Publications

1. Published Journal Articles (Refereed)

  • Albert S. Berahas, Frank E. Curtis, Daniel P. Robinson, and Baoyu Zhou. Sequential quadratic optimization for nonlinear equality constrained stochastic optimization. Accepted to siopt, 2021. [Bibtex] 
    @article{BerCRZ20,
AUTHOR = {Albert S. Berahas and Frank E. Curtis and Daniel P. Robinson and Baoyu Zhou},
TITLE = {Sequential Quadratic Optimization For Nonlinear Equality Constrained Stochastic Optimization},
JOURNAL = {accepted to SIOPT},
YEAR = 2021,
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis, Daniel P. Robinson, Clément W. Royer, and Stephen J. Wright. Trust-region Newton-CG with strong second-order complexity guarantees for nonconvex optimization. Siam j. optim., 31(1):518-544, 2021. [Bibtex] 
    @article{CurRRW21,
TITLE = {Trust-Region {N}ewton-{CG} with Strong Second-Order Complexity Guarantees for Nonconvex Optimization},
AUTHOR = {Frank E. Curtis and Daniel P. Robinson and Cl\'{e}ment W. Royer and Stephen J. Wright},
JOURNAL = SIOPT,
VOLUME = {31},
NUMBER = {1},
PAGES = {518-544},
YEAR = {2021},
PUBLISHER = {SIAM},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Mustafa D. Kaba, Mengnan Zhao, René Vidal, Daniel P. Robinson, and Enrique Mallada. What is the largest sparsity pattern that can be recovered by $1$-norm minimization. Accepted to ieee transactions on information theory, 2021. [Bibtex] 
    @article{KabZVRM21,
title={What is the Largest Sparsity Pattern that Can Be Recovered by $1$-Norm Minimization},
author={Mustafa D. Kaba and Mengnan Zhao and Ren{\'e} Vidal and Daniel P. Robinson and Enrique Mallada},
journal={Accepted to IEEE Transactions on Information Theory},
year={2021},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis, Yutong Dai, and Daniel P. Robinson. A subspace acceleration method for minimization involving a group sparsity-inducing regularizer. Siam j. optim., 2021. [Bibtex] 
    @article{CurDR21,
title={A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer},
author={Frank E. Curtis and Yutong Dai and Daniel P. Robinson},
journal=SIOPT,
year={2021},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Guilherme França, Daniel P. Robinson, and René Vidal. Gradient flows and proximal splitting methods: a unified view on accelerated and stochastic optimization. Physical review e, 103(5), 2021. [Bibtex] 
    @article{FraRV21,
AUTHOR = {Guilherme Fran\c{c}a and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization},
Journal = {Physical Review E},
Volume = {103},
Number = {5},
YEAR = {2021},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Philip E. Gill, Vyacheslav Kungurtsev, and Daniel P. Robinson. A shifted primal-dual penalty-barrier method for nonlinear optimization. Siam j. optim., 30(2):1067–1093, 2020. [Bibtex] 
    @article{GilKR20,
AUTHOR = {Philip E. Gill and Vyacheslav Kungurtsev and Daniel P. Robinson},
TITLE = {A Shifted Primal-Dual Penalty-Barrier Method for Nonlinear Optimization},
JOURNAL = SIOPT,
volume = {30},
number = {2},
pages = {1067--1093},
YEAR = {2020},
publisher = {SIAM},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Guilherme França, Jeremias Sulam, Daniel P. Robinson, and René Vidal. Conformal symplectic and relativistic optimization. Journal of statistical mechanics: theory and experiment, 2020. [Bibtex] 
    @article{FraRSV20,
AUTHOR = {Guilherme Fran\c{c}a and Jeremias Sulam and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {Conformal Symplectic and Relativistic Optimization},
Journal = {Journal of Statistical Mechanics: Theory and Experiment
},
YEAR = {2020},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis and Daniel P. Robinson. Regional complexity analysis of algorithms for nonconvex smooth optimization. Math. program., page 1–37, 2020. [Bibtex] 
    @article{CurR20,
author = {Frank E. Curtis and Daniel P. Robinson},
title = {Regional Complexity Analysis of Algorithms for Nonconvex Smooth Optimization},
journal = MP,
pages = {1--37},
year = {2020},
publisher={Springer},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis, Daniel P. Robinson, and Baoyu Zhou. A self-correcting variable-metric algorithm for nonsmooth optimization. Ima j. numer. anal., 40(2):1154–1187, 2020. [Bibtex] 
    @article{CurRZ20,
AUTHOR = {Frank E. Curtis and Daniel P. Robinson and Baoyu Zhou},
TITLE = {A Self-Correcting Variable-Metric Algorithm for Nonsmooth Optimization},
JOURNAL = IMAJNA,
VOLUME = {40},
number = {2},
pages = {1154--1187},
YEAR = {2020},
publisher = {Oxford University Press},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Chong You, Chi Li, Daniel P. Robinson, and René Vidal. Self-representation based unsupervised exemplar selection in a union of subspaces. Transactions on pattern analysis and machine intelligence (tpami), 2020. [Bibtex] 
    @article{YouLRV20,
title={Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces},
author={Chong You and Chi Li and Daniel P. Robinson and Ren\'{e} Vidal},
