Graph segmentation method and device and electronic equipment

Abstract

The invention provides a graph segmentation method, relates to the field of graph theory, in particular to the field of artificial intelligence such as image processing, can be applied to scenes such as statistical physics, network design, super-large-scale integrated circuit design and data clustering analysis, and comprises the following steps: acquiring a to-be-segmented graph, wherein the graph comprises a plurality of vertexes and edges between the vertexes; generating variable parameter vectors of the vertexes, and iterating a minimum loss function of the graph according to the variable parameter vectors of the vertexes; obtaining a target value of a variable parameter vector corresponding to the vertex at the generation moment of the minimum loss function; and for each vertex, determining the number of cut edges of the vertex according to the target value corresponding to the vertex. According to the method, the maximum cut problem is converted into the optimization problem of the variable parameter vector, the solving difficulty of the maximum cut problem is reduced, the solving efficiency of the maximum cut problem is improved, the practicability of the maximum cut problem is enhanced, and the solving efficiency of the maximum cut problem is enhanced.

Description

technical field [0001] The disclosure relates to the field of graph theory, especially to the field of artificial intelligence such as image processing, and can be applied to scenarios such as statistical physics, network design, VLSI design, and data clustering analysis. Background technique [0002] With the development and maturity of computer technology, computers can handle many complex computing tasks. However, there are still some problems that current computers cannot solve quickly and efficiently, such as the approximate solution of the maximum cut problem. [0003] In related technologies, the process of finding the optimal solution for the maximum cut problem often takes a long time, resulting in low efficiency in obtaining the optimal solution, which affects the practicability of the maximum cut to a certain extent. Therefore, how to improve the efficiency of obtaining the optimal approximate solution of the maximum cut is a problem that needs to be solved at pre...

AI Technical Summary

Problems solved by technology

[0002] With the development and maturity of computer technology, computers can handle many complex computing tasks. However, there are still some problems that current computers cannot solve quickly and efficiently, such as the approximate solution of the maximum cut problem.

[0003] In related technologies, the process of solving the optimal solution for the maximum cut problem often takes a long time, resulting in low efficiency in obtaining the optimal solution, which affects the practicability of the maximum cut problem to a certain extent.

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