Plenary Lectures

Plenary Lectures
(Partial list)

Professor Aharon Ben-Tal
Faculty of Industrial Engineering and Management
Technion - Israel Institute of Technology, Israel

Title: The Role of Robust Optimization in Deriving Practible Solutions to Uncertain Optimization Problems



We demonstrate by two examples the crucial role of robust optimization (RO) in designing reliable engineering structures.  We then briefly summarize cases (models) where this can be achieved by solving efficiently a tractable optimization problem.

Next, results of RO for dynamic (multistage) uncertain problems are presented for which tractable robust counterparts are achieved exactly or approximately.

We further show how RO can come to the rescue of, otherwise computation-hard, chance constraint problems, even when only partial information is available on the distribution function of the underlying random parameters (“Distributional Ambiguity”).

Finally, we present a classical linear estimation problem (with a signal processing example in mind).  While a Least Squares approach produces a poor estimator of the signal, we show that using RO, an accurate estimator of the signal is obtained (in fact, analytically!).

Brief bio: Aharon Ben-Tal is a Professor of Operations Research Management at the Technion – Israel Institute of Technology.  He received his Ph.D. in Applied Mathematics from Northwestern University in 1973.  He has been a Visiting Professor at the University of Michigan, University of Copenhagen, Delft University of Technology, MIT and CWI Amsterdam, Columbia and NYU.  His interests are in Continuous Optimization, particularly nonsmooth and large-scale problems, conic and robust optimization, as well as convex and nonsmooth analysis. In recent years the focus of his research is on optimization problems affected by uncertainty. In the last 20 years he has devoted much effort to engineering applications of optimization methodology and computational schemes. Some of the algorithms developed in the MINERVA Optimization Center are in use by Industry (Medical Imaging, Aerospace). He has published more than 135 papers in professional journals and co-authored three books.   Prof. Ben-Tal was Dean of the Faculty of Industrial Engineering and Management at the Technion (1989-1992) and (2011-2014). He served in the editorial board of all major OR/Optimization journals. He gave numerous plenary and keynote lectures in international conferences.

In 2007 Professor Ben-Tal was awarded the EURO Gold Medal - the highest distinction of Operations Research within Europe.

In 2009 he was named Fellow of INFORMS.

In 2015 he was named Fellow of SIAM.

In 2016 he was awarded by INFORMS the Khchiyan prize for Lifetime Achievement in the area of Optimization.

In 2017, the Operation Research Society of Israel (ORSIS) awarded him the Lifetime Achievement Prize.
As of September 2018 his work has over 22,900 citations (Google scholar).



Professor Masao Fukushima
Nanzan University, Japan

Title: Optimality Conditions for nonlinear Conic Programs via Squared Slack Variables



Nonlinear symmetric cone programs (NSCPs) constitute a general and important class of optimization problems that contains as special cases nonlinear semidefinite programs (NSDPs), nonlinear second order cone programs (NSOCPs) and traditional nonlinear programs (NLPs). We consider reformulating an NSCP as an ordinary NLP by means of squared slack variables. It is clear that the reformulated NLP is equivalent to the original NSCP in terms of not only global but also local optimality. This, however, is not the case in regard to optimality conditions. We discuss the first-order, i.e., Karush-Kuhn-Tucker (KKT) conditions and, in particular, the second-order necessary conditions as well as sufficient conditions for the NSCP and the reformulated NLP. Working with the reformulated NLP enables us to obtain the second-order optimality conditions for NSCPs in an easy manner, thereby bypassing a number of difficulties associated to the usual variational analytical approach. We also mention the possibility of importing convergence results from nonlinear programming, which we illustrate by means of a simple augmented Lagrangian method for NSCPs.

