Weighted part maximum satisfiability problem solving method based on parallel gradient descent

By employing a clause error term-global error function-parallel backpropagation gradient descent method in WPMS solving, the problems of high computational complexity and poor parallelism in existing technologies are solved, achieving efficient and stable WPMS solving, especially with excellent performance in large-scale instances.

CN121882297APending Publication Date: 2026-04-17CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
0 Cites 0 Cited by

Patent Information

Application Number
CN202610015193.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-07
Publication Date
2026-04-17

AI Technical Summary

Technical Problem

Existing WPMS/MaxSAT solving techniques face significant computational and storage challenges in large-scale instances. Graph modeling and offline training lead to computational complexity, making it difficult to fully utilize global information and perform clause-level parallel optimization.

Method used

We employ a clause error term-global error function-parallel backpropagation gradient descent method, which directly utilizes gradient information in a continuous space, reduces the search space through unit propagation, adaptively selects activation functions and learning rates, and combines three layers of weight management and periodic reset to achieve efficient parallel solution.

Benefits of technology

It achieves efficient and stable WPMS solving, reduces computational complexity, and improves the solving efficiency and solution quality of large-scale instances, especially performing well when the proportion of long clauses is high or the weight span is large.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention belongs to the technical field of combinatorial optimization and intelligent solving, and discloses a weighted partial maximum satisfiability (WPMS) problem solving method based on parallel gradient descent. According to the method, a MaxSAT instance is no longer converted into a point and edge graph structure required by a graph neural network and depends on off-line training, but each clause is directly mapped into a differentiable clause error term, and an overall error function is constructed after the clause error terms are weighted and summed; and enabling the numerical value and the current assignment value not to meet the soft clause weight sum (and the hard clause violation penalty) to be kept consistent with the optimization direction of the soft clause weight sum (and the hard clause violation penalty). And then, calculating a gradient by adopting a chain rule of neural network back propagation, carrying out gradient descent updating on the continuous variable y, and carrying out gradient descent updating on the continuous variable y through an activation function driven by problem complexity, a parameter adaptive mechanism, a soft / hard clause differential weight scaling and smooth updating mechanism and an adaptive learning rate scheduling and rollback strategy for an oscillation / platform period. And the convergence speed and stability are improved. Due to the fact that each clause error term and the gradient contribution thereof are mutually independent in calculation, fine-grained parallelization can be carried out on the multi-core CPU according to the clause dimension, and the solving time of large-scale instances is remarkably shortened. Experiments show that the method has good convergence and solution quality on a plurality of standard data sets, and is especially suitable for large-scale WPMS instances with large weight span and high long clause proportion.
Need to check novelty before this filing date? Find Prior Art