Converting an explicit series to a recursive form

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SUMMARY

The discussion focuses on the recursive representation of the expression $$y_N = \sum_{k=0}^N {\frac{{{X^k} e_i}}{\|{{{X^k} e_i}\|}_2}}$$ where X is an n × n square matrix and e_i is a unit vector. The initial recursive formula is established as $$y_{k+1} = X y_{k} + e_i$$ with $$y_0 = e_i$$. The participants debate the validity of a modified recursive expression that includes normalization, ultimately demonstrating that the proposed formula does not hold for the first few iterations.

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regina_lee
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Let e_i be a unit vector with one 1 in the i-th element. Is the following expression has a recursive presentation?

$$y_N = \sum_{k=0}^N {\frac{{{X^k} e_i}}{\|{{{X^k} e_i}\|}_2}} $$

where X is a n \times n square matrix, and {\| \cdot \|}_2 is a vector norm defined as {\|z\|}_2 = \sqrt{|z_1|^2+|z_2|^2+...+|z_n|^2}.

---

EDIT: I know that if y_N = \sum_{k=0}^N {{X^k} e_i}, it is easy to obtain the following recursive formula:

$$y_{k+1} = X y_{k} + e_i, \quad (k=0,1,2,...) \textrm{ with } \ \ \ y_0=e_i$$

However, after we add a normalized factor, is there a similar recursive expression? Thanks.
 
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Isn't it just
y_{k+1}= \frac{Xy_k+e_i}{|Xy_k+ e_i|}

(And I don't see why you wouldn't just write that as
y_{k+1}= \frac{Xy_k+ y_0}{|Xy_k+ y_0|})
 
regina_lee said:
Let e_i be a unit vector with one 1 in the i-th element. Is the following expression has a recursive presentation?

$$y_N = \sum_{k=0}^N {\frac{{{X^k} e_i}}{\|{{{X^k} e_i}\|}_2}} $$

where X is a n \times n square matrix, and {\| \cdot \|}_2 is a vector norm defined as {\|z\|}_2 = \sqrt{|z_1|^2+|z_2|^2+...+|z_n|^2}.

---

EDIT: I know that if y_N = \sum_{k=0}^N {{X^k} e_i}, it is easy to obtain the following recursive formula:

$$y_{k+1} = X y_{k} + e_i, \quad (k=0,1,2,...) \textrm{ with } \ \ \ y_0=e_i$$

However, after we add a normalized factor, is there a similar recursive expression? Thanks.

Hi, it seems your formula is not right because when I checked y_0,y_1,y_2, I find the following:

$$\begin{align}
& {{y}_{1}}=\frac{X{{e}_{i}}+{{e}_{i}}}{\left\| X{{e}_{i}}+{{e}_{i}} \right\|} \\
& {{y}_{2}}=\frac{X{{y}_{1}}+{{e}_{i}}}{\left\| X{{y}_{1}}+{{e}_{i}} \right\|} \\
& =\frac{X\frac{X{{e}_{i}}+{{e}_{i}}}{\left\| X{{e}_{i}}+{{e}_{i}} \right\|}+{{e}_{i}}}{\left\| X\frac{X{{e}_{i}}+{{e}_{i}}}{\left\| X{{e}_{i}}+{{e}_{i}} \right\|}+{{e}_{i}} \right\|} \\
& =\frac{\frac{{{X}^{2}}{{e}_{i}}+X{{e}_{i}}}{\left\| X{{e}_{i}}+{{e}_{i}} \right\|}+{{e}_{i}}}{\left\| \frac{{{X}^{2}}{{e}_{i}}+X{{e}_{i}}}{\left\| X{{e}_{i}}+{{e}_{i}} \right\|}+{{e}_{i}} \right\|} \\
\end{align}$$
 

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