Talks

1. Invited Conference and Workshop Presentations

• Daniel P. Robinson. Reduced-Space Optimization for Problems with Group Sparsity. Institute for Operations Research and the Management Sciences (INFORMS), Seattle, Washington, October 2019. [Bibtex]

  @article{informs-2019a-robinson,
  author = {Robinson, Daniel P.},
  title = {{Reduced-Space Optimization for Problems with Group Sparsity}},
  journal = {{Institute for Operations Research and the Management Sciences (INFORMS), Seattle, Washington}},
  month = {October},
  address = {Seattle, Washington},
  year = {2019},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Scalable Optimization Algorithms for Subspace Clustering. Institute for Operations Research and the Management Sciences (INFORMS), Seattle, Washington, October 2019. [Bibtex]

  @article{informs-2019b-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Optimization Algorithms for Subspace Clustering}},
  journal = {{Institute for Operations Research and the Management Sciences (INFORMS), Seattle, Washington}},
  month = {October},
  year = {2019},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Second-Order Reduced-Space Methods for Sparse Optimization. International Conference on Continuous Optimization (ICCOPT), Berlin, Germany, August 2019. [Bibtex]

  @article{iccopt-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Second-Order Reduced-Space Methods for Sparse Optimization}},
  journal = {{International Conference on Continuous Optimization (ICCOPT), Berlin, Germany}},
  month = {August},
  year = {2019},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Upper-Bounding Models for Optimization. DIMACS, TRIPODS, and MOPTA at Lehigh University, Bethlehem, Pennsylvania, August 2018. [Bibtex]

  @article{lehigh-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{Upper-Bounding Models for Optimization}},
  journal = {{DIMACS, TRIPODS, and MOPTA at Lehigh University, Bethlehem, Pennsylvania}},
  month = {August},
  year = {2018},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Positive Perspective on Negative Curvature. International Symposium on Mathematical Programming (ISMP), Bordeaux, France, July 2018. [Bibtex]

  @article{ismp-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Positive Perspective on Negative Curvature}},
  journal = {{International Symposium on Mathematical Programming (ISMP), Bordeaux, France}},
  month = {July},
  year = {2018},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. ADMM, Accelerated-ADMM, and Continuous Dynamical Systems. DIMAC Workshop on ADMM and Proximal Splitting Methods in Optimization, Rutgers University, New Jersey, June 2018. [Bibtex]

  @article{rutgers-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{ADMM, Accelerated-ADMM, and Continuous Dynamical Systems}},
  journal = {{DIMAC Workshop on ADMM and Proximal Splitting Methods in Optimization, Rutgers University, New Jersey}},
  month = {June},
  year = {2018},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Negative Curvature in Deterministic and Stochastic Nonconvex Optimization. 11th U.S.-Mexico Workshop on Optimization and its Applications, Huatulco, Mexico, January 2018. [Bibtex]

  @article{mexico-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{Negative Curvature in Deterministic and Stochastic Nonconvex Optimization}},
  journal = {{11th U.S.-Mexico Workshop on Optimization and its Applications, Huatulco, Mexico}},
  month = {January},
  year = {2018},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Low-rank Convex Optimization. The 15th EUROPT Workshop on Advances in Continuous Optimization, Montréal, Québec, Canada, July 2017. [Bibtex]

  @article{quebec-2017-robinson,
  author = {Robinson, Daniel P.},
  title = {{Low-rank Convex Optimization}},
  journal = {{The 15th EUROPT Workshop on Advances in Continuous Optimization, Montr\'{e}al, Qu\'{e}bec, Canada}},
  month = {July},
  year = {2017},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Large-scale Subspace Clustering Algorithms. The Third International Conference on Engineering and Computational Mathematics (ECM), Workshop on Big Data Analysis with Applications, Hong Kong Polytechnic University, June 2017. [Bibtex]

  @article{ecm-2017-robinson,
  author = {Robinson, Daniel P.},
  title = {{Large-scale Subspace Clustering Algorithms}},
  journal = {{The Third International Conference on Engineering and Computational Mathematics (ECM), Workshop on Big Data Analysis with Applications, Hong Kong Polytechnic University}},
  month = {June},
  year = {2017},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. An Enhanced Truncated-Newton Algorithm for Large-scale Optimization. SIAM Conference on Optimization (SIOPT), Vancouver, British Columbia, Canada, May 2017. [Bibtex]

  @article{siopt-2017-robinson,
  author = {Robinson, Daniel P.},
  title = {{An Enhanced Truncated-Newton Algorithm for Large-scale Optimization}},
  journal = {{SIAM Conference on Optimization (SIOPT), Vancouver, British Columbia, Canada}},
  month = {May},
  year = {2017},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Scalable Subspace Clustering via the Elastic Net. INFORMS Annual Meeting, Nashville, Tennessee, November 2016. [Bibtex]

  @article{informs-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Subspace Clustering via the Elastic Net}},
  journal = {{INFORMS Annual Meeting, Nashville, Tennessee}},
  month = {November},
  year = {2016},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Geometry Driven Active-set Method for Elastic-net Minimization. Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, Pennsylvania, August 2016. [Bibtex]

  @article{mopta-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Geometry Driven Active-set Method for Elastic-net Minimization}},
  journal = {{Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, Pennsylvania}},
  month = {August},
  year = {2016},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Low-rank Convex Optimization. International Conference on Continuous Optimization (ICCOPT), Tokyo, Japan, August 2016. [Bibtex]

  @article{iccopt-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{Low-rank Convex Optimization}},
  journal = {{International Conference on Continuous Optimization (ICCOPT), Tokyo, Japan}},
  month = {August},
  year = {2016},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Scalable Subspace Clustering via the Elastic Net. Workshop on Nonlinear Optimization Algorithms and Industrial Applications, Fields Institute, Toronto, Canada, June 2016. [Bibtex]

