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 …
Frameworks and results in distributionally robust optimization
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 …
Certifying some distributional robustness with principled adversarial training
Neural networks are vulnerable to adversarial examples and researchers have proposed
many heuristic attack and defense mechanisms. We address this problem through the …
many heuristic attack and defense mechanisms. We address this problem through the …
The big data newsvendor: Practical insights from machine learning
We investigate the data-driven newsvendor problem when one has n observations of p
features related to the demand as well as historical demand data. Rather than a two-step …
features related to the demand as well as historical demand data. Rather than a two-step …
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 …
Distributionally robust optimization under moment uncertainty with application to data-driven problems
Stochastic programming can effectively describe many decision-making problems in
uncertain environments. Unfortunately, such programs are often computationally demanding …
uncertain environments. Unfortunately, such programs are often computationally demanding …
Distributionally robust joint chance constraints with second-order moment information
We develop tractable semidefinite programming based approximations for distributionally
robust individual and joint chance constraints, assuming that only the first-and second-order …
robust individual and joint chance constraints, assuming that only the first-and second-order …
Theory and applications of robust optimization
In this paper we survey the primary research, both theoretical and applied, in the area of
robust optimization (RO). Our focus is on the computational attractiveness of RO …
robust optimization (RO). Our focus is on the computational attractiveness of RO …