journal= TPAMI,
year={2020},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis and Daniel P. Robinson. Exploiting negative curvature in deterministic and stochastic optimization. Mathematical programming (special issue on nonconvex optimization for statistical learning), 176(1-2):69–94, 2019. [Bibtex] 
    @article{CurR18b,
AUTHOR = {Frank E. Curtis and Daniel P. Robinson},
TITLE = {Exploiting Negative Curvature in Deterministic and Stochastic Optimization},
JOURNAL = {Mathematical Programming (Special Issue on Nonconvex Optimization for Statistical Learning)},
VOLUME = {176},
NUMBER = {1-2},
PAGES = {69--94},
YEAR = {2019},
PUBLISHER = {Springer},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Frank E. Curtis, Zachary Lubberts, and Daniel P. Robinson. Concise complexity analyses for trust region methods. Optimization letters, 12(8):1713–1724, Dec 2018. [Bibtex] 
    @Article{CurLR18,
author="Frank E. Curtis and Zachary Lubberts and Daniel P. Robinson",
title="Concise complexity analyses for trust region methods",
journal="Optimization Letters",
year="2018",
month="Dec",
day="01",
volume="12",
number="8",
pages="1713--1724",
abstract="Concise complexity analyses are presented for simple trust region algorithms for solving unconstrained optimization problems. In contrast to a traditional trust region algorithm, the algorithms considered in this paper require certain control over the choice of trust region radius after any successful iteration. The analyses highlight the essential algorithm components required to obtain certain complexity bounds. In addition, a new update strategy for the trust region radius is proposed that offers a second-order complexity bound.",
issn="1862-4480",
doi="10.1007/s11590-018-1286-2",
url="https://doi.org/10.1007/s11590-018-1286-2",
papercite = {1. Published Journal Articles (Refereed)}
}
  • Tianyi Chen, Frank E. Curtis, and Daniel P. Robinson. FaRSA for $\ell_1$-regularized convex optimization: local convergence and numerical experience. Optimization methods and software, 33(2):396–415, 2018. [Bibtex] 
    @article{CheCR18,
title={{FaRSA} for {$\ell_1$}-regularized convex optimization: local convergence and numerical experience},
author={Tianyi Chen and Frank E. Curtis and Daniel P. Robinson},
journal=OMS,
volume={33},
number={2},
pages={396--415},
year={2018},
publisher={Taylor \& Francis},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Frank E. Curtis, Daniel P. Robinson, and Mohammadreza Samadi. An inexact regularized Newton framework with a worst-case iteration complexity of ${O(\varepsilon^{-3/2})}$ for nonconvex optimization. Ima j. numer. anal., 00:1–32, 2018. [Bibtex] 
    @article{CurRS18,
author = {Frank E. Curtis and Daniel P. Robinson and Mohammadreza Samadi},
title = {An Inexact Regularized {N}ewton Framework with a Worst-Case Iteration Complexity of ${O(\varepsilon^{-3/2})}$ for Nonconvex Optimization},
journal=IMAJNA,
pages = {1--32},
volume={00},
year = {2018},
doi = {10.1093/imanum/dry022},
url = {https://dx.doi.org/10.1093/imanum/dry022},
eprint = {http://oup.prod.sis.lan/imajna/advance-article-pdf/doi/10.1093/imanum/dry022/24797066/dry022.pdf},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis, Daniel P. Robinson, and Mohammadreza Samadi. Complexity analysis of a trust funnel algorithm for equality constrained optimization. Siam j. optim., 28(2):1533–1563, 2018. [Bibtex] 
    @article{CurRS18b,
title={Complexity analysis of a trust funnel algorithm for equality constrained optimization},
author={Frank E. Curtis and Daniel P. Robinson and Mohammadreza Samadi},
journal=SIOPT,
volume={28},
number={2},
pages={1533--1563},
year={2018},
publisher={SIAM},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Hao Jiang, Daniel P. Robinson, René Vidal, and Chong You. A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis. Computational optimization and applications, 70(2):395–418, 2018. [Bibtex] 
    @article{JiaRVY18,
title={A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis},
author={Hao Jiang and Daniel P. Robinson and Ren{\'e} Vidal and Chong You},
journal=COAP,
volume={70},
number={2},
pages={395--418},
year={2018},
publisher={Springer},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Tianyi Chen, Franke E. Curtis, and Daniel P. Robinson. A reduced-space algorithm for minimizing $\ell_1$-regularized convex functions. Siam j. optim., 27(3):1583–1610, 2017. [Bibtex] 
    @article{CheCR17,
title={A Reduced-Space Algorithm for Minimizing {$\ell_1$}-Regularized Convex Functions},
author={Tianyi Chen and Franke E. Curtis and Daniel P. Robinson},
journal=SIOPT,
volume={27},
number={3},
pages={1583--1610},
year={2017},
publisher={SIAM},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Philip E. Gill, Vyacheslav Kungurtsev, and Daniel P. Robinson. A stabilized SQP method: global convergence. Ima j. numer. anal., 37(1):407–443, 2017. [Bibtex] 
    @article{GilKR17,
