This may be a redundant question because I'm still studying this field. I'm attempting to write an algorithm that removes linearly dependent vectors from a matrix in a simple way.
First approach was to rewrite the definition $c_1v_1 + \cdots + c_nv_n = 0$ as
$ \begin{pmatrix} v_1 \\ \vdots \\ v_n \end{pmatrix} \begin{pmatrix} c_1 & \cdots & c_n \end{pmatrix} = 0 $
and solve for the constants, not sure if this is an optimal or even correct approach but I can't seem to reach some identification of a linearly dependent vector.
how can one approach this correctly?
You can do QR decomposition programmatically (e.g., using numpy.linalg.qr), then every $v_i$ corresponding zero diagonal component of $R$ are what you're supposed to remove!
That is, once you get the upper triangular matrix, $R$, every $v_i$ for which $R_{ii}=0$ is the vector linearly dependent on $v_1, \ldots, v_{i-1}$.