Work

Data-Driven Chance Constrained Programs over Wasserstein Balls

Year: 2018

Type: preprint

Abstract: We provide an exact deterministic reformulation for data-driven chance constrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand side uncertainty, our reformulation amounts to a mixed-integer conic program. In the special case of a Wasserstein ball with the $1$-norm or the $\infty$-norm, the cone is the nonnegative orthant, and the chance constrained program can be reformulated as a mixed-integer linear program. Our reformulation compares favourably to several state-of-the-art data-driven optimization schemes in our numerical experiments.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1809.00210

Authors: Chen, Zhi, Kuhn, Daniel, Wiesemann, Wolfram

Institutions: City University of Hong Kong, École Polytechnique Fédérale de Lausanne, Imperial College London

Language: English

Topic: Risk and Portfolio Optimization

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funders: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Engineering and Physical Sciences Research Council

Awards: Optimization under Unknown or Uncertain Uncertainty (Optimization4U), Machine Learning, Robust Optimisation, and Verification: Creating Synergistic Capabilities in Cybersecurity Research

OpenAlex ID: W4308894625

Cites:

Cited by: 45

Related to: 10