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LANS Informal Seminar: Todd Munson, MCS Division, Argonne National Laboratory
February 28, 2018 @ 10:30 CST
Seminar Title: The Toolkit for Advanced Optimization: Past, Present, and Future
Speaker: Todd Munson, MCS Division, Argonne National Laboratory, Computational Scientist
Date/Time: 2018-02-28 10:30
Location: Bldg. 240, 4301
Description:
The mission of the Toolkit for Advanced Optimization (TAO) is to produce numerical optimization software for high-performance computers. We focus our efforts to deliver reliable, efficient algorithms in areas where our efforts will have broad, long-term impacts on both the optimization community and applications; to engage the user community to identify barriers and growth opportunities; and to prepare future leaders in the computational optimization field.
In this talk, I will focus on three aspects of the TAO effort. With regards to the past, I will talk about the basic unconstrained optimization methods and our efforts to modernize and improve the reliability and efficiency of those methods. For the present, I will discuss our efforts to develop methods for optimization problems with partial differential equation constraints. For the future, I will provide an overview of our TAO 2025 Initiative to extend these methods for use in new areas including optimization problems with partial differential equation constraints and state and design constraints.
Biosketch:
Dr. Todd Munson is a computational scientist in the Mathematics and Computer Science Division at Argonne National Laboratory, where he is currently the lead developer of the TAO, the deputy director for the ECP Center for Online Data Analysis and Reduction, and the area lead in numerical optimization for the FASTMath SciDAC Institute. He received a Presidential Early Career Award for Scientists and Engineers in 2006 for his pioneering work in the development of algorithms, software, and problem-solving environments for the solution of large-scale optimization problems” and the Beale-Orchard-Hayes Prize from the Mathematical Optimization Society in 2003. PATH, his implementation of the Josephy-Newton method, is currently the most widely used code for solving nonlinear complementarity problems.”