Oliver Hinder
Oliver Hinder
Assistant Professor, Industrial Engineering Department, University of Pittsburgh
Zweryfikowany adres z pitt.edu - Strona główna
Cytowane przez
Cytowane przez
Accelerated methods for nonconvex optimization
Y Carmon, JC Duchi, O Hinder, A Sidford
SIAM Journal on Optimization 28 (2), 1751-1772, 2018
Lower bounds for finding stationary points I
Y Carmon, JC Duchi, O Hinder, A Sidford
Mathematical Programming 184 (1-2), 71-120, 2020
“Convex Until Proven Guilty”: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions
Y Carmon, JC Duchi, O Hinder, A Sidford
International conference on machine learning, 654-663, 2017
Near-optimal methods for minimizing star-convex functions and beyond
O Hinder, A Sidford, N Sohoni
Conference on learning theory, 1894-1938, 2020
Lower bounds for finding stationary points ii: first-order methods
Y Carmon, JC Duchi, O Hinder, A Sidford
Mathematical Programming 185 (1-2), 315-355, 2021
Practical large-scale linear programming using primal-dual hybrid gradient
D Applegate, M Díaz, O Hinder, H Lu, M Lubin, B O'Donoghue, W Schudy
Advances in Neural Information Processing Systems 34, 20243-20257, 2021
Faster first-order primal-dual methods for linear programming using restarts and sharpness
D Applegate, O Hinder, H Lu, M Lubin
Mathematical Programming 201 (1-2), 133-184, 2023
A one-phase interior point method for nonconvex optimization
O Hinder, Y Ye
arXiv preprint arXiv:1801.03072, 2018
A novel integer programing formulation for scheduling with family setup times on a single machine to minimize maximum lateness
O Hinder, A Mason
European Journal of Operations Research 262 (2), 411–423, 2017
On the behavior of Lagrange multipliers in convex and nonconvex infeasible interior point methods
G Haeser, O Hinder, Y Ye
Mathematical Programming 186 (1-2), 257-288, 2021
Worst-Case Iteration Bounds for Log Barrier Methods on Problems with Nonconvex Constraints
O Hinder, Y Ye
Mathematics of Operations Research, 2023
Making SGD Parameter-Free
Y Carmon, O Hinder
Conference on Learning Theory, 2360-2389, 2022
An efficient nonconvex reformulation of stagewise convex optimization problems
R Bunel, O Hinder, S Bhojanapalli, D Krishnamurthy
Advances in Neural Information Processing Systems 33, 2020
Cutting plane methods can be extended into nonconvex optimization
O Hinder
Conference On Learning Theory, 1451-1454, 2018
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule
M Ivgi, O Hinder, Y Carmon
International Conference on Machine Learning, 2023
Optimal Diagonal Preconditioning
Z Qu, W Gao, O Hinder, Y Ye, Z Zhou
arXiv preprint arXiv:2209.00809, 2022
The stable matching linear program and an approximate rural hospital theorem with couples
O Hinder
Proceedings of WINE 15, 433, 2015
A generic adaptive restart scheme with applications to saddle point algorithms
O Hinder, M Lubin
arXiv preprint arXiv:2006.08484, 2020
Conic descent and its application to memory-efficient optimization over positive semidefinite matrices
JC Duchi, O Hinder, A Naber, Y Ye
Advances in Neural Information Processing Systems 33, 8308-8317, 2020
Worst-case analysis of restarted primal-dual hybrid gradient on totally unimodular linear programs
O Hinder
arXiv preprint arXiv:2309.03988, 2023
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