Distributionally Robust Chance Constrained Optimization Model for the Minimum Cost Consensus

摘要: As a solution method that not only considers the probability distribution information of data, but also ensures results are too conservative, more and researches have been made on distributionally robust optimization method. Based minimum cost consensus model, this paper proposes new model with chance constraints (DRO-MCC). Firstly, Conditional Value-at-Risk (CVaR) is used to approximate in model. Secondly, when first second moments random variables affecting unit adjustment known, min-max problem obtained based moment dual theory, tractable semidefinite programming can be easily processed through further transformation. Finally, order evaluate robustness proposed different parameters compared, DRO-MCC compared (RO-MCC) (MCC). The example proves MCC optimistic RO-MCC conservative. In contrast, overcomes conservatism so result ideal.