Parameter Optimization via Dynamic Local Search Ranges
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Solution Overview
Problem
Existing methods for optimizing high-dimensional parameter settings are inefficient due to inappropriate search ranges, leading to prolonged calculation times and potential inaccuracies, especially when dealing with large numbers of dimensions and variables.
Innovation Solution
An information processing apparatus that trains a prediction model using combined setting and evaluation values, generates a narrower local search range, shifts it to include the center value of the parameter range, and calculates new setting values using an optimized acquisition function, thereby efficiently optimizing parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the search range is increased to cover more parameter dimensions, then the optimization coverage is improved, but the calculation time increases significantly
Solution Approach 1:
The patent divides the high-dimensional parameter search space into multiple local search ranges, each centered around promising parameter settings identified during optimization. Instead of searching the entire parameter space uniformly, the system segments the search into focused local regions, reducing the overall computational burden while maintaining comprehensive coverage through multiple localized searches.
Solution Approach 2:
The patent applies different search strategies to different regions of the parameter space by generating local search ranges adaptively based on the characteristics of each parameter setting. Each local search range is tailored to the specific local landscape, allowing the system to allocate computational resources more effectively to regions that are more likely to contain optimal solutions.
2Measurement precision
If the number of search data pieces is increased to improve optimization accuracy, then the precision is improved, but the calculation time increases
Solution Approach 1:
The patent performs preliminary identification of promising parameter settings before conducting the full optimization search. By using initial evaluations to identify regions of interest, the system prepares targeted local search ranges in advance, which reduces the number of random explorations needed and accelerates the convergence to accurate optimal solutions.
Solution Approach 2:
The patent implements feedback mechanisms where evaluation results from previous search iterations are used to inform and adjust subsequent local search ranges. This feedback loop allows the system to concentrate search efforts on regions that show promise based on accumulated evidence, improving accuracy while reducing unnecessary computations in unproductive areas.
3Speed
If the search range is narrowed to reduce calculation time, then the speed is improved, but the optimization accuracy deteriorates
Solution Approach 1:
The patent employs dynamic adjustment of local search ranges based on the optimization progress and identified promising regions. The search ranges are not fixed but adapt their size and position during the optimization process, expanding in directions that show promise and contracting in unproductive directions, thereby maintaining accuracy while improving speed.
Solution Approach 2:
The patent changes the parameters defining the search range (such as range boundaries and center points) adaptively based on evaluation results. By modifying these parameters dynamically, the system can narrow the search range to improve speed when confidence is high, while maintaining the capability to expand the range if accuracy requirements demand it.
4Adaptability or versatility
If the dimensionality of parameters is increased to handle complex optimization problems, then the problem-solving capability is improved, but the calculation time increases exponentially
Solution Approach 1:
The patent segments the high-dimensional parameter space into multiple lower-dimensional local search ranges, each focusing on a specific region. This segmentation transforms the intractable high-dimensional search into multiple manageable lower-dimensional searches, reducing the exponential computational complexity while maintaining the ability to handle complex multi-parameter optimization problems.
Solution Approach 2:
The patent introduces a new dimension of organization by structuring the search around multiple local ranges rather than a single global search space. This dimensional reorganization allows the system to navigate high-dimensional parameter spaces more efficiently by exploiting the local structure of the optimization landscape.
Data Source
AI summary
An information processing apparatus comprising processing circuitry, the processing circuitry configured to train a prediction model based on a data set in which a setting value of a parameter and an evaluation value of the setting value are combined, generate a second range narrower than a first range that is a maximum variable range of the setting value inputtable to the prediction model based on the data set and the trained prediction model, shift the second range to include a center value of the first range, and calculate a new setting value to be evaluated next, based on an acquisition function optimized by inputting the setting value within the shifted second range to the trained prediction model.