.png){kind=icon}
返回
Constrained Bayesian Optimization: A Review
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3522876.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Bayesian optimization is a sequential optimization method that is particularly well suited for problems with limited computational budgets involving expensive and non-convex black-box functions. Though it has been widely used to solve various optimization tasks, most of the literature has focused on unconstrained settings, while many real-world problems are characterized by constraints. This paper reviews the current literature on single-objective constrained Bayesian optimization, classifying it according to three main algorithmic aspects: (i) the metamodel, (ii) the acquisition function, and (iii) the identification procedure. We discuss the current methods in each of these categories and conclude by a discussion of real-world applications and highlighting the main shortcomings in the literature, providing some promising directions for future research.
Keyword:
Optimization
Bayes methods
Reviews
Closed box
Noise
Uncertainty
Noise measurement
Linear programming
Three-dimensional displays
Surveys
Bayesian optimization
constrained optimization
expensive black-box functions
Gaussian processes
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏Rate of acyl migration in lysophosphatidylcholine (LPC) is dependent upon the nature of the acyl group. Greater stability of sn-2 docosahexaenoyl LPC compared to the more saturated LPC species.png)
PLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容