Frank E. Curtis

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Daniel P. RobinsonAssociate Professor, Lehigh UniversityPatvirtintas el. paštas lehigh.edu
Jorge NocedalProfessor, Industrial Engineering, Northwestern UniversityPatvirtintas el. paštas NORTHWESTERN.EDU
Andreas WaechterNorthwestern UniversityPatvirtintas el. paštas northwestern.edu
Leon BottouFacebook AI ResearchPatvirtintas el. paštas bottou.org
James V BurkeUniversity of WashingtonPatvirtintas el. paštas uw.edu
Michael L. OvertonProfessor of Computer Science and Mathematics, New York UniversityPatvirtintas el. paštas cs.nyu.edu
Baoyu ZhouArizona State UniversityPatvirtintas el. paštas asu.edu
Mohammadreza SamadiPhD, Industrial and Systems Engineering, Lehigh UniversityPatvirtintas el. paštas lehigh.edu
Katya ScheinbergCornell UniversityPatvirtintas el. paštas cornell.edu
Richard ByrdUniversity of ColoradoPatvirtintas el. paštas colorado.edu
Hao WangShanghaiTech UniversityPatvirtintas el. paštas shanghaitech.edu.cn
Olaf SchenkProfessor, Institute of Computing, Universita della Svizzera italiana, SIAM FellowPatvirtintas el. paštas usi.ch
Nick GouldSTFC-Rutherford Appleton LaboratoryPatvirtintas el. paštas stfc.ac.uk
Xiaocun QuePhD, Lehigh UniversityPatvirtintas el. paštas alum.lehigh.edu
Albert S. BerahasAssistant Professor, University of MichiganPatvirtintas el. paštas umich.edu
Zheng HanLehigh UniversityPatvirtintas el. paštas lehigh.edu
Rui ShiPhD Candidate, Lehigh UniversityPatvirtintas el. paštas lehigh.edu
Jiashan WangGooglePatvirtintas el. paštas google.com
Elizabeth WongUC San DiegoPatvirtintas el. paštas ucsd.edu
Daniel K. MolzahnAssistant Professor at the Georgia Institute of Technology. Computational Engineer at ArgonnePatvirtintas el. paštas gatech.edu

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Frank E. Curtis

Lehigh University

Patvirtintas el. paštas lehigh.edu - Pagrindinis puslapis

Mathematical Optimization Scientific Computing Machine Learning


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Optimization methods for large-scale machine learning L Bottou, FE Curtis, J Nocedal SIAM Review 60 (2), 223-311, 2018	4074	2018
A trust region algorithm with a worst-case iteration complexity of for nonconvex optimization FE Curtis, DP Robinson, M Samadi Mathematical Programming, 1-32, 2016	198*	2016
A sequential quadratic programming algorithm for nonconvex, nonsmooth constrained optimization FE Curtis, ML Overton SIAM Journal on Optimization 22 (2), 474-500, 2012	163	2012
A BFGS-SQP Method for Nonsmooth, Nonconvex, Constrained Optimization and its Evaluation using Relative Minimization Profiles FE Curtis, T Mitchell, ML Overton Optimization Methods and Software 31 (1), 148-181, 2017	149	2017
An inexact SQP method for equality constrained optimization RH Byrd, FE Curtis, J Nocedal SIAM Journal on Optimization 19 (1), 351-369, 2008	117	2008
A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees FE Curtis, X Que Mathematical Programming Computation 7 (4), 399-428, 2015	114	2015
Gradient sampling methods for nonsmooth optimization JV Burke, FE Curtis, AS Lewis, ML Overton, LEA Simões Numerical nonsmooth optimization: State of the art algorithms, 201-225, 2020	113	2020
Infeasibility detection and SQP methods for nonlinear optimization RH Byrd, FE Curtis, J Nocedal SIAM Journal on Optimization 20 (5), 2281-2299, 2010	95	2010
An interior-point algorithm for large-scale nonlinear optimization with inexact step computations FE Curtis, O Schenk, A Wächter SIAM Journal on Scientific Computing 32 (6), 3447-3475, 2010	84	2010
An adaptive gradient sampling algorithm for nonsmooth optimization FE Curtis, X Que Optimization Methods and Software 28 (6), 1302-1324, 2013	81	2013
Optimization methods for supervised machine learning: From linear models to deep learning FE Curtis, K Scheinberg Leading developments from INFORMS communities, 89-114, 2017	80	2017
Exploiting negative curvature in deterministic and stochastic optimization FE Curtis, DP Robinson Mathematical Programming 176, 69-94, 2019	66	2019
Sequential quadratic optimization for nonlinear equality constrained stochastic optimization AS Berahas, FE Curtis, D Robinson, B Zhou SIAM Journal on Optimization 31 (2), 1352-1379, 2021	64	2021
An inexact Newton method for nonconvex equality constrained optimization RH Byrd, FE Curtis, J Nocedal Mathematical Programming 122 (2), 273-299, 2010	63	2010
An adaptive augmented Lagrangian method for large-scale constrained optimization FE Curtis, H Jiang, DP Robinson Mathematical Programming 152 (1-2), 201-245, 2015	58	2015
A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization FE Curtis, Z Han, DP Robinson Computational Optimization and Applications 60 (2), 311-341, 2015	54	2015
ADMM for multiaffine constrained optimization W Gao, D Goldfarb, FE Curtis Optimization Methods and Software 35 (2), 257-303, 2020	53	2020
A stochastic trust region algorithm based on careful step normalization FE Curtis, K Scheinberg, R Shi Informs Journal on Optimization 1 (3), 200-220, 2019	51	2019
A sequential algorithm for solving nonlinear optimization problems with chance constraints FE Curtis, A Wächter, VM Zavala SIAM Journal on Optimization 28 (1), 930–958, 2018	48	2018
A matrix-free algorithm for equality constrained optimization problems with rank-deficient Jacobians FE Curtis, J Nocedal, A Wächter SIAM Journal on Optimization 20 (3), 1224-1249, 2009	48	2009

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