J
Jon Lee
Researcher at University of Michigan
Publications - 210
Citations - 6342
Jon Lee is an academic researcher from University of Michigan. The author has contributed to research in topics: Matroid & Polytope. The author has an hindex of 32, co-authored 200 publications receiving 5797 citations. Previous affiliations of Jon Lee include Otto-von-Guericke University Magdeburg & Yale University.
Papers
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Journal ArticleDOI
An algorithmic framework for convex mixed integer nonlinear programs
Pierre Bonami,Lorenz T. Biegler,Andrew R. Conn,Gérard Cornuéjols,Ignacio E. Grossmann,Carl D. Laird,Jon Lee,Andrea Lodi,François Margot,Nicolas Sawaya,Andreas Wächter +10 more
TL;DR: A class of hybrid algorithms, of which branch-and-bound and polyhedral outer approximation are the two extreme cases, are proposed and implemented and Computational results that demonstrate the effectiveness of this framework are reported.
Journal ArticleDOI
Branching and bounds tighteningtechniques for non-convex MINLP
TL;DR: An sBB software package named couenne (Convex Over- and Under-ENvelopes for Non-linear Estimation) is developed and used for extensive tests on several combinations of BT and branching techniques on a set of publicly available and real-world MINLP instances and is compared with a state-of-the-art MINLP solver.
Journal ArticleDOI
An Exact Algorithm for Maximum Entropy Sampling
TL;DR: An upper bound for the entropy is established, based on the eigenvalue interlacing property, and incorporated in a branch-and-bound algorithm for the exact solution of the experimental design problem of selecting a most informative subset, having prespecified size, from a set of correlated random variables.
BookDOI
Mixed Integer Nonlinear Programming
Jon Lee,Sven Leyffer +1 more
TL;DR: Mixed-integer nonlinear programming (MINLP) problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables.
Book ChapterDOI
Nonlinear Integer Programming
TL;DR: This chapter is a study of a simple version of general nonlinear integer problems, where all constraints are still linear, and focuses on the computational complexity of the problem, which varies significantly with the type of nonlinear objective function in combination with the underlying combinatorial structure.