Towards a Generic Algorithm for Identifying High-Quality Decompositions of Optimization Problems

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

Optimization is a ubiquitous tool in process systems engineering which is being used on models of ever-increasing size and complexity. Decomposition solution approaches can be powerful tools for solving difficult optimization problems but are dependent on finding a partition of variables and constraints amenable to the solution approach. In this paper, we propose an automated, generic method for decomposing optimization problems using community detection. This method generates subproblems which are tightly interacting amongst themselves but weakly interacting with other subproblems, thus minimizing the amount of coordination required in the solution approach. We demonstrate the ability of our method to find solutions faster than solving the original problem in many cases.

Original languageEnglish (US)
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages943-948
Number of pages6
DOIs
StatePublished - Jan 1 2018

Publication series

NameComputer Aided Chemical Engineering
Volume44
ISSN (Print)1570-7946

Keywords

  • community detection
  • decomposition
  • optimization

Fingerprint Dive into the research topics of 'Towards a Generic Algorithm for Identifying High-Quality Decompositions of Optimization Problems'. Together they form a unique fingerprint.

  • Cite this

    Allman, A., Tang, W., & Daoutidis, P. (2018). Towards a Generic Algorithm for Identifying High-Quality Decompositions of Optimization Problems. In Computer Aided Chemical Engineering (pp. 943-948). (Computer Aided Chemical Engineering; Vol. 44). Elsevier B.V.. https://doi.org/10.1016/B978-0-444-64241-7.50152-X