title={A stabilized {SQP} method: global convergence},
author={Philip E. Gill and Vyacheslav Kungurtsev and Daniel P. Robinson},
journal=IMAJNA,
volume={37},
number={1},
pages={407--443},
year={2017},
publisher={Oxford University Press},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Daniel P. Robinson and Rachael Tappenden. A flexible ADMM algorithm for big data applications. Journal of scientific computing, page 1–33, 2016. [Bibtex] 
    @article{RobT16,
title={A flexible {ADMM} algorithm for big data applications},
author={Daniel P. Robinson and Rachael Tappenden},
journal= JSC,
pages={1--33},
year={2016},
publisher={Springer},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Nicholas I. M. Gould and Daniel P. Robinson. A dual gradient-projection method for large-scale strictly convex quadratic problems. Computational optimization and applications, page 1–38, 2016. [Bibtex] 
    @article{GouR16,
title={A dual gradient-projection method for large-scale strictly convex quadratic problems},
author={Nicholas I. M. Gould and Daniel P. Robinson},
journal=COAP,
pages={1--38},
year={2016},
publisher={Springer},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Philip E. Gill, Vyacheslav Kungurtsev, and Daniel P. Robinson. A stabilized SQP method: superlinear convergence. Math. program., page 1–42, 2016. [Bibtex] 
    @article{GilKR16c,
title={A stabilized {SQP} method: superlinear convergence},
author={Philip E. Gill and Vyacheslav Kungurtsev and Daniel P. Robinson},
journal=MP,
pages={1--42},
year={2016},
publisher={Springer},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] Frank E. Curtis, Nicholas I. M. Gould, Hao Jiang, and Daniel P. Robinson. Adaptive augmented Lagrangian methods: algorithms and practical numerical experience. Optimization methods and software, 31(1):157-186, 2016. [Bibtex] 
    @article{CurGJR16,
author = {Frank E. Curtis and Nicholas I. M. Gould and Hao Jiang and Daniel P. Robinson},
title = {Adaptive augmented {L}agrangian methods: algorithms and practical numerical experience},
journal = OMS,
volume = {31},
number = {1},
pages = {157-186},
year = {2016},
doi = {10.1080/10556788.2015.1071813},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis, Daniel P. Robinson, and Mohammadreza Samadi. A trust region algorithm with a worst-case iteration complexity of ${O(\varepsilon^{-3/2})}$ for nonconvex optimization. Math. program., page 1–32, 2016. [Bibtex] 
    @article{CurRS16,
title={A trust region algorithm with a worst-case iteration complexity of ${O(\varepsilon^{-3/2})}$ for nonconvex optimization},
author={Frank E. Curtis and Daniel P. Robinson and Mohammadreza Samadi},
journal=MP,
pages={1--32},
year={2016},
publisher={Springer},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Frank E. Curtis, Nicholas I. M. Gould, Daniel P. Robinson, and Philippe L. Toint. An interior-point trust-funnel algorithm for nonlinear optimization. Math. program., page 1–62, 2016. [Bibtex] 
    @article{CurGRT16,
author="Frank E. Curtis and Nicholas I. M. Gould and Daniel P. Robinson and Philippe L. Toint",
title="An interior-point trust-funnel algorithm for nonlinear optimization",
journal=MP,
year="2016",
pages="1--62",
abstract="We present an interior-point trust-funnel algorithm for solving large-scale nonlinear optimization problems. The method is based on an approach proposed by Gould and Toint (Math Prog 122(1):155--196, 2010) that focused on solving equality constrained problems. Our method is similar in that it achieves global convergence guarantees by combining a trust-region methodology with a funnel mechanism, but has the additional capability of being able to solve problems with both equality and inequality constraints. The prominent features of our algorithm are that (i) the subproblems that define each search direction may be solved with matrix-free methods so that derivative matrices need not be formed or factorized so long as matrix-vector products with them can be performed; (ii) the subproblems may be solved approximately in all iterations; (iii) in certain situations, the computed search directions represent inexact sequential quadratic optimization steps, which may be desirable for fast local convergence; (iv) criticality measures for feasibility and optimality aid in determining whether only a subset of computations need to be performed during a given iteration; and (v) no merit function or filter is needed to ensure global convergence.",
issn="1436-4646",
doi="10.1007/s10107-016-1003-9",
url="http://dx.doi.org/10.1007/s10107-016-1003-9",
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Hassan Mohy-Ud-Din and Daniel P. Robinson. A solver for nonconvex bound-constrained quadratic optimization. Siam j. optim., 25(4):2385-2407, 2015. [Bibtex] 
    @article{MoyR15,
author = {Hassan {Mohy-Ud-Din} and Daniel P. Robinson},
title = {A Solver for Nonconvex Bound-Constrained Quadratic Optimization},
journal = SIOPT,
volume = {25},
