Decomposition in derivative-free optimization

Kaiwen Ma, Nikolaos V. Sahinidis (), Sreekanth Rajagopalan, Satyajith Amaran and Scott J Bury
Additional contact information
Kaiwen Ma: Carnegie Mellon University
Nikolaos V. Sahinidis: Georgia Institute of Technology
Sreekanth Rajagopalan: The Dow Chemical Company
Satyajith Amaran: The Dow Chemical Company
Scott J Bury: The Dow Chemical Company

Journal of Global Optimization, 2021, vol. 81, issue 2, No 1, 269-292

Abstract: Abstract This paper proposes a novel decomposition framework for derivative-free optimization (DFO) algorithms. Our framework significantly extends the scope of current DFO solvers to larger-scale problems. We show that the proposed framework closely relates to the superiorization methodology that is traditionally used for improving the efficiency of feasibility-seeking algorithms for constrained optimization problems in a derivative-based setting. We analyze the convergence behavior of the framework in the context of global search algorithms. A practical implementation is developed and exemplified with the global model-based solver Stable Noisy Optimization by Branch and Fit (SNOBFIT) [36]. To investigate the decomposition framework’s performance, we conduct extensive computational studies on a collection of over 300 test problems of varying dimensions and complexity. We observe significant improvements in the quality of solutions for a large fraction of the test problems. Regardless of problem convexity and smoothness, decomposition leads to over 50% improvement in the objective function after 2500 function evaluations for over 90% of our test problems with more than 75 variables.

Keywords: Derivative-free optimization; Superiorization; SNOBFIT (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10898-021-01051-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jglopt:v:81:y:2021:i:2:d:10.1007_s10898-021-01051-w

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/10898

DOI: 10.1007/s10898-021-01051-w

Access Statistics for this article

Journal of Global Optimization is currently edited by Sergiy Butenko

More articles in Journal of Global Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().