Learning to Optimize Non-Convex Sum-Rate Maximization Problems

Solving optimization problems through machine learning is a promising research direction. In this position paper, we sketch a general framework motivated by first-order necessary conditions to solve non-convex sum-rate optimization problems arising from practical resource allocation problems in cellular networks. We construct two parameter matrices to update matrix-form decision variables of the given objective function. We inherently enhance the learning efficiency by increasing the dimensionality of decision variables with a learnable parameter matrix. Our preliminary evaluation shows that our approach achieves up to 98\% optimality over state-of-the-art numerical algorithms while being up to 38$\times$ faster in various settings.