Teaching Networks to Solve Optimization Problems. (arXiv:2202.04104v1 [cs.LG])

Leveraging machine learning to optimize the optimization process is an
emerging field which holds the promise to bypass the fundamental computational
bottleneck caused by traditional iterative solvers in critical applications
requiring near-real-time optimization. The majority of existing approaches
focus on learning data-driven optimizers that lead to fewer iterations in
solving an optimization. In this paper, we take a different approach and
propose to replace the iterative solvers altogether with a trainable parametric
set function that outputs the optimal arguments/parameters of …

arxiv networks optimization teaching