  @article{toronto-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Subspace Clustering via the Elastic Net}},
  journal = {{Workshop on Nonlinear Optimization Algorithms and Industrial Applications, Fields Institute, Toronto, Canada}},
  month = {June},
  year = {2016},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Adaptive Subspace Methods for Optimization. 10th U.S.-Mexico Workshop on Optimization and its Applications, Mérida, Mexico, January 2016. [Bibtex]

  @article{mexico-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{Adaptive Subspace Methods for Optimization}},
  journal = {{10th U.S.-Mexico Workshop on Optimization and its Applications, M\'{e}rida, Mexico}},
  month = {January},
  year = {2016},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Solver for $\ell_1$-regularized Convex Optimization. INFORMS Annual Meeting, Philadelphia, Pennsylvania, November 2015. [Bibtex]

  @article{informs-2015-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Solver for $\ell_1$-regularized Convex Optimization}},
  journal = {{INFORMS Annual Meeting, Philadelphia, Pennsylvania}},
  month = {November},
  year = {2015},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Scalable, Efficient, and Robust NLP Solvers. International Symposium of Mathematical Programming (ISMP), Pittsburgh, Pennsylvania, July 2015. [Bibtex]

  @article{ismp-2015a-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable, Efficient, and Robust NLP Solvers}},
  journal = {{International Symposium of Mathematical Programming (ISMP), Pittsburgh, Pennsylvania}},
  month = {July},
  year = {2015},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Cost Sensitive Regularization in Model Prediction. International Symposium of Mathematical Programming (ISMP), Pittsburgh, Pennsylvania, July 2015. [Bibtex]

  @article{ismp-2015b-robinson,
  author = {Robinson, Daniel P.},
  title = {{Cost Sensitive Regularization in Model Prediction}},
  journal = {{International Symposium of Mathematical Programming (ISMP), Pittsburgh, Pennsylvania}},
  month = {July},
  year = {2015},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Shifted Penalty-barrier Method for NLP. Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, Pennsylvania, July 2015. [Bibtex]

  @article{mopta-2015-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Shifted Penalty-barrier Method for NLP}},
  journal = {{Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, Pennsylvania}},
  month = {July},
  year = {2015},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Flexible ADMM for Big Data Applications. Optimization and Big Data workshop (invited spotlight talk and poster), University of Edinburgh, Edinburgh, May 2015. [Bibtex]

  @article{edinburgh-2015-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Flexible ADMM for Big Data Applications}},
  journal = {{Optimization and Big Data workshop (invited spotlight talk and poster), University of Edinburgh, Edinburgh}},
  month = {May},
  year = {2015},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Dostál and Schöberl’s Method for Nonconvex Problem. Foundations of Computational Mathematics (FoCM), Montevideo, Uruguay, December 2014. [Bibtex]

  @article{montevideo-2014-robinson,
  author = {Robinson, Daniel P.},
  title = {{Dost\'{a}l and Sch\"{o}berl’s Method for Nonconvex Problem}},
  journal = {{Foundations of Computational Mathematics (FoCM), Montevideo, Uruguay}},
  month = {December},
  year = {2014},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Large-scale Subspace Clustering. Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, Pennsylvania, August 2014. [Bibtex]

  @article{mopta-2014-robinson,
  author = {Robinson, Daniel P.},
  title = {{Large-scale Subspace Clustering}},
  journal = {{Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, Pennsylvania}},
  month = {August},
  year = {2014},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Trust-funnel Algorithm. Incorporating Cost with Model Prediction. Southern California Optimization Day, San Diego, California, May 2014. [Bibtex]

  @article{sandiego-2014-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Trust-funnel Algorithm. Incorporating Cost with Model Prediction}},
  journal = {{Southern California Optimization Day, San Diego, California}},
  month = {May},
  year = {2014},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Matrix-free Trust-funnel Barrier-SQP Method for Extreme-scale Constrained Optimization. SIAM Conference on Optimization (SIOPT), San Diego, California, May 2014. [Bibtex]

  @article{siopt-2014-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Matrix-free Trust-funnel Barrier-SQP Method for Extreme-scale Constrained Optimization}},
  journal = {{SIAM Conference on Optimization (SIOPT), San Diego, California}},
  month = {May},
  year = {2014},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Trust-funnel Algorithm for Nonlinear Programming. INFORMS Optimization Society Conference, Houston, Texas, March 2014. [Bibtex]

  @article{informs-2014-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Trust-funnel Algorithm for Nonlinear Programming}},
  journal = {{INFORMS Optimization Society Conference, Houston, Texas}},
  month = {March},
  year = {2014},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Trust-funnel Algorithm for Nonlinear Programming. AMS Sectional Meeting (special session), UMBC, Baltimore, Maryland, March 2014. [Bibtex]

  @article{ams-2014-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Trust-funnel Algorithm for Nonlinear Programming}},
  journal = {{AMS Sectional Meeting (special session), UMBC, Baltimore, Maryland}},
  month = {March},
  year = {2014},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Filter SQP method. International Conference on Continuous Optimization (ICCOPT), Lisbon, Portugal, July 2013. [Bibtex]

  @article{lisbon-2013-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Filter SQP method}},
  journal = {{International Conference on Continuous Optimization (ICCOPT), Lisbon, Portugal}},
  month = {July},
  year = {2013},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Two-phase Matrix Splitting Methods for Asymmetric and Symmetric LCP. Workshop on Complementarity and Related Problems, National University of Singapore, Singapore, December 2012. [Bibtex]

