Final answer:
The max-cut problem on graphs involves partitioning the vertices of a graph into two sets to maximize the number of edges between the sets. While it may be challenging to find articles with specific configurations behind the max-cut value, there are general techniques such as spectral methods that can be used to approximate the max-cut value.
Step-by-step explanation:
The max-cut problem on graphs is a fundamental problem in graph theory and optimization. It involves partitioning the vertices of a graph into two sets, such that the number of edges between the two sets is maximized. The Ising model is a mathematical model used to study interactions between particles.
Although there are many articles on solving the Ising problem or max-cut problem on graphs, it may be difficult to find specific articles that include the configurations behind the max-cut value. This is because the problem is NP-hard, and finding the optimal solution for large graphs can be computationally challenging. Researchers often focus on developing algorithms to approximate the max-cut value rather than providing specific configurations.
However, there are some general techniques that can be applied to find good approximate solutions for max-cut. One common approach is to use spectral methods, such as the spectral relaxation or the semidefinite programming relaxation. These methods provide a lower bound on the max-cut value and can be used to obtain near-optimal partitionings of the graph.