Primal–dual hybrid gradient method for distributionally robust optimization problems

Yongchao Liu, Xiaoming YUAN, Shangzhi Zeng, Jin ZHANG*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

8 Citations (Scopus)


We focus on the discretization approach to distributionally robust optimization (DRO) problems and propose a numerical scheme originated from the primal–dual hybrid gradient (PDHG) method that recently has been well studied in convex optimization area. Specifically, we consider the cases where the ambiguity set of the discretized DRO model is defined through the moment condition and Wasserstein metric, respectively. Moreover, we apply the PDHG to a portfolio selection problem modelled by DRO and verify its efficiency.

Original languageEnglish
Pages (from-to)625-630
Number of pages6
JournalOperations Research Letters
Issue number6
Publication statusPublished - Nov 2017

Scopus Subject Areas

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

User-Defined Keywords

  • Discretization method
  • Distributionally robust optimization
  • Moment conditions
  • Primal–dual hybrid gradient
  • Wasserstein metric


Dive into the research topics of 'Primal–dual hybrid gradient method for distributionally robust optimization problems'. Together they form a unique fingerprint.

Cite this