BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//ARC - ECPv5.1.5//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:ARC
X-ORIGINAL-URL:https://arc.m3hosting.www.umich.edu
X-WR-CALDESC:Events for ARC
BEGIN:VTIMEZONE
TZID:America/Detroit
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20140309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20141102T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20141015T000000
DTEND;TZID=America/Detroit:20141015T000000
DTSTAMP:20240725T042654
CREATED:20141013T040000Z
LAST-MODIFIED:20141013T040000Z
UID:15990-1413331200-1413331200@arc.m3hosting.www.umich.edu
SUMMARY:MICDE Seminar: Nikolaos Sahinidis\, Carnegie Mellon University\, on Automatic Learning of Algebraic Models for Optimization — Oct. 15
DESCRIPTION:ALAMO: Automatic Learning of Algebraic Models for Optimization \n Nikolaos Sahinidis\, John E. Swearingen Professor\, Chemical Engineering\, Carnegie Mellon University 4 – 5 p.m.\, October 15\, 2014 IOE 1610 Professor Sahinidis will address the problem of discovering algebraic relationships that are hidden in a set of data\, an experimental process\, or a simulation model. The problem lies at the interfaces between statistical experimental design\, optimization\, and machine learning. This talk will present a methodology for developing models that are simple and accurate\, while minimizing the number of experiments or simulations of the system under study. The methodology begins by building a low-complexity model of the system using integer optimization techniques. The model is then tested\, exploited\, and improved through the use of derivative-free optimization to adaptively sample new experimental or simulation points. Semi-infinite optimization techniques facilitate a combined data- and theory-driven approach to model building. The talk provides computational comparisons between ALAMO\, the computational implementation of the proposed methodology\, and a variety of machine learning and statistical techniques\, including Latin hypercube sampling\, simple least squares regression\, and the lasso. Finally\, the talk presents an application in the optimal design of CO2 capture systems using a detailed process simulator. Nick Sahinidis is John E. Swearingen Professor at Carnegie Mellon University. His research has included the development of theory\, algorithms\, and the BARON software for global optimization of mixed-integer nonlinear programs. Scientists and engineers have used BARON in many application areas\, including the development of new Runge-Kutta methods for partial differential equations\, energy policy making\, modeling and design of metabolic processes\, product and process design\, engineering design\, and automatic control. Several companies have also used BARON in the automotive\, financial\, and chemical process industries. Professor Sahinidis’s research activities have been recognized by a National Science Foundation CAREER award in 1995\, the 2004 INFORMS Computing Society Prize\, the 2006 Beale-Orchard-Hays Prize from the Mathematical Programming Society\, and the 2010 Computing in Chemical Engineering Award. \n
URL:https://arc.m3hosting.www.umich.edu/event/micde-seminar-nikolaos-sahinidis-carnegie-mellon-university-on-automatic-learning-of-algebraic-models-for-optimization-oct-15/
END:VEVENT
END:VCALENDAR