A Graph-Based, Distributed Memory, Modeling Abstraction for Optimization

TL;DR

This paper introduces RemoteOptiGraph, a flexible distributed modeling abstraction for optimization problems that enables efficient decomposition and solving of large-scale models across distributed memory systems.

Contribution

It extends the OptiGraph model to distributed environments, allowing for unified modeling and development of general-purpose distributed optimization algorithms.

Findings

RemoteOptiGraph enables scalable distributed optimization modeling.

Using Benders decomposition with RemoteOptiGraph accelerates solving large models.

Achieved 7.5x speedup on a large-scale capacity expansion problem.

Abstract

We present a general, flexible modeling abstraction for building and working with distributed optimization problems called a RemoteOptiGraph. This abstraction extends the OptiGraph model in Plasmo$.$jl, where optimization problems are represented as hypergraphs with nodes that define modular subproblems (variables, constraints, and objectives) and edges that encode algebraic linking constraints between nodes. The RemoteOptiGraph allows OptiGraphs to be utilized in distributed memory environments through InterWorkerEdges, which manage linking constraints that span workers. This abstraction offers a unified approach for modeling optimization problems on distributed memory systems (avoiding bespoke modeling approaches), and provides a basis for developing general-purpose meta-algorithms that can exploit distributed memory structure such as Benders or Lagrangian decompositions. We implement this abstraction in the open-source package, Plasmo$.$jl and we illustrate how it can be used by solving a mixed integer capacity expansion model for the western United States containing over 12 million variables and constraints. The RemoteOptiGraph abstraction together with Benders decomposition performs 7.5 times faster than solving the same problem without decomposition.