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SUMMARY:MICDE Seminar: Katya Scheinberg\,Professor of Operations Research and Information Engineering\, Cornell University
DESCRIPTION:WATCH THE RECORDING HERE. \nBio: Dr. Katya Scheinberg is a Professor and Director of Graduate Studies at the School of Operations Research and Information Engineering at Cornell University. Prior to joining Cornell she was the Harvey E. Wagner Endowed Chair Professor at the Industrial and Systems Engineering Department at Lehigh University. She attended Moscow University for her undergraduate studies and received her PhD degree from Columbia University. She worked at the IBM T.J. Watson Research Center as a research staff member for over a decade before joining Lehigh in 2010.\nProf. Scheinberg’s main research areas are related to developing practical algorithms (and their theoretical analysis) for various problems in continuous optimization\, such as convex optimization\, derivative free optimization\, machine learning\, quadratic programming\, etc. She is a recipient of the Lagrange Prize from SIAM and MOS\, the Farkas Prize from Informs Optimization Society and the Outstanding Simulation Publication award from Informs Simulation Society.\nProf. Scheinberg is currently the editor-in-chief of Mathematics of Operations Research\, and a co-editor of Mathematical Programming. \n\nOverview of Adaptive Optimization Methods for Stochastic Oracles\nContinuous optimization is a mature field\, which has recently undergone major expansion and change. One of the key new directions is the development of methods that do not require exact information about the objective function. Nevertheless\, the majority of these methods\, from stochastic gradient descent to “zero-th order” methods use some kind of approximate first order information. We will introduce a general definition of a stochastic and show how this definition applies in a variety of familiar settings\, including simple stochastic gradient via sampling\, traditional and randomized finite difference methods and more. We will overview several stochastic methods and how the general definition extends to the oracles used by these methods. \n  \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery and Engineering (MICDE) and the Department of Industrial and Operations Engineering. Dr. Scheinberg will be hosted by Dr. Albert Berahas\, Assistant Professor of Industrial and Operations Engineering. \nThis is a hybrid event and will be held in-person and broadcasted online via Zoom.  \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-katya-scheinberg/
LOCATION:1500 EECS
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20190201T110000
DTEND;TZID=America/Detroit:20190201T120000
DTSTAMP:20230905T171423Z
CREATED:20230905T171423Z
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SUMMARY:MICDE Seminar: Amir Ali Ahmadi\, Assistant Professor\, Operations Research and Financial Engineering\, Princeton University
DESCRIPTION:Bio: Amir Ali Ahmadi is an Assistant Professor at the Department of Operations Research and Financial Engineering at Princeton University and an Associated Faculty member of the Program in Applied and Computational Mathematics\, the Department of Computer Science\, the Department of Mechanical and Aerospace Engineering\, and the Center for Statistics and Machine Learning. Amir Ali received his PhD in EECS from MIT and was a Goldstine Fellow at the IBM Watson Research Center prior to joining Princeton. His research interests are in optimization theory\, computational aspects of dynamics and control\, and algorithms and complexity. Amir Ali’s distinctions include the Sloan Fellowship in Computer Science\, a MURI award from the AFOSR\, the NSF CAREER Award\, the AFOSR Young Investigator Award\, the DARPA Faculty Award\, the Google Faculty Award\, the Howard B. Wentz Junior Faculty Award as well as the Innovation Award of Princeton University\, the Goldstine Fellowship of IBM Research\, and the Oberwolfach Fellowship of the NSF. His undergraduate course at Princeton (ORF 363\, “Computing and Optimization’’) has received the 2017 Excellence in Teaching of Operations Research Award of the Institute for Industrial and Systems Engineers and the 2017 Phi Beta Kappa Award for Excellence in Undergraduate Teaching at Princeton University. Amir Ali is also the recipient of a number of best-paper awards\, including the INFORMS Optimization Society’s Young Researchers Prize\, the INFORMS Computing Society Prize (for best series of papers at the interface of operations research and computer science)\, the Best Conference Paper Award of the IEEE International Conference on Robotics and Automation\, and the prize for one of two most outstanding papers published in the SIAM Journal on Control and Optimization in 2013-2015. \nPOLYNOMIAL OPTIMIZATION AND DYNAMICAL SYSTEMS\nIn recent years\, there has been a surge of exciting research activity at the