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 …
Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming
C Ning, F You - Computers & Chemical Engineering, 2019 - Elsevier
This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …
modern data lens, highlights key research challenges and promise of data-driven …
Distributionally robust chance constrained data-enabled predictive control
In this article we study the problem of finite-time constrained optimal control of unknown
stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive …
stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive …
A distributionally robust optimization based method for stochastic model predictive control
B Li, Y Tan, AG Wu, GR Duan - IEEE Transactions on Automatic …, 2021 - ieeexplore.ieee.org
Two stochastic model predictive control algorithms, which are referred to as distributionally
robust model predictive control algorithms, are proposed in this article for a class of discrete …
robust model predictive control algorithms, are proposed in this article for a class of discrete …
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 …
Robot learning from randomized simulations: A review
The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
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 …
Distributionally robust linear quadratic control
Abstract Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is
studied in various fields such as engineering, computer science, economics, and …
studied in various fields such as engineering, computer science, economics, and …
Distributionally robust learning
R Chen, IC Paschalidis - Foundations and Trends® in …, 2020 - nowpublishers.com
This monograph develops a comprehensive statistical learning framework that is robust to
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …
(distributional) perturbations in the data using Distributionally Robust Optimization (DRO) …
Wasserstein distributionally robust stochastic control: A data-driven approach
I Yang - IEEE Transactions on Automatic Control, 2020 - ieeexplore.ieee.org
Standard stochastic control methods assume that the probability distribution of uncertain
variables is available. Unfortunately, in practice, obtaining accurate distribution information …
variables is available. Unfortunately, in practice, obtaining accurate distribution information …