Robust Optimization

By Aharon Ben-Tal, Laurent El Ghaoui, Arkadi Nemirovski. Princeton Series in Applied Mathematics, 2021

1,042 citations recorded by the source corpus.

Course aims What this course is This is a PhD course on robust optimization. The decision-making problem requires the parameters of underlying models. While applying data to estimate parameters, the uncertainty is unavoidable. Robust optimization is an emerging area to incorporate uncertainty into mathematical programming models. This course will cover various aspects of robust optimization and satisficing frameworks, including nonstochastic and stochastic models.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0dc44c3dbaeec29ed5e9b9dd196ff637b5def17d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. Dimitris J. Bertsimas
2. Nathan Kallus
3. John C. Duchi
4. Hongseok Namkoong
5. Tomer Koren
6. Shie Mannor
7. Elisabeth Köbis
8. Aharon Ben-Tal

In Statistics and Probability

1. Dimitris J. Bertsimas
2. Nathan Kallus
3. Elisabeth Köbis
4. Shie Mannor
5. Aharon Ben-Tal
6. David Simchi-Levi
7. Yehua Dennis Wei
8. He Wang

Citations in the corpus

• From Predictive to Prescriptive Analytics (2014, background): “A notable alternative approach to decision making under uncertainty in OR/MS problems is robust optimization (cf. Ben-Tal et al. 2009) and its data-driven variants (cf. Bertsimas et al. 2018a).”
• Increasing Supply Chain Robustness through Process Flexibility and Inventory (2018, methodology): “Our approach to model uncertainties follows the recent proposals in the robust optimization literature (e.g., Ben-Tal et al. 2009, Bandi and Bertsimas 2012), which propose to replace probabilistic distributions with uncertainty sets.”
• Oracle-Based Robust Optimization via Online Learning (2014, background): “The Robust Optimization (RO; see Ben-Tal and Nemirovski (2002); Ben-Tal et al. (2009); Bertsimas et al. (2011)) framework addresses a fundamental problem of many convex optimization problems: slight inaccuracies in data give rise to significant fluctuations in the solution.”
• Adversarial Examples on Object Recognition (2020, background): “In optimization, a robust solution has the ability to perform well under a certain level of uncertainty [11].”
• On Robust Optimization (2015, background): “Except for ε-constraint robust OPs, which were just recently introduced in [5], the mentioned concepts are well-known in the literature, see [2, 3, 4, 6, 7].”