 70 | 2015 |
| A unified convergence analysis for shuffling-type gradient methods LM Nguyen, Q Tran-Dinh, DT Phan, PH Nguyen, M Van Dijk Journal of Machine Learning Research 22 (207), 1-44, 2021 | 67 | 2021 |
| Convexity in source separation: Models, geometry, and algorithms MB McCoy, V Cevher, Q Tran-Dinh, A Asaei, L Baldassarre IEEE Signal Processing Magazine 31 (3), 87-95, 2014 | 57 | 2014 |
| Sequential convex programming methods for solving nonlinear optimization problems with DC constraints Q Tran-Dinh, M Diehl arXiv preprint arXiv:1107.5841, 2011 | 55 | 2011 |
| An inexact perturbed path-following method for Lagrangian decomposition in large-scale separable convex optimization Q Tran-Dinh, I Necoara, C Savorgnan, M Diehl SIAM Journal on Optimization 23 (1), 95-125, 2013 | 52 | 2013 |
Moritz DiehlProfessor of Systems, Control and Optimization at the University of Freiburg在 imtek.uni-freiburg.de 的电子邮件经过验证
