Distributed quasi-Newton derivative-free optimization method for optimization problems with multiple local optima

Gao, Guohua; Wang, Yixuan; Vink, Jeroen C.; Wells, Terence; Saaf, Fredrik

doi:10.1007/s10596-021-10101-x

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…14 One technique that we have not mentioned is the distributed quasi-Newton algorithm of (Gao et al, 2020) (see also (Gao et al, 2021)), which is designed for extremely expensive situations on the order of ≥ 1 day/evaluation. This and related algorithms such as SPMI do not appear to be publically available and/or comprehensively benchmarked in the literature: indeed, SPMI is described in (Alpak et al, 2013) as "an in-house massively parallel mixed integer and real variable optimization tool" (emphasis added).…”

Section: Remarksmentioning

confidence: 99%

Parallel black-box optimization of expensive high-dimensional multimodal functions via magnitude

Huntsman¹

2022

Preprint

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Building on the recently developed theory of magnitude, we introduce the optimization algorithm EXPLO2 and carefully benchmark it. EXPLO2 advances the state of the art for optimizing highdimensional (D ⪆ 40) multimodal functions that are expensive to compute and for which derivatives are not available, such as arise in hyperparameter optimization or via simulations. Example 1. Let {x j } 3 j=1 ⊂ R 2 have pairwise distances d jk ∶= d(x j , x k ) given by d 12 = d 13 = 1 = d 21 = d 31 and d 23 = δ = d 32 with δ < 2. It turns out that w 1 = e (δ+2)t − 2e (δ+1)t + e 2t e (δ+2)t − 2e δt + e 2t ;

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Section: Remarksmentioning

confidence: 99%