| Optimization methods for large-scale machine learning L Bottou, FE Curtis, J Nocedal SIAM Review 60 (2), 223-311, 2018 | 3724 | 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 | 194* | 2016 |
| A sequential quadratic programming algorithm for nonconvex, nonsmooth constrained optimization FE Curtis, ML Overton SIAM Journal on Optimization 22 (2), 474-500, 2012 | 153 | 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 | 141 | 2017 |
| An inexact SQP method for equality constrained optimization RH Byrd, FE Curtis, J Nocedal SIAM Journal on Optimization 19 (1), 351-369, 2008 | 114 | 2008 |
| 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 | 110 | 2020 |
| A quasi-Newton algorithm for nonconvex, nonsmooth optimization with global convergence guarantees FE Curtis, X Que Mathematical Programming Computation 7 (4), 399-428, 2015 | 103 | 2015 |
| Infeasibility detection and SQP methods for nonlinear optimization RH Byrd, FE Curtis, J Nocedal SIAM Journal on Optimization 20 (5), 2281-2299, 2010 | 92 | 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 | 78 | 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 | 71 | 2017 |
| An inexact Newton method for nonconvex equality constrained optimization RH Byrd, FE Curtis, J Nocedal Mathematical Programming 122 (2), 273-299, 2010 | 62 | 2010 |
| Exploiting negative curvature in deterministic and stochastic optimization FE Curtis, DP Robinson Mathematical Programming 176, 69-94, 2019 | 61 | 2019 |
| An adaptive augmented Lagrangian method for large-scale constrained optimization FE Curtis, H Jiang, DP Robinson Mathematical Programming 152 (1-2), 201-245, 2015 | 57 | 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 | 53 | 2015 |
| 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 | 52 | 2021 |
| ADMM for multiaffine constrained optimization W Gao, D Goldfarb, FE Curtis Optimization Methods and Software 35 (2), 257-303, 2020 | 51 | 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 | 50 | 2019 |
| 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 |
| 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 | 47 | 2018 |
Daniel P. RobinsonAssociate Professor, Lehigh University在 lehigh.edu 的电子邮件经过验证
