论文标题
分支并在预测中进行递归学习和迭代解决的问题+优化
Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize
论文作者
论文摘要
本文提出了Branch＆Learn，这是一个预测+优化的框架，以解决求解时未知参数的优化问题。考虑到可以通过满足简单条件的递归算法来解决的优化问题，我们展示了如何直接从递归算法中直接且有条理地构建相应的学习算法。我们的框架也通过将其视为退化形式的递归形式，也适用于迭代算法。广泛的实验显示了我们对古典和最先进方法的提议的表现更好。
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.
