Information Processing Optimization with Phased Bayesian Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing optimization techniques using simulated bifurcation machines and quantum annealers face inefficiencies in processing time and accuracy due to the need for multiple repetitions of parameter search, which can lead to prolonged computation times and potential failure to find optimal parameters.
Innovation Solution
A method involving Bayesian optimization is employed to divide the total computation time into multiple phases, adjusting the calculation time for each phase to balance speed and accuracy, with thresholds for repetition and solution updates to efficiently converge on optimal parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple repetitions of parameter search are performed to improve accuracy, then the accuracy of parameter search is improved, but the processing time increases
Solution Approach 1:
The patent divides the parameter search process into multiple phases with different repetition counts. In earlier phases, fewer repetitions are performed to quickly explore the parameter space, while later phases perform more repetitions to refine the optimal parameters. This segmentation allows the system to achieve high accuracy without requiring all repetitions to be performed uniformly across all phases, thereby reducing overall processing time.
Solution Approach 2:
The patent dynamically adjusts the number of repetitions for parameter search based on the current phase and the progress made so far. The repetition count is not fixed but adapts according to the optimization process, allowing the system to allocate computational resources more efficiently and reduce total processing time while maintaining accuracy.
2Measurement precision
If the calculation time for each phase is increased to improve solution accuracy, then the accuracy is improved, but the overall processing time increases
Solution Approach 1:
The patent segments the optimization process into multiple phases where each phase has a specific time allocation and repetition count. This allows the system to perform fewer, faster calculations in early phases and reserve more time for later phases where higher accuracy is needed, optimizing the trade-off between total time and final accuracy.
Solution Approach 2:
The patent changes the parameters of the optimization process (such as the number of repetitions and calculation time per phase) based on the current state of the optimization. By adjusting these parameters dynamically, the system can achieve high accuracy solutions without wasting time on unnecessary calculations in early phases.
Data Source
AI summary
An information processing device includes an optimization unit, a control unit, and a search unit. The optimization unit executes optimization calculation processing of obtaining an optimal solution by repeatedly executing solving processing of obtaining a solution to an optimization problem by using one or more set parameters. The control unit sets a calculation time of the optimization calculation processing for each of a plurality of search times obtained by dividing a specified time that is specified. The search unit repeatedly executes search processing of setting the parameter different from parameters of other search times for each of the plurality of search times, causing the optimization unit to execute the optimization calculation processing within the set calculation time by using the set parameter, and searching for an optimal value of the parameter by Bayesian optimization by using the optimal solution obtained by the optimization calculation processing.