interface of optimization (in particular polynomial\, semidefinite\, and sum of squares optimization) and the theory of dynamical systems. In this talk\, we focus on two of our current research directions that are at this interface. In part (i)\, we propose more scalable alternatives to sum of squares optimization and show how they impact verification problems in control and robotics\, as well as some classic questions in polynomial optimization and statistics. Our new algorithms do not rely on semidefinite programming\, but instead use linear programming\, or second-order cone programming\, or are altogether free of optimization. In particular\, we present the first Positivstellensatz that certifies infeasibility of a set of polynomial inequalities simply by multiplying certain fixed polynomials together and checking nonnegativity of the coefficients of the resulting product.\nIn part (ii)\, we introduce a new class of optimization problems whose constraints are imposed by trajectories of a dynamical system. As a concrete example\, we consider the problem of optimizing a linear function over the set of initial conditions that forever remain inside a given polyhedron under the action of a linear\, or a switched linear\, dynamical system. We present a hierarchy of linear and semidefinite programs that respectively lower and upper bound the optimal value of such problems to arbitrary accuracy. \nThis seminar is co-sponsored by the department of Industrial and Operations Engineering. Prof. Ahmadi is being hosted by Prof. Shen (IOE). If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-amir-ali-ahmadi-assistant-professor-operations-research-and-financial-engineering-princeton-university/
LOCATION:2717 IOE\, 1205 BEAL AVE\, ANN ARBOR\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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DTSTART;TZID=UTC:20161026T161000
DTEND;TZID=UTC:20161026T170000
DTSTAMP:20260522T154116Z
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SUMMARY:MICDE Seminar: Andrea Lodi\, Polytechnique Montréal
DESCRIPTION:Bio:  Andrea Lodi received a PhD in System Engineering from the University of Bologna in 2000 and he was a Herman Goldstine Fellow at the IBM Mathematical Sciences Department\, NY from 2005–2006. He was a full professor of Operations Research at DEI\, University of Bologna between 2007 and 2015. Since 2015 he has been the Canada Excellence Research Chair in “Data Science for Real-time Decision Making” at the Polytechnique Montréal. His main research interests are in Mixed-Integer Linear and Nonlinear Programming and Data Science and his work has received recognition including the IBM and Google faculty awards. He is author of more than 80 publications in the top journals of the field of Mathematical Optimization. He serves as Associate Editor for several prestigious journals in the area. He has been the network coordinator and principal investigator of two large EU projects/networks\, and\, since 2006\, consultant of the IBM CPLEX research and development team. Finally\, Andrea Lodi is the co-principal investigator (with Yoshua Bengio) of the project “Data Serving Canadians: Deep Learning and Optimization for the Knowledge Revolution”\, recently funded by the Canadian Federal Government under the Apogée Programme. \nOn Wide Split Cuts for Mixed-Integer Programming\nCutting planes (or simply cuts) are a fundamental component of modern Mixed-Integer Linear Programming (MILP) solvers because they help in strengthening the linear programming relaxation\, a proxy to make the branchand-bound tree small. A classical way of devising cuts is to exploit disjunctions\, for example in the domain of an integer variable\, where\, of course\, no fractional value leads to any feasible solution. Cutting planes of this type\, called split cuts\, classically exploit disjunctions whose ‘width’ is always equal to one\, i.e.\, no fractional value is feasible between two consecutive integer values. We investigate cutting planes that arise when widening the associated disjunctions. This allows\, e.g.\, to model non contiguous domains of (integer) variables (or\, stated differently\, ‘holes’ in the domains). The validity of the disjunctions in a MILP can come from either primal or dual information\, and we present examples and computational results in both cases. We further explore an exact MILP approach based on these cutting planes\, that in addition tackles non-contiguity directly via branching and as a side-effect reduces the model size. (Joint work with P. Bonami\, F. Serrano\, A. Tramontani\, S. Wiese.) \nThis seminar is co-sponsored by the U-M Department of Industrial & Operations Engineering
URL:https://micde.umich.edu/event/micde-seminar-andrea-lodi-ecole-polytechnique-montreal/
LOCATION:Boeing Auditorium –  1109 Francois-Xavier Bagnoud Building\, 1320 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
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