Parameter Optimization Using Gaussian Process Regression
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Solution Overview
Problem
Complex systems with many adjustable parameters pose challenges in optimizing performance, especially when function evaluations are expensive and gradients or Hessians are unknown, making it difficult to efficiently find the global optimum.
Innovation Solution
A computer-implemented method using non-parametric regression and transfer learning with Gaussian Process regressors to determine early-stopping and suggest optimal parameter values, allowing for dynamic switching between optimization techniques and incorporating user input for custom policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive parameter evaluation is performed to ensure finding the global optimum, then optimization reliability is improved, but computational resource expenditure increases
Solution Approach 1:
The patent applies partial action by performing only the necessary subset of parameter evaluations needed to achieve satisfactory optimization results. Instead of exhaustively evaluating all possible parameter combinations, the system uses non-parametric regression to predict performance and identifies only the most promising parameter variants for actual evaluation, thereby reducing computational resource expenditure while maintaining acceptable optimization reliability
Solution Approach 2:
The patent introduces non-parametric regression as an intermediary tool that estimates parameter variant performance without requiring actual system evaluations. This intermediary model allows the optimization process to screen and rank parameter variants theoretically, reducing the number of expensive actual evaluations needed while maintaining reliable identification of optimal parameters
2Use of energy by moving object
If the number of parameter evaluations is reduced to save computational resources, then resource expenditure is improved, but the ability to find the global optimum deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the results of each parameter evaluation are fed back into the non-parametric regression model to refine performance predictions. This feedback loop allows the system to learn from actual evaluations and improve the accuracy of its predictions, enabling reliable identification of the global optimum even with a limited number of evaluations by continuously improving the predictive model's accuracy
Solution Approach 2:
The patent applies preliminary action by using non-parametric regression to pre-screen and rank parameter variants before actual evaluation. This preliminary theoretical assessment identifies the most promising variants that are most likely to contain the global optimum, ensuring that limited evaluation resources are concentrated on the most fruitful search directions and maintaining optimization reliability with fewer actual evaluations
3Use of energy by moving object
If non-parametric regression with early-stopping is used, then computational resource expenditure is reduced, but system complexity increases
Solution Approach 1:
The patent extracts the complex non-parametric regression computations and early-stopping logic into a separate optimization module that operates independently from the core system being optimized. This extraction allows the complex optimization machinery to be applied as a black-box service layer, reducing the apparent complexity of the target system while concentrating computational complexity in the dedicated optimization component that can be managed and controlled separately
Data Source
AI summary
A computer-implemented method is provided for optimization of parameters of a system, product, or process. The method includes establishing an optimization procedure for a system, product, or process. The system, product, or process has an evaluable performance that is dependent on values of one or more adjustable parameters. The method includes receiving one or more prior evaluations of performance of the system, product, or process. The one or more prior evaluations are respectively associated with one or more prior variants of the system, product, or process. The one or more prior variants are each defined by a set of values for the one or more adjustable parameters. The method includes utilizing an optimization algorithm to generate a suggested variant based at least in part on the one or more prior evaluations of performance and the associated set of values.