Unusual normalization related to the eigenvector perturbation

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In the Magnus and Neudecker's book "Matrix differential calculus with applications", Sec. 8.9, the authors derive an expression for the derivative of the right eigenvector $u(t)$ corresponding to a simple eigenvalue $\lambda(t)$ of a matrix $Z(t)\in M_n(\mathbb C)$: $$u'(0)=(\lambda I -Z(0))^\dagger \left(I-\frac{u(0)v^*(0)}{v^*(0)u(0)}\right)Z'(0)u(0),$$ where $()^\dagger$ is the Moore-Penrose inversion operator and $v^T(t)$ is the left eigenvector.

When deriving this expression they normalize $u(t)$ s.t. $\langle u(t),u(0)\rangle=1$ for small $t$, thus basically requiring that $\langle u'(0),u(0)\rangle=0$.

So my questions are:

Is it always possible to perform such a normalization?

How would you do this in practice when you do not know $u(t)$, which is obviously the case as otherwise you wouldn't need to use the above expression?

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Sure, it's possible to perform this normalization except in the case that the right eigenvector corresponding to eigenvalue $\lambda$ becomes orthogonal to $u(0)$, which is presumably why they consider only small $t$.

Think about it this way: the eigenvector corresponding to a given eigenvalue is only defined up to a complex constant, and it is not clear how to differentiate $u(t)$ if you allow its image to be an entire one-dimensional subspace, rather than a vector. So instead of considering all possible rescalings of the eigenvector, Magnus and Neudecker are selecting one particular representative element in the equivalence class of vectors which have $\lambda$ as an eigenvalue; this representative element sweeps out a curve in $\mathbb{C}^n$ as $t$ varies and can be differentiated.

In the real case, the natural normalization is to require $\|u(t)\|^2=1$, but obviously this does not work in the complex domain.

Finally I'm not sure what the "this" is you're asking about in the second part of the question: the formula you've listed only requires knowing $u(0$), not $u(t)$ for other $t$, but in any case performing the normalization is easy: if $v$ is an eigenvector with eigenvalue $\lambda$ at some $t$, you normalize it by computing $$u(t) = \frac{v}{\langle v, u(0)\rangle}.$$