QUBO Matrix Block Division for Combinatorial Optimization
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Solution Overview
Problem
Existing methods for solving QUBO-modeled combinatorial optimization problems often struggle to obtain near-optimal solutions efficiently, particularly when directly using the QUBO model without appropriate optimization techniques.
Innovation Solution
The proposed optimization device and method determine the type of combinatorial optimization problem from a QUBO matrix with a two-way one-hot condition, and then perform an optimization process tailored to that type, using techniques such as problem division and subproblem optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a QUBO model of a combinatorial optimization problem is simply used as it is, then the problem can be solved by an annealing machine, but it may take a lot of time and near-optimal solutions are difficult to obtain
Solution Approach 1:
The patent applies segmentation by dividing the QUBO matrix into multiple blocks based on the two-way one-hot condition structure. This block division allows the optimization problem to be processed in smaller, more manageable segments, reducing the overall computational time while maintaining solution quality through targeted optimization on each block.
Solution Approach 2:
The patent implements preliminary action by performing block division and identifying two-way one-hot conditions before the main optimization process. This preprocessing step prepares the QUBO matrix in advance, enabling more efficient optimization execution and avoiding time-consuming operations during the actual solving phase.
2Productivity
If appropriate optimization techniques are applied to QUBO-modeled problems, then solution quality and efficiency can be improved, but the complexity of the optimization process increases
Solution Approach 1:
The patent reduces process complexity through segmentation by dividing the QUBO matrix into distinct blocks based on structural characteristics. This segmentation creates a systematic framework that simplifies the optimization process, making it more manageable and less complex while improving efficiency through targeted processing of each block.
Solution Approach 2:
The patent applies parameter changes by transforming the QUBO matrix parameters through block division and reorganization. This parameter transformation simplifies the optimization landscape, enabling more efficient solving while the systematic approach to parameter changes keeps the process complexity manageable.
3Reliability
If the QUBO model is used without problem-specific optimization, then the optimization process is simpler, but near-optimal solutions are difficult to obtain
Solution Approach 1:
The patent applies local quality by treating different blocks of the QUBO matrix with specialized optimization approaches tailored to their specific characteristics. This localized optimization strategy improves solution optimality by addressing the unique properties of each block while maintaining overall process manageability through the systematic block framework.
Solution Approach 2:
The patent implements preliminary action by performing systematic block division and identifying two-way one-hot conditions before optimization. This preliminary structuring enables more effective optimization strategies to be applied, improving solution optimality while the pre-established framework keeps the overall process complexity manageable.
Data Source
AI summary
The optimization device 90 includes a determining means 91 and an optimizing means 92. The determining means 91 determines a type of combinatorial optimization problem from a QUBO matrix obtained by QUBO modeling of a combinatorial optimization problem that includes a two-way one-hot condition as a constraint condition. The optimizing means 92 performs optimization process according to the determined type of combinatorial optimization problem.