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Matt Menickelly
Matt Menickelly
Computational Mathematician, Argonne National Laboratory
Adresă de e-mail confirmată pe anl.gov - Pagina de pornire
Titlu
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Derivative-free optimization methods
J Larson, M Menickelly, SM Wild
Acta Numerica 28, 287-404, 2019
5262019
Stochastic optimization using a trust-region method and random models
R Chen, M Menickelly, K Scheinberg
Mathematical Programming 169, 447-487, 2018
1942018
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
1382019
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
1072021
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
642020
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
422016
Derivative-free robust optimization by outer approximations
M Menickelly, SM Wild
Mathematical Programming 179 (1), 157-193, 2020
402020
Manifold Sampling for Nonconvex Optimization
J Larson, M Menickelly, SM Wild
SIAM Journal on Optimization 26 (4), 2540-2563, 2016
342016
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
222021
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
152020
Optimal generalized decision trees via integer programming
O Gunluk, J Kalagnanam, M Li, M Menickelly, K Scheinberg
arXiv preprint arXiv:1612.03225, 2016
152016
Latency considerations for stochastic optimizers in variational quantum algorithms
M Menickelly, Y Ha, M Otten
Quantum 7, 949, 2023
132023
Learning sparsity-constrained gaussian graphical models in anomaly detection
D Phan, M Menickelly, JR Kalagnanam, T Ide
US Patent 11,216,743, 2022
132022
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
132018
Manifold sampling for optimizing nonsmooth nonconvex compositions
J Larson, M Menickelly, B Zhou
SIAM Journal on Optimization 31 (4), 2638-2664, 2021
122021
A stochastic quasi-Newton method in the absence of common random numbers
M Menickelly, SM Wild, M Xie
arXiv preprint arXiv:2302.09128, 2023
62023
Structure-aware methods for expensive derivative-free nonsmooth composite optimization
J Larson, M Menickelly
Mathematical Programming Computation 16 (1), 1-36, 2024
42024
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
42017
Avoiding geometry improvement in derivative-free model-based methods via randomization
M Menickelly
arXiv preprint arXiv:2305.17336, 2023
32023
Two-stage estimation and variance modeling for latency-constrained variational quantum algorithms
Y Ha, S Shashaani, M Menickelly
INFORMS Journal on Computing 37 (1), 125-145, 2025
22025
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