Faculty

Daniel Robinson

Assistant Professor

Research Interests

  • Optimization
  • Numerical Analysis
  • Matrix Analysis
Education
  • Ph.D. 2007, University of California, San Diego
  • MA 2003, University of California, San Diego
  • B.A. 2001, James Madison University
Experience
  • 2011 - Present:  Assistant Professor, Johns Hopkins University
  • June 1, 2014 - July 31, 2014:  Visiting Professor, Northwestern University
  • 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, University of California, San Diego
Research Areas
  • Designing, analyzing, and implementing algorithms for solving convex and nonconvex optimization problems with applications in Optimal Control, Machine Learning, and Computer Vision.
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 (2010-2011)
  • 2007:  Exeter College Research Member , University of Oxford, UK (2007-2010)
  • 2007:  Research assistant to Nicholas Gould, University of Oxford, UK (2007-2010)
  • 2001:  Teaching Assistantship with Scholarship , University of California, San Diego (2001-2007)
  • 2001:  Outstanding Senior Mathematics Student , James Madison University, Virginia
Journal Articles
  • Gould, N. I., Robinson, D. (2016).  A dual gradient-projection method for large-scale strictly convex quadratic problems.  Computational Optimization and Applications.  1--38.
  • Robinson, D., Tappenden, R. E. (2016).  A flexible ADMM algorithm for big data applications.  Journal of Scientific Computing.  1--33.
  • Gill, P. E., Kungurtsev, V., Robinson, D. (2016).  A Stabilized SQP Method: Global Convergence.  IMA Journal on Numerical Analysis.  drw004.
  • Gill, P. E., Kungurtsev, V., Robinson, D. (2016).  A Stabilized SQP Method: Superlinear Convergence.  Mathematical Programming.  1--42.
  • Curtis, F. E., 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.
  • Curtis, F. E., Gould, N. I., Jiang, H., Robinson, D. (2016).  Adaptive Augmented Lagrangian Methods: Algorithms and Practical Numerical Experience.  Optimization Methods and Software.  31(1).  157-186.
  • Curtis, F. E., Gould, N. I., Robinson, D., Toint, P. L. (2016).  An Interior-Point Trust-Funnel Algorithm for Nonlinear Optimization.  Mathematical Programming.  1-62.
  • Gould, N. I., Loh, Y., Robinson, D. (2015).  A nonmonotone filter SQP method: local convergence and numerical results.  SIAM Journal on Optimization.  25(3).  1885-1911.
  • Robinson, D., Moyh-ud-Din, H. (2015).  A solver for nonconvex bound-constrained quadratic optimization.  SIAM Journal on Optimization.  25(4).  2385-2407.
  • 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.
  • Gould, N. I., Loh, Y., Robinson, D. (2014).  A filter method with unified step computation for nonlinear optimization.  SIAM Journal on Optimization.  24(1).  175–209.
  • Curtis, F. E., 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.
  • Curtis, F. E., Jiang, H., Robinson, D. (2014).  An adaptive augmented Lagrangian method for large-scale constrained optimization.  Mathematical Programming.  1–45.
  • Curtis, F. E., Johnson, T. C., Robinson, D., Wächter, A. (2014).  An Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Optimization.  SIAM Journal on Optimization.  24(3).  1041–1074.
  • Gill, P. E., Robinson, D. (2013).  A Globally Convergent Stabilized SQP Method.  SIAM J. Optim..  23(4).  1983–2010.
  • Robinson, D., Feng, L., Nocedal, J. M., Pang, J. (2013).  Subspace Accelerated Matrix Splitting Algorithms for Asymmetric and Symmetric Linear Complementarity Problems.  SIAM Journal on Optimization.  23(3).  1371–1397.
  • Gould, N. I., Orban, D., Robinson, D. (2013).  Trajectory-following methods for large-scale degenerate convex quadratic programming.  Mathematical Programming Computation.  5(2).  113–142.
  • Gill, P. E., Robinson, D. (2012).  A primal-dual augmented Lagrangian.  Computational Optimization and Applications.  51.  pp. 1-25.
  • Gould, N. I., Robinson, D. (2012).  A second-derivative SQP method with a 'trust-region-free' predictor step.  IMA Journal of Numerical Analysis.  40(4).  580-601.
  • Gould, N. I., Orban, D., Robinson, D. (2012).  CQP: an interior-point code for quadratic programming.  Mathematical Programming Computation.
  • Gould, N. I., Robinson, D. (2010).  A second derivative SQP method: global convergence.  SIAM Journal on Optimization.  20(4).  pp. 2023-2048.
