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Daniel P. Robinson
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2020 – today
- 2024
- [j36]Frank E. Curtis
, Michael O'Neill
, Daniel P. Robinson
:
Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization. Math. Program. 205(1): 431-483 (2024) - 2023
- [j35]Guilherme França
, Daniel P. Robinson
, René Vidal
:
A Nonsmooth Dynamical Systems Perspective on Accelerated Extensions of ADMM. IEEE Trans. Autom. Control. 68(5): 2966-2978 (2023) - [j34]Yutong Dai, Tianyi Chen, Guanyi Wang, Daniel P. Robinson:
An Adaptive Half-Space Projection Method for Stochastic Optimization Problems with Group Sparse Regularization. Trans. Mach. Learn. Res. 2023 (2023) - [c19]Yutong Dai, Guanyi Wang, Frank E. Curtis, Daniel P. Robinson:
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization. AISTATS 2023: 5107-5133 - [i14]Frank E. Curtis, Vyacheslav Kungurtsev
, Daniel P. Robinson, Qi Wang:
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems. CoRR abs/2304.14907 (2023) - 2022
- [j33]Chong You
, Chi Li
, Daniel P. Robinson, René Vidal
:
Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 44(5): 2698-2711 (2022) - [j32]Frank E. Curtis
, Yutong Dai, Daniel P. Robinson
:
A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer. SIAM J. Optim. 32(2): 545-572 (2022) - 2021
- [j31]Frank E. Curtis
, Daniel P. Robinson
:
Regional complexity analysis of algorithms for nonconvex smooth optimization. Math. Program. 187(1): 579-615 (2021) - [j30]Frank E. Curtis
, Daniel P. Robinson
, Clément W. Royer
, Stephen J. Wright
:
Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization. SIAM J. Optim. 31(1): 518-544 (2021) - [j29]Albert S. Berahas
, Frank E. Curtis
, Daniel P. Robinson
, Baoyu Zhou
:
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. SIAM J. Optim. 31(2): 1352-1379 (2021) - [j28]Mustafa Devrim Kaba
, Mengnan Zhao
, René Vidal
, Daniel P. Robinson
, Enrique Mallada
:
What is the Largest Sparsity Pattern That Can Be Recovered by 1-Norm Minimization? IEEE Trans. Inf. Theory 67(5): 3060-3074 (2021) - [c18]Tianyu Ding, Zhihui Zhu, Manolis C. Tsakiris, René Vidal, Daniel P. Robinson:
Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms. AISTATS 2021: 2944-2952 - [c17]Tianyu Ding, Zhihui Zhu, René Vidal, Daniel P. Robinson:
Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach. ICML 2021: 2739-2748 - [c16]Mustafa Devrim Kaba, Chong You, Daniel P. Robinson, Enrique Mallada, René Vidal:
A Nullspace Property for Subspace-Preserving Recovery. ICML 2021: 5180-5188 - [i13]Yunchen Yang, Xinyue Zhang, Tianjiao Ding, Daniel P. Robinson, René Vidal, Manolis C. Tsakiris:
Boosting RANSAC via Dual Principal Component Pursuit. CoRR abs/2110.02918 (2021) - 2020
- [j27]Philip E. Gill, Vyacheslav Kungurtsev
, Daniel P. Robinson
:
A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization. SIAM J. Optim. 30(2): 1067-1093 (2020) - [c15]Tianjiao Ding, Yunchen Yang, Zhihui Zhu, Daniel P. Robinson, René Vidal, Laurent Kneip, Manolis C. Tsakiris:
Robust Homography Estimation via Dual Principal Component Pursuit. CVPR 2020: 6079-6088 - [c14]Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal:
Conformal Symplectic and Relativistic Optimization. NeurIPS 2020 - [i12]Chong You, Chun-Guang Li, Daniel P. Robinson, René Vidal:
Is an Affine Constraint Needed for Affine Subspace Clustering? CoRR abs/2005.03888 (2020) - [i11]Chong You, Chi Li, Daniel P. Robinson, René Vidal:
Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces. CoRR abs/2006.04246 (2020) - [i10]Albert S. Berahas, Frank E. Curtis
, Daniel P. Robinson, Baoyu Zhou:
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization. CoRR abs/2007.10525 (2020)
2010 – 2019
- 2019
- [j26]Frank E. Curtis
, Daniel P. Robinson:
Exploiting negative curvature in deterministic and stochastic optimization. Math. Program. 176(1-2): 69-94 (2019) - [c13]Chong You, Chun-Guang Li, Daniel P. Robinson, René Vidal:
