Question

I have been doing this using an svd computation

[U, S, V] = svd(A)

wherein I use the last column of A as my null space approximation. Since A gets really large, I realized that this is slowing down my computation.

For null(A), the documentation seems to suggest that it does an SVD anyways. Also, it does not work if A is full rank. An SVD proceeds by finding the largest singular value, then the next one and so on whereas I just need the smallest one.

This seems to be a big bottleneck. Will really appreciate help on this.
Am using MATLAB.

Thanks.

Answer 1

If all points are from a plane, call SVD with just a sample.