  • Gould, N. I., Robinson, D. (2010).  A second derivative SQP method: local convergence and practical issues.  SIAM Journal on Optimization.  20(4).  pp. 2049-2079.
  • Gould, N. I., Robinson, D., Thorne, H. S. (2010).  On solving trust-region and other regularised subproblems in optimization.  Mathematical Programming Computation.  2.  pp. 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
  • Gill, P. E., Kungurtsev, V., Robinson, D.  A Primal-Dual Shifted Predictor-Correct Interior-Point Algorithm.  The Johns Hopkins University.
  • Gill, P. E., Robinson, D., Kungurtsev, V. (2016).  Distance-to-solution estimates for optimization problems with constraints in standard form.  (Report CCoM 16-01).
  • Robinson, D., Gould, N. I., Loh, Y. (2014).  A nonmonotone filter sqp method: local convergence and numerical results.  RAL-P-2014-012R.
  • Gould, N. I., Robinson, D., Toint, P. (2011).  Corrigendum: Nonlinear programming without a penalty function or a filter.  STFC Rutherford Appleton Laboratory, RAL Technical Reports, RAL-TR-2011-006.
  • Gould, N. I., Robinson, D. (2008).  A second-derivative SQP method with imposed descent.  Oxford University Computing Laboratory, report number NA 08/09.
Conference Proceedings
  • You, C., Donnat, C., Robinson, D., Vidal, R. (2016).  A divide-and-conquer framework for large-scale subspace clustering.  Asilomar conference on Signals, Systems, and computers.
  • Robinson, D., Saria, S. (2016).  Incorporating User Preferences in Predictive Models Via Structured Regularizers.  International Joint Conferences on Artificial Intelligence (IJCAI).
  • You, C., Robinson, D., Vidal, R. (2016).  Oracle based active set algorithm for scalable elastic net subspace clustering.  Conference on Computer Vision and Pattern Recognition (CVPR).
  • You, C., Robinson, D., Vidal, R. (2016).  Sparse subspace clustering by orthogonal matching pursuit.  International Conference on Computer Vision and Pattern Recognition (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.
  • "Learning a Union of Subspaces from Big and Corrupted Data", Big Data PI Meeting.  Arlington, VA.  2016
  • "Scalable subspace clustering via the elastic net", INFORMS Annual Meeting.  Nashville, Tennessee.  2016
  • "Cost-Sensitive Prediction: Applications in Healthcare", Institute for Data Intensive Engineering and Sciences (IDIES) Annual Symposium.  Johns Hopkins University.  2016
  • "Low-rank convex optimization", International Conference on Continuous Optimization (ICCOPT).  Tokyo, Japan.  2016
  • "Incorporating User Preferences in Predictive Models Via Structured Regularizers", International Joint Conference on Artificial Intelligence.  New York City, NY.  2016
  • "A geometry driven active-set method for elastic-net minimization", Modeling and Optimization: Theory and Applications (MOPTA).  Bethlehem, PA.  2016
  • "Scalable Subspace Clustering Via the Elastic Net", Workshop on Nonlinear Optimization Algorithms and Industrial Applications.  Toronto, Canada.  2016
  • Institute for Mathematics and its Applications (IMA).  University of Minnesota.  2016
  • "Adaptive subspace methods for optimization", U.S.-Mexico Workshop on Optimization and its Applications.  Merida, Mexico.  2016
  • "A Solver for $ell_1$ Regularized Convex Optimization", INFORMS Annual Meeting.  Philadelphia, Pennsylvania.  2015
  • Cost-Sensitive Prediction: Applications in Healthcare.  Baltimore, Maryland.  2015
  • "Cost sensitive regularization in model prediction", International Conference on Continuous Optimization.  Pittsburgh, Pennsylvania.  2015
  • "Scalable, efficient, and robust NLP solvers", International Conference on Continuous Optimization.  Pittsburgh, Pennsylvania.  2015
  • "A shifted penalty-barrier method for NLP", Modeling and Optimization: Theory and Applications (MOPTA).  Bethlehem, Pennsylvania.  2015
  • A QP solver for bound-constrained problems.  Oxford.  2015
  • A trust-region method with optimal complexity.  Prague, Czech Republic.  2015
  • Globalizing local methods.  Zurich, Switzerland.  2015
  • "A flexible ADMM for big data applications", Optimization and Big Data Workshop.  Edinburgh, UK.  2015
  • "Dostal and Schoberl's method for nonconvex problems", Foundations of Computational Mathematics (FoCM).  Montevideo, Uruguay.  2014
  • Inexactness in large-scale active-set methods for large-scale optimization.  2014
  • "Large-scale subspace clustering", Modeling and Optimization: Theory and Applications (MOPTA).  Lehigh University, Bethlehem, Pennsylvania.  2014
