000150721 001__ 150721 000150721 005__ 20251017144642.0 000150721 0247_ $$2doi$$a10.3390/e27010058 000150721 0248_ $$2sideral$$a142707 000150721 037__ $$aART-2025-142707 000150721 041__ $$aeng 000150721 100__ $$0(orcid)0000-0001-7251-0753$$aGarcia-Barcos, Javier 000150721 245__ $$aAdvanced Monte Carlo for Acquisition Sampling in Bayesian Optimization 000150721 260__ $$c2025 000150721 5060_ $$aAccess copy available to the general public$$fUnrestricted 000150721 5203_ $$aOptimizing complex systems usually involves costly and time-consuming experiments, where selecting the experiments to perform is fundamental. Bayesian optimization (BO) has proved to be a suitable optimization method in these situations thanks to its sample efficiency and principled way of learning from previous data, but it typically requires that experiments are sequentially performed. Fully distributed BO addresses the need for efficient parallel and asynchronous active search, especially where traditional centralized BO faces limitations concerning privacy in federated learning and resource utilization in high-performance computing settings. Boltzmann sampling is an embarrassingly parallel method that enables fully distributed BO using Monte Carlo sampling. However, it also requires sampling from a continuous acquisition function, which can be challenging even for advanced Monte Carlo methods due to its highly multimodal nature, constrained search space, and possibly numerically unstable values. We introduce a simplified version of Boltzmann sampling, and we analyze multiple Markov chain Monte Carlo (MCMC) methods with a numerically improved log EI implementation for acquisition sampling. Our experiments suggest that by introducing gradient information during MCMC sampling, methods such as the MALA or CyclicalSGLD improve acquisition sampling efficiency. Interestingly, a mixture of proposals for the Metropolis–Hastings approach proves to be effective despite its simplicity. 000150721 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T45-23R$$9info:eu-repo/grantAgreement/ES/MICINN-AEI/PID2021-125209OB-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131150B-I00 000150721 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es 000150721 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000150721 700__ $$0(orcid)0000-0002-6741-844X$$aMartinez-Cantin, Ruben$$uUniversidad de Zaragoza 000150721 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000150721 773__ $$g27, 1 (2025), 58 [21 pp.]$$pEntropy$$tENTROPY$$x1099-4300 000150721 8564_ $$s977025$$uhttps://zaguan.unizar.es/record/150721/files/texto_completo.pdf$$yVersión publicada 000150721 8564_ $$s2480927$$uhttps://zaguan.unizar.es/record/150721/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000150721 909CO $$ooai:zaguan.unizar.es:150721$$particulos$$pdriver 000150721 951__ $$a2025-10-17-14:32:48 000150721 980__ $$aARTICLE