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A Bayesian optimization method based on a sequential Monte Carlo method

A technique of sequential Monte Carlo, optimization methods, applied in directions based on specific mathematical models, computational models, calculations, etc.

Inactive Publication Date: 2019-05-10
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

The introduction of the helper optimizer brings other problems: how to understand that this helper indeed finds the maximum of the acquisition function, and that the use of helper may be an unnecessary cost since it has to be performed in each iteration

Method used

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  • A Bayesian optimization method based on a sequential Monte Carlo method
  • A Bayesian optimization method based on a sequential Monte Carlo method
  • A Bayesian optimization method based on a sequential Monte Carlo method

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Embodiment Construction

[0065] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0066] Such as figure 1 As shown, a Bayesian optimization method based on sequential Monte Carlo method, including the following steps:

[0067] S1. Establish the objective function and obtain the posterior distribution of the objective function; the specific implementation method is:

[0068] For a d-dimensional random variable X, the multivariate Student-t distribution is given by:

[0069]

[0070] Among them, M d (x, μ, ∑) = (x-μ) T ∑ -1 (x-μ); M d (x, μ, ∑) represents the squared Mahalanobis distance from x to the mean μ with respect to the covariance matrix Σ; Explanatory; the additional parameter ν>2 describes the degrees of freedom of the distribution and acts as a smoothing factor when changing; when ν→∞, the Student-t distribution converges to a Gaussian distribution with the same mean and covariance matrix;

[0071] The ob...

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Abstract

The invention discloses a Bayesian optimization method based on a sequential Monte Carlo method, and the method comprises the following steps: S1, building an objective function, and obtaining the posterior distribution of the objective function; S2, adding a noise signal, and calculating an edge Stuent-t distribution of the objective function; and S3, optimizing the objective function through a sequential Monte Carlo approximation method. Weight distribution is calculated through a sequential Monte Carlo method; the improved sequential Monte Carlo method is expanded to global optimization through Student-t process regression instead of Gaussian process regression, maximum value distribution can be more effectively obtained under the condition that the number of samples is small, and higher exploration capacity and abnormal value adaptability are achieved.

Description

technical field [0001] The invention belongs to the technical field of global optimization, and in particular relates to a Bayesian optimization method based on a sequential Monte Carlo method. Background technique [0002] Many problems in science and engineering can be described as finding the minimum or maximum of an unknown function that is difficult to estimate. Bayesian optimization is a widely used probabilistic method for solving this problem. In order to explain the characteristics of unknown objective functions, Gaussian processes are particularly suitable for the interpretation of model predictions and provide a reasonable framework for learning and model selection. Due to its advantages in modulation and learning, it has become the most popular nonparametric kernel-based global optimization method in machine learning. It is widely used in many applications or research areas, for example, in reinforcement learning, big data, wireless sensor networks, and many ot...

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Application Information

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IPC IPC(8): G06N7/00
Inventor 王伟东于秦余俊良
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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