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

TL;DR

This paper introduces Branch & Learn, a framework that integrates learning with recursive and iterative optimization algorithms to improve decision-making in Predict+Optimize problems with unknown parameters.

Contribution

It provides a systematic method to derive learning algorithms from recursive and iterative optimization procedures, enhancing Predict+Optimize solutions.

Findings

Outperforms classical approaches in experiments

Applicable to recursive and iterative algorithms

Improves decision quality in Predict+Optimize tasks

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.