| Exact and inexact subsampled Newton methods for optimization R Bollapragada, RH Byrd, J Nocedal IMA Journal of Numerical Analysis 39 (2), 545-578, 2019 | 138 | 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 | 99 | 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 | 85 | 2020 |
| Adaptive sampling strategies for stochastic optimization R Bollapragada, R Byrd, J Nocedal SIAM Journal on Optimization 28 (4), 3312-3343, 2018 | 78 | 2018 |
| 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 | 63 | 2018 |
| Nonlinear acceleration of momentum and primal-dual algorithms R Bollapragada, D Scieur, A d'Aspremont arXiv preprint arXiv:1810.04539, 2018 | 21 | 2018 |
| Adaptive sampling quasi-Newton methods for derivative-free stochastic optimization R Bollapragada, SM Wild arXiv preprint arXiv:1910.13516, 2019 | 7 | 2019 |
| 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 | 6 | 2020 |
| 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 | 4 | 2021 |
| Nonlinear acceleration of primal-dual algorithms R Bollapragada, D Scieur, A d’Aspremont The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 4 | 2019 |
| Constrained and composite optimization via adaptive sampling methods Y Xie, R Bollapragada, R Byrd, J Nocedal arXiv preprint arXiv:2012.15411, 2020 | 3 | 2020 |
| Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization R Bollapragada, SM Wild arXiv preprint arXiv:2109.12213, 2021 | | 2021 |
| Retrospective Approximation for Smooth Stochastic Optimization D Newton, R Bollapragada, R Pasupathy, NK Yip arXiv preprint arXiv:2103.04392, 2021 | | 2021 |
| Optimization and Machine Learning Training Algorithms for Fitting Numerical Physics Models R Bollapragada, M Menickelly, W Nazarewicz, J O’Neal, PG Reinhard, ... arXiv preprint arXiv:2010.05668, 2020 | | 2020 |
| An Investigation of Newton-Sketch and Subsampled Newton Methods: Supplementary Materials AS Berahas, R Bollapragada, J Nocedal | | 2020 |
| Kinematical Design of a Precision Cylindrical Grinder with Volumetric Error Modelling and Sensitivity Analysis V Rajagopal, VR Bollapragada, RB Nimmagadda International Journal of Precision Technology 6, 193-215, 2016 | | 2016 |
Jorge NocedalProfessor, Industrial Engineering, Northwestern UniversityVerified email at NORTHWESTERN.EDU
