Robinson, Daniel

Assistant Professor
Applied Mathematics And Statistics
AMS Profile
Personal Website

Whitehead Hall 202B
(410) 516-7582
daniel.p.robinson@jhu.edu

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About

Education
  • Ph.D. 2007, UNIV CALIF SAN DIEGO
Experience
  • 2014 - 2016:  Officer, INFORMS
  • 2014 - 2014:  Visiting Professor, Northwestern University
  • 2013 - 2013:  Other, Department Chair of Elections Committee
  • 2013 - 2013:  Other, Introductory Examination Committee
  • 2010 - 2011:  Research Assistant to Jorge Nocedal, Northwestern University
  • 2008 - 2010:  Visitor, Rutherford Appleton Laboratory
  • 2007 - 2010:  Research Assistant to Nicholas Gould, University of Oxford
  • 2000 - 2007:  Teaching Assistant, Unspecified
Research Areas
  • Algorithms for solving convex optimization problems
  • Algorithms for solving nonconvex optimization problems
  • COMPUTER vision
  • MACHINE learning
  • Optimal control
Awards
  • 2014:  Vice Chair for Nonlinear Optimization
  • 2012:  Professor Joel Dean Award for Excellence in Teaching, Johns Hopkins
  • 2010:  Research assistant to Jorge Nocedal, Northwestern University, Evanston
  • 2007:  Exeter College Research Member , University of Oxford, UK
  • 2007:  Research assistant to Nicholas Gould, University of Oxford, UK
  • 2001:  Outstanding Senior Mathematics Student , James Madison University, Virginia
  • 2001:  Teaching Assistantship with Scholarship , University of California, San Diego (2001-2007)
Presentations
  • "Negative Curvature in Deterministic and Stochastic Nonconvex Optimization", US-Mexico Workshop on Optimization and Its Applications.  Huatulco, Mexico.  January 1, 2018
  • "FaRSA: A Fast Reduced-Space Algorithm for Sparse Convex Optimization", The Institute for Data Intensive Engineering and Science (IDIES).  Baltimore Maryland, United States of America (the).  October 1, 2017
  • "Low-Rank Convex Optimization", The 15th EUROPT Workshop on Advances in Continuous Optimization.  Montreal Quebec, Canada.  July 1, 2017
  • "Large-scale Subspace Clustering Algorithms", The Third International Conference on Engineering and Computational Mathematics.  Hong Kong.  June 1, 2017
  • "An Enhanced Truncated-Newton Algorithm for Large-Scale Optimization", SIAM Conference on Optimization.  Vancouver British Columbia, Canada.  May 1, 2017
  • "Scalable Optimization Algorithms for Large-Scale Subspace Clustering", Department Seminar.  San Diego California, United States of America (the).  March 1, 2017
  • "Scalable Optimization Algorithms for Large-Scale Subspace Clustering", Department Seminar.  Berkeley California, United States of America (the).  March 1, 2017
  • "Scalable Optimization Algorithms for Large-Scale Subspace Clustering", Department Seminar.  Stanford California, United States of America (the).  March 1, 2017
  • "Scalable Optimization Algorithms for Large-Scale Subspace Clustering", Department Seminar.  Los Angeles California, United States of America (the).  March 1, 2017
  • Institute for Mathematics and its Applications (IMA).  University of Minnesota.  August 1, 2016
  • "Incorporating User Preferences in Predictive Models Via Structured Regularizers", International Joint Conference on Artificial Intelligence.  New York City, NY.  January 1, 2016
  • "Learning a Union of Subspaces from Big and Corrupted Data", Big Data PI Meeting.  Arlington, VA.  January 1, 2016
  • "Cost-Sensitive Prediction: Applications in Healthcare", Institute for Data Intensive Engineering and Sciences (IDIES) Annual Symposium.  Johns Hopkins University.  January 1, 2016
  • "Scalable Subspace Clustering Via the Elastic Net", Workshop on Nonlinear Optimization Algorithms and Industrial Applications.  Toronto, Canada.  January 1, 2016
  • "Low-rank convex optimization", International Conference on Continuous Optimization (ICCOPT).  Tokyo, Japan.  January 1, 2016
  • "A geometry driven active-set method for elastic-net minimization", Modeling and Optimization: Theory and Applications (MOPTA).  Bethlehem, PA.  January 1, 2016
  • "Scalable subspace clustering via the elastic net", INFORMS Annual Meeting.  Nashville, Tennessee.  January 1, 2016
