| Derivative-free optimization methods J Larson, M Menickelly, SM Wild Acta Numerica 28, 287-404, 2019 | 454 | 2019 |
| Stochastic optimization using a trust-region method and random models R Chen, M Menickelly, K Scheinberg Mathematical Programming 169, 447-487, 2018 | 178 | 2018 |
| Convergence rate analysis of a stochastic trust-region method via supermartingales J Blanchet, C Cartis, M Menickelly, K Scheinberg INFORMS journal on optimization 1 (2), 92-119, 2019 | 116 | 2019 |
| Optimal decision trees for categorical data via integer programming O Günlük, J Kalagnanam, M Li, M Menickelly, K Scheinberg Journal of global optimization 81, 233-260, 2021 | 94 | 2021 |
| A survey of nonlinear robust optimization S Leyffer, M Menickelly, T Munson, C Vanaret, SM Wild INFOR: Information Systems and Operational Research 58 (2), 342-373, 2020 | 59 | 2020 |
| Derivative-free robust optimization by outer approximations M Menickelly, SM Wild Mathematical Programming 179 (1), 157-193, 2020 | 40 | 2020 |
| Convergence rate analysis of a stochastic trust region method for nonconvex optimization J Blanchet, C Cartis, M Menickelly, K Scheinberg arXiv preprint arXiv:1609.07428 5, 2016 | 40 | 2016 |
| Manifold Sampling for Nonconvex Optimization J Larson, M Menickelly, SM Wild SIAM Journal on Optimization 26 (4), 2540-2563, 2016 | 34 | 2016 |
| Tuning multigrid methods with robust optimization and local Fourier analysis J Brown, Y He, S MacLachlan, M Menickelly, SM Wild SIAM Journal on Scientific Computing 43 (1), A109-A138, 2021 | 23 | 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 |
| Optimal generalized decision trees via integer programming O Gunluk, J Kalagnanam, M Li, M Menickelly, K Scheinberg arXiv preprint arXiv:1612.03225, 2016 | 15 | 2016 |
| Latency considerations for stochastic optimizers in variational quantum algorithms M Menickelly, Y Ha, M Otten arXiv preprint arXiv:2201.13438, 2022 | 13 | 2022 |
| Learning sparsity-constrained gaussian graphical models in anomaly detection D Phan, M Menickelly, JR Kalagnanam, T Ide US Patent 11,216,743, 2022 | 11 | 2022 |
| Accurate, rapid identification of dislocation lines in coherent diffractive imaging via a min-max optimization formulation A Ulvestad, M Menickelly, SM Wild AIP Advances 8 (1), 2018 | 11 | 2018 |
| Manifold sampling for optimizing nonsmooth nonconvex compositions J Larson, M Menickelly, B Zhou SIAM Journal on Optimization 31 (4), 2638-2664, 2021 | 10 | 2021 |
| A Stochastic Quasi-Newton Method in the Absence of Common Random Numbers M Menickelly, SM Wild, M Xie arXiv preprint arXiv:2302.09128, 2023 | 5 | 2023 |
| A novel l0-constrained gaussian graphical model for anomaly localization DT Phan, T Idé, J Kalagnanam, M Menickelly, K Scheinberg 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 830-833, 2017 | 4 | 2017 |
| Structure-aware methods for expensive derivative-free nonsmooth composite optimization J Larson, M Menickelly Mathematical Programming Computation 16 (1), 1-36, 2024 | 3 | 2024 |
| On the Solution of ℓ0-Constrained Sparse Inverse Covariance Estimation Problems DT Phan, M Menickelly INFORMS Journal on Computing 33 (2), 531-550, 2021 | 2 | 2021 |
| Robust learning of trimmed estimators via manifold sampling M Menickelly, SM Wild arXiv preprint arXiv:1807.02736, 2018 | 2 | 2018 |
Stefan M. WildBerkeley Lab, Applied Mathematics & Computational Research Division在 lbl.gov 的电子邮件经过验证