number = {4},
pages = {2385-2407},
year = {2015},
doi = {10.1137/15M1022100},
URL = {http://epubs.siam.org/doi/abs/10.1137/15M1022100},
eprint = {http://epubs.siam.org/doi/pdf/10.1137/15M1022100},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Daniel P. Robinson. Primal-dual active-set methods for large-scale optimization. Journal of optimization theory and applications, 166(1):137–171, 2015. [Bibtex] 
    @article{Rob15,
author = {Daniel P. Robinson},
title = {Primal-Dual Active-Set Methods for Large-Scale Optimization},
journal = JOTA,
issue_date = {July 2015},
volume = {166},
number = {1},
month = jul,
year = {2015},
issn = {0022-3239},
pages = {137--171},
numpages = {35},
url = {http://dx.doi.org/10.1007/s10957-015-0708-x},
doi = {10.1007/s10957-015-0708-x},
acmid = {2801566},
publisher = {Plenum Press},
address = {New York, NY, USA},
keywords = {49M37, 65K05, 65K10, 90C06, 90C26, 90C30, 90C55, Augmented Lagrangian, Constrained optimization, Large scale, Primal-dual},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Daniel P. Robinson. Comments on: critical Lagrange multipliers: what we currently know about them, how they spoil our lives, and what we can do about it. An official journal of the spanish society of statistics and operations research (top), pages 1-5, 2015. [Bibtex] 
    @article{Rob15b,
year={2015},
issn={1134-5764},
journal={An Official Journal of the Spanish Society of Statistics and Operations Research (TOP)},
doi={10.1007/s11750-015-0371-2},
title={Comments on: Critical {L}agrange multipliers: what we currently know about them, how they spoil our lives, and what we can do about it},
url={http://dx.doi.org/10.1007/s11750-015-0371-2},
publisher={Springer Berlin Heidelberg},
author={Daniel P. Robinson},
pages={1-5},
language={English},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Nicholas I. M. Gould, Yueling Loh, and Daniel P. Robinson. A nonmonotone filter SQP method: local convergence and numerical results. Siam j. optim., 25(3):1885-1911, 2015. [Bibtex] 
    @article{GouLR15,
author = {Nicholas I. M. Gould and Yueling Loh and Daniel P. Robinson},
title = {A Nonmonotone Filter {SQP} Method: Local Convergence and Numerical Results},
journal = SIOPT,
volume = {25},
number = {3},
pages = {1885-1911},
year = {2015},
doi = {10.1137/140996677},
URL = {http://dx.doi.org/10.1137/140996677},
eprint = {http://dx.doi.org/10.1137/140996677},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Nicholas I. M. Gould, Yueling Loh, and Daniel P. Robinson. A filter method with unified step computation for nonlinear optimization. Siam j. optim., 24(1):175–209, 2014. [Bibtex] 
    @article{GouLR14,
title={A filter method with unified step computation for nonlinear optimization},
author={Nicholas I. M. Gould and Yueling Loh and Daniel P. Robinson},
journal=SIOPT,
volume={24},
number={1},
pages={175--209},
year={2014},
publisher={SIAM},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis, Travis C. Johnson, Daniel P. Robinson, and Andreas Wächter. An inexact sequential quadratic optimization algorithm for nonlinear optimization. Siam j. optim., 24(3):1041–1074, 2014. [Bibtex] 
    @article{CurJRW14,
TITLE = {An Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Optimization},
AUTHOR = {Frank E. Curtis and Travis C. Johnson and Daniel P. Robinson and Andreas W\"{a}chter},
JOURNAL = SIOPT,
VOLUME = {24},
NUMBER = {3},
PAGES = {1041--1074},
YEAR = {2014},
PUBLISHER = {SIAM},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Frank E. Curtis, Zheng Han, and Daniel P. Robinson. A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization. Computational optimization and applications, pages 1-31, 2014. [Bibtex] 
    @article{CurHR14,
year={2014},
issn={0926-6003},
journal=COAP,
doi={10.1007/s10589-014-9681-9},
title={A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization},
url={http://dx.doi.org/10.1007/s10589-014-9681-9},
publisher={Springer US},
keywords={Convex quadratic optimization; Active-set methods; Large-scale optimization; Semi-smooth Newton methods; 49M05; 49M15; 65K05; 65K10; 65K15},
author={Frank E. Curtis and Zheng Han and Daniel P. Robinson},
pages={1-31},
language={English},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Frank E. Curtis, Hao Jiang, and Daniel P. Robinson. An adaptive augmented Lagrangian method for large-scale constrained optimization. Math. program., page 1–45, 2014. [Bibtex] 
    @article{CurJR14,
title={An adaptive augmented {L}agrangian method for large-scale constrained optimization},
author={Frank E. Curtis and Hao Jiang and Daniel P. Robinson},
journal=MP,
pages={1--45},
year={2014},
publisher={Springer},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [DOI] [Download PDF] Nicholas I. M. Gould, Dominique Orban, and Daniel P. Robinson. Trajectory-following methods for large-scale degenerate convex quadratic programming. Math. program. comp., 5(2):113–142, 2013. [Bibtex] 
    @article{GouOR13,
