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Let us assume that there is a feedforward neural network with two layers. and weights of each layer are constrained such that sum of the weights is a constant value in each layer and their values are non-negative. You may wonder why should we have such assumptions? Answer: I have an optimization problem with unknown variables that can be mapped to a neural network in that weights represent my variables that's why. Can anyone suggest to me a way to handle these constraints? for now, I just integrated these constraints into the cost function, though the way I did is not working very well. I just added the constraints to the main cost function using max. for example when A(x)<x I just added its cost as max(A(x)/x-1,0) to the main cost function.

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Maybe instead of mapping nn weights Wi to your problem, map Constant * Softmax(Wi) instead for each layer?

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  • $\begingroup$ can you explain what you mean? because this is applied to the outputs, not to bare weights. $\endgroup$ Jul 4, 2022 at 17:42

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