Weight Coefficient Calculation for Combinatorial Optimization
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Solution Overview
Problem
Existing methods for determining weight coefficients of constraint terms in combinatorial optimization problems are time-consuming, as they require repeated simulated annealing processes to ensure constraint satisfaction.
Innovation Solution
A device and method that calculate weight coefficients for constraint terms based on automatic establishment rates, energy increase amounts at constraint breakdown, and the number of spins associated with each constraint term, allowing for rapid calculation without iterative simulated annealing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If repeated simulated annealing is performed to determine weight coefficients of constraint terms, then constraint satisfaction reliability is improved, but calculation time increases
Solution Approach 1:
The patent calculates the number of spins associated with each constraint term in advance, before performing simulated annealing. This preliminary calculation allows the system to determine weight coefficients more efficiently during the optimization process, reducing the overall calculation time while maintaining constraint satisfaction reliability.
Solution Approach 2:
The patent replaces the traditional trial-and-error approach of repeatedly performing simulated annealing with a more efficient method that uses pre-calculated spin information. By substituting the mechanical iterative process with a calculation-based approach using spin counts, the system achieves faster weight coefficient determination while maintaining reliability.
2Measurement precision
If iterative weight coefficient adjustment is performed, then solution accuracy is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary calculation of the number of spins for each constraint term before the main optimization process. This advance preparation enables more accurate weight coefficient determination without requiring excessive iterative adjustments, thereby improving solution accuracy while maintaining high calculation speed and productivity.
3Measurement precision
If multiple simulated annealing runs are conducted, then constraint term weight accuracy is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex iterative simulated annealing processes with a more streamlined approach that utilizes pre-calculated spin information. By substituting the mechanical iterative adjustment process with calculation-based weight determination using spin counts, the system achieves accurate weight coefficients while reducing process complexity.
Data Source
AI summary
Each constraint term in an expression representing energy in a combinatorial optimization problem is input the input means 71. The automatic establishment rate calculation means 73 calculates an automatic establishment rate for each constraint term, wherein the automatic establishment rate is a probability that a constraint represented by a constraint term is satisfied when all other constraints associated with individual spins associated with the constraint term are satisfied. The energy increase amount determination means 74 determines amount of energy increase at constraint breakdown for each constraint term, wherein the amount of energy increase at constraint breakdown is amount of energy increase when a constraint represented by a constraint term is no longer satisfied. The spin number derivation means 75 derives the number of spins associated with a constraint represented by a constraint term, for each constraint term.