February 23-27, 2015 IMA Workshops ORGANIZERS Nina Balcan, Carnegie-Mellon University Henrik Christensen, Georgia Institute of Technology William Cook, University of Waterloo Satoru Iwata, University of Tokyo Prasad Tetali, Georgia Institute of Technology SPEAKERS Alexander 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

Convexity and Optimization: Theory and Applications

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Page 1: Convexity and Optimization: Theory and Applications

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