scholarly journals Distributionally robust joint chance constraints with second-order moment information

2011 ◽  
Vol 137 (1-2) ◽  
pp. 167-198 ◽  
Author(s):  
Steve Zymler ◽  
Daniel Kuhn ◽  
Berç Rustem
2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Ke-wei Ding

We discuss and develop the convex approximation for robust joint chance constraints under uncertainty of first- and second-order moments. Robust chance constraints are approximated by Worst-Case CVaR constraints which can be reformulated by a semidefinite programming. Then the chance constrained problem can be presented as semidefinite programming. We also find that the approximation for robust joint chance constraints has an equivalent individual quadratic approximation form.


Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 564
Author(s):  
Hong Shen ◽  
Longkun Yu ◽  
Xu Jing ◽  
Fengfu Tan

The turbulence moment of order m (μm) is defined as the refractive index structure constant Cn2 integrated over the whole path z with path-weighting function zm. Optical effects of atmospheric turbulence are directly related to turbulence moments. To evaluate the optical effects of atmospheric turbulence, it is necessary to measure the turbulence moment. It is well known that zero-order moments of turbulence (μ0) and five-thirds-order moments of turbulence (μ5/3), which correspond to the seeing and the isoplanatic angles, respectively, have been monitored as routine parameters in astronomical site testing. However, the direct measurement of second-order moments of turbulence (μ2) of the whole layer atmosphere has not been reported. Using a star as the light source, it has been found that μ2 can be measured through the covariance of the irradiance in two receiver apertures with suitable aperture size and aperture separation. Numerical results show that the theoretical error of this novel method is negligible in all the typical turbulence models. This method enabled us to monitor μ2 as a routine parameter in astronomical site testing, which is helpful to understand the characteristics of atmospheric turbulence better combined with μ0 and μ5/3.


AIChE Journal ◽  
2012 ◽  
Vol 58 (12) ◽  
pp. 3653-3675 ◽  
Author(s):  
Juhui Chen ◽  
Shuyan Wang ◽  
Dan Sun ◽  
Huilin Lu ◽  
Dimitri Gidaspow ◽  
...  

2016 ◽  
Vol 56 (1) ◽  
pp. 125-136 ◽  
Author(s):  
Joviša Žunić ◽  
Dragiša Žunić

2021 ◽  
Vol 49 (3) ◽  
pp. 291-299 ◽  
Author(s):  
Christos Ordoudis ◽  
Viet Anh Nguyen ◽  
Daniel Kuhn ◽  
Pierre Pinson

2015 ◽  
Vol 32 (01) ◽  
pp. 1540004 ◽  
Author(s):  
Chenchen Wu ◽  
Dachuan Xu ◽  
Jiawei Zhang

In this paper, we present a bilinear second-order cone programming safe approximation for the distributionally robust chance constrained program (DRCCP), assuming that the support of the uncertain parameters, and the first and second marginal moments of the probability with respect to the interval constraint imposed on the sum of the uncertain parameters are given. If we further know the covariance matrix, we can obtain a bilinear semi-definite programming safe approximation. Preliminary numerical tests indicate that the proposed models are competitive.


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