| Exact and inexact subsampled Newton methods for optimization R Bollapragada, RH Byrd, J Nocedal IMA Journal of Numerical Analysis 39 (2), 545-578, 2019 | 192 | 2019 |
| A progressive batching L-BFGS method for machine learning R Bollapragada, J Nocedal, D Mudigere, HJ Shi, PTP Tang International Conference on Machine Learning, 620-629, 2018 | 165 | 2018 |
| Adaptive sampling strategies for stochastic optimization R Bollapragada, R Byrd, J Nocedal SIAM Journal on Optimization 28 (4), 3312-3343, 2018 | 129 | 2018 |
| An investigation of Newton-sketch and subsampled Newton methods AS Berahas, R Bollapragada, J Nocedal Optimization Methods and Software 35 (4), 661-680, 2020 | 120 | 2020 |
| Balancing communication and computation in distributed optimization AS Berahas, R Bollapragada, NS Keskar, E Wei IEEE Transactions on Automatic Control 64 (8), 3141-3155, 2018 | 118 | 2018 |
| Nonlinear acceleration of momentum and primal-dual algorithms R Bollapragada, D Scieur, A d'Aspremont arXiv preprint arXiv:1810.04539, 2018 | 31* | 2018 |
| On the fast convergence of minibatch heavy ball momentum R Bollapragada, T Chen, R Ward arXiv preprint arXiv:2206.07553, 2022 | 19 | 2022 |
| Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization R Bollapragada, SM Wild Mathematical Programming Computation 15 (2), 327-364, 2023 | 18* | 2023 |
| On the convergence of nested decentralized gradient methods with multiple consensus and gradient steps AS Berahas, R Bollapragada, E Wei IEEE Transactions on Signal Processing 69, 4192-4203, 2021 | 17 | 2021 |
| Optimization and supervised machine learning methods for fitting numerical physics models without derivatives R Bollapragada, M Menickelly, W Nazarewicz, J O’Neal, PG Reinhard, ... Journal of Physics G: Nuclear and Particle Physics 48 (2), 024001, 2020 | 15 | 2020 |
| Constrained and composite optimization via adaptive sampling methods Y Xie, R Bollapragada, R Byrd, J Nocedal IMA Journal of Numerical Analysis 44 (2), 680-709, 2024 | 9 | 2024 |
| An adaptive sampling sequential quadratic programming method for equality constrained stochastic optimization AS Berahas, R Bollapragada, B Zhou arXiv preprint arXiv:2206.00712, 2022 | 8 | 2022 |
| Scalable unidirectional Pareto optimality for multi-task learning with constraints S Gupta, G Singh, R Bollapragada, M Lease arXiv preprint arXiv:2110.15442, 2021 | 7 | 2021 |
| Nonlinear acceleration of primal-dual algorithms R Bollapragada, D Scieur, A d’Aspremont The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 7 | 2019 |
| Balancing communication and computation in gradient tracking algorithms for decentralized optimization AS Berahas, R Bollapragada, S Gupta arXiv preprint arXiv:2303.14289, 2023 | 5 | 2023 |
| An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints R Bollapragada, C Karamanli, B Keith, B Lazarov, S Petrides, J Wang Computers & Mathematics with Applications 149, 239-258, 2023 | 4 | 2023 |
| Adaptive Consensus: A network pruning approach for decentralized optimization SM Shah, AS Berahas, R Bollapragada arXiv preprint arXiv:2309.02626, 2023 | 1 | 2023 |
| A stochastic gradient tracking algorithm for decentralized optimization with inexact communication SM Shah, R Bollapragada arXiv preprint arXiv:2307.14942, 2023 | 1 | 2023 |
| Retrospective approximation for smooth stochastic optimization D Newton Purdue University, 2023 | 1 | 2023 |
| Modified Line Search Sequential Quadratic Methods for Equality-Constrained Optimization with Unified Global and Local Convergence Guarantees AS Berahas, R Bollapragada, J Shi arXiv preprint arXiv:2406.11144, 2024 | | 2024 |
Jorge NocedalProfessor, Industrial Engineering, Northwestern University在 NORTHWESTERN.EDU 的电子邮件经过验证
