Distributionally robust optimization: A review
H Rahimian, S Mehrotra - arXiv preprint arXiv:1908.05659, 2019 - arxiv.org
The concepts of risk-aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. Statistical learning community has also …
have developed significantly over the last decade. Statistical learning community has also …
A survey of adjustable robust optimization
Static robust optimization (RO) is a methodology to solve mathematical optimization
problems with uncertain data. The objective of static RO is to find solutions that are immune …
problems with uncertain data. The objective of static RO is to find solutions that are immune …
Wasserstein distributionally robust optimization: Theory and applications in machine learning
Many decision problems in science, engineering, and economics are affected by uncertain
parameters whose distribution is only indirectly observable through samples. The goal of …
parameters whose distribution is only indirectly observable through samples. The goal of …
Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations
P Mohajerin Esfahani, D Kuhn - Mathematical Programming, 2018 - Springer
We consider stochastic programs where the distribution of the uncertain parameters is only
observable through a finite training dataset. Using the Wasserstein metric, we construct a …
observable through a finite training dataset. Using the Wasserstein metric, we construct a …
A practical guide to robust optimization
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 …
the last 15 years. Robust optimization is very useful for practice, since it is tailored to the …
Frameworks and results in distributionally robust optimization
H Rahimian, S Mehrotra - Open Journal of Mathematical Optimization, 2022 - numdam.org
The concepts of risk aversion, chance-constrained optimization, and robust optimization
have developed significantly over the last decade. The statistical learning community has …
have developed significantly over the last decade. The statistical learning community has …
Data-driven robust optimization
The last decade witnessed an explosion in the availability of data for operations research
applications. Motivated by this growing availability, we propose a novel schema for utilizing …
applications. Motivated by this growing availability, we propose a novel schema for utilizing …
Recent advances in robust optimization: An overview
This paper provides an overview of developments in robust optimization since 2007. It seeks
to give a representative picture of the research topics most explored in recent years …
to give a representative picture of the research topics most explored in recent years …
Adaptive distributionally robust optimization
We develop a modular and tractable framework for solving an adaptive distributionally
robust linear optimization problem, where we minimize the worst-case expected cost over an …
robust linear optimization problem, where we minimize the worst-case expected cost over an …
Data-driven decision making under uncertainty integrating robust optimization with principal component analysis and kernel smoothing methods
C Ning, F You - Computers & Chemical Engineering, 2018 - Elsevier
This paper proposes a novel data-driven robust optimization framework that leverages the
power of machine learning and big data analytics for decision-making under uncertainty. By …
power of machine learning and big data analytics for decision-making under uncertainty. By …