2021-07-28, 19:00–19:30 (UTC), JuMP Track
We present InfiniteOpt.jl which facilitates a coherent unifying abstraction for characterizing infinite-dimensional optimization problems rigorously through a common lens. This decouples models from discretized forms and promotes the use of novel transformations. This new perspective encourages new theoretical crossover and novel problem formulations (creating new disciplines like random field optimization).
Infinite-dimensional optimization problems are a challenging problem class that cover a wide breadth of optimization areas and embed complex modeling elements such as infinite-dimensional variables, measures, and derivatives. Typical modeling approaches (e.g., those behind Gekko and Pyomo.dae) often only consider discretized formulations and do not provide a unified paradigm across the various disciplines.