Paper ID: 2205.01672

Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize

Xinyi Hu, Jasper C. H. Lee, Jimmy H. M. Lee, Allen Z. Zhong

This paper proposes Branch & Learn, a framework for Predict+Optimize to tackle optimization problems containing parameters that are unknown at the time of solving. Given an optimization problem solvable by a recursive algorithm satisfying simple conditions, we show how a corresponding learning algorithm can be constructed directly and methodically from the recursive algorithm. Our framework applies also to iterative algorithms by viewing them as a degenerate form of recursion. Extensive experimentation shows better performance for our proposal over classical and state-of-the-art approaches.

Submitted: May 1, 2022