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A platform for research: civil engineering, architecture and urbanism
Rectangular chance constrained geometric optimization
Abstract This paper discusses joint rectangular chance or probabilistic constrained geometric programs. We present a new reformulation of the joint rectangular chance constrained geometric programs where the random parameters are elliptically distributed and pairwise independent. As this reformulation is not convex, we propose new convex approximations based on the variable transformation together with piecewise linear approximation methods. For the latter, we provide a theoretical bound for the number of segments in the worst case. Our numerical results show that our approximations are asymptotically tight.
Rectangular chance constrained geometric optimization
Abstract This paper discusses joint rectangular chance or probabilistic constrained geometric programs. We present a new reformulation of the joint rectangular chance constrained geometric programs where the random parameters are elliptically distributed and pairwise independent. As this reformulation is not convex, we propose new convex approximations based on the variable transformation together with piecewise linear approximation methods. For the latter, we provide a theoretical bound for the number of segments in the worst case. Our numerical results show that our approximations are asymptotically tight.
Rectangular chance constrained geometric optimization
Liu, Jia (author) / Peng, Shen (author) / Lisser, Abdel (author) / Chen, Zhiping (author)
2019
Article (Journal)
English
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