Restricted Boltzmann Machine Assignment Algorithm: Application to solve many-to-one matching problems on weighted bipartite graph
This work addresses matching problems in domains like resource allocation, but it appears incremental as it applies an existing RBM framework to a specific graph problem.
The authors tackled the perfect matching problem on weighted bipartite graphs by developing an iterative algorithm based on a Restricted Boltzmann Machine (RBM) to maximize an energy function and assign elements, demonstrating its potential with a real-world application.
In this work an iterative algorithm based on unsupervised learning is presented, specifically on a Restricted Boltzmann Machine (RBM) to solve a perfect matching problem on a bipartite weighted graph. Iteratively is calculated the weights $w_{ij}$ and the bias parameters $θ= ( a_i, b_j) $ that maximize the energy function and assignment element $i$ to element $j$. An application of real problem is presented to show the potentiality of this algorithm.