# Solving Overdetermined Systems with the QR Decomposition

# Solving Overdetermined Systems with the QR Decomposition

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*Ax=b*where A is a m by n matrix and b is a m dimensional vector b but m is greater than n. In this case, the vector b cannot be expressed as a linear combination of the columns of A. Hence, we can't find x so that satisfies the problem

*Ax=b*(except in specific cases) but it is possible to determine x so that Ax is as close to b as possible. So we wish to find x which minimizes the following error

Considering the QR decomposition of A we have that

*Ax=b*becomes

multiplying by Q^T we obtain

and since Q^T is orthogonal (this means that Q^T*Q=I) we have

Now, this is a well defined system, R is an upper triangular matrix and Q^T*b is a vector. More precisely b is the orthogonal projection of b onto the range of A. And,

The function linalg.lstsq() provided by numpy returns the least-squares solution to a linear system equation and is able to solve overdetermined systems. Let's compare the solutions of linalg.lstsq() with the ones computed using the QR decomposition:

from numpy import * # generating a random overdetermined system A = random.rand(5,3) b = random.rand(5,1) x_lstsq = linalg.lstsq(A,b)[0] # computing the numpy solution Q,R = linalg.qr(A) # qr decomposition of A Qb = dot(Q.T,b) # computing Q^T*b (project b onto the range of A) x_qr = linalg.solve(R,Qb) # solving R*x = Q^T*b # comparing the solutions print 'qr solution' print x_qr print 'lstqs solution' print x_lstsq

This is the output of the script above:

qr solution [[ 0.08704059] [-0.10106932] [ 0.56961487]]

lstqs solution [[ 0.08704059] [-0.10106932] [ 0.56961487]]

As we can see, the solutions are the same.

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Published at DZone with permission of Giuseppe Vettigli , DZone MVB. See the original article here.

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