[arijo]

I find the geometrical intuition of rotating a vector in high dimensional space to minimize its largest values (vector basis projections) beautiful.

I'm no expert and I'm sure this has been tried by many people already - but would it be possible to reduce the computational effort instead by using SVD decomposition, spreading the singular values and then reapplying the original singular values and recomposing the matrix using the quantized versions of the SVD matrices?