  • "Adaptive subspace methods for optimization", U.S.-Mexico Workshop on Optimization and its Applications.  Merida, Mexico.  January 1, 2016
  • "A Solver for $ell_1$ Regularized Convex Optimization", INFORMS Annual Meeting.  Philadelphia, Pennsylvania.  November 1, 2015
  • "Cost-Sensitive Prediction: Applications in Healthcare".  Baltimore, Maryland.  October 1, 2015
  • "A shifted penalty-barrier method for NLP", Modeling and Optimization: Theory and Applications (MOPTA).  Bethlehem, Pennsylvania.  July 1, 2015
  • "Cost sensitive regularization in model prediction", International Conference on Continuous Optimization.  Pittsburgh, Pennsylvania.  July 1, 2015
  • "Scalable, efficient, and robust NLP solvers", International Conference on Continuous Optimization.  Pittsburgh, Pennsylvania.  July 1, 2015
  • "A trust-region method with optimal complexity".  Prague, Czech Republic.  May 1, 2015
  • "A QP solver for bound-constrained problems".  Oxford.  May 1, 2015
  • "A flexible ADMM for big data applications", Optimization and Big Data Workshop.  Edinburgh, UK.  May 1, 2015
  • "Globalizing local methods".  Zurich, Switzerland.  May 1, 2015
  • "Dostal and Schoberl's method for nonconvex problems", Foundations of Computational Mathematics (FoCM).  Montevideo, Uruguay.  December 1, 2014
  • "Inexactness in large-scale active-set methods for large-scale optimization".  November 1, 2014
  • "Large-scale subspace clustering", Modeling and Optimization: Theory and Applications (MOPTA).  Lehigh University, Bethlehem, Pennsylvania.  August 1, 2014
  • "A trust-funnel algorithm. Incorporating cost with model prediction.", Southern California Optimization Day.  San Diego, California.  May 1, 2014
  • "A matrix-free trust-funnel barrier-SQP method for extreme-scale constrained optimization", SIAM Conference on Optimization.  San Diego, California.  May 1, 2014
  • "A trust-funnel algorithm for nonlinear programming", INFORMS Optimization Society.  Houston, Texas.  March 1, 2014
  • "A trust-funnel algorithm for nonlinear programming", American Mathematical Society (AMS).  Baltimore, Maryland.  March 1, 2014
  • "Contributions in Nonlinear Optimization".  Wisconsin Institutes of Discovery, Madison, Wisconsin.  November 1, 2013
  • "Steering augmented Lagrangian methods".  October 1, 2013
  • "A filter SQP method", International Conference on Continuous Optimization.  Lisbon, Portugal.  July 1, 2013
  • "Steering augmented Lagrangian methods".  Argonne, Illinois.  July 1, 2013
  • Duke University, Electrical and Computer Engineering, Durham, NC.  January 1, 2013
  • "Numerical optimization: interesting applications and recent research".  Harrisonburg, VA.  January 1, 2013
  • "Steering augmented Lagrangian methods", International Symposium of Mathematical Programming, Berlin, Germany.  January 1, 2012
  • "Two-phase subspace accelerated methods for asymmetric and symmetric LCP", Workshop on Complementarity and Related Problems, National University of Singapore.  January 1, 2012
  • "Pushing the limits of sequential quadratic programming", University of Maryland Baltimore County, Baltimore, MD.  January 1, 2012
  • "Large-scale optimization methods based on the augmented Lagrangian", New York University (NYU), Department of Computer Science, New York City.  January 1, 2012
  • "Adaptive augmented Lagrangian methods for large-scale optimization", INFORMS Annual Meeting, Phoenix.  January 1, 2012
  • "Matrix splitting methods for BQP and LCP", Johns Hopkins University, Center for Imaging Science, Baltimore, MD.  January 1, 2011
  • "Using active-set phases to accelerate algorithms", Johns Hopkins University, Applied Mathematics and Statistics, Baltimore, MD.  January 1, 2011
  • "LCP and SQP", SIAM Conference on Optimization, Darmstadt, Germany.  January 1, 2011
  • "Two-phase matrix splitting methods for BQP and LCP", Argonne National Laboratory, Mathematics and Computer Science Division.  January 1, 2011
  • "Two-phase matrix splitting methods for BQP and LCP", Johns Hopkins University, Applied Mathematics and Statistics, Baltimore, MD.  January 1, 2011
  • "Using active-set phases to accelerate algorithms", Lehigh University, Industrial and Systems Engineering, Bethlehem, PA.  January 1, 2011
