• Corpus ID: 219558321

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

  title={A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl},
  author={Jordan Jalving and Sungho Shin and Victor M. Zavala},
  journal={arXiv: Optimization and Control},
We present a general graph-based modeling abstraction for optimization that we call an OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical hypergraph in which nodes represent optimization subproblems and edges represent connectivity between such subproblems. The abstraction enables the modular construction of highly complex models in an intuitive manner, facilitates the use of graph analysis tools (to perform partitioning, aggregation, and visualization… 
A Julia Framework for Graph-Structured Nonlinear Optimization
This work presents a Julia framework for modeling and solving graph-structured nonlinear optimization problems and demonstrates the scalability of the framework by targeting a large-scale, stochastic gas network problem that contains over 1.7 million variables.
GBOML: Graph-Based Optimization Modeling Language
The Graph-Based Optimization Modeling Language (GBOML) is a modeling language for mathematical programming enabling the easy implementation of a broad class of structured mixed-integer linear
A Unifying Modeling Abstraction for Infinite-Dimensional Optimization


Graph-based modeling and simulation of complex systems
A graph-based computational framework for simulation and optimisation of coupled infrastructure networks
The authors present a graph-based computational framework that facilitates the construction, instantiation, and analysis of large-scale optimisation and simulation applications of coupled infrastructure networks and discusses how to use these capabilities to target coupled natural gas and electricity systems.
Decentralized Schemes With Overlap for Solving Graph-Structured Optimization Problems
The proposed approach provides a bridge between fully decentralized and centralized architectures and is flexible in that it enables the implementation of asynchronous schemes, handling of constraints, and balancing of computing, communication, and data privacy needs.
Computational Experience with Hypergraph-Based Methods for Automatic Decomposition in Discrete Optimization
Preliminary experiments using hypergraph partitioning as a means of performing automatic decomposition of branch-and-price algorithms based on Dantzig-Wolfe decomposition for unstructured MILPs are reported on.
HYPE: Massive Hypergraph Partitioning with Neighborhood Expansion
HYPE is proposed, a hypergraph partitionier that exploits the neighborhood relations between vertices in the hypergraph using an efficient implementation of neighborhood expansion and improves partitioning quality and reduces runtime compared to streaming partitioning.
Distillating knowledge about SCOTCH
  • F. Pellegrini
  • Computer Science
    Combinatorial Scientific Computing
  • 2009
This tutorial will show how to add a simple genetic algorithm routine to the graph bipartitioning methods of Scotch, and describe its visible objects and data structures.
JuMP: A Modeling Language for Mathematical Optimization
JuMP is an open-source modeling language that allows users to express a wide range of optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and nonlinear) in a
Efficient Stochastic Programming in Julia
The StochasticPrograms.jl framework can reduce the barrier to entry for incoming practitioners of stochastic programming by providing both an intuitive interface for new users and an extensive development environment for expert users by providing strong scaling properties of the distributed algorithms on numerical benchmarks in a multinode setup.
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
This work presents a new coarsening heuristic (called heavy-edge heuristic) for which the size of the partition of the coarse graph is within a small factor of theSize of the final partition obtained after multilevel refinement, and presents a much faster variation of the Kernighan--Lin (KL) algorithm for refining during uncoarsening.
HyperX: A Scalable Hypergraph Framework
This paper proposes HyperX, a general-purpose distributed hypergraph processing framework built on top of Spark that achieves an order of magnitude improvement for running hypergraph learning algorithms compared with graph conversion based approaches in terms of running time, network communication costs, and memory consumption.