AUTHOR = {Nicholas I. M. Gould and Dominique Orban and Daniel P. Robinson},
TITLE = {Trajectory-following methods for large-scale degenerate convex quadratic programming},
ISSN = {1867-2949},
JOURNAL = MPC,
VOLUME = {5},
NUMBER = {2},
DOI = {10.1007/s12532-012-0050-3},
URL = {http://dx.doi.org/10.1007/s12532-012-0050-3},
PUBLISHER = {Springer-Verlag},
KEYWORDS = {Convex quadratic programming; Path-following methods; Degenerate problems;
Software; 65K05; 90C20; 90C25; 90C51},
PAGES = {113--142},
LANGUAGE = {English},
YEAR = {2013},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Philip E. Gill and Daniel P. Robinson. A globally convergent stabilized SQP method. Siam j. optim., 23(4):1983–2010, 2013. [Bibtex] 
    @article{GilR13,
AUTHOR = {Philip E. Gill and Daniel P. Robinson},
TITLE = {A Globally Convergent Stabilized {SQP} Method},
JOURNAL = SIOPT,
VOLUME = {23},
YEAR = {2013},
NUMBER = {4},
PAGES = {1983--2010},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Daniel P. Robinson, Liming Feng, Jorge M. Nocedal, and Jong-Shi Pang. Subspace accelerated matrix splitting algorithms for asymmetric and symmetric linear complementarity problems. Siam j. optim., 23(3):1371–1397, 2013. [Bibtex] 
    @article{RobFNP13,
title={Subspace Accelerated Matrix Splitting Algorithms for Asymmetric and Symmetric Linear Complementarity Problems},
author={Daniel P. Robinson and Liming Feng and Jorge M. Nocedal and Jong-Shi Pang},
journal=SIOPT,
volume={23},
number={3},
pages={1371--1397},
year={2013},
publisher={SIAM},
papercite = {1. Published Journal Articles (Refereed)}
}
  • [Download PDF] Philip E. Gill and Daniel P. Robinson. A primal-dual augmented Lagrangian. Computational optimization and applications, 51:1–25, 2012. [Bibtex] 
    @article{GilR12,
AUTHOR = {Philip E. Gill and Daniel P. Robinson},
TITLE = {A primal-dual augmented {Lagrangian}},
JOURNAL = COAP,
PUBLISHER = {Springer Netherlands},
VOLUME = {51},
ISSN = {0926-6003},
KEYWORD = {Computer Science},
PAGES = {1--25},
URL = {http://dx.doi.org/10.1007/s10589-010-9339-1},
YEAR = {2012},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Nicholas I. M. Gould and Daniel P. Robinson. A second derivative SQP method with a “trust-region-free” predictor step. Ima j. numer. anal., 32(2):580–601, 2011. [Bibtex] 
    @article{GouR10c,
AUTHOR = {Nicholas I. M. Gould and Daniel P. Robinson},
TITLE = {A Second Derivative {SQP} Method with a "trust-region-free" predictor step},
JOURNAL = IMAJNA,
VOLUME = {32},
NUMBER = {2},
YEAR = {2011},
PAGES = {580--601},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Nicholas I. M. Gould and Daniel P. Robinson. A second derivative SQP method: global convergence. Siam j. optim., 20(4):2023–2048, 2010. [Bibtex] 
    @article{GouR10a,
AUTHOR = {Nicholas I. M. Gould and Daniel P. Robinson},
TITLE = {A Second Derivative {SQP} Method: Global Convergence},
JOURNAL = SIOPT,
VOLUME = {20},
YEAR = {2010},
NUMBER = {4},
PAGES = {2023--2048},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Nicholas I. M. Gould and Daniel P. Robinson. A second derivative SQP method: local convergence and practical issues. Siam j. optim., 20(4):2049–2079, 2010. [Bibtex] 
    @article{GouR10b,
AUTHOR = {Nicholas I. M. Gould and Daniel P. Robinson},
TITLE = {A Second Derivative {SQP} Method: Local Convergence and Practical Issues},
JOURNAL = SIOPT,
VOLUME = {20},
YEAR = {2010},
NUMBER = {4},
PAGES = {2049--2079},
papercite = {1. Published Journal Articles (Refereed)}
}
  • Nicholas I. M. Gould, Daniel P. Robinson, and Sue H. Thorne. On solving trust-region and other regularised subproblems in optimization. Math. program. comp., 2(1):21–57, 2010. [Bibtex] 
    @article{GouRT10,
title={On solving trust-region and other regularised subproblems in optimization},
author={Nicholas I. M. Gould and Daniel P. Robinson and Sue H. Thorne},
journal=MPC,
volume={2},
number={1},
pages={21--57},
year={2010},
publisher={Springer},
papercite = {1. Published Journal Articles (Refereed)}
}

2. Published Conference and Proceedings Articles (Refereed)

  • Tianyu Ding, Zhihui Zhu, Manolis Tsakiris, René Vidal, and Daniel P. Robinson. Dual principal component pursuit for learning a union of hyperplanes: theory and algorithms. In International conference on artificial intelligence and statistics (aistats), 2021. [Bibtex] 
    @conference{DinZTVR21,
AUTHOR = {Tianyu Ding and Zhihui Zhu and Manolis Tsakiris and Ren\'{e} Vidal and Daniel P. Robinson},
TITLE = {Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms},
BOOKTITLE = AISTATS,
YEAR = {2021},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Tianyu Ding, Zhihui Zhu, René Vidal, and Daniel P. Robinson. Dual principal component pursuit for robust subspace learning: theory and algorithms for a holistic approach. In International conference on machine learning (icml), 2021. [Bibtex] 
    @conference{DinZVR21,
AUTHOR = {Tianyu Ding and Zhihui Zhu and Ren\'{e} Vidal and Daniel P. Robinson},