  • "Optimization in machine/statistical learning", University of San Diego, Mathematics and Computer Science, San Diego, CA.  January 1, 2011
  • "High-order methods for overcoming degeneracy in interior-point methods for QP", International Conference on Continuous Optimization (ICCOPT), Santiago, Chile.  January 1, 2010
  • "Recent work in sequential quadratic programming methods", Argonne National Laboratory, Mathematics and Computer Science Division.  January 1, 2010
  • "Recent advances in second derivative sequential quadratic programming methods", Edinburgh Research Group in Optimization (ERGO), Edinburgh, UK.  January 1, 2010
  • "An introduction to sequential quadratic programming methods", Trinity University, Mathematics Department, San Antonio, TX.  January 1, 2010
  • "Recent advances in second derivative sequential quadratic programming methods", University of Warwick, Coventry, UK.  January 1, 2010
  • "An interior-point trust-funnel algorithm for large-scale nonconvex optimization", INFORMS annual meeting 2009, San Diego.  January 1, 2009
  • "Second derivative SQP methods for solving large nonlinear optimization problems", Bath-RAL Numerical Analysis Day, Rutherford Appleton Laboratory, UK.  January 1, 2009
  • "A trust-funnel algorithm for general nonlinear optimization", Biennial Conference on Numerical Analysis, University of Strathclyde.  January 1, 2009
  • "S2QP - a second derivative SQP method for nonlinear optimization", International Symposium of Mathematical Programming, Chicago.  January 1, 2009
  • "A primal-dual SQP-like method", SIAM Annual Meeting, San Diego.  January 1, 2008
  • "A second derivative SQP method with imposed descent", SIAM Conference on Optimization, Boston.  January 1, 2008
  • "A generalized primal-dual augmented Lagrangian", Computational Mathematics and Applications Seminar, University of Oxford, UK.  January 1, 2007
  • "Poster session for undergraduate research", Joint Mathematics Meeting, New Orleans.  January 1, 2001
  • "Existence of a derivative on a finite set", MD-DC-VA Section Meeting, Loyola College.  January 1, 2000

Publications

Journal Articles
  • Jiang H, Robinson DP, Vidal R, You C (2018).  A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis.  Computational Optimization and Applications.  (2).
  • Chen T, Curtis FE, Robinson DP (2018).  FaRSA for ℓ1-regularized convex optimization: local convergence and numerical experience.  Optimization Methods and Software.  33(2).
  • Chen T, Curtis FE, Robinson DP (2018).  FaRSA for $ell_1$-regularized convex optimization: local convergence and numerical experience.  Optimization Methods and Software.  Taylor & Francis.  33.  396--415.
  • You C, Robinson DP, Vidal R (2017).  Provable self-representation based outlier detection in a union of subspaces.  Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.  2017-January.
  • You C, Donnat C, Robinson DP, Vidal R (2017).  A divide-and-conquer framework for large-scale subspace clustering.  Conference Record - Asilomar Conference on Signals, Systems and Computers.
  • Amitabh Basu, Tianyu Ding, Daniel P. Robinson (2017).  A reduced-space accelerated optimization algorithm for low-rank optimization.  In preparation for submission to SIAM Journal on Optimization.
  • Chen T, Curtis FE, Robinson DP (2017).  A Reduced-Space Algorithm for Minimizing $ell_1$-Regularized Convex Functions.  SIAM Journal on Optimization.  SIAM.  27.  1583--1610.
  • Philip E. Gill, Vyacheslav Kungurtsev, Daniel P. Robinson (2017).  A Primal-Dual Shifted Penalty-Barrier Algorithm.  In preparation for submission to Mathematical Programming.
  • Chen T, Curtis FE, Robinson DP (2017).  A reduced-space algorithm for minimizing1-regularized convex functions.  SIAM Journal on Optimization.  27(3).
  • Gill PE, Kungurtsev V, Robinson DP (2017).  A stabilized SQP method: Global convergence.  IMA Journal of Numerical Analysis.  37(1).
  • Jiang H, Robinson DP, Vidal R, You C (2017).  A Nonconvex Formulation for Low Rank Subspace Clustering: Algorithms and Convergence Analysis.  Submitted to the Journal on Computational Optimization and Applications.