Is an Affine Constraint Needed for Affine Subspace Clustering? ICCV 2019: 9914-9923 - [c12]Tianyu Ding, Zhihui Zhu, Tianjiao Ding, Yunchen Yang, Daniel P. Robinson, Manolis C. Tsakiris, René Vidal:
Noisy Dual Principal Component Pursuit. ICML 2019: 1617-1625 - [c11]Zhihui Zhu, Tianyu Ding, Daniel P. Robinson, Manolis C. Tsakiris, René Vidal:
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning. NeurIPS 2019: 9437-9447 - [i9]Guilherme França, Daniel P. Robinson, René Vidal:
Gradient Flows and Accelerated Proximal Splitting Methods. CoRR abs/1908.00865 (2019) - [i8]Mustafa Devrim Kaba, Mengnan Zhao, René Vidal, Daniel P. Robinson, Enrique Mallada:
Generalized Nullspace Property for Structurally Sparse Signals. CoRR abs/1910.05652 (2019) - [i7]Daniel P. Robinson, René Vidal, Chong You:
Basis Pursuit and Orthogonal Matching Pursuit for Subspace-preserving Recovery: Theoretical Analysis. CoRR abs/1912.13091 (2019) - 2018
- [j25]Hao Jiang, Daniel P. Robinson
, René Vidal, Chong You:
A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis. Comput. Optim. Appl. 70(2): 395-418 (2018) - [j24]Frank E. Curtis
, Zachary Lubberts
, Daniel P. Robinson
:
Concise complexity analyses for trust region methods. Optim. Lett. 12(8): 1713-1724 (2018) - [j23]Tianyi Chen
, Frank E. Curtis
, Daniel P. Robinson:
FaRSA for ℓ1-regularized convex optimization: local convergence and numerical experience. Optim. Methods Softw. 33(2): 396-415 (2018) - [j22]Frank E. Curtis
, Daniel P. Robinson
, Mohammadreza Samadi:
Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization. SIAM J. Optim. 28(2): 1533-1563 (2018) - [c10]Mengnan Zhao, M. Devrim Kaba, René Vidal, Daniel P. Robinson, Enrique Mallada
:
Sparse Recovery over Graph Incidence Matrices. CDC 2018: 364-371 - [c9]Chong You, Chi Li, Daniel P. Robinson, René Vidal:
A Scalable Exemplar-Based Subspace Clustering Algorithm for Class-Imbalanced Data. ECCV (9) 2018: 68-85 - [c8]Guilherme França, Daniel P. Robinson, René Vidal:
ADMM and Accelerated ADMM as Continuous Dynamical Systems. ICML 2018: 1554-1562 - [c7]Zhihui Zhu, Yifan Wang, Daniel P. Robinson, Daniel Q. Naiman, René Vidal, Manolis C. Tsakiris:
Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms. NeurIPS 2018: 2175-2185 - [i6]Mengnan Zhao, M. Devrim Kaba, René Vidal, Daniel P. Robinson, Enrique Mallada:
Sparse Recovery over Graph Incidence Matrices: Polynomial Time Guarantees and Location Dependent Performance. CoRR abs/1803.09631 (2018) - [i5]Zhihui Zhu, Yifan Wang, Daniel P. Robinson, Daniel Q. Naiman, René Vidal, Manolis C. Tsakiris:
Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms. CoRR abs/1812.09924 (2018) - 2017
- [j21]Nicholas I. M. Gould
, Daniel P. Robinson
:
A dual gradient-projection method for large-scale strictly convex quadratic problems. Comput. Optim. Appl. 67(1): 1-38 (2017) - [j20]Daniel P. Robinson, Rachael Tappenden:
A Flexible ADMM Algorithm for Big Data Applications. J. Sci. Comput. 71(1): 435-467 (2017) - [j19]Frank E. Curtis
, Nicholas I. M. Gould
, Daniel P. Robinson, Philippe L. Toint
:
An interior-point trust-funnel algorithm for nonlinear optimization. Math. Program. 161(1-2): 73-134 (2017) - [j18]Frank E. Curtis
, Daniel P. Robinson, Mohammadreza Samadi:
A trust region algorithm with a worst-case iteration complexity of O(ϵ -3/2) for nonconvex optimization. Math. Program. 162(1-2): 1-32 (2017) - [j17]Philip E. Gill, Vyacheslav Kungurtsev
, Daniel P. Robinson
:
A stabilized SQP method: superlinear convergence. Math. Program. 163(1-2): 369-410 (2017) - [j16]Tianyi Chen
, Frank E. Curtis
, Daniel P. Robinson:
A Reduced-Space Algorithm for Minimizing ℓ1-Regularized Convex Functions. SIAM J. Optim. 27(3): 1583-1610 (2017) - [c6]Chong You, Daniel P. Robinson, René Vidal:
Provable Self-Representation Based Outlier Detection in a Union of Subspaces. CVPR 2017: 4323-4332 - [i4]Chong You, Daniel P. Robinson, René Vidal:
