Data-driven chance constrained stochastic program

By Ruiwei Jiang, Yongpei Guan. Mathematical programming, 2015

507 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/1ce3282145d5d1810dfc40a0dfd59707a4c05314

How researchers see this paper

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

In Control and Systems Engineering

In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

Wasserstein Distributional Robustness and Regularization in Statistical Learning (2017, background): “Popular choices of the statistical distance include φ -divergences (Ben-Tal et al. 2013, Jiang and Guan 2015, Bayraksan and Love 2015, Wang et al. 2016), Prokhorov metric (Erdo˘gan and Iyengar 2006), and Wasserstein distances (Wozabal 2014, Blanchet and Murthy 2016, Gao and Kleywegt 2016, Esfahani…”
Outlier detection using distributionally robust optimization under the Wasserstein metric (2017, methodology): “ene Pas a ball of distributions using some probability distance functions such as the ˚-divergences [Bayraksan and Love, 2015], which include the Kullback-Leibler (KL) divergence [Hu and Hong, 2013, Jiang and Guan, 2015] as a special case, the Prokhorov metric [Erdogan and Iyengar, 2006], and the Wa”
Multi-stage distributionally robust optimization with risk aversion (2021, background): “If P(ρ) represents the ambiguity set as a function of ρ > 0, [19] founded P2(ρ) ⊆P(ρ) ⊆P(ρ) ⊆PHD(√ρ) ⊆P (2ρ 14 ).”
Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, methodology): “Most, if not all, of the papers on data-driven optimization under uncertainty are restricted to such learning of data [25, 116, 144, 157], so they fail to account for real-time data.”
Quantifying Distributional Model Risk via Optimal Transport (2016, unknown): “he most popular choice for d, thanks to the tractability of (1) when dis chosen as relative entropy or other likelihood based metrics (see [14,36,23,4] for the use in robust performance analysis, and [26,32,41,38,33,9,55,34,29,8] and references therein for applications to distributionally robust opt”
Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization (2016, background): “As a by-product, our proposed method also resolves some technical challenges reported in the previous literature in calibrating divergence balls (e.g., Jiang and Guan (2012), Esfahani and Kuhn (2015)).”
Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach (2016, methodology): “Delage and Ye [26] made initial progress in this direction by constructing conﬁdence regions based on mean and covariance matrices from the data, and Jiang and Guan [42] expand this line of research to other moment constraints.”
Distributionally Robust Chance Constrained Optimal Power Flow with Renewables: A Conic Reformulation (2018, result): “Similar to [35], [43], [44], let us consider the ambiguity set defined by first and second moments as”
Distributionally Robust Chance-Constrained Optimal Power Flow With Uncertain Renewables and Uncertain Reserves Provided by Loads (2017, methodology): “After applying Lemma 1 in [22], the SDP formulation (16)–(20) directly follows from the dual formulation (21)–(24) by replacing the semi-infinite constraints (22) by finite SDP constraints.”
Distributionally Robust Stochastic Optimization with Wasserstein Distance (2016, background): “The results of Esfahani and Kuhn [20] and Zhao and Guan [50] and other strong duality results in the literature are based on the established Hahn-Banach theorem for certain inﬁnite dimensional vector spaces.”
Distributionally Robust Optimization: A Review (2019, background): “[157] and Jiang and Guan [166] show that distributionally robust chanceconstrained programs formed via φ-divergences can be transformed into a chanceconstrained problem under the nominal distribution but with an adjusted risk level.”