Stephen M. Robinson

Professor Emeritus

Room: 3015
Mechanical Engineering Building
1513 University Avenue
Madison, WI 53706-1539

Ph: (608) 263-6862
Fax: (608) 262-8454
smrobins@wisc.edu

Primary Affiliation:
Industrial and Systems Engineering

Additional Affiliations:
Computer Sciences,


Profile Summary

My research is in the development of quantitative methods for making the best use of scarce resources, which is part of the broad category of operations research methods. Within that category, I work particularly on nonlinear and stochastic optimization methods for both optimization and equilibrium problems, trying both to develop the underlying theory and to find better numerical methods for solving applied problems. I have developed algorithms for deterministic nonlinear problems, as well as sample-path algorithms for stochastic optimization problems. My recent work has focused especially on the mathematical properties of solutions of variational conditions, considered as functions of the data appearing in those conditions. For these problems I am working on efficient reparametrization methods for problem expression, on implicit-function methods for representing solutions as functions of the data, and on conditions to ensure Lipschitz continuity of solutions. Using these tools I am also developing numerical methods for efficient solution of variational conditions, based on generalized linearization among other techniques.

Education

  • Diploma 1986, U.S. Army War College
  • Ph.D. 1971, Computer Sciences, University of Wisconsin-Madison
  • M.S. 1963, Mathematics, New York University
  • B.A. 1962, Mathematics, University of Wisconsin

Research Interests

  • Variational analysis and optimization
  • Quantitative methods in managerial economics
  • Methods to support decision under uncertainty

Awards, Honors and Societies

  • George E. Kimball Medal, Institute for Operations Research and the Management Sciences (INFORMS), 2011
  • Fellow of the Society for Industrial and Applied Mathematics (SIAM), 2009
  • National Associate of the National Research Council, 2008
  • Member of the National Academy of Engineering, 2008
  • Fellow of the Institute for Operations Research and the Management Sciences (INFORMS), 2004
  • John K. Walker, Jr. Award, 2001, Military Operations Research Society
  • George B. Dantzig Prize, 1997, Mathematical Programming Society and Society for Industrial and Applied Mathematics
  • Byron Bird Award, 1996, College of Engineering, University of Wisconsin-Madison
  • Doctor honoris causa, 1996, Universitaet Zuerich, Switzerland

Publications

  • For a complete list, please see link to current curriculum vitae below.
  • S. Sridhar, P. F. Brennan, S. J. Wright, and S. M. Robinson, Optimizing financial effects of HIE: A multi-party linear programming approach. Journal of the American Medical Informatics Association, published online June 25, 2012: doi: 10.1136/amiajnl-2011-000606
  • S. M. Robinson, Equations on monotone graphs. Mathematical Programming Series A, published online 6 Jan 2012: DOI 10.1007/s10107-011-0509-4
  • S. M. Robinson, A short derivation of the conjugate of a supremum function. Journal of Convex Analysis 19 (2012) 569–574
  • S. M. Robinson, A point-of-attraction result for Newton’s method with point-based approximations. Optimization 60 (2011) 89–99; first published online 2010
  • Shu Lu and S.M. Robinson, Variational inequalities over perturbed polyhedral convex sets. Mathematics of Operations Research 33 (2008) 689-711

Links

Courses

Fall 2014-2015

  • ISYE 727 - Convex Analysis
  • Profile Summary

    My research is in the development of quantitative methods for making the best use of scarce resources, which is part of the broad category of operations research methods. Within that category, I work particularly on nonlinear and stochastic optimization methods for both optimization and equilibrium problems, trying both to develop the underlying theory and to find better numerical methods for solving applied problems. I have developed algorithms for deterministic nonlinear problems, as well as sample-path algorithms for stochastic optimization problems. My recent work has focused especially on the mathematical properties of solutions of variational conditions, considered as functions of the data appearing in those conditions. For these problems I am working on efficient reparametrization methods for problem expression, on implicit-function methods for representing solutions as functions of the data, and on conditions to ensure Lipschitz continuity of solutions. Using these tools I am also developing numerical methods for efficient solution of variational conditions, based on generalized linearization among other techniques.


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