Provable Self-Representation Based Outlier Detection in a Union of Subspaces. CoRR abs/1704.03925 (2017) - [i3]Frank E. Curtis, Daniel P. Robinson, Mohammadreza Samadi:
Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization. CoRR abs/1707.00337 (2017) - 2016
- [j15]Frank E. Curtis
, Nicholas I. M. Gould
, Hao Jiang, Daniel P. Robinson:
Adaptive augmented Lagrangian methods: algorithms and practical numerical experience. Optim. Methods Softw. 31(1): 157-186 (2016) - [c5]Chong You, Claire Donnat
, Daniel P. Robinson, René Vidal:
A divide-and-conquer framework for large-scale subspace clustering. ACSSC 2016: 1014-1018 - [c4]Chong You, Daniel P. Robinson, René Vidal:
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit. CVPR 2016: 3918-3927 - [c3]Chong You, Chun-Guang Li, Daniel P. Robinson, René Vidal:
Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering. CVPR 2016: 3928-3937 - [c2]Daniel P. Robinson, Suchi Saria:
Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive Models. IJCAI 2016: 1974-1982 - [i2]Daniel P. Robinson, Suchi Saria:
Trading-Off Cost of Deployment Versus Accuracy in Learning Predictive Models. CoRR abs/1604.05819 (2016) - [i1]Chong You, Chun-Guang Li, Daniel P. Robinson, René Vidal:
Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering. CoRR abs/1605.02633 (2016) - 2015
- [j14]Frank E. Curtis
, Zheng Han, Daniel P. Robinson:
A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization. Comput. Optim. Appl. 60(2): 311-341 (2015) - [j13]Daniel P. Robinson:
Primal-Dual Active-Set Methods for Large-Scale Optimization. J. Optim. Theory Appl. 166(1): 137-171 (2015) - [j12]Frank E. Curtis
, Hao Jiang, Daniel P. Robinson:
An adaptive augmented Lagrangian method for large-scale constrained optimization. Math. Program. 152(1-2): 201-245 (2015) - [j11]Nicholas I. M. Gould
, Yueling Loh, Daniel P. Robinson:
A Nonmonotone Filter SQP Method: Local Convergence and Numerical Results. SIAM J. Optim. 25(3): 1885-1911 (2015) - [j10]Hassan Mohy-ud-Din, Daniel P. Robinson:
A Solver for Nonconvex Bound-Constrained Quadratic Optimization. SIAM J. Optim. 25(4): 2385-2407 (2015) - [c1]Congyuan Yang, Daniel P. Robinson, René Vidal:
Sparse Subspace Clustering with Missing Entries. ICML 2015: 2463-2472 - 2014
- [j9]Nicholas I. M. Gould
, Yueling Loh, Daniel P. Robinson:
A Filter Method with Unified Step Computation for Nonlinear Optimization. SIAM J. Optim. 24(1): 175-209 (2014) - [j8]Frank E. Curtis
, Travis C. Johnson, Daniel P. Robinson, Andreas Wächter:
An Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Optimization. SIAM J. Optim. 24(3): 1041-1074 (2014) - 2013
- [j7]Nicholas I. M. Gould
, Dominique Orban, Daniel P. Robinson:
Trajectory-following methods for large-scale degenerate convex quadratic programming. Math. Program. Comput. 5(2): 113-142 (2013) - [j6]Daniel P. Robinson, Liming Feng, Jorge Nocedal, Jong-Shi Pang:
Subspace Accelerated Matrix Splitting Algorithms for Asymmetric and Symmetric Linear Complementarity Problems. SIAM J. Optim. 23(3): 1371-1397 (2013) - [j5]Philip E. Gill, Daniel P. Robinson:
A Globally Convergent Stabilized SQP Method. SIAM J. Optim. 23(4): 1983-2010 (2013) - 2012
- [j4]Philip E. Gill, Daniel P. Robinson:
A primal-dual augmented Lagrangian. Comput. Optim. Appl. 51(1): 1-25 (2012) - 2010
- [j3]Nicholas I. M. Gould
, Daniel P. Robinson, H. Sue Thorne:
On solving trust-region and other regularised subproblems in optimization. Math. Program. Comput. 2(1): 21-57 (2010) - [j2]Nicholas I. M. Gould
, Daniel P. Robinson:
A Second Derivative SQP Method: Global Convergence. SIAM J. Optim. 20(4): 2023-2048 (2010) - [j1]Nicholas I. M. Gould
, Daniel P. Robinson:
A Second Derivative SQP Method: Local Convergence and Practical Issues. SIAM J. Optim. 20(4): 2049-2079 (2010)
Coauthor Index
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last updated on 2024-08-02 19:15 CEST by the dblp team
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