Online Mixed-Integer Optimization in Milliseconds

Understand

We propose a method to solve online mixed-integer optimization (MIO) problems at very high speed using machine learning.

• By exploiting the repetitive nature of online optimization, we are able to greatly speedup the solution time.
• Our approach encodes the optimal solution into a small amount of information denoted as strategy using the Voice of Optimization framework proposed in [BS21].
• In this way the core part of the optimization algorithm becomes a multiclass classification problem which can be solved very quickly.