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We present a general graph-based modeling abstraction for optimization thatwe call an OptiGraph. Under this abstraction, any optimization problem istreated as a hierarchical hypergraph in which nodes represent optimizationsubproblems and edges represent connectivity between such subproblems. Theabstraction enables the modular construction of highly complex models in anintuitive manner, facilitates the use of graph analysis tools (to performpartitioning, aggregation, and visualization tasks), and facilitatescommunication of structures to decomposition algorithms. We provide anopen-source implementation of the abstraction in the Julia-based packagePlasmo.jl. We provide tutorial examples and large application case studies toillustrate the capabilities.

A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl