Jorge Nocedal
Jorge Nocedal
Professor, Industrial Engineering, Northwestern University
Verified email at NORTHWESTERN.EDU - Homepage
Cited by
Cited by
Numerical Optimization, Second Edition
J Nocedal, SJ Wright
Numerical optimization, 497-528, 2006
On the limited memory BFGS method for large scale optimization
DC Liu, J Nocedal
Mathematical programming 45 (1-3), 503-528, 1989
A limited memory algorithm for bound constrained optimization
RH Byrd, P Lu, J Nocedal, C Zhu
SIAM Journal on scientific computing 16 (5), 1190-1208, 1995
Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
C Zhu, RH Byrd, P Lu, J Nocedal
ACM Transactions on mathematical software (TOMS) 23 (4), 550-560, 1997
Updating quasi-Newton matrices with limited storage
J Nocedal
Mathematics of computation 35 (151), 773-782, 1980
Optimization methods for large-scale machine learning
L Bottou, FE Curtis, J Nocedal
SIAM review 60 (2), 223-311, 2018
On large-batch training for deep learning: Generalization gap and sharp minima
NS Keskar, D Mudigere, J Nocedal, M Smelyanskiy, PTP Tang
arXiv preprint arXiv:1609.04836, 2016
An interior point algorithm for large-scale nonlinear programming
RH Byrd, ME Hribar, J Nocedal
SIAM Journal on Optimization 9 (4), 877-900, 1999
A trust region method based on interior point techniques for nonlinear programming
RH Byrd, JC Gilbert, J Nocedal
Mathematical programming 89, 149-185, 2000
Global convergence properties of conjugate gradient methods for optimization
JC Gilbert, J Nocedal
SIAM Journal on optimization 2 (1), 21-42, 1992
Numerical optimization
S Wright, J Nocedal
Springer Science 35 (67-68), 7, 1999
Knitro: An Integrated Package for Nonlinear Optimization
RH Byrd, J Nocedal, RA Waltz
Large-scale nonlinear optimization, 35-59, 2006
An interior algorithm for nonlinear optimization that combines line search and trust region steps
RA Waltz, JL Morales, J Nocedal, D Orban
Mathematical programming 107 (3), 391-408, 2006
Representations of quasi-Newton matrices and their use in limited memory methods
RH Byrd, J Nocedal, RB Schnabel
Mathematical Programming 63 (1-3), 129-156, 1994
A tool for the analysis of quasi-Newton methods with application to unconstrained minimization
RH Byrd, J Nocedal
SIAM Journal on Numerical Analysis 26 (3), 727-739, 1989
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
Theory of algorithms for unconstrained optimization
J Nocedal
Acta numerica 1, 199-242, 1992
Remark on “Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”
JL Morales, J Nocedal
ACM Transactions on Mathematical Software (TOMS) 38 (1), 1-4, 2011
Global convergence of a cass of quasi-Newton methods on convex problems
RH Byrd, J Nocedal, YX Yuan
SIAM Journal on Numerical Analysis 24 (5), 1171-1190, 1987
Sample size selection in optimization methods for machine learning
RH Byrd, GM Chin, J Nocedal, Y Wu
Mathematical programming 134 (1), 127-155, 2012
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