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Modern Trends in Optimization and Its Application

Workshop II: Numerical Methods for Continuous Optimization

October 11 - 15, 2010

Organizing Committee | Scientific Overview | Speaker List

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Organizing Committee

Don Goldfarb (Columbia University, IEOR)
Renato Monteiro (Georgia Institute of Technology, School of Industrial and Systems Engineering)
Yurii Nesterov (Université Catholique de Louvain)
Michael Overton (New York University)
Kim Toh (National University of Singapore)
Stephen Wright (University of Wisconsin-Madison, Computer Science)

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Scientific Overview

The field of optimization has recently been challenged by applications that require structured, approximate solutions, rather than the exact solutions that are the traditional goal of optimization algorithms. Instances of structure include sparsity of the solution vector (as occurs in compressed sensing and support vector machines), low matrix rank (as required in matrix completion, distance matrix estimation, kernel regularization), and low total variation, as needed in image processing applications. Structured solutions can be obtained in some cases by modifying the optimization formulation, for example by adding regularization terms and additional constraints. The algorithms that are appropriate to solve these modified formulations may be quite different from those that work for the original formulations. This happens in part because the regularization terms frequently introduce nonsmoothness, and because highly accurate solutions (even of the regularized formulation) may not be needed by the application. Add to these factors the large size of such applications and the frequent need to solve them in real time, and we have a significant challenge to current optimization methodology.

This workshop brings together experts on techniques that are currently being used (or that could potentially be used) to solve sparse/structured problems and other problem classes of recent interest. We mention in particular techniques for conic optimization formulations (which have applications also in robust optimization), fast gradient and subgradient methods, stochastic approximation techniques, and semismooth Newton and other methods that use second-order information. The workshop will also involve nonlinear programming researchers, with a view to making tighter connections between recent research in that area and the emerging paradigms discussed above.

This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.

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Confirmed Speakers

Michel Baes (ETH Zürich)
Emmanuel Candes (Stanford University)
Coralia Cartis (University of Edinburgh)
Frank Curtis (Lehigh University)
Alexandre dAspremont (Princeton University)
Maryam Fazel (University of Washington)
Masakazu Kojima (Tokyo Institute of Technology)
Guanghui Lan (University of Florida)
Zhaosong Lu (Simon Fraser University)
Arkadi Nemirovski (Georgia Institute of Technology)
Yurii Nesterov (Université Catholique de Louvain)
Pablo Parrilo (Massachusetts Institute of Technology)
Javier Pena (Carnegie-Mellon University)
Benjamin Recht (University of Wisconsin-Madison)
Katya Scheinberg (Lehigh University)
Nathan Srebro (Toyota Technological Institute at Chicago)
Defeng Sun (National University of Singapore)
Jared Tanner (University of Edinburgh)
Marc Teboulle (Tel Aviv University)
Michael Todd (Cornell University)
Andreas Waechter (IBM Thomas J. Watson Research Center)
Henry Wolkowicz (University of Waterloo)
Yinyu Ye (Stanford University)
Yin Zhang (Rice University)

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Contact Us:

Institute for Pure and Applied Mathematics (IPAM)
Attn: OPWS2
460 Portola Plaza
Los Angeles CA 90095-7121
Phone: 310 825-4755
Fax: 310 825-4756
Email: ipam@ucla.edu
Website: http://www.ipam.ucla.edu/programs/opws2/

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