TITLE = {Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach},
BOOKTITLE = ICML,
YEAR = {2021},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Mustafa D. Kaba, Chong You, Daniel P. Robinson, Enrique Mallada, and René Vidal. A nullspace property for subspace-preserving recovery. International conference on machine learning (icml), 2021. [Bibtex] 
    @article{KabYRMV21,
title={A Nullspace Property for Subspace-Preserving Recovery},
author={Mustafa D. Kaba and Chong You and Daniel P. Robinson and Enrique Mallada and Ren{\'e} Vidal},
journal= ICML,
year={2021},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Tianjiao Ding, Yuchen Yang, Zhihui Zhu, Daniel P. Robinson, René Vidal, Laurent Kneip, and Manolis Tsakiris. Homography estimation via dual principal component pursuit. In Ieee conference on computer vision and pattern recognition (cvpr), 2020. [Bibtex] 
    @conference{DinYZRVKM20,
AUTHOR = {Tianjiao Ding and Yuchen Yang and Zhihui Zhu and Daniel P. Robinson and Ren\'{e} Vidal and Laurent Kneip and Manolis Tsakiris},
TITLE = {Homography Estimation via Dual Principal Component Pursuit},
BOOKTITLE = CVPR,
YEAR = {2020},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Guilherme França, Daniel P. Robinson, Jeremias Sulam, and René Vidal. Conformal symplectic and relativistic optimization. Conference on neural information processing systems (neurips), 2020. [Bibtex] 
    @article{FraRSV19,
AUTHOR = {Guilherme Fran\c{c}a and Daniel P. Robinson and Jeremias Sulam and Ren\'{e} Vidal},
TITLE = {Conformal Symplectic and Relativistic Optimization},
Journal = {Conference on Neural Information Processing Systems (NeurIPS)},
YEAR = {2020},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Tianyu Ding, Zhihui Zhu, Daniel P. Robinson, René Vidal, and Manolis Tsakiris. Noisy dual principal component pursuit. In International conference on machine learning (icml), 2019. [Bibtex] 
    @conference{DinZRVT19,
AUTHOR = {Tianyu Ding and Zhihui Zhu and Daniel P. Robinson and Ren\'{e} Vidal and Manolis Tsakiris},
TITLE = {Noisy Dual Principal Component Pursuit},
BOOKTITLE = ICML,
YEAR = {2019},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Chong You, Chun-Guang Li, Daniel P. Robinson, and René Vidal. Is an affine constraint needed for affine subspace clustering?. In International conference on computer vision (iccv), 2019. [Bibtex] 
    @conference{YouLRV19,
AUTHOR = {Chong You and Chun-Guang Li and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {Is an Affine Constraint Needed for Affine Subspace Clustering?},
BOOKTITLE=ICCV,
YEAR = {2019},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Zhihui Zhu, Daniel P. Robinson, René Vidal, and Manolis Tsakiris. A linearly convergent method for non-smooth non-convex optimization on the grassmannian with applications to robust subspace and dictionary learning. In Conference on neural information processing systems (neurips), 2019. [Bibtex] 
    @conference{ZhuRVT19,
AUTHOR = {Zhihui Zhu and Daniel P. Robinson and Ren\'{e} Vidal and Manolis Tsakiris},
TITLE = {A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning},
BOOKTITLE = {Conference on Neural Information Processing Systems (NeurIPS)},
YEAR = {2019},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Guilherme França, Daniel P. Robinson, and René Vidal. ADMM and accelerated ADMM as continuous dynamical system. In International conference on machine learning (icml), 2018. [Bibtex] 
    @conference{FraRV18,
AUTHOR = {Guilherme Fran\c{c}a and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {{ADMM} and Accelerated {ADMM} as Continuous Dynamical System},
BOOKTITLE = {International Conference on Machine Learning (ICML)},
YEAR = {2018},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Chong You, Daniel P. Robinson, and René Vidal. A scalable exemplar-based subspace clustering algorithm for class-imbalanced data. In European conference on computer vision (eccv), 2018. [Bibtex] 
    @conference{YouRV17b,
AUTHOR = {Chong You and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {A Scalable Exemplar-based Subspace Clustering Algorithm for Class-Imbalanced Data},
BOOKTITLE = ECCV,
YEAR = {2018},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Mengnan Zhao, Mustafa Devrim Kaba, René Vidal, Daniel P. Robinson, and Enrique Mallada. Sparse recovery over graph incidence matrices: polynomial time guarantees and location dependent performance. In Ieee conference on decision and control, 2018. [Bibtex] 
    @conference{ZhaKVRM18,
AUTHOR = {Mengnan Zhao and Mustafa Devrim Kaba and Ren\'{e} Vidal and Daniel P. Robinson and Enrique Mallada},
TITLE = {Sparse Recovery over Graph Incidence Matrices: Polynomial Time Guarantees and Location Dependent Performance},
BOOKTITLE = {IEEE Conference on Decision and Control},
