Unifying nonlinearly constrained optimization

Nonlinearly constrained optimization problems arise in a broad range of applications, including optimal experimental design, the control and operation of the power-grid, and the analysis of experimental campaigns. We present a motivating example arising in the design of scanning-type experiments, and discuss the basic building block of iterative solvers for nonlinearly constrained optimization problems. We show that these building blocks can be presented as a double loop framework that allows us to express a broad range of state-of-the-art nonlinear optimization solvers within a common framework.  We have implemented this framework in Uno, a modern, lightweight and extensible C++ solver that unifies the workflow of most derivative-based iterative nonlinear optimization solvers. Uno is meant to enable researchers to experiment with novel optimization strategies while leveraging established subproblem solvers and interfaces to modeling languages. We close by showing some extensions and open questions.

Bio
Sven Leyffer joined the Mathematics and Computer Science Division at Argonne in 2002, where he is now a senior computational mathematician, and a deputy division director. Sven is a SIAM Fellow, and a senior fellow of the University of Chicago/Argonne Computation Institute. He is SIAM's Past-President and serves on the editorial boards of Computational Optimization and Applications. In 2006, Leyffer (along with two colleagues) received the Lagrange Prize in Continuous Optimization, which is awarded only once every 3 years. In 2016, he received the Farkas Prize from the INFORMS Optimization Society. Leyffer obtained his Ph.D. in 1994 from the University of Dundee, Scotland, and held postdoctoral research positions at Dundee, Argonne, and Northwestern University.
https://www.anl.gov/profile/sven-leyffer