Dynamic Optimization Problems

Numerical Optimization
Dynamic Optimization Problems


Developing a framework in the Toolkit for Advance Optimization for PDE-constrained optimization with nontrivial design and state constraints that may include discrete variables (using MINOTAUR) and multiple objectives (using POUNDERS and APOSMM).

We will develop a framework in TAO for PDE-constrained optimization problems with nontrivial design and state constraints. Our framework will use as much information as the application developer can provide and use automatic differentiation and surrogate models when necessary. Hard constraints that must be satisfied by all iterates of the numerical method and relaxable constraints that need only be satisfied at the solution will be supported. We will extend this framework to support discrete variables and multiple objectives.


Todd Munson

Mathematics and Computer Science Division, Argonne National Laboratory, 9700 South Cass Avenue, Building 240, Argonne, IL 60439