Argument Value Determination for Global Optima in Bayesian Optimization
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Solution Overview
Problem
Existing methods for determining argument values that maximize or minimize function values are inefficient, particularly when the probability distribution of function values is estimated using Bayesian optimization, as they often focus on local solutions rather than global optima.
Innovation Solution
An information processing device and method that acquire a probability distribution of a function, then use an evaluation function based on a distribution with a different average than the original function's distribution to determine argument values for sampling, emphasizing the possibility of finding global optimal solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Bayesian optimization is used to estimate the probability distribution of function values, then the search process can be automated and structured, but the method tends to focus on local solutions rather than global optima
Solution Approach 1:
The patent changes the parameter used for evaluating argument values from the standard probability distribution mean to a transformed distribution with different statistical characteristics. By using a distribution transformation that emphasizes tail probabilities or extreme values, the method shifts focus from local optima to global optima, resolving the contradiction between reliability of global solution finding and efficiency of search.
2Productivity
If the argument value with the highest estimated function value is selected for observation, then the search efficiency is improved, but the risk of converging to local optima increases
Solution Approach 1:
Instead of selecting the argument value with the highest estimated function value (standard exploitation approach), the patent inverts the selection criterion by using a transformed distribution that prioritizes argument values with high potential for global optimality. This inversion of the selection logic allows the method to balance exploration and exploitation, improving both search efficiency and observation accuracy.
3Measurement precision
If multiple observation points are evaluated to find the global optimum, then the accuracy of function value observation improves, but the computational cost and time increase
Solution Approach 1:
The patent performs preliminary transformation of the probability distribution into a form that directly highlights global optimal candidates. By pre-processing the distribution transformation before the selection process, the method identifies promising argument values in advance, reducing the number of observations needed and thus decreasing computational time while maintaining high measurement precision.
Data Source
AI summary
An information processing device acquires, based on a set including a plurality of samples in which a first value and a second value are associated with each other, a first distribution that is a probability distribution of a first function for calculating the second value from the first value. The information processing device acquires, based on a second distribution having an average different from an average of each argument of the first function in the first distribution, a second function for calculating an evaluation value for an argument value of the first function. The information processing device determines, based on the evaluation value by the second function, an argument value for sampling a function value of the first function.