# A Scalable Algorithm for Two-Stage Adaptive Linear Optimization

arXiv: https://arxiv.org/abs/1807.02812 · HTML: https://mlsanity.com/paper/1807.02812/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market: extended version](https://mlsanity.com/paper/2105.13801/) (2021)
- [An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories](https://mlsanity.com/paper/2310.12183/) (2023)

## Abstract (arXiv)

The column-and-constraint generation (CCG) method was introduced by citetZeng2013 for solving two-stage adaptive optimization. We found that the CCG method is quite scalable, but sometimes, and in some applications often, produces infeasible first-stage solutions, even though the problem is feasible. In this research, we extend the CCG method in a way that (a) maintains scalability and (b) always produces feasible first-stage decisions if they exist. We compare our method to several recently proposed methods and find that it reaches high accuracies faster and solves significantly larger problems.