-

 

 

 




Optimization Online





 

Convergence Analysis and a DC Approximation Method for Data-driven Mathematical Programs with Distributionally Robust Chance Constraints

Sun Hailin(hlsun***at***njnu.edu.cn)
Zhang Dali(zhangdl***at***sjtu.edu.cn)
Chen Yannan(ynchen***at***scnu.edu.cn)

Abstract: In this paper, we consider the convergence analysis of data-driven mathematical programs with distributionally robust chance constraints (MPDRCC) under weaker conditions without continuity assumption of distributionally robust probability functions. Moreover, combining with the data-driven approximation, we propose a DC approximation method to MPDRCC without some special tractable structures. We also give the convergence analysis of the DC approximation method without continuity assumption of distributionally robust probability functions and apply a recent DC algorithm to solve them. The numerical tests verify the theoretical results and show the effectiveness of the data-driven approximated DC approximation method.

Keywords: Distributionally robust optimization, chance constraints, data-driven, convergence analysis, DC approximation

Category 1: Robust Optimization

Category 2: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 11/07/2019
Entry Accepted: 11/07/2019
Entry Last Modified: 11/07/2019

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society