Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize. (arXiv:2205.01672v1 [cs.LG])

Web: http://arxiv.org/abs/2205.01672

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 …

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