  @article{singapore-2012-robinson,
  author = {Robinson, Daniel P.},
  title = {{Two-phase Matrix Splitting Methods for Asymmetric and Symmetric LCP}},
  journal = {{Workshop on Complementarity and Related Problems, National University of Singapore, Singapore}},
  month = {December},
  year = {2012},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Steering Augmented Lagrangian Methods. INFORMS Annual Meeting, Phoenix, Arizona, October 2012. [Bibtex]

  @article{informs-2012-robinson,
  author = {Robinson, Daniel P.},
  title = {{Steering Augmented Lagrangian Methods}},
  journal = {{INFORMS Annual Meeting, Phoenix, Arizona}},
  month = {October},
  year = {2012},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Steering Augmented Lagrangian Methods. International Symposium of Mathematical Programming (ISMP), Berlin, Germany, August 2012. [Bibtex]

  @article{ismp-2012-robinson,
  author = {Robinson, Daniel P.},
  title = {{Steering Augmented Lagrangian Methods}},
  journal = {{International Symposium of Mathematical Programming (ISMP), Berlin, Germany}},
  month = {August},
  year = {2012},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. LCP and SQP. SIAM Conference on Optimization, Darmstadt, Germany, May 2011. [Bibtex]

  @article{siam-2011-robinson,
  author = {Robinson, Daniel P.},
  title = {{LCP and SQP}},
  journal = {{SIAM Conference on Optimization, Darmstadt, Germany}},
  month = {May},
  year = {2011},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. High-order Methods for Overcoming Degeneracy in Interior-point Methods for Quadratic Programming. International Conference on Continuous Optimization (ICCOPT), Santiago, Chile, July 2010. [Bibtex]

  @article{chile-2010-robinson,
  author = {Robinson, Daniel P.},
  title = {{High-order Methods for Overcoming Degeneracy in Interior-point Methods for Quadratic Programming}},
  journal = {{International Conference on Continuous Optimization (ICCOPT), Santiago, Chile}},
  month = {July},
  year = {2010},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. An Interior-point Trust-funnel Algorithm for Large-scale Nonconvex Optimization. INFORMS Annual Meeting, San Diego, California, October 2009. [Bibtex]

  @article{informs-2009-robinson,
  author = {Robinson, Daniel P.},
  title = {{An Interior-point Trust-funnel Algorithm for Large-scale Nonconvex Optimization}},
  journal = {{INFORMS Annual Meeting, San Diego, California}},
  month = {October},
  year = {2009},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. Second Derivative SQP Methods for Solving Large Nonlinear Optimization Problems. Bath-RAL Numerical Analysis Day, Rutherford Appleton Laboratory, Didcot, England, September 2009. [Bibtex]

  @article{england-2009b-robinson,
  author = {Robinson, Daniel P.},
  title = {{Second Derivative SQP Methods for Solving Large Nonlinear Optimization Problems}},
  journal = {{Bath-RAL Numerical Analysis Day, Rutherford Appleton Laboratory, Didcot, England}},
  month = {September},
  year = {2009},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. S2QP – a second derivative SQP method. International Symposium of Mathematical Programming (ISMP), Chicago, Illinois, August 2009. [Bibtex]

  @article{ismp-2009-robinson,
  author = {Robinson, Daniel P.},
  title = {{S2QP -- a second derivative SQP method}},
  journal = {{International Symposium of Mathematical Programming (ISMP), Chicago, Illinois}},
  month = {August},
  year = {2009},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Trust-funnel Algorithm for Nonlinear Programming. Biennial Conference on Numerical Analysis, University of Strathclyde, England, June 2009. [Bibtex]

  @article{england-2009a-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Trust-funnel Algorithm for Nonlinear Programming}},
  journal = {{Biennial Conference on Numerical Analysis, University of Strathclyde, England}},
  month = {June},
  year = {2009},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Generalized Primal-dual Augmented Lagrangian. SIAM Annual Meeting, San Diego, California, July 2008. [Bibtex]

  @article{siam-2008b-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Generalized Primal-dual Augmented Lagrangian}},
  journal = {{SIAM Annual Meeting, San Diego, California}},
  month = {July},
  year = {2008},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

• Daniel P. Robinson. A Second Derivative SQP Method with Imposed Descent. SIAM Conference on Optimization, Boston, Massachusetts, May 2008. [Bibtex]

  @article{siam-2008a-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Second Derivative SQP Method with Imposed Descent}},
  journal = {{SIAM Conference on Optimization, Boston, Massachusetts}},
  month = {May},
  year = {2008},
  papercite = {1. Invited Conference and Workshop Presentations}
  }

2. Invited Seminar Presentations at Universities and Research Centers

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. Southern Methodist University, Department of Engineering Management, Information, and Systems, Dallas, Texas, April 2019. [Bibtex]

  @article{smu-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{Southern Methodist University, Department of Engineering Management, Information, and Systems, Dallas, Texas}},
  month = {April},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Scalable Optimization Algorithms for Large-scale Subspace Clustering. New York University (NYU), Courant Institute, Department of Mathematics, New York, New York, March 2019. [Bibtex]

  @article{nyu-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Optimization Algorithms for Large-scale Subspace Clustering}},
  journal = {{New York University (NYU), Courant Institute, Department of Mathematics, New York, New York}},
  month = {March},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. Rutgers University, School of Business, Newark and New Brunswick, New Jersey, March 2019. [Bibtex]

  @article{rutgers-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{Rutgers University, School of Business, Newark and New Brunswick, New Jersey}},
  month = {March},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. University of Colorado Denver, Department of Mathematical and Statistical Sciences, Denver, Colorado, January 2019. [Bibtex]

