AdvOL Student Seminars and Defences
Adrian Burlacu,September 19, 2017, 15:30-16:00, ITB 201
Speaker:   Adrian Burlacu

Title:  Minimized asynchronous scalable domain transactions
 
AdvOL Optimization Seminars
Reza Samavi, September 19, 2017, 16:30-17:30, ITB 201
Speaker:   Reza Samavi
Department of Computing and Software
McMaster University

Title:  Optimizing data utility with privacy constraints
Read more...
 
Fields Institute Industrial Optimization Seminar, June 6, 2017
Speakers:   Yuriy Zinchenko (University of Calgarys)
Pooyan Shirvani (TD Bank)

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 September 2017
 
 
Main Menu
Home
People
Publications
Software
Events
Awards
Photogallery
Internal pages
Latest Theses
File Icon Computational Determination of the Largest Lattice Polytope Diameter
File Icon Computational Framework for the Generalized Berge Sorting Conjecture
File Icon Structural Factorization of Squares in Strings
Latest Reports
Visitors by region
Totals Top 20
 61 % Unknown
 14 % Commercial
 8 % networks
 6 % Canada
 3 % Germany
 2 % China
 2 % Russia
 < 1.0 % Educational
 < 1.0 % Brazil
 < 1.0 % United Kingdom
 < 1.0 % Ukraine
 < 1.0 % Poland
 < 1.0 % 
 < 1.0 % Italy
 < 1.0 % France
 < 1.0 % Netherlands
 < 1.0 % Japan
 < 1.0 % India
 < 1.0 % Australia
 < 1.0 % Organization

Visitors: 5142800
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.
 
< Prev   Next >
McMaster University
McMaster University
Faculty of Engineering
Faculty of Engineering
Faculty of Science
Faculty of Science
Computing & Software
Computing & Software
Comput. Eng. & Sci.

School Website >>>


Latest Publications
Publication Downloads
Error cannot find GD extension
 
Top!
Top!