eprintid: 10148855
rev_number: 12
eprint_status: archive
userid: 699
dir: disk0/10/14/88/55
datestamp: 2022-05-20 09:57:27
lastmod: 2023-05-24 09:18:52
status_changed: 2022-05-20 09:57:27
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Marousi, Asimina
creators_name: Kokossis, Antonis
title: On the acceleration of global optimization algorithms by coupling cutting plane decomposition algorithms with machine learning and advanced data analytics
ispublished: pub
divisions: C05
divisions: F43
divisions: B04
divisions: UCL
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Data-driven technologies have demonstrated their potential on various scientific and industrial applications. Their use in the development of generic optimization algorithms is relatively unexplored. The paper presents such an application to design a global optimization algorithm that is generic and suitable to address quadratic box constraint problems. The new method reformulates cutting plane decomposition methods substituting the solution of the master problem by a data-driven selection of cutting planes. The paper presents the theoretical background, data technologies used and computational results that compare the new against state-of-the-art methods. Computational experiments include 100 quadratic programming (QP) problems featuring a wide range of density (25-75%), size (40-100 variables), and complexity. Results are particularly encouraging and demonstrate significant reductions in the duality gap, as high as 40-60% scope on average. Largest improvements are traced in larger formulations (over 100 variables, 75% density). The research is based solely on data produced at a particular iteration. Future work is intended to extend the analysis comparing and considering data patterns across different iterations, also to apply the methodology in other classes of optimization problems.
date: 2022-07
date_type: published
publisher: Elsevier BV
official_url: https://doi.org/10.1016/j.compchemeng.2022.107820
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1956774
doi: 10.1016/j.compchemeng.2022.107820
lyricists_name: Marousi, Asimina
lyricists_id: AMARO88
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: Computers & Chemical Engineering
volume: 163
article_number: 107820
citation:        Marousi, Asimina;    Kokossis, Antonis;      (2022)    On the acceleration of global optimization algorithms by coupling cutting plane decomposition algorithms with machine learning and advanced data analytics.                   Computers & Chemical Engineering , 163     , Article 107820.  10.1016/j.compchemeng.2022.107820 <https://doi.org/10.1016/j.compchemeng.2022.107820>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10148855/1/1-s2.0-S0098135422001582-main.pdf