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

arXiv: https://arxiv.org/abs/2205.01672 · HTML: https://mlsanity.com/paper/2205.01672/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 1 May 2022.

## Builds on (most cited first, 4 of 4)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [Interior Point Solving for LP-based prediction+optimisation](https://mlsanity.com/paper/2010.13943/) (2020)
- [Decision Trees for Decision-Making under the Predict-then-Optimize Framework](https://mlsanity.com/paper/2003.00360/) (2020)

## Built on by (most cited first, 5 of 5)

- [Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities](https://mlsanity.com/paper/2307.13565/) (2023)
- [Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information](https://mlsanity.com/paper/2307.08964/) (2023)
- [Branch & Learn with Post-hoc Correction for Predict+Optimize with Unknown Parameters in Constraints](https://mlsanity.com/paper/2303.06698/) (2023)
- [torchmSAT: A GPU-Accelerated Approximation To The Maximum Satisfiability Problem](https://mlsanity.com/paper/2402.03640/) (2024)
- [Two-Stage Predict+Optimize for Mixed Integer Linear Programs with Unknown Parameters in Constraints](https://mlsanity.com/paper/2311.08022/) (2023)

## Abstract (arXiv)

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.