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 …
On distributionally robust chance constrained programs with Wasserstein distance
W **e - Mathematical Programming, 2021 - Springer
This paper studies a distributionally robust chance constrained program (DRCCP) with
Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a …
Wasserstein ambiguity set, where the uncertain constraints should be satisfied with a …
Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness
S Küçükyavuz, R Jiang - EURO Journal on Computational Optimization, 2022 - Elsevier
Chance-constrained programming (CCP) is one of the most difficult classes of optimization
problems that has attracted the attention of researchers since the 1950s. In this survey, we …
problems that has attracted the attention of researchers since the 1950s. In this survey, we …
Distributionally robust joint chance-constrained dispatch for integrated transmission-distribution systems via distributed optimization
This paper focuses on the distributionally robust dispatch for integrated transmission-
distribution (ITD) systems via distributed optimization. Existing distributed algorithms usually …
distribution (ITD) systems via distributed optimization. Existing distributed algorithms usually …
Stochastic-distributionally robust frequency-constrained optimal planning for an isolated microgrid
Microgrid is a typical low-inertia system with uncertainty due to the high penetration of power
electronics and renewable energy. Therefore, it is necessary to consider the issue of …
electronics and renewable energy. Therefore, it is necessary to consider the issue of …
Frequency constrained scheduling under multiple uncertainties via data-driven distributionally robust chance-constrained approach
The declining system inertia in renewable-rich power systems raises a concern about the
frequency stability problem. The wind farm equipped with the power electronic controller is …
frequency stability problem. The wind farm equipped with the power electronic controller is …
Distributionally robust fault detection design and assessment for dynamical systems
We present a novel distributionally robust optimization approach for integrated design and
assessment of fault detection system. Its salient feature is the guaranteed robustness against …
assessment of fault detection system. Its salient feature is the guaranteed robustness against …
Wasserstein distributionally robust look-ahead economic dispatch
We consider the problem of look-ahead economic dispatch (LAED) with uncertain
renewable energy generation. The goal of this problem is to minimize the cost of …
renewable energy generation. The goal of this problem is to minimize the cost of …
Wasserstein distributionally robust motion control for collision avoidance using conditional value-at-risk
A Hakobyan, I Yang - IEEE Transactions on Robotics, 2021 - ieeexplore.ieee.org
In this article, a risk-aware motion control scheme is considered for mobile robots to avoid
randomly moving obstacles when the true probability distribution of uncertainty is unknown …
randomly moving obstacles when the true probability distribution of uncertainty is unknown …