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

• URL: http://arxiv.org/abs/2205.01672v1
• Date: Sun, 1 May 2022 08:41:30 GMT
• Title: Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
• Authors: Xinyi Hu, Jasper C.H. Lee, Jimmy H.M. Lee and Allen Z. Zhong
• Abstract summary: This paper proposes Branch & Learn, a framework for Predict+ to tackle optimization problems containing parameters that are unknown at the time of solving. Our framework applies also to iterative algorithms by viewing them as a degenerate form of recursion.
• Score: 9.772500430303285
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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