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I would like some hints on how to derive the following function with respect to the matrix $X$:

$$ f(X)=\left\| y - \sum_k (h_k X^k)x \right\|_2^2$$

where $X$ is a matrix, $x$ and $y$ are two given vectors and $h_k$ is a (given) scalar coefficient that weights the matrix. This is the composition of a convex function (the norm) with a non affine function. I want to linearize the function with respect to X, so I would like to compute the derivative. Some hints?

Thank you.

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    $\begingroup$ What you have written is a function of $y, x$, and $\{h_k\}$. But it is not a function of $X$, since $X$ is a dummy variable in the expression. Did you perhaps mean $\min_x$ instead? $\endgroup$ – Paul Sinclair Jan 28 at 4:10
  • $\begingroup$ @PaulSinclair, I edited the question by explaining what each variable represents. All is given except the matrix $X$, which is the optimization variable. So the function is $f(X)$ $\endgroup$ – Alberto Jan 28 at 10:08
  • $\begingroup$ If you are talking about $f(X)$, why did you put an extraneous "$\min_X$ in front? It serves no purpose except to seriously obfuscate. $\endgroup$ – Paul Sinclair Jan 28 at 14:44
  • $\begingroup$ @PaulSinclair you are right. $\endgroup$ – Alberto Jan 28 at 15:28
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If you are just trying to linearize $f(X)$ around $X= 0$, this is easily accomplished

$$f(X) =\left(y - \sum_k h_k X^kx\right)^T\left(y - \sum_k h_k X^kx\right)\\ =\left(y^T - \sum_k h_kx^T(X^T)^k\right)^T\left(y - \sum_k h_k X^kx\right)\\ =y^Ty - \sum_k h_ky^TX^kx - \sum_k h_kx^T(X^T)^ky + \sum_j\sum_k h_jh_k x^T(X^T)^jX^kx\\ = y^Ty + h_0^2x^Tx - h_0(y^Tx + x^Ty) - h_1(y^TXx + x^TX^Ty) + 2h_0h_1x^TXx + O(X^2)$$

Linearization just means ignoring higher order terms, so the linearization for $X$ near $0$ is $$y^Ty + h_0^2x^Tx - h_0(y^Tx + x^Ty) - h_1(y^TXx + x^TX^Ty) + 2h_0h_1x^TXx$$ If there are no terms for $k = 0$, it simplifies to $$y^Ty - h_1(y^TXx + x^TX^Ty)$$

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