Energy Function Derivation for Combinatorial Optimization
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Solution Overview
Problem
Users face challenges in modifying existing solutions to combinatorial optimization problems due to changes in circumstances, such as altering work schedules, without significantly affecting other workers' attendance status.
Innovation Solution
An energy function derivation device and method that generates terms based on the desired changes to spin states, allowing for the modification of existing energy functions by adding specific terms to the original energy function, enabling the derivation of a new energy function that reflects the user's desired modifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the energy function is modified to change spin states, then the solution can be adjusted to reflect new circumstances, but the complexity of the energy function increases
Solution Approach 1:
The energy function is segmented into an original energy function and additional terms. The additional terms are generated separately based on the desired spin state changes, allowing the modification process to be modular and manageable without overwhelming complexity.
Solution Approach 2:
The invention changes parameters of the energy function by introducing new terms with specific coefficients that encode the desired spin state modifications. This allows flexible adjustment of the energy function to reflect new circumstances while maintaining a structured approach to complexity management.
2Manufacturing precision
If multiple terms are added to the energy function to achieve desired changes, then the solution can be precisely modified, but the computational complexity increases
Solution Approach 1:
The additional terms are designed to have local effects, each term specifically targeting particular spin states for modification. This localized approach allows precise control over which spins change state while minimizing the overall computational burden compared to modifying the entire energy function uniformly.
Data Source
AI summary
The term generation means 73 generates a term, when a solution to a combinatorial optimization problem, an energy function, which is used to obtain the solution, of a model representing states of individual spins by a first value or a second value, and a pair of identification information of spin and degree of wish to change which indicates degree to which an user wishes to change state of spin corresponding to the identification information in the solution are given, wherein the term is generated for each pair of the identification information and the degree of wish to change, based on the state of spin corresponding to the identification information in the solution and the degree of wish to change. The energy function derivation means 74 deriving a new energy function by adding terms generated by the term generation means 73 for each pair to the energy function.


