Solving Max-cut problem Gset instances with Neural Network using PyTorch
Given an undirected graph
Maximization of the
The max-cut problem has important applications in various fields, including computer vision, statistical physics, and computational biology. It is also known to be NP-hard, which means that it is computationally difficult to solve optimally for large instances of the problem. Therefore, various approximation algorithms and heuristics have been developed to tackle the problem.
We are using Multi-Layer Perceptron (MLP) as our Minimizer Neural Network. The MLP takes an
We feed the output
to achieve a max-cut solution (for example, displayed by the graph below).
Best known max cut value on the Gset are given in Una Benlic and Jin-Kao Hao, Breakout Local Search for the Max-Cut problem