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Hi,
I have trouble understanding the proof of Proposition 1 of your paper (https://arxiv.org/pdf/1609.07152.pdf). Can you provide supplementary steps why a fully connected ICNN (defined in equation (2) of your paper) is convex. Especially, why W^(y)_i can have negative values?
For example, when setting all W^(z)_i = 0 I expect the network not to be convex in general.
I would appreciate any help. Thanks.
The text was updated successfully, but these errors were encountered:
Please refer to my paper, which has the answer you want. 《Nonlinear model predictive control of USC boiler-turbine power units in flexible operations via input convex neural network》
Hi,
I have trouble understanding the proof of Proposition 1 of your paper (https://arxiv.org/pdf/1609.07152.pdf). Can you provide supplementary steps why a fully connected ICNN (defined in equation (2) of your paper) is convex. Especially, why W^(y)_i can have negative values?
For example, when setting all W^(z)_i = 0 I expect the network not to be convex in general.
I would appreciate any help. Thanks.
The text was updated successfully, but these errors were encountered: