Text

Distributionally Robust Chance Constrained Geometric Optimization

This paper discusses distributionally robust geometric programs with individual or joint chance constraints. Several groups of uncertainty sets are considered: uncertainty sets with first two order moments information; uncertainty sets with known first order or first two order moments information under nonnegative support; uncertainty sets constrained by the Kullback–Leibler divergence with a normal or discrete reference distribution; uncertainty sets constrained by the Wasserstein distance under discrete, full, or nonnegative real-space support; and joint uncertainty sets for the product of random variables. Under each group of uncertainty sets, we find deterministic reformulations of the distributionally robust geometric programs with individual or joint chance constraints. Convexity, solution methods, and relationships of the reformulation programs are discussed. Finally, numerical tests are carried out on a shape optimization problem.

Ketersediaan

Informasi Detail

Judul Seri

MATHEMATICS OF OPERATIONS RESEARCH; Vol.47 No.4 November 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 2950-2988

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

GEOMETRIC OPTIMIZATION
CHANCE CONSTRAINTS
DISTRIBUTIONALLY ROBUST
UNCERTAINTY SETS

Info Detail Spesifik

https://doi.org/10.1287/moor.2021.1233

Pernyataan Tanggungjawab

Jia Liu, Abdel Lisser, Zhiping Chen

Versi lain/terkait

Lampiran Berkas

Komentar