  @article{ucdenver-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{University of Colorado Denver, Department of Mathematical and Statistical Sciences, Denver, Colorado}},
  month = {January},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. Worcester Polytechnic Institute, Data Science, Worcester, Massachusetts, January 2019. [Bibtex]

  @article{wpi-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{Worcester Polytechnic Institute, Data Science, Worcester, Massachusetts}},
  month = {January},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. University of Iowa, Department of Industrial and Systems Engineering, Iowa City, Iowa, January 2019. [Bibtex]

  @article{uofiowa-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{University of Iowa, Department of Industrial and Systems Engineering, Iowa City, Iowa}},
  month = {January},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. University of Florida, Department of Mathematics, Gainesville, Florida, Feburary 2019. [Bibtex]

  @article{uoff-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{University of Florida, Department of Mathematics, Gainesville, Florida}},
  month = {Feburary},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. Lehigh University, Department of Industrial and Systems Engineering, Bethlehem, Pennsylvania, Feburary 2019. [Bibtex]

  @article{lehigh-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{Lehigh University, Department of Industrial and Systems Engineering, Bethlehem, Pennsylvania}},
  month = {Feburary},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. University of Wisconsin-Madison, Department of Industrial and Systems Engineering, Madison, Wisconsin, Feburary 2019. [Bibtex]

  @article{uw-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{University of Wisconsin-Madison, Department of Industrial and Systems Engineering, Madison, Wisconsin}},
  month = {Feburary},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering. Colorado State University, Department of Mathematics, Fort Collins, Colorado, Feburary 2019. [Bibtex]

  @article{colorado-2019-robinson,
  author = {Robinson, Daniel P.},
  title = {{Effectively Using Negative Curvature in Optimization and Scalable Methods for Subspace Clustering}},
  journal = {{Colorado State University, Department of Mathematics, Fort Collins, Colorado}},
  month = {Feburary},
  year = {2019},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Recent Work on Effectively Using Negative Curvature in Optimization, and Scalable Methods for Subspace Clustering. University of Michigan, Department of Industrial and Operations Engineering, Ann Arbor, Michigan, December 2018. [Bibtex]

  @article{umichigan-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{Recent Work on Effectively Using Negative Curvature in Optimization, and Scalable Methods for Subspace Clustering}},
  journal = {{University of Michigan, Department of Industrial and Operations Engineering, Ann Arbor, Michigan}},
  month = {December},
  year = {2018},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. A Positive Perspective on Negative Curvature. University of Minnesota-Twin Cities, Department of Industrial and Systems Engineering, Minneapolis, Minnesota, November 2018. [Bibtex]

  @article{minnesota-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Positive Perspective on Negative Curvature}},
  journal = {{University of Minnesota-Twin Cities, Department of Industrial and Systems Engineering, Minneapolis, Minnesota}},
  month = {November},
  year = {2018},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. A Positive Perspective on Negative Curvature. University of North Carolina at Charlotte, Department of Systems Engineering and Engineering Management, Charlotte, North Carolina, October 2018. [Bibtex]

  @article{uncc-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Positive Perspective on Negative Curvature}},
  journal = {{University of North Carolina at Charlotte, Department of Systems Engineering and Engineering Management, Charlotte, North Carolina}},
  month = {October},
  year = {2018},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Scalable Optimization Algorithms for Large-scale Subspace Clustering. George Mason University, Department of Mathematics, Fairfax, Virginia, March 2018. [Bibtex]

  @article{gmu-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Optimization Algorithms for Large-scale Subspace Clustering}},
  journal = {{George Mason University, Department of Mathematics, Fairfax, Virginia}},
  month = {March},
  year = {2018},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Scalable Optimization Algorithms for Large-scale Subspace Clustering. University of California at San Diego, Department of Mathematics, San Diego, California, March 2017. [Bibtex]

  @article{ucsd-2017-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Optimization Algorithms for Large-scale Subspace Clustering}},
  journal = {{University of California at San Diego, Department of Mathematics, San Diego, California}},
  month = {March},
  year = {2017},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Scalable Optimization Algorithms for Large-scale Subspace Clustering. University of California at Berkeley, Department of Mathematics, Berkeley, California, March 2017. [Bibtex]

  @article{berkeley-2017-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Optimization Algorithms for Large-scale Subspace Clustering}},
  journal = {{University of California at Berkeley, Department of Mathematics, Berkeley, California}},
  month = {March},
  year = {2017},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Scalable Optimization Algorithms for Large-scale Subspace Clustering. Stanford University, Department of Management Science and Engineering, Stanford, California, March 2017. [Bibtex]

  @article{stanford-2017-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Optimization Algorithms for Large-scale Subspace Clustering}},
  journal = {{Stanford University, Department of Management Science and Engineering, Stanford, California}},
  month = {March},
  year = {2017},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Scalable Optimization Algorithms for Large-scale Subspace Clustering. University of Southern California, Department of Industrial and Systems Engineering, Los Angeles, California, March 2017. [Bibtex]

  @article{usc-2017-robinson,
  author = {Robinson, Daniel P.},
  title = {{Scalable Optimization Algorithms for Large-scale Subspace Clustering}},
  journal = {{University of Southern California, Department of Industrial and Systems Engineering, Los Angeles, California}},
  month = {March},
  year = {2017},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Cost-sensitive Prediction: Applications in Healthcare. The Johns Hopkins University, Institute for Data Intensive Engineering and Sciences (IDIES) Annual Symposium, Baltimore, Maryland, October 2016. [Bibtex]