  • Robinson DP (2017).  A Truncated-Newton Algorithm for Unconstrained Optimization with Strong Global and Local Convergence Properties.  Submitted to the Optimization Methods and Software.
  • Robinson DP, Curtis FE (2017).  Exploiting Negative Curvature Directions in Deterministic and Stochastic Optimization.  Submitted to Mathematical Programming.
  • Curtis FE, Robinson DP, Samadi M (2017).  Complexity Analysis of a Trust-Funnel Algorithm for Equality Constrained Optimization.  Accepted for publication in SIAM Journal on Optimization.
  • Curtis FE, Robinson DP, Samadi M (2017).  An Inexact Regularized Newton Framework with a Worst-Case Iteration Complexity of $O(varepsilon^-3/2)$ for Nonconvex Optimization.  Submitted to the IMA Journal on Numerical Analysis.
  • Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou (2017).  Self-Correcting Variable-Metric Algorithms for Nonsmooth Optimization.  Submitted to SIAM Journal on Optimization.
  • Gould NIM, Robinson DP (2016).  A dual gradient-projection method for large-scale strictly convex quadratic problems.  Computational Optimization and Applications.  (1).
  • Robinson DP, Tappenden R (2016).  A Flexible ADMM Algorithm for Big Data Applications.  Journal of Scientific Computing.  (1).
  • Gill PE, Kungurtsev V, Robinson DP (2016).  A stabilized SQP method: superlinear convergence.  Mathematical Programming.  (1-2).
  • Curtis FE, Robinson DP, Samadi M (2016).  A trust region algorithm with a worst-case iteration complexity of O(ϵ-3/2) for nonconvex optimization.  Mathematical Programming.  (1-2).
  • Curtis FE, Gould NIM, Robinson DP, Toint PL (2016).  An interior-point trust-funnel algorithm for nonlinear optimization.  Mathematical Programming.  (1-2).
  • Curtis FE, Gould NIM, Jiang H, Robinson DP (2016).  Adaptive augmented Lagrangian methods: Algorithms and practical numerical experience.  Optimization Methods and Software.  31(1).
  • Gill PE, Kungurtsev V, Robinson DP (2016).  A stabilized SQP method: global convergence.  IMAJNA.  Oxford University Press.  drw004.
  • Gill PE, Kungurtsev V, Robinson DP (2016).  A stabilized SQP method: superlinear convergence.  MP.  Springer.  1--42.
  • Gould NIM, Robinson DP (2016).  A dual gradient-projection method for large-scale strictly convex quadratic problems.  Computational Optimization and Applications.  Springer.  1--38.
  • You C, Li CG, Robinson DP, Vidal R (2016).  Oracle based active set algorithm for scalable elastic net subspace clustering.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.  2016-January.
  • Robinson DP, Tappenden R (2016).  A flexible ADMM algorithm for big data applications.  JSC.  Springer.  1--33.
  • You C, Robinson DP, Vidal R (2016).  Rapidly Adapting Working Set Methods for Learning Sparse Models from Large-Scale Datasets.  Submitted to Neural Information Processing Systems (NIPS).
  • Curtis FE, Robinson DP, Samadi M (2016).  A trust region algorithm with a worst-case iteration complexity of $O(varepsilon^-3/2)$ for nonconvex optimization.  MP.  Springer.  1--32.
  • Robinson DP, Saria S (2016).  Trading-off cost of deployment versus accuracy in learning predictive models.  IJCAI International Joint Conference on Artificial Intelligence.  2016-January.
  • Curtis FE, Gould NI, Robinson D, Toint PL (2016).  An Interior-Point Trust-Funnel Algorithm for Nonlinear Optimization.  Mathematical Programming.  1-62.
  • Robinson D, Gould NI (2016).  A dual gradient-projection method for large-scale strictly convex quadratic problems.  Computational Optimization and Applications.  1--38.
  • Gill PE, Kungurtsev V, Robinson D (2016).  A Stabilized SQP Method: Global Convergence.  IMA Journal on Numerical Analysis.  drw004.
  • Curtis FE, Gould NI, Jiang H, Robinson D (2016).  Adaptive Augmented Lagrangian Methods: Algorithms and Practical Numerical Experience.  Optimization Methods and Software.  31(1).  157-186.
  • You C, Robinson DP, Vidal R (2016).  Scalable sparse subspace clustering by orthogonal matching pursuit.  Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.  2016-January.
