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Xiao Jiao Wang, March 27, 2018, 15:30-16:00, ITB 201
Speaker:   Xiao Jiao Wang

Title:  On inventory allocation for periodic review assemble-to-order systems
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Michael Metel, March 27, 2018, 16:30-17:30, ITB 201
Speaker:   Michael Metel
DeGroote School of Business
McMaster University

Title:  Electric car sharing charging station location optimization with limited vehicle relocation
Fields Institute Industrial Optimization Seminar, April 30, 2018
Speakers:   Reza Samavi (McMaster University and Vector Institute for Artificial Intelligence)
Ragavan Thurairatnam and Hashiam Kadhim ( Toronto)

On the first Tuesday of each month, the Industrial Optimization Seminar is held at the Fields Institute. See the seminar series website for further information.
Home arrow Seminars arrow Invited seminars arrow Kamil A. Khan, April 4, 2017, 16:30-17:30, ITB 201
Sunday, 24 June 2018
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Kamil A. Khan, April 4, 2017, 16:30-17:30, ITB 201
Speaker:   Kamil A. Khan
Department of Chemical Engineering
McMaster University

Title:  Generating convex underestimators for use in global optimization

Several applications in engineering, physics, and economics involve nonconvex optimization problems that must be solved to guaranteed global optimality. Methods for deterministic continuous nonconvex minimization typically proceed by computing progressively tighter upper and lower bounds on the unknown optimal solution value. While upper bounds may be obtained by applying local solvers, lower bounds are less straightforward to compute, and fundamentally require global knowledge of the considered system. Convex underestimators of the functions involved may be used to provide this knowledge, since any local minimum of a convex underestimator yields a guaranteed lower bound for the original problem. However, generating useful underestimators for nontrivial functions is itself a nontrivial task, as even compositions of convex functions are not necessarily convex. This presentation describes a pioneering approach by McCormick (1976) for generating useful convex underestimators for composite functions automatically, and details several recent improvements and generalizations of this approach. Implications and examples are discussed.
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