  @article{jhu-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{Cost-sensitive Prediction: Applications in Healthcare}},
  journal = {{The Johns Hopkins University, Institute for Data Intensive Engineering and Sciences (IDIES) Annual Symposium, Baltimore, Maryland}},
  month = {October},
  year = {2016},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. New Directions Short Course on Mathematical Optimization (invitation declined). Institute for Mathematics and its Applications (IMA), August 2016. [Bibtex]

  @article{ima-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{New Directions Short Course on Mathematical Optimization (invitation declined)}},
  journal = {{Institute for Mathematics and its Applications (IMA)}},
  month = {August},
  year = {2016},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Cost-sensitive Prediction: Applications in Healthcare. The Johns Hopkins University, Institute for Data Intensive Engineering and Sciences (IDIES) Annual Symposium, Baltimore, Maryland, October 2015. [Bibtex]

  @article{jhu-2015-robinson,
  author = {Robinson, Daniel P.},
  title = {{Cost-sensitive Prediction: Applications in Healthcare}},
  journal = {{The Johns Hopkins University, Institute for Data Intensive Engineering and Sciences (IDIES) Annual Symposium, Baltimore, Maryland}},
  month = {October},
  year = {2015},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. A trust-region method with optimal complexity. Czech Technical University in Prague, Department of Computer Science, Prague, Czech Republic, May 2015. [Bibtex]

  @article{prague-2015-robinson,
  author = {Robinson, Daniel P.},
  title = {{A trust-region method with optimal complexity}},
  journal = {{Czech Technical University in Prague, Department of Computer Science, Prague, Czech Republic}},
  month = {May},
  year = {2015},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Globalizing local methods. Eidgen\:ossische Technische Hochschule (ETH) Z\:urich, Institut f\:ur Operations Research, Departement Mathematik, Z\:urich,Switzerland, May 2015. [Bibtex]

  @article{zurich-2015-robinson,
  author = {Robinson, Daniel P.},
  title = {{Globalizing local methods}},
  journal = {{Eidgen\:{o}ssische Technische Hochschule (ETH) Z\:{u}rich, Institut f\:{u}r Operations Research, Departement Mathematik, Z\:{u}rich,Switzerland}},
  month = {May},
  year = {2015},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. A QP solver for Bound-constrained Problems. University of Oxford, Numerical Analysis Group, Mathematical Institute, Oxford, England, May 2015. [Bibtex]

  @article{oxford-2015-robinson,
  author = {Robinson, Daniel P.},
  title = {{A QP solver for Bound-constrained Problems}},
  journal = {{University of Oxford, Numerical Analysis Group, Mathematical Institute, Oxford, England}},
  month = {May},
  year = {2015},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Inexactness in Large-scale Active-set Methods for Large-scale Optimization. University of Maryland, Baltimore County, Department of Mathematics and Statistics, Baltimore, Maryland, November 2014. [Bibtex]

  @article{uofmaryland-2014-robinson,
  author = {Robinson, Daniel P.},
  title = {{Inexactness in Large-scale Active-set Methods for Large-scale Optimization}},
  journal = {{University of Maryland, Baltimore County, Department of Mathematics and Statistics, Baltimore, Maryland}},
  month = {November},
  year = {2014},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Contributions in Nonlinear Optimization. University of Wisconsin-Madison, Wisconsin Institutes of Discovery, Madison, Wisconsin, November 2013. [Bibtex]

  @article{uw-2013-robinson,
  author = {Robinson, Daniel P.},
  title = {{Contributions in Nonlinear Optimization}},
  journal = {{University of Wisconsin-Madison, Wisconsin Institutes of Discovery, Madison, Wisconsin}},
  month = {November},
  year = {2013},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Steering Augmented Lagrangian Methods. Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, Illinois, July 2013. [Bibtex]

  @article{argonne-2013-robinson,
  author = {Robinson, Daniel P.},
  title = {{Steering Augmented Lagrangian Methods}},
  journal = {{Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, Illinois}},
  month = {July},
  year = {2013},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Numerical Optimization: Interesting Applications and Recent Research. James Madison University, Department of Mathematics and Statistics, Harrisonburg, Virginia, April 2013. [Bibtex]

  @article{jmu-2013-robinson,
  author = {Robinson, Daniel P.},
  title = {{Numerical Optimization: Interesting Applications and Recent Research}},
  journal = {{James Madison University, Department of Mathematics and Statistics, Harrisonburg, Virginia}},
  month = {April},
  year = {2013},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Rapidly Converging Active-set Methods for Convex Quadratic Programming. Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, April 2013. [Bibtex]

  @article{duke-2013-robinson,
  author = {Robinson, Daniel P.},
  title = {{Rapidly Converging Active-set Methods for Convex Quadratic Programming}},
  journal = {{Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina}},
  month = {April},
  year = {2013},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Rapidly Converging Active-set Methods for Convex Quadratic Programming. Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina, April 2013. [Bibtex]

  @article{duke-2013-robinson,
  author = {Robinson, Daniel P.},
  title = {{Rapidly Converging Active-set Methods for Convex Quadratic Programming}},
  journal = {{Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina}},
  month = {April},
  year = {2013},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Large-scale Optimization Methods Based on the Augmented Lagrangian. New York University (NYU), Department of Computer Science, New York City, New York, November 2012. [Bibtex]

  @article{nyu-2012-robinson,
  author = {Robinson, Daniel P.},
  title = {{Large-scale Optimization Methods Based on the Augmented Lagrangian}},
  journal = {{New York University (NYU), Department of Computer Science, New York City, New York}},
  month = {November},
  year = {2012},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Pushing the Limits of Sequential Quadratic Programming. University of Maryland, Baltimore County, Department of Mathematics and Statistics, Baltimore, Maryland, March 2012. [Bibtex]

