LLSComp

# LLSComp - Linear Least Squares Computations Assuming that A...

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Linear Least Squares Computations Assuming that A R m × n has linearly independent columns, the problem arg min x k Ax - b k 2 (1) has a unique solution, say x LS , which is also the unique solution to the normal equations A t Ax = A t b. (2) This suggests the normal equations approach to computing x LS : 1. Form C = A t A , and w = A t b . 2. Compute the Cholesky factorization C = LL t . 3. Solve Ly = w by forsub and then L t x = y by backsub. This algorithm requires mn 2 + 1 3 n 3 + O ( mn ) ﬂops (taking advantage of the symmetry of C ). It is an important method because it is fast and doesn’t use very much memory. Cx = w can be viewed as a compressed form of arg min x k Ax - b k 2 . We have other methods that, while more costly, are more robust in the face of rounding errors. The other methods arrive at x LS by a diﬀerent route. Recall that the normal equations were a result of requiring that b - Ax be orthogonal (normal) to the subspace S = ColSp( A ). That is another way of saying that Ax is the orthogonal projection of b onto S . The solution to the normal equations is therefore
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## This note was uploaded on 12/18/2010 for the course PHYS 5073 taught by Professor Mark during the Fall '10 term at Arkansas.

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