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The number of gPC terms increases dramatically as the dimension of the random input variables increases.
This reduction in computational cost becomes more pronounced as the number of plant design variables increases.
However, as the number of variables increases, the computation time increases exponentially.
In all engineering problems, as the number of variables increases, the corresponding data matrix extends.
Such models require extensive computational effort that increases drastically as the number of decision variables increases.
However, it is difficult to evaluate this probability when the number of random variables increases.
We study analytically the limiting behavior of the algorithm as the number of hidden variables increases indefinitely.
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Manhattan alone has several spheres of influence, uptown and downtown, and when the other boroughs are considered the variables increase exponentially.
All these variables increased significantly with increasing feed ration size.
The variables increase exponentially both in centralized and decentralized cases.
The importance of precipitation variables increased with time.
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