Brief bio: Professor Masao Fukushima obtained all academic degrees in Engineering from Kyoto University. Currently he is a full professor at the Faculty of Science and Engineering, Nanzan University, and Professor Emeritus of Kyoto University. His research interests include nonlinear optimization, variational inequality and complementarily problems, parallel optimization, nonsmooth optimization, global optimization, game theory, and applications in transportation, finance, data mining, etc. He has published over 200 papers in peer reviewed journals and was selected as an ISI Highly Cited Researcher in Mathematics in 2010. He has been awarded a number of academic prizes, including Kondo Prize from the Operations Research Society of Japan, and Paul Y. Tseng Memorial Lectureship from the Mathematical Optimization Society. Professor Fukushima is one of the founders of the Pacific Optimization Research Activity Group, and had served as the Chairman of the Working Committee. He is also the founder and the Co-Editor of Pacific Journal of Optimization. Besides, he is currently on the editorial boards of 15 international journals in optimization and operations research, including Computational Optimization and Applications, Optimization Methods and Software, Journal of Optimization Theory and Applications, etc.



Professor Panos M. Pardalos
Center for Applied Optimization, University of Florida, USA


Title: Network Models, Optimization and Control in Neurosciences


The human brain is probably one of the most complex objects in nature. In recent years many network optimization models have been proposed to analyze brain dynamics and study certain neurological disorders.
In nearly every study conducted on human brain networks the questions asked were "what are the hubs of the network", e.g. the nodes with highest degree?

There is however another important network characteristic set of nodes, arising from network controllability theory, which for the time being remained beyond the attention of researchers: identify a minimum set of driver nodes, providing controllability of the network.

In this talk we are going to discuss a spectrum of problems in computational neuroscience whose solution needs tools from data sciences, optimization and control.

Brief bio: Panos Pardalos is a Distinguished Professor and the Paul and Heidi Brown Preeminent Professor in the Departments of Industrial and Systems Engineering at the University of Florida, and a world-renowned leader in Global Optimization, Mathematical Modeling, and Data Sciences. He is a Fellow of AAAS, AIMBE, and INFORMS and was awarded the 2013 Constantin Caratheodory Prize of the International Society of Global Optimization. In addition, Dr. Pardalos has been awarded the 2013 EURO Gold Medal prize bestowed by the Association for European Operational Research Societies. This medal is the preeminent European award given to Operations Research (OR) professionals for “scientific contributions that stand the test of time.”

Dr. Pardalos has been awarded a prestigious Humboldt Research Award (2018-2019). The Humboldt Research Award is granted in recognition of a researcher’s entire achievements to date – fundamental discoveries, new theories, insights that have had significant impact on their discipline.

Dr. Pardalos is also a Member of the New York Academy of Sciences, the Lithuanian Academy of Sciences, the Royal Academy of Spain, and the National Academy of Sciences of Ukraine. He is the Founding Editor of Optimization Letters, Energy Systems, and Co-Founder of the International Journal of Global Optimization, and Computational Management Science. He has published over 500 papers, edited/authored over 200 books and organized over 80 conferences. He has a google h-index of 95 and has graduated 63 PhD students so far.



Professor Immanuel M. Bomze
University of Vienna, Austria


Title: Global optimality certificates and tighter dual bounds by copositivity



Optimization problems with a (nonconvex) objective subject to (nonconvex) constraints and additional linear constraints, are ubiquitous in applications. Special cases include, for instance, the QCQP.

The potential weakness of Lagrangian dual bounds is well-known in nonconvex optimization. Classical Shor lifting shows that Lagrangian relaxation for QCQPs leads to an SDP, we will characterize the Semi-Lagrangian dual of problems of above type as more general conic optimization problems based upon copositivity. It turns out that even the weakest (lowest degree) approximation of the latter improves upon the former. Moreover, also some global optimality conditions can be formulated easily in terms of copositivity.

The approach will also give rise to an apparently new approximation hierarchy which avoids memory problems with large Hessians because it mainly focuses on linear formulations, and only uses SDPs of a size of the original problem, in sharp contrast to higher-order sum-of-squares or moment approximation hierarchies which employ matrices of an order which increases with a power of the problem dimension.

As a special case, we will consider the so-called extended CDT problem (nonconvex quadratic optimization over the intersection of two ellipsoids with a polyhedron, APX-hard). Both the global optimality criterion and the approximation hierarchy leading to tighter dual bounds will be specified, along with a condition guaranteeing exactness of them.

This talk is partly based upon research results obtained jointly with M. Overton, G. Li and V. Jeyakumar.







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