scholarly journals A data-driven distributionally robust bound on the expected optimal value of uncertain mixed 0-1 linear programming

2018 ◽  
Vol 15 (1) ◽  
pp. 111-134 ◽  
Author(s):  
Guanglin Xu ◽  
Samuel Burer
Author(s):  
Yoshihiro Kanno

AbstractThis study considers structural optimization under a reliability constraint, in which the input distribution is only partially known. Specifically, when it is only known that the expected value vector and the variance-covariance matrix of the input distribution belong to a given convex set, it is required that the failure probability of a structure should be no greater than a specified target value for any realization of the input distribution. We demonstrate that this distributionally-robust reliability constraint can be reduced equivalently to deterministic constraints. By using this reduction, we can handle a reliability-based design optimization problem under the distributionally-robust reliability constraint within the framework of deterministic optimization; in particular, nonlinear semidefinite programming. Two numerical examples are solved to demonstrate the relation between the optimal value and either the target reliability or the uncertainty magnitude.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 135087-135098 ◽  
Author(s):  
Zhichao Shi ◽  
Hao Liang ◽  
Venkata Dinavahi

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