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DTSTART;TZID=Europe/London:20190702T140000
DTEND;TZID=Europe/London:20190702T150000
DTSTAMP:20260511T154330
CREATED:20190425T090204Z
LAST-MODIFIED:20190425T090204Z
UID:1229-1562076000-1562079600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Chordal Completions - Semidefinite Programming and Minimum Completions
DESCRIPTION:Title: Chordal Completions – Semidefinite Programming and Minimum Completions\nSpeaker: Dr Arvind Raghunathan\nAffiliation: Mitsubishi Electric Research Laboratories (MERL)\nLocation: 217 Huxley Building\nTime: 14:00 – 15:00 \nAbstract. A graph is chordal if every cycle of length at least four contains a chord\, that is\, an edge connecting two nonconsecutive vertices of the cycle. Chordal completion of a given undirected graph G is a chordal graph\, on the same vertex set\, that has G as a subgraph. Several classical applications in sparse linear systems\, database management\, computer vision\, and SemiDefinite Programming (SDP) utilize chordal completions. The computation workload that results can be related to the number of edges that are added. Hence\, finding the minimum number of edges that makes a graph chordal is important. We refer to this as the Minimum Chordal Completion Problem (MCCP). In this talk\, we will present results on an application of completions in SDPs and the solution of MCCP. \nConversion approach\, a decomposition based on chordal completions\, is routinely used for solving large-scale SDPs. We show that the SDP resulting from the conversion approach is numerically degenerate under very mild assumptions. Numerical experiments on SDPLIB are provided to demonstrate the impact on solvers such as SDPT3 and SeDuMi. \nWe propose a new formulation for the MCCP which does not rely on finding perfect elimination orderings of the graph\, as has been considered in previous work. We introduce several families of facet-defining inequalities for cycle subgraphs. Numerical studies combining heuristic separation methods based on a threshold rounding and lazy-constraint generation indicate that our approach substantially outperforms existing methods for the MCCP\, solving many benchmark graphs to optimality for the first time. \nBiography. Arvind Raghunathan is a Senior Principal Scientist at Mitsubishi Electric Research Laboratories (MERL). His research interests are in the development of algorithms for the solution of nonlinear and mixed integer programming problems with applications in electric grid operations\, model predictive control\, and transportation. Arvind’s research has found business impact in Mitsubishi’s products and has won top technical honors within MERL and Mitsubishi Electric Corporation. Arvind currently serves as an associate editor for Optimization & Engineering journal and as an expert of ANSI on the ISO Working Group on Smart Transportation. He obtained a Ph.D. from Carnegie Mellon University and a B.Tech from Indian Institute of Technology (Madras) both in Chemical Engineering. He worked for United Technologies Research Center for 7 years prior to joining MERL.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-chordal-completions-semidefinite-programming-and-minimum-completions/
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DTSTART;TZID=UTC:20190722T140000
DTEND;TZID=UTC:20190722T150000
DTSTAMP:20260511T154330
CREATED:20190703T163349Z
LAST-MODIFIED:20190703T163349Z
UID:1273-1563804000-1563807600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Largest Small n-Polygons: Numerical Results and Conjectured Optima
DESCRIPTION:Title: Largest Small n-Polygons: Numerical Results and Conjectured Optima \nSpeaker: János D. Pintér \nAffiliation: Department of Industrial and Systems Engineering\, Lehigh University \nLocation: 218 Huxley Building \nTime: 14:00 – 15:00 \n  \nAbstract. LSP(n)\, the largest small polygon with n vertices\, is defined as the polygon of unit diameter that\nhas maximal area A(n). Finding the configuration LSP(n) and the corresponding A(n) for even\nvalues n >= 6 is a long-standing challenge that can be also perceived as class of hard global\noptimization problems. We present numerical solution estimates for all even values 6 <= n <= 80\,\nusing the AMPL model development environment with the LGO global-local solver engine option.\nOur results are in close agreement with the results obtained by other researchers who tackled the\nproblem using exact approaches (for 6 <= n <= 20)\, and with the best results obtained using general\npurpose numerical optimization software (for selected values from the range 6 <= n <= 100). Based\non our numerical results\, we also present a regression model based estimate of {A(n)} for all even\nvalues n >= 6. \n  \nBio. János D. Pintér is a researcher and practitioner with over four decades of experience. His\ngeneral professional interests are related to Computational Optimization\, Data Analytics\, and\nOperations Research (O.R.). His more specific primary area of expertise is nonlinear optimization\,\nincluding model\, algorithm and software development\, with a range of applications.\nHe received his M.Sc. in the area of Applied Mathematics / Operations Research from\nEötvös Loránd University\, Hungary; Ph.D. in Probability Theory / Stochastic Optimization from\nMoscow State University; and D.Sc. in Mathematics / Global Optimization from the Hungarian\nAcademy of Sciences. \nAs of 2019\, Dr. Pintér wrote and edited ten books. He is also the author/co-author of more\nthan 200 journal articles\, book chapters\, proceedings contributions\, book reviews\, and research\nreports. His monograph titled Global Optimization in Action received the 2000 INFORMS\nComputing Society Prize for Research Excellence. \nAmong other professional affiliations\, he serves on the editorial board of the Journal of\nGlobal Optimization\, and he is an editor of the book series SpringerBriefs in Optimization. He also\nserved as Global Optimization vice-chair of the INFORMS Optimization Society\, and as a member\n(later chair) of the Managing Board of EUROPT. Currently\, he is a member of the Canadian and\nthe Hungarian Operations Research Societies\, INFORMS\, and EUROPT.\nHe has worked and presented lectures in about 40 countries of the Americas\, Europe\, the\nMiddle East\, and the Pacific Region. His LGO software – with links to modeling languages and\nscientific-technical computing systems – has been in use at hundreds of academic\, business\,\ngovernment\, and research organizations. \nIn 2016\, Dr. Pintér joined the Department of Industrial and Systems Engineering at Lehigh\nUniversity as a Professor of Practice. Since that time\, he has been teaching a range of O.R. related\ncourses for undergraduate and graduate students\, as well as ISE in-class and online (distance)\ncourses for healthcare engineering professionals. \n 
URL:http://optimisation.doc.ic.ac.uk/event/seminar-largest-small-n-polygons-numerical-results-and-conjectured-optima/
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