  • Gill PE, Kungurtsev V, Robinson D (2016).  A Stabilized SQP Method: Superlinear Convergence.  Mathematical Programming.  1--42.
  • Curtis FE, Robinson D, Samadi M (2016).  A Trust Region Algorithm with a Worst-Case Iteration Complexity of $O(eps-3/2)$ for Nonconvex Optimization.  Mathematical Programming.  1-32.
  • Frank E. Curtis, Nicholas I. M. Gould, Hao Jiang, Daniel P. Robinson (2016).  Adaptive augmented Lagrangian methods: algorithms and practical numerical experience.  OMS.  31.  157-186.
  • Robinson D, Tappenden RE (2016).  A flexible ADMM algorithm for big data applications.  Journal of Scientific Computing.  1--33.
  • Curtis FE, Gould NIM, Robinson DP, Toint PL (2016).  An interior-point trust-funnel algorithm for nonlinear optimization.  MP.  1--62.
  • Curtis FE, Jiang H, Robinson DP (2015).  An adaptive augmented Lagrangian method for large-scale constrained optimization.  Mathematical Programming.  152(1-2).
  • Robinson DP (2015).  Primal-Dual Active-Set Methods for Large-Scale Optimization.  JOTA.  Plenum Press.  166.  137--171.
  • Robinson D (2015).  Comments on: "Critical Lagrange Multipliers: what we currently know about them, how they spoil our lives, and what we can do about it".  Operations Research Journal of the Spanish Society of Statistics and Operations Research.  1-5.
  • Robinson D (2015).  Primal-Dual Active-Set Methods for Large-Scale Optimization.  Journal of Optimization Theory and Applications.  166(1).  137-171.
  • Robinson D, Moyh-ud-Din H (2015).  A solver for nonconvex bound-constrained quadratic optimization.  SIAM Journal on Optimization.  25(4).  2385-2407.
  • Yang C, Robinson D, Vidal R (2015).  Sparse subspace clustering with missing entries.  32nd International Conference on Machine Learning, ICML 2015.  3.
  • Robinson DP (2015).  Primal-dual active-set methods for large-scale optimization.  Journal of Optimization Theory and Applications.  166(1).
  • Mohy-Ud-din H, Robinson DP (2015).  A solver for nonconvex bound-constrained quadratic optimiza tion.  SIAM Journal on Optimization.  25(4).
  • Gould NIM, Loh Y, Robinson DP (2015).  A nonmonotone filter SQP method: Local convergence and numerical results.  SIAM Journal on Optimization.  25(3).
  • Robinson DP (2015).  Comments on: Critical Lagrange multipliers: what we currently know about them, how they spoil our lives, and what we can do about it.  TOP.  23(1).
  • Curtis FE, Han Z, Robinson DP (2015).  A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization.  Computational Optimization and Applications.  60(2).
  • Nicholas I. M. Gould, Yueling Loh, Daniel P. Robinson (2015).  A Nonmonotone Filter SQP Method: Local Convergence and Numerical Results.  SIOPT.  25.  1885-1911.
  • Robinson DP (2015).  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).  Springer Berlin Heidelberg.  1-5.
  • Gould NI, Loh Y, Robinson D (2015).  A nonmonotone filter SQP method: local convergence and numerical results.  SIAM Journal on Optimization.  25(3).  1885-1911.
  • Hassan Mohy-Ud-Din, Daniel P. Robinson (2015).  A Solver for Nonconvex Bound-Constrained Quadratic Optimization.  SIOPT.  25.  2385-2407.
  • Curtis FE, Johnson TC, Robinson D, Wächter A (2014).  An Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Optimization.  SIAM Journal on Optimization.  24(3).  1041-1074.
  • Curtis FE, Jiang H, Robinson DP (2014).  An adaptive augmented Lagrangian method for large-scale constrained optimization.  MP.  Springer.  1--45.
  • Curtis FE, Han Z, Robinson DP (2014).  A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization.  COAP.  Springer US.  1-31.
  • Gould NIM, Loh Y, Robinson DP (2014).  A filter method with unified step computation for nonlinear optimization.  SIOPT.  SIAM.  24.  175--209.
  • Curtis FE, Johnson TC, Robinson DP, Wächter A (2014).  An inexact sequential quadratic optimization algorithm for nonlinear optimization.  SIAM Journal on Optimization.  24(3).
  • Gould NIM, Loh Y, Robinson DP (2014).  A filter method with unified step computation for nonlinear optimization.  SIAM Journal on Optimization.  24(1).
