  In this paper, we propose a machine learning (ML) method to learn how to
solve a generic constrained continuous optimization problem. To the best of our
knowledge, the generic methods that learn to optimize, focus on unconstrained
optimization problems and those dealing with constrained problems are not
easy-to-generalize. This approach is quite useful in optimization tasks where
the problem's parameters constantly change and require resolving the
optimization task per parameter update. In such problems, the computational
complexity of optimization algorithms such as gradient descent or interior
point method preclude near-optimal designs in real-time applications. In this
paper, we propose an unsupervised deep learning (DL) solution for solving
constrained optimization problems in real-time by relegating the main
computation load to offline training phase. This paper's main contribution is
proposing a method for enforcing the equality and inequality constraints to the
DL-generated solutions for generic optimization tasks.
