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

Xinyi Hu · Jasper Lee · Jimmy Lee · Allen Z. Zhong

Hall J #719


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.

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