Nondifferentiable optimization with applications to economics Event
- Time:
- 16:00 - 17:00
- Date:
- 21 July 2016
- Venue:
- Room 3041 Building 2, Southampton Business School
For more information regarding this event, please email Dr Yuan Huang at yuan.huang@soton.ac.uk .
Event details
This talk discusses some tools of modern optimization the development of which has been largely motivated by applications to problems of operations research, economics, behavioral sciences, etc. One of the most characteristic features of modern optimization theory and its applications is dealing with nondifferentiable functions, which naturally enter not only through the initial data of optimization problems but often via variational principles and perturbation techniques applied to a variety of problems even with standard smooth data and in non-optimization frameworks. Besides considering such optimization tools and results, we present their applications to some models in microeconomics related to Pareto-type equilibria and marginal prices.
Speaker information
Professor Boris Mordukhovich,Wayne State University,Bio: Boris Mordukhovich is Distinguished University Professor of Mathematics at Wayne State University, where he is a Lifetime Scholar and was President of the Academy of Scholars in 2010 – 2011. He is one of the world’s leading experts in Variational Analysis and has published around 400 research articles and 4 books on Optimization Theory and its Applications to Systems Control, Operations Research, Nonlinear Dynamics, Economics, Engineering, Mechanics, and Behavioral Science. Boris holds a PhD in Applied Mathematics from Belarus State University and has been awarded Honoris Causa Honorary Doctorates by 6 different Universities. He is also a Fellow of the Society for Industrial and Applied Mathematics (SIAM) and of the American Mathematical Society (AMS). Professor Mordukhovich has supervised more than 30 PhD students and has served as Editor in Chief, Associate Editor or Member of the Editorial Board for about 30 journals.