In the special case n=1 , the general least squares problem reduces to finding a scalar x that minimizes axb2 , where aandbarem-vectors. (We write the matrix A here in lower case, since it is an m-vector.) Assuming aandb are nonzero, show that ax^b2=b2(sin)2, where = (a, b). This shows that the optimal relative error in approximating one vector by a multiple of another one depends on their angle.