Articoli con mandati relativi all'accesso pubblico - Jorge NocedalUlteriori informazioni
Non disponibile pubblicamente: 1
On the numerical performance of finite-difference-based methods for derivative-free optimization
HJM Shi, M Qiming Xuan, F Oztoprak, J Nocedal
Optimization Methods and Software 38 (2), 289-311, 2023
Mandati: US National Science Foundation, US Department of Defense
Disponibili pubblicamente: 18
Optimization methods for large-scale machine learning
L Bottou, FE Curtis, J Nocedal
SIAM review 60 (2), 223-311, 2018
Mandati: US National Science Foundation, US Department of Energy, US Department of …
A stochastic quasi-Newton method for large-scale optimization
RH Byrd, SL Hansen, J Nocedal, Y Singer
SIAM Journal on Optimization 26 (2), 1008-1031, 2016
Mandati: US National Science Foundation, US Department of Energy
Exact and inexact subsampled Newton methods for optimization
R Bollapragada, RH Byrd, J Nocedal
IMA Journal of Numerical Analysis 39 (2), 545-578, 2019
Mandati: US National Science Foundation, US Department of Energy, US Department of …
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
Mandati: US National Science Foundation, US Department of Energy
A multi-batch L-BFGS method for machine learning
AS Berahas, J Nocedal, M Takác
Advances in Neural Information Processing Systems 29, 2016
Mandati: US National Science Foundation, US Department of Energy
Adaptive sampling strategies for stochastic optimization
R Bollapragada, R Byrd, J Nocedal
SIAM Journal on Optimization 28 (4), 3312-3343, 2018
Mandati: US National Science Foundation, US Department of Energy, US Department of …
An investigation of Newton-sketch and subsampled Newton methods
AS Berahas, R Bollapragada, J Nocedal
Optimization Methods and Software 35 (4), 661-680, 2020
Mandati: US National Science Foundation, US Department of Energy, US Department of …
Derivative-free optimization of noisy functions via quasi-Newton methods
AS Berahas, RH Byrd, J Nocedal
SIAM Journal on Optimization 29 (2), 965-993, 2019
Mandati: US National Science Foundation, US Department of Energy, US Department of …
An inexact successive quadratic approximation method for L-1 regularized optimization
RH Byrd, J Nocedal, F Oztoprak
Mathematical Programming 157 (2), 375-396, 2016
Mandati: US National Science Foundation, US Department of Energy
A family of second-order methods for convex -regularized optimization
RH Byrd, GM Chin, J Nocedal, F Oztoprak
Mathematical Programming 159, 435-467, 2016
Mandati: US National Science Foundation, US Department of Energy, Natural Sciences …
A noise-tolerant quasi-Newton algorithm for unconstrained optimization
HJM Shi, Y Xie, R Byrd, J Nocedal
SIAM Journal on Optimization 32 (1), 29-55, 2022
Mandati: US National Science Foundation, US Department of Defense
A second-order method for convex 1-regularized optimization with active-set prediction
N Keskar, J Nocedal, F Öztoprak, A Waechter
Optimization Methods and Software 31 (3), 605-621, 2016
Mandati: US National Science Foundation, US Department of Energy
An algorithm for quadratic ℓ1-regularized optimization with a flexible active-set strategy
S Solntsev, J Nocedal, RH Byrd
Optimization Methods and Software 30 (6), 1213-1237, 2015
Mandati: US Department of Energy
Analysis of the BFGS method with errors
Y Xie, RH Byrd, J Nocedal
SIAM Journal on Optimization 30 (1), 182-209, 2020
Mandati: US National Science Foundation, US Department of Defense
Adaptive finite-difference interval estimation for noisy derivative-free optimization
HJM Shi, Y Xie, MQ Xuan, J Nocedal
SIAM Journal on Scientific Computing 44 (4), A2302-A2321, 2022
Mandati: US National Science Foundation, US Department of Defense
A trust region method for noisy unconstrained optimization
S Sun, J Nocedal
Mathematical Programming 202 (1), 445-472, 2023
Mandati: US National Science Foundation, US Department of Defense
Constrained optimization in the presence of noise
F Oztoprak, R Byrd, J Nocedal
SIAM Journal on Optimization 33 (3), 2118-2136, 2023
Mandati: US National Science Foundation, US Department of Defense
An Investigation of Newton-Sketch and Subsampled Newton Methods: Supplementary Materials
AS Berahas, R Bollapragada, J Nocedal
Mandati: US National Science Foundation, US Department of Defense
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