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A dynamic fix for large-scale uncertainty

Frank Curtis, assistant professor of industrial and systems engineering, recently received a five-year Early Career Research Program Award from the U.S. Department of Energy to develop dynamic, scalable algorithms for large-scale constrained optimization problems.

The grant will enable Curtis and his students to develop algorithms, or step-by-step mathematical methods, and work with researchers at DOE's Argonne National Laboratory to implement them in software that monitors and controls processes in complex systems.

“Our goal,” says Curtis, “is to solve problems that require the use of continuous nonlinear optimization techniques. These problems include the design and construction of a network that has to be able to make countless decisions in real time. The key features of these new algorithms are that they will be fast, dynamic, and scalable to meet the computational requirements of scientists and researchers working to optimize large‐scale, complex systems.”

The DOE award is the second major grant Curtis has received in the last three years. In 2010, he received a three-year single-investigator grant from the National Science Foundation to develop nonlinear optimization algorithms for large-scale and non-smooth applications.

Read the full story at the Lehigh Universtiy News Archive.

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Hector Munoz-Avila

Frank Curtis, assistant professor of industrial systems and engineering