  @article{uofmaryland-2012-robinson,
  author = {Robinson, Daniel P.},
  title = {{Pushing the Limits of Sequential Quadratic Programming}},
  journal = {{University of Maryland, Baltimore County, Department of Mathematics and Statistics, Baltimore, Maryland}},
  month = {March},
  year = {2012},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Matrix Splitting Methods for BQP and LCP. The Johns Hopkins University, Center for Imaging Science, Baltimore, Maryland, October 2011. [Bibtex]

  @article{jhu-2012-robinson,
  author = {Robinson, Daniel P.},
  title = {{Matrix Splitting Methods for BQP and LCP}},
  journal = {{The Johns Hopkins University, Center for Imaging Science, Baltimore, Maryland}},
  month = {October},
  year = {2011},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Two-phase Matrix Splitting Methods for BQP and LCP. Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, Illinois, July 2011. [Bibtex]

  @article{argonne-2011-robinson,
  author = {Robinson, Daniel P.},
  title = {{Two-phase Matrix Splitting Methods for BQP and LCP}},
  journal = {{Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, Illinois}},
  month = {July},
  year = {2011},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Optimization in Machine/statistical Learning. University of San Diego, Department of Mathematics and Computer Science, San Diego, California, March 2011. [Bibtex]

  @article{ucsd-2011-robinson,
  author = {Robinson, Daniel P.},
  title = {{Optimization in Machine/statistical Learning}},
  journal = {{University of San Diego, Department of Mathematics and Computer Science, San Diego, California}},
  month = {March},
  year = {2011},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Using Active-set Phases to Accelerate Algorithms. Lehigh University, Department of Industrial and Systems Engineering, Bethlehem, Pennsylvania, March 2011. [Bibtex]

  @article{lehigh-2011-robinson,
  author = {Robinson, Daniel P.},
  title = {{Using Active-set Phases to Accelerate Algorithms}},
  journal = {{Lehigh University, Department of Industrial and Systems Engineering, Bethlehem, Pennsylvania}},
  month = {March},
  year = {2011},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Recent Advances in Second Derivative Sequential Quadratic Programming Methods. University of Warwick, Department of Mathematics, Coventry, UK, March 2010. [Bibtex]

  @article{warwick-2010-robinson,
  author = {Robinson, Daniel P.},
  title = {{Recent Advances in Second Derivative Sequential Quadratic Programming Methods}},
  journal = {{University of Warwick, Department of Mathematics, Coventry, UK}},
  month = {March},
  year = {2010},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. An Introduction to Sequential Quadratic Programming Methods. Trinity University, Department of Mathematics, San Antonio, Texas, February 2010. [Bibtex]

  @article{trinity-2010-robinson,
  author = {Robinson, Daniel P.},
  title = {{An Introduction to Sequential Quadratic Programming Methods}},
  journal = {{Trinity University, Department of Mathematics, San Antonio, Texas}},
  month = {February},
  year = {2010},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Recent Advances in Second Derivative Sequential Quadratic Programming Methods. Edinburgh Research Group in Optimization (ERGO), Edinburgh, UK, February 2010. [Bibtex]

  @article{edinburgh-2010-robinson,
  author = {Robinson, Daniel P.},
  title = {{Recent Advances in Second Derivative Sequential Quadratic Programming Methods}},
  journal = {{Edinburgh Research Group in Optimization (ERGO), Edinburgh, UK}},
  month = {February},
  year = {2010},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. Recent Work in Sequential Quadratic Programming. Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, Illinois, January 2010. [Bibtex]

  @article{argonne-2010-robinson,
  author = {Robinson, Daniel P.},
  title = {{Recent Work in Sequential Quadratic Programming}},
  journal = {{Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, Illinois}},
  month = {January},
  year = {2010},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

• Daniel P. Robinson. A Generalized Primal-dual Augmented Lagrangian. University of Oxford, Computational Mathematics and Applications Seminar, Oxford, UK, October 2007. [Bibtex]

  @article{oxford-2007-robinson,
  author = {Robinson, Daniel P.},
  title = {{A Generalized Primal-dual Augmented Lagrangian}},
  journal = {{University of Oxford, Computational Mathematics and Applications Seminar, Oxford, UK}},
  month = {October},
  year = {2007},
  papercite = {2. Invited Seminar Presentations at Universities and Research Centers}
  }

3. Internal Department and Other Presentations

• Daniel P. Robinson. Learning a Union of Subspaces from Big and Corrupted Data. The National Science Foundation joint PI Meeting: BIGDATA and Big Data Hubs and Spokes (poster session), Arlington, Virginia, June 2018. [Bibtex]

  @article{nsf-2018-robinson,
  author = {Robinson, Daniel P.},
  title = {{Learning a Union of Subspaces from Big and Corrupted Data}},
  journal = {{The National Science Foundation joint PI Meeting: BIGDATA and Big Data Hubs and Spokes (poster session), Arlington, Virginia}},
  month = {June},
  year = {2018},
  papercite = {3. Internal Department and Other Presentations}
  }

• Daniel P. Robinson. FaRSA: a Fast Reduced-space Algorithm for Sparse Convex Optimization. The Institute for Data Intensive Engineering and Science (IDIES), Annual Symposium, Johns Hopkins University (poster session), Baltimore, MD, October 2017. [Bibtex]

  @article{idies-2017-robinson,
  author = {Robinson, Daniel P.},
  title = {{FaRSA: a Fast Reduced-space Algorithm for Sparse Convex Optimization}},
  journal = {{The Institute for Data Intensive Engineering and Science (IDIES), Annual Symposium, Johns Hopkins University (poster session), Baltimore, MD}},
  month = {October},
  year = {2017},
  papercite = {3. Internal Department and Other Presentations}
  }

