EconPapers    
Economics at your fingertips  
 

Basin Hopping with synched multi L-BFGS local searches. Parallel implementation in multi-CPU and GPUs

Ferreiro-Ferreiro, Ana M., García-Rodríguez, José A., Luis Souto and Carlos Vázquez

Applied Mathematics and Computation, 2019, vol. 356, issue C, 282-298

Abstract: In this work, a technique for improving the convergence properties (speed and reliability) of a non monotonic Basin Hopping algorithm is presented. This modification of Basin Hopping happens to be highly parallelizable and therefore the parallel implementation is shown both for multi-CPU and GPU architectures. A benchmark of classical global optimization tests is run, focussing in a number of tests in the literature that result to be particularly hard for Basin Hopping.

Keywords: Global optimization; Basin Hopping; L-BFGS; Parallel; Multi-CPU; GPU (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300319301444
Full text for ScienceDirect subscribers only

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:eee:apmaco:v:356:y:2019:i:c:p:282-298

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-08-10
Handle: RePEc:eee:apmaco:v:356:y:2019:i:c:p:282-298