Questions about the OptNet paper derivations I have the following questions related to the paper OptNet:

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*First of all, according to my understanding, the paper uses neural networks to solve QP problems. The advantage is it can solve multiple QP problems in parallel and it is faster than Gurobi solver. Am I right?

*Does it solve QP problems by solving its KKT condition?

*I am very confused about the differential start from eq(5). Why take the differential of the KKT condition? In particular, what are the variable and dependent variables of following differential:


*Why take gradient with respect to $Q, A, G, q, b, h$. I thought they are the data of the QP problem and should be fixed.

I must be misunderstanding something, any answer and references will be welcome.

I think based on KKT condition it really takes diffrentials of optimization parameters Q(zi), q(zi), A(zi), b(zi), G(zi), and h(zi) with respect to zi. For the QP problem yes they are fixed cause now z is the optimization variable, and the result of this optimization is noted as z* or zi+1.