  • "A matrix-free trust-funnel barrier-SQP method for extreme-scale constrained optimization", SIAM Conference on Optimization.  San Diego, California.  2014
  • "A trust-funnel algorithm. Incorporating cost with model prediction.", Southern California Optimization Day.  San Diego, California.  2014
  • "A trust-funnel algorithm for nonlinear programming", American Mathematical Society (AMS).  Baltimore, Maryland.  2014
  • "A trust-funnel algorithm for nonlinear programming", INFORMS Optimization Society.  Houston, Texas.  2014
  • Duke University, Electrical and Computer Engineering, Durham, NC.  2013
  • Numerical optimization: interesting applications and recent research.  Harrisonburg, VA.  2013
  • Contributions in Nonlinear Optimization.  Wisconsin Institutes of Discovery, Madison, Wisconsin.  2013
  • Steering augmented Lagrangian methods.  2013
  • "A filter SQP method", International Conference on Continuous Optimization.  Lisbon, Portugal.  2013
  • Steering augmented Lagrangian methods.  Argonne, Illinois.  2013
  • "Adaptive augmented Lagrangian methods for large-scale optimization", INFORMS Annual Meeting, Phoenix.  2012
  • "Steering augmented Lagrangian methods", International Symposium of Mathematical Programming, Berlin, Germany.  2012
  • "Large-scale optimization methods based on the augmented Lagrangian", New York University (NYU), Department of Computer Science, New York City.  2012
  • "Pushing the limits of sequential quadratic programming", University of Maryland Baltimore County, Baltimore, MD.  2012
  • "Two-phase subspace accelerated methods for asymmetric and symmetric LCP", Workshop on Complementarity and Related Problems, National University of Singapore.  2012
  • "Two-phase matrix splitting methods for BQP and LCP", Argonne National Laboratory, Mathematics and Computer Science Division.  2011
  • "Two-phase matrix splitting methods for BQP and LCP", Johns Hopkins University, Applied Mathematics and Statistics, Baltimore, MD.  2011
  • "Using active-set phases to accelerate algorithms", Johns Hopkins University, Applied Mathematics and Statistics, Baltimore, MD.  2011
  • "Matrix splitting methods for BQP and LCP", Johns Hopkins University, Center for Imaging Science, Baltimore, MD.  2011
  • "Using active-set phases to accelerate algorithms", Lehigh University, Industrial and Systems Engineering, Bethlehem, PA.  2011
  • "LCP and SQP", SIAM Conference on Optimization, Darmstadt, Germany.  2011
  • "Optimization in machine/statistical learning", University of San Diego, Mathematics and Computer Science, San Diego, CA.  2011
  • "Recent work in sequential quadratic programming methods", Argonne National Laboratory, Mathematics and Computer Science Division.  2010
  • "Recent advances in second derivative sequential quadratic programming methods", Edinburgh Research Group in Optimization (ERGO), Edinburgh, UK.  2010
  • "High-order methods for overcoming degeneracy in interior-point methods for QP", International Conference on Continuous Optimization (ICCOPT), Santiago, Chile.  2010
  • "An introduction to sequential quadratic programming methods", Trinity University, Mathematics Department, San Antonio, TX.  2010
  • "Recent advances in second derivative sequential quadratic programming methods", University of Warwick, Coventry, UK.  2010
  • "Second derivative SQP methods for solving large nonlinear optimization problems", Bath-RAL Numerical Analysis Day, Rutherford Appleton Laboratory, UK.  2009
  • "A trust-funnel algorithm for general nonlinear optimization", Biennial Conference on Numerical Analysis, University of Strathclyde.  2009
  • "An interior-point trust-funnel algorithm for large-scale nonconvex optimization", INFORMS annual meeting 2009, San Diego.  2009
  • "S2QP - a second derivative SQP method for nonlinear optimization", International Symposium of Mathematical Programming, Chicago.  2009
  • "A primal-dual SQP-like method", SIAM Annual Meeting, San Diego.  2008
  • "A second derivative SQP method with imposed descent", SIAM Conference on Optimization, Boston.  2008
  • "A generalized primal-dual augmented Lagrangian", Computational Mathematics and Applications Seminar, University of Oxford, UK.  2007
  • "Poster session for undergraduate research", Joint Mathematics Meeting, New Orleans.  2001
  • "Existence of a derivative on a finite set", MD-DC-VA Section Meeting, Loyola College.  2000
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