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Optimization and Automatic Differentiation

Primary Contact: Teresa Parks (

John Dennis (Project Director), Jason Abate, Ali Bouaricha, Christian Bischof, Gwyneth Owens Butera, Alan Carle, Indraneel Das, Mahmoud El-Alem, Mike Fagan, Matthias Heinkenschloss, Mary Beth Hribar, Peyvand Khademi, Andrew Mauer, Doug Moore, Jorge Moré, Teresa Parks, Lucas Roh, David Serafini, Virginia Torczon, Michael Trosset, Luis Vicente, Po-Ting Wu, and Zhijun Wu.

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Abstract. Optimization is a staple of numerical computation. In industry, optimization is used in planning, design, marketing, and distribution. Many important problems (both in industry and in academia) cannot be solved satisfactorily with current algorithms due to limitations of available computing hardware. The Parallel Optimization and Automatic Differentiation project is dedicated to solving these problems through algorithms designed to exploit the capabilities of parallel computers. Engineering problems currently being studied include multidisciplinary design optimization and optimal control. Other research areas include automatic differentiation tools and problem-solving environments for large-scale optimization. This research is making fundamental contributions to the way we formulate and solve large optimization problems arising from applications by exploring new technologies, including distributed computing over the Web.  

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