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Levinson recursion
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=== Using the backward vectors === The above steps give the ''N'' backward vectors for '''''M'''''. From there, a more arbitrary equation is: : <math> \vec y = \mathbf M \ \vec x. </math> The solution can be built in the same recursive way that the backwards vectors were built. Accordingly, <math>\vec x</math> must be generalized to a sequence of intermediates <math>\vec x^n</math>, such that <math>\vec x^N = \vec x</math>. The solution is then built recursively by noticing that if : <math> \mathbf T^{n-1} \begin{bmatrix} x_1^{n-1} \\ x_2^{n-1} \\ \vdots \\ x_{n-1}^{n-1} \\ \end{bmatrix} = \begin{bmatrix} y_1 \\ y_2 \\ \vdots \\ y_{n-1} \end{bmatrix}.</math> Then, extending with a zero again, and defining an error constant where necessary: : <math> \mathbf T^{n} \begin{bmatrix} x_1^{n-1} \\ x_2^{n-1} \\ \vdots \\ x_{n-1}^{n-1} \\ 0 \end{bmatrix} = \begin{bmatrix} y_1 \\ y_2 \\ \vdots \\ y_{n-1} \\ \varepsilon_x^{n-1} \end{bmatrix}.</math> We can then use the ''n''<sup>th</sup> backward vector to eliminate the error term and replace it with the desired formula as follows: : <math> \mathbf T^{n} \left ( \begin{bmatrix} x_1^{n-1} \\ x_2^{n-1} \\ \vdots \\ x_{n-1}^{n-1} \\ 0 \\ \end{bmatrix} + (y_n - \varepsilon_x^{n-1}) \ \vec b^n \right ) = \begin{bmatrix} y_1 \\ y_2 \\ \vdots \\ y_{n-1} \\ y_n \end{bmatrix}.</math> Extending this method until ''n'' = ''N'' yields the solution <math>\vec x</math>. In practice, these steps are often done concurrently with the rest of the procedure, but they form a coherent unit and deserve to be treated as their own step.
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