Optimization Online


A Composite Risk Measure Framework for Decision Making under Uncertainty

Pengyu Qian(pengyu.qian***at***pku.edu.cn)
Zizhuo Wang(zwang***at***umn.edu)
Zaiwen Wen(wenzw***at***math.pku.edu.cn)

Abstract: In this paper, we present a unified framework for decision making under uncertainty. Our framework is based on the composite of two risk measures, where the inner risk measure accounts for the risk of decision given the exact distribution of uncertain model parameters, and the outer risk measure quantifies the risk that occurs when estimating the parameters of distribution. We show that the model is tractable under mild conditions. The framework is a generalization of several existing models, including stochastic programming, robust optimization, distributionally robust optimization, etc. Using this framework, we study a few new models which imply probabilistic guarantees for solutions and yield less conservative results comparing to traditional models. Numerical experiments are performed on portfolio selection problems to demonstrate the strength of our models.


Category 1: Robust Optimization

Category 2: Stochastic Programming


Download: [PDF]

Entry Submitted: 01/06/2015
Entry Accepted: 01/06/2015
Entry Last Modified: 01/06/2015

Modify/Update this entry

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


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