A graph classification approach to determine when to decompose optimization problems

Ilias Mitrai; Prodromos Daoutidis

doi:10.1016/B978-0-443-15274-0.50104-9

A graph classification approach to determine when to decompose optimization problems

Ilias Mitrai, Prodromos Daoutidis

Research output: Chapter in Book/Report/Conference proceeding › Chapter

3 Scopus citations

Abstract

Decomposition-based optimization algorithms are widely used for the solution of optimization problems. However, the choice of whether a decomposition-based solution approach should be selected over a monolithic one is not apparent in general. In this work, we propose a graph classification approach for determining a-priori when to use a decomposition-based solution approach for the solution of convex mixed integer nonlinear optimization problems. We apply the proposed approach to benchmark optimization problems and analyze the predictive performance of the classifier.

Original language	English (US)
Title of host publication	Computer Aided Chemical Engineering
Publisher	Elsevier B.V.
Pages	655-660
Number of pages	6
DOIs	https://doi.org/10.1016/B978-0-443-15274-0.50104-9
State	Published - Jan 2023
Externally published	Yes

Publication series

Name	Computer Aided Chemical Engineering
Volume	52
ISSN (Print)	1570-7946

Bibliographical note

Funding Information:
This work was supported by the National Science Foundation (NSF-CBET, award number 1926303) and a Doctoral Dissertation Fellowship (DDF) from University of Minnesota

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

convex MINLP
Decomposition-based solution algorithms
Graph Classification

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A graph classification approach to determine when to decompose optimization problems

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