convexity and optimization: theory and applications
TRANSCRIPT
February 23-27, 2015
IMA Workshops
ORGANIZERS
Nina Balcan, Carnegie-Mellon UniversityHenrik Christensen, Georgia Institute of TechnologyWilliam Cook, University of WaterlooSatoru Iwata, University of TokyoPrasad Tetali, Georgia Institute of Technology
SPEAKERSAlexander Barvinok, University of Michigan
Jeff Bilmes, University of Washington
Natashia Boland, Georgia Institute of Technology
Sébastien Bubeck, Microsoft
Niao He, Georgia Institute of Technology
Stefanie Jegelka, University of California, Berkeley
Fatma Kilinc-Karzan, Carnegie Mellon University
Andreas Krause, ETH
Yingyu Liang, Princeton University
Jeff Linderoth, University of Wisconsin, Madison
Ruta Mehta, Georgia Institute of Technology
Kazuo Murota, University of Tokyo
Sebastian Pokutta, Georgia Institute of Technology
Nati Srebro, Technion-Israel Institute of Technology
Karthik Sridharan, Cornell University
Larry Sweet, Symbotic
Akiko Takeda, University of Tokyo
Cynthia Vinzant, North Carolina State University
Jan Vondrak, IBM Research Division
Eric Xing, Carnegie Mellon University
Tong Zhang, Rutgers, The State University of New Jersey
Convexity and Optimization: Theory and Applications
Optimization formulations and methods have been at the heart of many modern machine learning algorithms, which have been used extensively in applications across science and engineering for automatically extracting essential knowledge from huge volumes of data. This workshop will begin with a look at supply chain optimization by bringing together researchers in industry and academia to discuss challenges, opportunities, and new trends. Then, focus will switch to discrete and continuous optimization, with a foray into machine learning. Submodular functions are discrete analogs of convex functions, arising in various fields of computer science and operations research. Submodularity has long been recognized as a common structure of many efficiently solvable combinatorial optimization problems. Researchers from various backgrounds will be brought together to exchange results, ideas, and problems on submodular optimization and its applications. Application domains have been numerous, ranging from sensor placement for water management to navigation of mobile robots.
www.ima.umn.edu/2014-2015/W2.23-27.15
The IMA is a NSF-funded institute