I'm working on a problem which naturally involves both linear and nonlinear operations, and I'd like some help understanding the best way to combine these into a neural network framework. To be more precise: suppose I have a matrix $A$, and some basis of matrices which it can be decomposed into, i.e.,

$$A = \sum_{j}a_{j}A_{j}$$

for some real numbers $a_{j}$ and some pre-specified basis matrices $A_{j}$. Once I have this matrix $A$, I want to apply a function $f$ to it to obtain $f(A) = X$. Note that in this context I mean actually applying $f$ to the full matrix $A$: it is not elementwise applications of $f$, i.e., $f(A)$ is defined by the Taylor series of $f$, and $f$ is a nonlinear function. (In case it matters, the function I have in mind is the matrix exponential, but I don't think this should make much of a difference in the statement of the problem.)

Now, I also have some objective matrix $Y$. My goal is to find $A$ such that $X$ approximates $Y$ as closely as possible. The key here is that $f$ is a nonlinear function, but I want to restrict $A$ to be a linear combination of the $A_{j}$'s. I don't know how to incorporate that constraint naturally in a machine learning context, but it's obviously desirable to do so since I would like to do gradient descent over both the $a_{j}$'s and the parameters which enforce the function $f$ to find the desired matrix $X$. Any guidance, broadly speaking about what neural network architectures are best to tackle this kind of problem would be very helpful.


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