The graph partitioning problem can take various complex forms or architectures. The problem can be as simple as a binary partition, but it can also be so complex that it is mapped onto a wide variety of combinatorial optimization NP-hard problems.
- In this project, what is demonstrated is the utilization of the pymetis and networkx libraries for optimization.
- It is equivalent to a benchmark, high-speed graph theory computation.
- However, in the previous work we shared about the spatial light Ising machine, it was a study on optical computing. Optical computing has been proven to be capable of rapidly solving combinatorial optimization problems, especially when the number of variables is large.
- The advantages of its fast and low-energy consumption are thus manifested.
Therefore, we can incorporate the coupling interaction matrix in the GPP problem into the spatial optical Ising machine for optimization and iteration, thereby achieving the cross-fusion of graph theory and optical computing.
The variable coupling matrix becomes more conducive to accelerating iterative optimization after reordering.
The logic of the problem is as follows: Given that the spatial optical Ising machine can optimize the Ising Hamiltonian form of the problem, then after representing the number of variables and the interaction matrices between variables for the graph partitioning problem. Thus, the image segmentation problem can be applied as a solution to the spatial Ising model.