  Effective resource management plays a pivotal role in wireless networks,
which, unfortunately, results in challenging mixed-integer nonlinear
programming (MINLP) problems in most cases. Machine learning-based methods have
recently emerged as a disruptive way to obtain near-optimal performance for
MINLPs with affordable computational complexity. There have been some attempts
in applying such methods to resource management in wireless networks, but these
attempts require huge amounts of training samples and lack the capability to
handle constrained problems. Furthermore, they suffer from severe performance
deterioration when the network parameters change, which commonly happens and is
referred to as the task mismatch problem. In this paper, to reduce the sample
complexity and address the feasibility issue, we propose a framework of
Learning to Optimize for Resource Management (LORM). Instead of the end-to-end
learning approach adopted in previous studies, LORM learns the optimal pruning
policy in the branch-and-bound algorithm for MINLPs via a sample-efficient
method, namely, imitation learning. To further address the task mismatch
problem, we develop a transfer learning method via self-imitation in LORM,
named LORM-TL, which can quickly adapt a pre-trained machine learning model to
the new task with only a few additional unlabeled training samples. Numerical
simulations will demonstrate that LORM outperforms specialized state-of-the-art
algorithms and achieves near-optimal performance, while achieving significant
speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by
relying on a few unlabeled samples, achieves comparable performance with the
model trained from scratch with sufficient labeled samples.