YEAR = {2018},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Zhihui Zhu, Yifan Wang, Daniel P. Robinson, Daniel Q. Naiman, René Vidal, and Manolis Tsakiris. Dual principal component pursuit: improved analysis and efficient algorithms. In Neural information processing systems (neurips), 2018. [Bibtex] 
    @conference{ZhuWRNVT18,
AUTHOR = {Zhihui Zhu and Yifan Wang and Daniel P. Robinson and Daniel Q. Naiman and Ren\'{e} Vidal and Manolis Tsakiris},
TITLE = {Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms},
BOOKTITLE = NIPS,
YEAR = {2018},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Chong You, Daniel P. Robinson, and René Vidal. Provable self-representation based outlier detection in a union of subspaces. In Ieee conference on computer vision and pattern recognition (cvpr), 2017. [Bibtex] 
    @conference{YouRV17,
AUTHOR = {Chong You and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {Provable Self-Representation Based Outlier Detection in a Union of Subspaces},
BOOKTITLE = CVPR,
YEAR = {2017},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Daniel P. Robinson and Suchi Saria. Incorporating user preferences in predictive models via structured regularizers. In International joint conferences on artificial intelligence (ijcai), 2016. [Bibtex] 
    @conference{RobS16a,
AUTHOR = {Daniel P. Robinson and Suchi Saria},
TITLE = {Incorporating User Preferences in Predictive Models Via Structured Regularizers},
BOOKTITLE = IJCAI,
YEAR = {2016},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Chong You, Chun-Guang Li, Daniel P. Robinson, and René Vidal. Oracle based active set algorithm for scalable elastic net subspace clustering. In Ieee conference on computer vision and pattern recognition (cvpr), 2016. [Bibtex] 
    @conference{YouLRV16,
AUTHOR = {Chong You and Chun-Guang Li and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering},
BOOKTITLE=CVPR,
YEAR = {2016},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Chong You, Daniel P. Robinson, and René Vidal. Sparse subspace clustering by orthogonal matching pursuit. In Ieee conference on computer vision and pattern recognition (cvpr), 2016. [Bibtex] 
    @conference{YouRV16,
AUTHOR = {Chong You and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {Sparse Subspace Clustering by Orthogonal Matching Pursuit},
BOOKTITLE=CVPR,
YEAR = {2016},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}
  • Congyuan Yang, Daniel P. Robinson, and René Vidal. Sparse subspace clustering with missing entries. In International conference on machine learning (icml), page 2463–2472, 2015. [Bibtex] 
    @conference{YanRV15,
AUTHOR = {Congyuan Yang and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {Sparse Subspace Clustering with Missing Entries},
PAGES={2463--2472},
BOOKTITLE = ICML,
YEAR = {2015},
papercite = {2. Published Conference and Proceedings Articles (Refereed)}
}

3. Invited Publications (Not Refereed)

  • Chong You, Claire Donnat, Daniel P. Robinson, and René Vidal. A divide-and-conquer framework for large-scale subspace clustering. In Asilomar conference on signals, systems, and computers, 2016. [Bibtex] 
    @conference{YouDRV16,
AUTHOR={Chong You and Claire Donnat and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE={A Divide-and-Conquer Framework for Large-Scale Subspace Clustering},
BOOKTITLE={Asilomar Conference on Signals, Systems, and Computers},
YEAR={2016},
papercite = {3. Invited Publications (Not Refereed)}
}

4. Published Books and Book Chapter

  • Suchi Saria and Daniel P. Robinson. Incorporating end-user preferences in predictive models. In David A. Clifton, editor, Machine learning for healthcare technologies, chapter 8. The institution of engineering and technologies, 2016. [Bibtex] 
    @incollection{RobS16b,
AUTHOR = {Suchi Saria and Daniel P. Robinson},
TITLE = {Incorporating End-User Preferences in Predictive Models},
CHAPTER={8},
BOOKTITLE = {Machine Learning for Healthcare Technologies},
EDITOR={David A. Clifton},
PUBLISHER={The Institution of Engineering and Technologies},
YEAR = {2016},
papercite = {4. Published Books and Book Chapter}
}

5. Articles Under Review

  • Daniel P. Robinson, René Vidal, and Chong You. Basis pursuit and orthogonal matching pursuit for subspace-preserving recovery: theoretical analysis. Journal of machine learning research, 2020. [Bibtex] 
    @article{YouRV20,
AUTHOR = {Daniel P. Robinson and Ren\'{e} Vidal and Chong You},
TITLE = {Basis Pursuit and Orthogonal Matching Pursuit for Subspace-preserving Recovery: Theoretical Analysis},
JOURNAL = JMLR,
YEAR = {2020},
papercite = {5. Articles Under Review}
}

6. Working Papers

  • Miju Ahn, Jong-Shi Pang, and Daniel P. Robinson. A mixed-integer nonlinear programming approach for cost-sensitive prediction. Working paper, 2020. [Bibtex] 
    @article{AhnPR20,
AUTHOR = {Miju Ahn and Jong-Shi Pang and Daniel P. Robinson},
TITLE = {A Mixed-Integer Nonlinear Programming Approach For Cost-Sensitive Prediction},
JOURNAL = {Working paper},
YEAR = 2020,
papercite = {6. Working Papers}
}