• Daniel P. Robinson. Incorporating User Preferences in Predictive Models via Structured Regularizers. International Joint Conference on Artificial Intelligence (poster session), New York, NY, July 2016. [Bibtex]

  @article{ai-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{Incorporating User Preferences in Predictive Models via Structured Regularizers}},
  journal = {{International Joint Conference on Artificial Intelligence (poster session), New York, NY}},
  month = {July},
  year = {2016},
  papercite = {3. Internal Department and Other Presentations}
  }

• Daniel P. Robinson. Learning a Union of Subspaces from Big and Corrupted Data. National Science Foundation Big Data PI Meeting, Arlington, Virginia, April 2016. [Bibtex]

  @article{nsf-2016-robinson,
  author = {Robinson, Daniel P.},
  title = {{Learning a Union of Subspaces from Big and Corrupted Data}},
  journal = {{National Science Foundation Big Data PI Meeting, Arlington, Virginia}},
  month = {April},
  year = {2016},
  papercite = {3. Internal Department and Other Presentations}
  }

• Daniel P. Robinson. Steering Augmented Lagrangian Methods. The Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, Maryland, October 2013. [Bibtex]

  @article{jhu-2013-robinson,
  author = {Robinson, Daniel P.},
  title = {{Steering Augmented Lagrangian Methods}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, Maryland}},
  month = {October},
  year = {2013},
  papercite = {3. Internal Department and Other Presentations}
  }

• Daniel P. Robinson. Two-phase Matrix Splitting Methods for BQP and LCP. The Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, Maryland, September 2011. [Bibtex]

  @article{jhu-2011a-robinson,
  author = {Robinson, Daniel P.},
  title = {{Two-phase Matrix Splitting Methods for BQP and LCP}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, Maryland}},
  month = {September},
  year = {2011},
  papercite = {3. Internal Department and Other Presentations}
  }

• Daniel P. Robinson. Using Active-set Phases to Accelerate Optimization Algorithms. The Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, Maryland, February 2011. [Bibtex]

  @article{jhu-2011b-robinson,
  author = {Robinson, Daniel P.},
  title = {{Using Active-set Phases to Accelerate Optimization Algorithms}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, Maryland}},
  month = {February},
  year = {2011},
  papercite = {3. Internal Department and Other Presentations}
  }

• Daniel P. Robinson. Existence of a Derivative on a Finite Set. Joint Mathematics Meeting (poster session for undergraduate research), New Orleans, Louisiana, ? 2001. [Bibtex]

  @article{joint-2001-robinson,
  author = {Robinson, Daniel P.},
  title = {{Existence of a Derivative on a Finite Set}},
  journal = {{Joint Mathematics Meeting (poster session for undergraduate research), New Orleans, Louisiana}},
  month = {?},
  year = {2001},
  papercite = {3. Internal Department and Other Presentations}
  }

• Daniel P. Robinson. Existence of a Derivative on a Finite Set. MD-DC-VA Fall Sectional Meeting, Loyola College, Baltimore, Maryland, ? 2000. [Bibtex]

  @article{loyola-2000-robinson,
  author = {Robinson, Daniel P.},
  title = {{Existence of a Derivative on a Finite Set}},
  journal = {{MD-DC-VA Fall Sectional Meeting, Loyola College, Baltimore, Maryland}},
  month = {?},
  year = {2000},
  papercite = {3. Internal Department and Other Presentations}
  }

4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations

• Tianyu Ding. Robust Subspace Learning with Dual Principal Component Pursuit. MathWorks Lakeside Campus, Natick, Massachusetts, June 2019. [Bibtex]

  @article{mathworks-2019-ding,
  author = {Tianyu Ding},
  title = {{Robust Subspace Learning with Dual Principal Component Pursuit}},
  journal = {{MathWorks Lakeside Campus, Natick, Massachusetts}},
  month = {June},
  year = {2019},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Tianyu Ding. Noisy Dual Principal Component Pursuit. International Conference on Machine Learning (ICML), Long Beach, California, June 2019. [Bibtex]

  @article{icml-2019-ding,
  author = {Tianyu Ding},
  title = {{Noisy Dual Principal Component Pursuit}},
  journal = {{International Conference on Machine Learning (ICML), Long Beach, California}},
  month = {June},
  year = {2019},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Tianyi Chen. Drawing Beautification. The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland, October 2017. [Bibtex]

  @article{jhu-2017-chen,
  author = {Tianyi Chen},
  title = {{Drawing Beautification}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland}},
  month = {October},
  year = {2017},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Tianyi Chen. A Fast Reduced-Space Algorithm for $\ell_1$-regularized Problem. Microsoft Research Lab, Redmond, Washington, June 2017. [Bibtex]

  @article{microsoft-2017-chen,
  author = {Tianyi Chen},
  title = {{A Fast Reduced-Space Algorithm for $\ell_1$-regularized Problem}},
  journal = {{Microsoft Research Lab, Redmond, Washington}},
  month = {June},
  year = {2017},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Tianyi Chen. FaRSA: Fast Reduced-space Algorithm for Minimizing $\ell_1$-regularized Convex Functions. Institute for Operations Research and Management Sciences (INFORMS), Nashville, Ten- nessee, November 2016. [Bibtex]

  @article{informs-2016-chen,
  author = {Tianyi Chen},
  title = {{FaRSA: Fast Reduced-space Algorithm for Minimizing $\ell_1$-regularized Convex Functions}},
  journal = {{Institute for Operations Research and Management Sciences (INFORMS), Nashville, Ten- nessee}},
  month = {November},
  year = {2016},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Tianyi Chen. FaRSA: Fast Reduced-space Algorithm for Minimizing $\ell_1$-regularized Convex Functions. The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland, October 2016. [Bibtex]

