Artikkelit, joihin on yleisen käytön mandaatti - Matt MenickellyLisätietoja
Saatavilla jossain: 15
Derivative-free optimization methods
J Larson, M Menickelly, SM Wild
Acta Numerica 28, 287-404, 2019
Mandaatit: US Department of Energy
Stochastic optimization using a trust-region method and random models
R Chen, M Menickelly, K Scheinberg
Mathematical Programming 169, 447-487, 2018
Mandaatit: US National Science Foundation, US Department of Defense
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
Mandaatit: US National Science Foundation, UK Engineering and Physical Sciences …
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
Mandaatit: US National Science Foundation, US Department of Energy
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
Mandaatit: US Department of Energy
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
Mandaatit: US National Science Foundation, UK Engineering and Physical Sciences …
Derivative-free robust optimization by outer approximations
M Menickelly, SM Wild
Mathematical Programming 179 (1), 157-193, 2020
Mandaatit: US Department of Energy
Manifold Sampling for Nonconvex Optimization
J Larson, M Menickelly, SM Wild
SIAM Journal on Optimization 26 (4), 2540-2563, 2016
Mandaatit: US Department of Energy
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
Mandaatit: US Department of Energy, Natural Sciences and Engineering Research Council …
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
Mandaatit: US Department of Energy
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
Mandaatit: US Department of Energy
Manifold sampling for optimizing nonsmooth nonconvex compositions
J Larson, M Menickelly, B Zhou
SIAM Journal on Optimization 31 (4), 2638-2664, 2021
Mandaatit: US Department of Energy
On the Solution of ℓ0-Constrained Sparse Inverse Covariance Estimation Problems
DT Phan, M Menickelly
INFORMS Journal on Computing 33 (2), 531-550, 2021
Mandaatit: US Department of Energy
Improving PyDDA's atmospheric wind retrievals using automatic differentiation and Augmented Lagrangian methods
R Jackson, R Gjini, SHK Narayanan, M Menickelly, P Hovland, ...
21st Python in Science Conference, 177-183, 2022
Mandaatit: US Department of Energy
TROPHY: Trust Region Optimization Using a Precision Hierarchy
RJ Clancy, M Menickelly, J Hückelheim, P Hovland, P Nalluri, R Gjini
22nd International Conference on Computational Science 13350, 445-459, 2022
Mandaatit: US Department of Energy
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