  • Curtis FE, Han Z, Robinson D (2014).  A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization.  Computational Optimization and Applications.  1-31.
  • Gould NI, Loh Y, Robinson D (2014).  A filter method with unified step computation for nonlinear optimization.  SIAM Journal on Optimization.  24(1).  175-209.
  • Curtis FE, Johnson TC, Robinson DP, Wächter A (2014).  An Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Optimization.  SIOPT.  SIAM.  24.  1041--1074.
  • Curtis FE, Jiang H, Robinson D (2014).  An adaptive augmented Lagrangian method for large-scale constrained optimization.  Mathematical Programming.  1-45.
  • Gill PE, Robinson DP (2013).  A globally convergent stabilized sqp method.  SIAM Journal on Optimization.  23(4).
  • Robinson DP, Feng L, Nocedal JM, Pang JS (2013).  Subspace accelerated matrix splitting algorithms for asymmetric and symmetric linear complementarity problems.  SIAM Journal on Optimization.  23(3).
  • Gould NIM, Orban D, Robinson DP (2013).  Trajectory-following methods for large-scale degenerate convex quadratic programming.  Mathematical Programming Computation.  5(2).
  • Robinson DP, Feng L, Nocedal JM, Pang J (2013).  Subspace Accelerated Matrix Splitting Algorithms for Asymmetric and Symmetric Linear Complementarity Problems.  SIOPT.  SIAM.  23.  1371--1397.
  • Gill PE, Robinson D (2013).  A Globally Convergent Stabilized SQP Method.  SIAM J. Optim..  23(4).  1983-2010.
  • Gould NI, Orban D, Robinson D (2013).  Trajectory-following methods for large-scale degenerate convex quadratic programming.  Mathematical Programming Computation.  5(2).  113-142.
  • Gould NIM, Orban D, Robinson DP (2013).  Trajectory-following methods for large-scale degenerate convex quadratic programming.  MPC.  Springer-Verlag.  5.  113--142.
  • Philip E. Gill, Daniel P. Robinson (2013).  A Globally Convergent Stabilized SQP Method.  SIOPT.  23.  1983--2010.
  • Robinson D, Feng L, Nocedal JM, Pang J (2013).  Subspace Accelerated Matrix Splitting Algorithms for Asymmetric and Symmetric Linear Complementarity Problems.  SIAM Journal on Optimization.  23(3).  1371-1397.
  • Gould NIM, Robinson DP (2012).  A second-derivative SQP method with a 'trust-region-free' predictor step.  IMA Journal of Numerical Analysis.  32(2).
  • Robinson D, Gould NI (2012).  A second-derivative SQP method with a 'trust-region-free' predictor step.  IMA Journal of Numerical Analysis.  40(4).  580-601.
  • Gould NIM, Daniel P. Robinson (2012).  A Second Derivative SQP Method with a "trust-region-free" predictor step.  IMAJNA.  32.  580--601.
  • Gill PE, Robinson DP (2012).  A primal-dual augmented Lagrangian.  Computational Optimization and Applications.  51(1).
  • Robinson D, Gould NI, Orban D (2012).  CQP: an interior-point code for quadratic programming.  Mathematical Programming Computation.
  • Philip E. Gill, Daniel P. Robinson (2012).  A primal-dual augmented Lagrangian.  COAP.  Springer Netherlands.  51.  1--25.
  • Robinson D, Gill PE (2012).  A primal-dual augmented Lagrangian.  Computational Optimization and Applications.  51.  pp. 1-25.
  • Gould NIM, Robinson DP (2010).  A second derivative sqp method: Global convergence.  SIAM Journal on Optimization.  20(4).
  • Gould NIM, Robinson DP (2010).  A second derivative SQP method: Local convergence and practical issues.  SIAM Journal on Optimization.  20(4).
  • Gould NIM, Robinson DP, Thorne HS (2010).  On solving trust-region and other regularised subproblems in optimization.  Mathematical Programming Computation.  2(1).
  • Gould NIM, Daniel P. Robinson (2010).  A Second Derivative SQP Method: Local Convergence and Practical Issues.  SIOPT.  20.  2049--2079.
  • Gould NIM, Daniel P. Robinson (2010).  A Second Derivative SQP Method: Global Convergence.  SIOPT.  20.  2023--2048.
