Comparing structured ambiguity sets for stochastic optimization: Application to uncertainty quantification

L. Chaouach; T.A.E. Oomen; D. Boskos

doi:10.1109/CDC49753.2023.10383381

Comparing structured ambiguity sets for stochastic optimization: Application to uncertainty quantification

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

89 Downloads (Pure)

Abstract

The aim of this paper is to compare two classes of structured ambiguity sets, which are data-driven and can reduce the conservativeness of their associated optimization problems. These two classes of structured sets, coined Wasserstein hyperrectangles and multi-transport hyperrectangles, are explored in their trade-offs in terms of reducing conservativeness and providing tractable reformulations. It follows that multi-transport hyperrectangles lead to tractable optimization problems for a significantly broader range of objective functions under a decent compromise in terms of conservativeness reduction. The results are illustrated in an uncertainty quantification case study.

Original language	English
Title of host publication	Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023)
Publisher	IEEE
Pages	8274-8279
Number of pages	6
ISBN (Electronic)	979-8-3503-0124-3
DOIs	https://doi.org/10.1109/CDC49753.2023.10383381
Publication status	Published - 2023
Event	62nd IEEE Conference on Decision and Control - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023 Conference number: 62

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	62nd IEEE Conference on Decision and Control
Abbreviated title	CDC 2023
Country/Territory	Singapore
City	Singapore
Period	13/12/23 → 15/12/23

Bibliographical note

Accepted Author Manuscript

Access to Document

10.1109/CDC49753.2023.10383381

IEEE_CDC_2023_submittedAccepted author manuscript, 358 KB

Cite this

@inproceedings{5e56b417e3094e9abdcf12f1ff1e7778,

title = "Comparing structured ambiguity sets for stochastic optimization: Application to uncertainty quantification",

abstract = "The aim of this paper is to compare two classes of structured ambiguity sets, which are data-driven and can reduce the conservativeness of their associated optimization problems. These two classes of structured sets, coined Wasserstein hyperrectangles and multi-transport hyperrectangles, are explored in their trade-offs in terms of reducing conservativeness and providing tractable reformulations. It follows that multi-transport hyperrectangles lead to tractable optimization problems for a significantly broader range of objective functions under a decent compromise in terms of conservativeness reduction. The results are illustrated in an uncertainty quantification case study.",

author = "L. Chaouach and T.A.E. Oomen and D. Boskos",

note = "Accepted Author Manuscript; 62nd IEEE Conference on Decision and Control, CDC 2023 ; Conference date: 13-12-2023 Through 15-12-2023",

year = "2023",

doi = "10.1109/CDC49753.2023.10383381",

language = "English",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "IEEE",

pages = "8274--8279",

booktitle = "Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023)",

address = "United States",

}

Chaouach, L , Oomen, TAE & Boskos, D 2023, Comparing structured ambiguity sets for stochastic optimization: Application to uncertainty quantification. in Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023). Proceedings of the IEEE Conference on Decision and Control, IEEE, pp. 8274-8279, 62nd IEEE Conference on Decision and Control, Singapore, Singapore, 13/12/23. https://doi.org/10.1109/CDC49753.2023.10383381

Comparing structured ambiguity sets for stochastic optimization: Application to uncertainty quantification. / Chaouach, L.; Oomen, T.A.E.; Boskos, D.
Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023). IEEE, 2023. p. 8274-8279 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Comparing structured ambiguity sets for stochastic optimization: Application to uncertainty quantification

AU - Chaouach, L.

AU - Oomen, T.A.E.

AU - Boskos, D.

N1 - Conference code: 62

PY - 2023

Y1 - 2023

N2 - The aim of this paper is to compare two classes of structured ambiguity sets, which are data-driven and can reduce the conservativeness of their associated optimization problems. These two classes of structured sets, coined Wasserstein hyperrectangles and multi-transport hyperrectangles, are explored in their trade-offs in terms of reducing conservativeness and providing tractable reformulations. It follows that multi-transport hyperrectangles lead to tractable optimization problems for a significantly broader range of objective functions under a decent compromise in terms of conservativeness reduction. The results are illustrated in an uncertainty quantification case study.

AB - The aim of this paper is to compare two classes of structured ambiguity sets, which are data-driven and can reduce the conservativeness of their associated optimization problems. These two classes of structured sets, coined Wasserstein hyperrectangles and multi-transport hyperrectangles, are explored in their trade-offs in terms of reducing conservativeness and providing tractable reformulations. It follows that multi-transport hyperrectangles lead to tractable optimization problems for a significantly broader range of objective functions under a decent compromise in terms of conservativeness reduction. The results are illustrated in an uncertainty quantification case study.

UR - http://www.scopus.com/inward/record.url?scp=85184821630&partnerID=8YFLogxK

U2 - 10.1109/CDC49753.2023.10383381

DO - 10.1109/CDC49753.2023.10383381

M3 - Conference contribution

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 8274

EP - 8279

BT - Proceedings of the 62nd IEEE Conference on Decision and Control (CDC 2023)

PB - IEEE

T2 - 62nd IEEE Conference on Decision and Control

Y2 - 13 December 2023 through 15 December 2023

ER -

Comparing structured ambiguity sets for stochastic optimization: Application to uncertainty quantification

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this