Multi-Dimensional Search Space Refinement Through Representative Sampling
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Solution Overview
Problem
Computational exploration of large multi-dimensional search spaces is prohibitive in both time and computing resources due to the vast number of possible combinations, which can take hundreds of years to search exhaustively.
Innovation Solution
A method involving sampling representative parameters based on a sampling threshold, determining a parameter range, and transforming a part of the search space using statistical guarantees to refine the search space, utilizing techniques like Latin Hypercube Sampling, Design of Experiment, and Information Gain to prune unimportant parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive search is performed on large multi-dimensional search spaces, then complete solution coverage is achieved, but computational time and resources become prohibitive
Solution Approach 1:
The patent divides the large multi-dimensional search space into multiple smaller sub-spaces or regions based on parameter ranges and statistical analysis. This segmentation allows the search to be performed on manageable portions rather than the entire space, reducing computational time while maintaining solution quality through targeted exploration of promising regions.
Solution Approach 2:
The patent performs partial search by sampling representative parameters and transforming only the necessary portions of the search space based on statistical guarantees. Instead of exhaustive exploration, the method applies partial action on selected parameter combinations that are most likely to yield solutions, achieving acceptable completeness with reduced computational effort.
2Reliability
If exhaustive search is performed on large multi-dimensional search spaces, then all possible solutions are found, but computing resources become prohibitive
Solution Approach 1:
The patent extracts and removes unnecessary parameters and combinations from the search space through statistical analysis and parameter transformation. By identifying and eliminating redundant or low-value search dimensions, the method reduces the effective search space size, thereby reducing computing resource requirements while preserving the ability to find meaningful solutions.
Solution Approach 2:
The patent transforms parameters and their ranges based on statistical guarantees and sampled data. By changing parameter representations, scaling ranges, or redefining parameter relationships, the method reduces the complexity of the search space without losing solution information, making the search computationally feasible.
3Productivity
If parameter sampling is performed to reduce search space, then computational efficiency is improved, but measurement precision of parameter ranges may be reduced
Solution Approach 1:
The patent employs iterative sampling and statistical analysis where results from sampled parameters feed back into refining the parameter range estimates. Through multiple rounds of sampling, evaluation, and transformation, the method progressively improves parameter range precision while maintaining computational efficiency, balancing accuracy and productivity through feedback-driven refinement.
Data Source
AI summary
An embodiment includes detecting a search request of a multi-dimensional search space by a system; responsive to detecting the search request, sampling representative parameters in the multi-dimensional search space based on a sampling threshold. The embodiment includes determining a parameter range of the representative parameters in the multi-dimensional search space. The embodiment also includes transforming a part of the multi-dimensional search space based on a statistical guarantee and the parameter range, where the part of the multi-dimensional search space is a refined space for a solution of the search request.