Learning to Optimize Under Constraints with Unsupervised Deep Neural Networks
This method addresses the real-time computational bottleneck for engineers and practitioners dealing with constrained optimization problems where parameters frequently change, offering a faster alternative to traditional optimization algorithms.
This paper proposes an unsupervised deep learning method to solve generic constrained continuous optimization problems. The method shifts the computational burden to an offline training phase, enabling real-time solutions for problems with frequently changing parameters.
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.