  Learning the optimized solution as a function of environmental parameters is
effective in solving numerical optimization in real time for time-sensitive
applications. Existing works of learning to optimize train deep neural networks
(DNN) with labels, and the learnt solution are inaccurate, which cannot be
employed to ensure the stringent quality of service. In this paper, we propose
a framework to learn the latent function with unsupervised deep learning, where
the property that the optimal solution should satisfy is used as the
"supervision signal" implicitly. The framework is applicable to both functional
and variable optimization problems with constraints. We take a variable
optimization problem in ultra-reliable and low-latency communications as an
example, which demonstrates that the ultra-high reliability can be supported by
the DNN without supervision labels.