  • Robinson D, Gould NI (2010).  A second derivative SQP method: global convergence.  SIAM Journal on Optimization.  20(4).  pp. 2023-2048.
  • Robinson D, Gould NI, Thorne HS (2010).  On solving trust-region and other regularised subproblems in optimization.  Mathematical Programming Computation.  2.  pp. 21-57.
  • Robinson D, Gould NI (2010).  A second derivative SQP method: local convergence and practical issues.  SIAM Journal on Optimization.  20(4).  pp. 2049-2079.
  • Gould NIM, Robinson DP, Thorne HS (2010).  On solving trust-region and other regularised subproblems in optimization.  MPC.  Springer.  2.  21--57.
Book Chapters
  • Robinson D, Saria S (2016).  Incorporating end-user preferences in predictive models.  Machine Learning for Healthcare Technologies.  Chapter 8.
Other Publications
  • Philip E. Gill, Vyacheslav Kungurtsev, Daniel P. Robinson (2016).  Distance-to-Solution Estimates for Optimization Problems with Constraints in Standard Form.
  • Robinson D, Gill PE, Kungurtsev V (2016).  Distance-to-solution estimates for optimization problems with constraints in standard form.  (Report CCoM 16-01).
  • Robinson D, Gould NI, Loh Y (2014).  A nonmonotone filter sqp method: local convergence and numerical results.  RAL-P-2014-012R.
  • Gould NIM, Loh Y, Robinson DP (2014).  A nonmonotone filter SQP method: local convergence and numerical results.
  • Robinson D, Gould NI, Toint P (2011).  Corrigendum: Nonlinear programming without a penalty function or a filter.  STFC Rutherford Appleton Laboratory, RAL Technical Reports, RAL-TR-2011-006.
  • Nicholas I. M. Gould, Daniel P. Robinson, Philippe L. Toint (2011).  Corrigendum: nonlinear programming without a penalty function or a filter.  Technical Report RAL-TR-2011-006 (2011).
  • Robinson D, Gould NI (2008).  A second-derivative SQP method with imposed descent.  Oxford University Computing Laboratory, report number NA 08/09.
  • Gould NIM, Daniel P. Robinson (2008).  A second derivative SQP method with imposed descent.
Conference Proceedings
  • Franc ca G, Robinson DP, Vidal R (2018).  A variational approach to accelerated ADMM.  Submitted to International Conference on Machine Learning (ICML).
  • You C, Robinson DP, Vidal R (2018).  Exemplar-based Subspace Clustering via a Farthest First Search Algorithm.  Submitted to Computational Vision and Pattern Recognition (CVPR).
  • You C, Robinson DP, Vidal R (2017).  Provable Self-Representation Based Outlier Detection in a Union of Subspaces.  Computer Vision and Pattern Recognition.
  • Robinson D, You C, Vidal R, Donnat C (2016).  A divide-and-conquer framework for large-scale subspace clustering.  Asilomar conference on Signals, Systems, and computers.
  • You C, Donnat C, Robinson DP, Vidal R (2016).  A Divide-and-Conquer Framework for Large-Scale Subspace Clustering.  Asilomar Conference on Signals, Systems, and Computers.
  • Robinson DP, Saria S (2016).  Incorporating User Preferences in Predictive Models Via Structured Regularizers.  IJCAI.
  • You C, Robinson DP, Vidal R (2016).  Sparse Subspace Clustering by Orthogonal Matching Pursuit.  CVPR.
  • Robinson D, You C, Vidal R (2016).  Sparse subspace clustering by orthogonal matching pursuit.  International Conference on Computer Vision and Pattern Recognition (CVPR).
  • Robinson D, You C, Vidal R (2016).  Oracle based active set algorithm for scalable elastic net subspace clustering.  Conference on Computer Vision and Pattern Recognition (CVPR).
  • Robinson D, Saria S (2016).  Incorporating User Preferences in Predictive Models Via Structured Regularizers.  International Joint Conferences on Artificial Intelligence (IJCAI).
  • You C, Li C, Robinson DP, Vidal R (2016).  Oracle Based Active Set Algorithm for Scalable Elastic Net Subspace Clustering.  CVPR.
  • Robinson D, Yang C, Vidal R (2015).  Sparse subspace clustering with missing entries.  Proceedings of The 32nd International Conference on Machine Learning.  2463-2472.
  • Yang C, Robinson DP, Vidal R (2015).  Sparse Subspace Clustering with Missing Entries.  ICML.  2463--2472.
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