About Optimization in AMCR

We are developing advanced numerical optimization algorithms to tackle unsolved problems that arise in DOE-relevant science applications. Many of our developments are motivated by the needs of domain scientists. Our main focus is on the development of efficient and effective algorithms for optimization problems whose objective functions are obtained by running computationally expensive black box simulations or time-consuming experiments and for which derivative information is not available (see the illustration below).

We are actively developing solution methods for optimization problems with

  • continuous, integer, and mixed-integer parameters

  • computationally cheap and expensive black box constraints

  • multiple conflicting black box objective functions

  • inexpensive objective functions and expensive black box constraints

  • multiple levels of simulation model fidelity

  • high dimensions

  • noisy objective functions