  @article{jhu-2016a-chen,
  author = {Tianyi Chen},
  title = {{FaRSA: Fast Reduced-space Algorithm for Minimizing $\ell_1$-regularized Convex Functions}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland}},
  month = {October},
  year = {2016},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Tianyi Chen. A Fast Reduced-Space Algorithm for $\ell_1$-regularized Problem. Amazon, Seattle, Washington, August 2016. [Bibtex]

  @article{amazon-2016-chen,
  author = {Tianyi Chen},
  title = {{A Fast Reduced-Space Algorithm for $\ell_1$-regularized Problem}},
  journal = {{Amazon, Seattle, Washington}},
  month = {August},
  year = {2016},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Tianyi Chen. A Reduced-space Algorithm for Minimizing $\ell_1$-regularized Convex Functions. The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland, April 2016. [Bibtex]

  @article{jhu-2016b-chen,
  author = {Tianyi Chen},
  title = {{A Reduced-space Algorithm for Minimizing $\ell_1$-regularized Convex Functions}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland}},
  month = {April},
  year = {2016},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Rachael E. Tappenden. Flexible ADMM for Big Data Applications. Control and Dynamical Systems Seminar at the University of Maryland, College Park, Maryland, November 2015. [Bibtex]

  @article{uofmaryland-2015-tappenden,
  author = {Rachael E. Tappenden},
  title = {{Flexible ADMM for Big Data Applications}},
  journal = {{Control and Dynamical Systems Seminar at the University of Maryland, College Park, Maryland}},
  month = {November},
  year = {2015},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Yueling Loh. A Stochastic Programming Model for Nurse Staffing in Post-Anesthesia Recovery Units. The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland, October 2015. [Bibtex]

  @article{jhu-2015-loh,
  author = {Yueling Loh},
  title = {{A Stochastic Programming Model for Nurse Staffing in Post-Anesthesia Recovery Units}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland}},
  month = {October},
  year = {2015},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Hassan Mohy-ud-Din. A QP Solver for Nonconvex Bound-constrained Problems. International Symposium on Mathematical Programming (ISMP), Pittsburgh, Pennsylvania, July 2015. [Bibtex]

  @article{ismp-2015-mohyuddin,
  author = {Hassan Mohy-ud-Din},
  title = {{A QP Solver for Nonconvex Bound-constrained Problems}},
  journal = {{International Symposium on Mathematical Programming (ISMP), Pittsburgh, Pennsylvania}},
  month = {July},
  year = {2015},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Rachael E. Tappenden. A Flexible ADMM Algorithm for Big Data Applications. International Symposium on Mathematical Programming (ISMP), Pittsburgh, Pennsylvania, July 2015. [Bibtex]

  @article{ismp-2015-tappenden,
  author = {Rachael E. Tappenden},
  title = {{A Flexible ADMM Algorithm for Big Data Applications}},
  journal = {{International Symposium on Mathematical Programming (ISMP), Pittsburgh, Pennsylvania}},
  month = {July},
  year = {2015},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Yueling Loh. Nonmonotone Filter SQO Algorithm: Local Convergence and Numerical Results. Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, Pennsylvania, July 2015. [Bibtex]

  @article{mopta-2015-loh,
  author = {Yueling Loh},
  title = {{Nonmonotone Filter SQO Algorithm: Local Convergence and Numerical Results}},
  journal = {{Modeling and Optimization: Theory and Applications (MOPTA), Bethlehem, Pennsylvania}},
  month = {July},
  year = {2015},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Rachael E. Tappenden. Coordinate Descent Methods for Modern Optimization Problems. The Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, Maryland, October 2014. [Bibtex]

  @article{jhu-2014-tappenden,
  author = {Rachael E. Tappenden},
  title = {{Coordinate Descent Methods for Modern Optimization Problems}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, Maryland}},
  month = {October},
  year = {2014},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Hao Jiang. Steering Augmented Lagrangian Methods. SIAM Conference on Optimization (SIOPT), San Diego, California, May 2014. [Bibtex]

  @article{siopt-2014-jiang,
  author = {Hao Jiang},
  title = {{Steering Augmented Lagrangian Methods}},
  journal = {{SIAM Conference on Optimization (SIOPT), San Diego, California}},
  month = {May},
  year = {2014},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Yueling Loh. A Filter Method with Unified Step Computation. SIAM Conference on Optimization (SIOPT), San Diego, California, May 2014. [Bibtex]

  @article{siopt-2014-loh,
  author = {Yueling Loh},
  title = {{A Filter Method with Unified Step Computation}},
  journal = {{SIAM Conference on Optimization (SIOPT), San Diego, California}},
  month = {May},
  year = {2014},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Yueling Loh. Filter Methods for Nonlinear Optimization. The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland, April 2014. [Bibtex]

  @article{jhu-2014-loh,
  author = {Yueling Loh},
  title = {{Filter Methods for Nonlinear Optimization}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland}},
  month = {April},
  year = {2014},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }

• Hao Jiang. The Augmented Lagrangian Method: Warm Start Strategies and Adaptive Penalty Updates. The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland, October 2013. [Bibtex]

  @article{jhu-2013-jiang,
  author = {Hao Jiang},
  title = {{The Augmented Lagrangian Method: Warm Start Strategies and Adaptive Penalty Updates}},
  journal = {{The Johns Hopkins University, Department of Applied Mathematics and Statistics student seminar, Baltimore, Maryland}},
  month = {October},
  year = {2013},
  papercite = {4. Undergraduate, Masters, PhD, and Postdoctoral Advisee Presentations}
  }