  • Amitabh Basu, Tianyu Ding, and Daniel P. Robinson. A reduced-space accelerated optimization algorithm for low-rank optimization. In preparation for submission to siam journal on optimization, 2020. [Bibtex] 
    @article{BasDR17,
AUTHOR = {Amitabh Basu and Tianyu Ding and Daniel P. Robinson},
TITLE = {A reduced-space accelerated optimization algorithm for low-rank optimization},
JOURNAL = {In preparation for submission to SIAM Journal on Optimization},
YEAR = 2020,
papercite = {6. Working Papers}
}
  • Guilherme França, Daniel P. Robinson, and René Vidal. A nonsmooth dynamical systems perspective on accelerated extensions of admm. Siam j. control optim., 2020. [Bibtex] 
    @article{FraRV20b,
AUTHOR = {Guilherme Fran\c{c}a and Daniel P. Robinson and Ren\'{e} Vidal},
TITLE = {A Nonsmooth Dynamical Systems Perspective on Accelerated Extensions of ADMM},
Journal = SICON,
YEAR = {2020},
papercite = {6. Working Papers}
}
  • Daniel P. Robinson. A primal-dual matrix-free algorithm for strictly convex quadratic problems. In preparation for submission to mathematical programming computation, 2020. [Bibtex] 
    @article{Rob15c,
author={Daniel P. Robinson},
title={A Primal-Dual Matrix-Free Algorithm for Strictly Convex Quadratic Problems},
journal={In preparation for submission to Mathematical Programming Computation},
year={2020},
papercite = {6. Working Papers}
}
  • Daniel P. Robinson. A truncated-Newton algorithm for unconstrained optimization with strong global and local convergence properties. In preparation for submission to the ima journal on numerical analysis, 2020. [Bibtex] 
    @article{Rob16,
author={Daniel P. Robinson},
title={A Truncated-{N}ewton Algorithm for Unconstrained Optimization with Strong Global and Local Convergence Properties},
journal={In preparation for submission to the IMA Journal on Numerical Analysis},
year={2020},
papercite = {6. Working Papers}
}

7. Technical Reports

  • Frank E. Curtis, Yutong Dai, and Daniel P. Robinson. A subspace acceleration method for minimization involving a group sparsity-inducing regularizer. ISE Technical Report 20T-015, Lehigh University, July 2020. [Bibtex] 
    @techreport{CurDR20Report,
title={A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer},
author={Frank E. Curtis and Yutong Dai and Daniel P. Robinson},
institution = {Lehigh University},
type = {ISE Technical Report},
month = {July},
number = {20T-015},
year={2020},
papercite = {7. Technical Reports}
}
  • Philip E. Gill, Vyacheslav Kungurtsev, and Daniel P. Robinson. Distance-to-solution estimates for optimization problems with constraints in standard form. Center for Computational Mathematics Report CCoM 16-01, University of California, San Diego, April 2016. [Bibtex] 
    @techreport{GilKR16b,
AUTHOR = {Philip E. Gill and Vyacheslav Kungurtsev and Daniel P. Robinson},
INSTITUTION = {University of California, San Diego},
TITLE = {Distance-to-Solution Estimates for Optimization Problems with Constraints in Standard Form},
TYPE = {Center for Computational Mathematics Report},
MONTH = {April},
NUMBER = {CCoM 16-01},
YEAR = 2016,
papercite = {7. Technical Reports}
}
  • Nicholas I. M. Gould, Yueling Loh, and Daniel P. Robinson. A nonmonotone filter SQP method: local convergence and numerical results. Numerical Analysis Report Preprint RAL-P-2014-012R, Computational Laboratory, University of Oxford, Oxford, UK, 2014. [Bibtex] 
    @techreport{GouLR14b,
author = {Nicholas I. M. Gould and Yueling Loh and Daniel P. Robinson},
title = {A nonmonotone filter {SQP} method: local convergence and numerical results},
institution = {Computational Laboratory, University of Oxford},
address = {Oxford, UK},
type = {Numerical Analysis Report},
number = {Preprint RAL-P-2014-012R},
year = {2014},
papercite = {7. Technical Reports}
}
  • Nicholas I. M. Gould, Daniel P. Robinson, and Philippe L. Toint. Corrigendum: nonlinear programming without a penalty function or a filter. Technical Report, Rutherford Appleton Laboratory, Chilton, England, 2011. [Bibtex] 
    @techreport{GouRT11,
AUTHOR = {Nicholas I. M. Gould and Daniel P. Robinson and Philippe L. Toint},
TITLE = {Corrigendum: nonlinear programming without a penalty function or a filter},
JOURNAL = {Technical Report RAL-TR-2011-006 (2011)},
INSTITUTION = {Rutherford Appleton Laboratory},
ADDRESS = {Chilton, England},
YEAR = {2011},
papercite = {7. Technical Reports}
}
  • Nicholas I. M. Gould and Daniel P. Robinson. A second derivative SQP method with imposed descent. Numerical Analysis Report 08/09, Computational Laboratory, University of Oxford, Oxford, UK, 2008. [Bibtex] 
    @techreport{GouR08,
AUTHOR = {Nicholas I. M. Gould and Daniel P. Robinson},
TITLE = {A second derivative {SQP} method with imposed descent},
INSTITUTION = {Computational Laboratory, University of Oxford},
ADDRESS = {Oxford, UK},
TYPE = {Numerical Analysis Report},
NUMBER = {08/09},
YEAR = {2008},
papercite = {7. Technical Reports}
}