Optimization Online


Data-driven satisficing measure and ranking

Wenjie Huang (wenjie_huang***at***u.nus.edu)

Abstract: We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions. The basic model is built based on satisficing measure (SM) which yields a single index for risk comparison. Since SM is a dual representation for a family of risk measures, we consider problems constrained by Conditional value-at-risk (CVaR) and by general coherent risk measure. Starting with offline optimization, we apply sample average approximation technique and argue the convergence rate and validation of optimal solutions. In online stochastic optimization case, we develop primal-dual stochastic approximation algorithms respectively for CVaR and general risk constrained problems, and derive their regret bounds. For both offline and online cases, we illustrate the relationship between risk ranking accuracy with sample size (or iterations).

Keywords: Risk measure; Satisficing measure; Online stochastic optimization; Stochastic approximation; Sample average approximation; Ranking;

Category 1: Stochastic Programming

Category 2: Robust Optimization

Category 3: Applications -- Science and Engineering (Civil and Environmental Engineering )


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Entry Submitted: 03/27/2017
Entry Accepted: 03/27/2017
Entry Last Modified: 03/27/2017

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