Combinatorial Optimization Method Selection via Feature Analysis
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Solution Overview
Problem
Current methods struggle to appropriately select a solution method suitable for a given combinatorial optimization problem, as information processing equipment lacks knowledge about the specific problem type when presented with an Ising model or QUBO, leading to inefficient solution execution.
Innovation Solution
A solution method selection device and method that derive feature information from the given model, select a suitable solution method from multiple types based on this information, and send a specified solution request to the appropriate solution device for execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple solution methods are available for combinatorial optimization problems, then the ability to solve various problem types improves, but the difficulty of selecting the appropriate method increases
Solution Approach 1:
The system derives feature information from the given model and uses this feedback to automatically select the most suitable solution method. The feature information acts as feedback about the problem characteristics, enabling the selection process to adapt to different problem types without manual intervention.
Solution Approach 2:
The system performs self-service by automatically analyzing the model features and selecting the appropriate solution method without requiring external expertise or manual selection. The feature information derivation and method selection are automated processes that serve the system itself.
2Ease of operation
If feature information derivation is performed to enable automatic method selection, then the ease of operation improves, but the device complexity increases
Solution Approach 1:
The feature information derivation process is segmented into distinct functional components that analyze different aspects of the model. This segmentation makes the complex derivation process more manageable and modular, allowing each segment to handle specific feature extraction tasks independently.
Solution Approach 2:
Feature information acts as an intermediary between the given model and the solution method selection. This intermediary layer translates the model characteristics into selectable features, bridging the gap between problem representation and solution selection without requiring direct complex analysis.
3Productivity
If the system derives and analyzes feature information to match model characteristics with solution methods, then the productivity improves, but the use of energy increases
Solution Approach 1:
The system performs partial feature information derivation by focusing on the most relevant features needed for method selection rather than analyzing all possible model characteristics. This partial action approach reduces computational energy while maintaining sufficient accuracy for effective method matching.
Data Source
AI summary
The feature information derivation means 72 derives feature information that represents a feature of a model used to solve a combinatorial optimization problem, when the model is given. The solution method selection means 73 selects a solution method for the combinatorial optimization problem from among predetermined multiple types of solution methods based on the feature information. The solution request means 74 send a solution request that includes information that can specify the model used to solve the combinatorial optimization problem to a solution device that solves the combinatorial optimization problem using the selected solution method.