A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
Autor (es)
Balázs, Csaba; van Beekveld, Melissa; Caron, Sascha; Dillon, Barry M.; Farmer, Ben; Fowlie, Andrew; Garrido Merchán, Eduardo César; Handley, Will; Hendriks, Luc; Jóhannesson, Guðlaugur; Leinweber, Adam; Mamužić, Judita; Martinez, Gregory D.; Otten, Sydney; de Austri, Roberto Ruiz; Scott, Pat; Searle, Zachary; Stienen, Bob; Vanschoren, Joaquin; White, MartinEntidad
UAM. Departamento de Ingeniería InformáticaEditor
International School for Advanced Studies (SISSA)Fecha de edición
2021-05-13Cita
10.1007/JHEP05(2021)108
Journal Of High Energy Physics 2021.5 (2021): 108
ISSN
1126-6708 (print); 1029-8479 (online)DOI
10.1007/JHEP05(2021)108Financiado por
MvB acknowledges support from the Science and Technology Facilities Council (grant number ST/T000864/1). R. RdA acknowledges partial funding/support from the Elusives ITN (Marie Skłodowska-Curie grant agreement No 674896), the “SOM Sabor y origen de la Materia” (FPA 2017-85985-P). BS, LH and SC acknowledges the support by the Netherlands eScience Center under the project iDark:The intelligent Dark Matter Survey.9 AF is supported by an NSFC Research Fund for International Young Scientists grant 11950410509. MJW and CB are funded by the Australian Research Council (ARC) Discovery Project DP180102209, MJW and AL by the ARC Centre of Excellence for Dark Matter Particle Physics CE200100008, and PS by ARC Future Fellowship FT190100814. JM acknowledges the support from the grant PGC2018-094856-B-I00 by the Spanish MICIU / AEI and the European Union / FEDER, and the APOSTD/2019/165 fellowship by the University of Valencia, Generalitat Valenciana and European Union. EGM gratefully acknowledges the use of the facilities of Centro de Computación Científica (CCC) at Universidad Autónoma de Madrid. EGM also acknowledge financial support from Spanish Plan Nacional I+D+i, grants TIN2016-76406-P and PID2019-106827GB-I00 / AEI / 10.13039/501100011033Proyecto
Gobierno de España. PGC2018-094856-B-I00; Gobierno de España. TIN2016-76406-P; Gobierno de España. PID2019-106827GB-I00Versión del editor
https://doi.org/10.1007/JHEP05(2021)108Materias
Phenomenology of Field Theories in Higher Dimensions; Supersymmetry Phenomenology; InformáticaNota
The version of record of this article, first published in Journal Of High Energy Physics, is available online at Publisher’s website: http://dx.doi.org/10.1007/JHEP05(2021)108Derechos
© The author(s)Resumen
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensive to evaluate. We describe a number of global optimisation algorithms that are not yet widely used in particle astrophysics, benchmark them against random sampling and existing techniques, and perform a detailed comparison of their performance on a range of test functions. These include four analytic test functions of varying dimensionality, and a realistic example derived from a recent global fit of weak-scale supersymmetry. Although the best algorithm to use depends on the function being investigated, we are able to present general conclusions about the relative merits of random sampling, Differential Evolution, Particle Swarm Optimisation, the Covariance Matrix Adaptation Evolution Strategy, Bayesian Optimisation, Grey Wolf Optimisation, and the PyGMO Artificial Bee Colony, Gaussian Particle Filter and Adaptive Memory Programming for Global Optimisation algorithms
Lista de ficheros
Google Scholar:Balázs, Csaba
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van Beekveld, Melissa
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Caron, Sascha
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Dillon, Barry M.
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Farmer, Ben
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Fowlie, Andrew
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Garrido Merchán, Eduardo César
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Handley, Will
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Hendriks, Luc
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Jóhannesson, Guðlaugur
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Leinweber, Adam
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Mamužić, Judita
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Martinez, Gregory D.
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Otten, Sydney
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de Austri, Roberto Ruiz
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Scott, Pat
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Searle, Zachary
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Stienen, Bob
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Vanschoren, Joaquin
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White, Martin
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