# A Practical Guide to Robust Optimization

arXiv: https://arxiv.org/abs/1501.02634 · HTML: https://mlsanity.com/paper/1501.02634/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2015.

## Built on by (most cited first, 16 of 16)

- [Conformal Uncertainty Sets for Robust Optimization](https://mlsanity.com/paper/2105.14957/) (2021)
- [Optimization with Constraint Learning: A Framework and Survey](https://mlsanity.com/paper/2110.02121/) (2021)
- [Zeroth-Order Methods for Convex-Concave Minmax Problems: Applications to Decision-Dependent Risk Minimization](https://mlsanity.com/paper/2106.09082/) (2021)
- [Distributed Distributionally Robust Optimization with Non-Convex Objectives](https://mlsanity.com/paper/2210.07588/) (2022)
- [Federated Distributionally Robust Optimization with Non-Convex Objectives: Algorithm and Analysis](https://mlsanity.com/paper/2307.14364/) (2023)
- [Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers](https://mlsanity.com/paper/2504.19000/) (2025)
- [Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control](https://mlsanity.com/paper/2207.13730/) (2022)
- [Learning and Testing Convex Functions](https://mlsanity.com/paper/2511.11498/) (2025)
- [Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty](https://mlsanity.com/paper/2411.02654/) (2024)
- [Generalized Neural Operator for Parametric and Boundary-Value Problems](https://mlsanity.com/paper/2607.21932/) (2026)
- [Ensemble Methods for Robust Support Vector Machines using Integer Programming](https://mlsanity.com/paper/2203.01606/) (2022)
- [Classifying with Uncertain Data Envelopment Analysis](https://mlsanity.com/paper/2209.01052/) (2022)
- [Distributionally and Adversarially Robust Logistic Regression via Intersecting Wasserstein Balls](https://mlsanity.com/paper/2407.13625/) (2024)
- [Stability Regularized Cross-Validation](https://mlsanity.com/paper/2505.06927/) (2025)
- [Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors](https://mlsanity.com/paper/2509.26511/) (2025)
- [On the Complexity of Offline Reinforcement Learning with Q^star-Approximation and Partial Coverage](https://mlsanity.com/paper/2602.12107/) (2026)

## Abstract (arXiv)

Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do's and don'ts for using it in practice. We use many small examples to illustrate our discussions.