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Large weighted least squares system

  1. Apr 17, 2015 #1
    I have a large weighted least squares system with millions of observations. I can't store the least squares matrix in memory, so I only store the normal equations matrix:

    A^T W A

    where [itex]A[/itex] is dense and n-by-p and [itex]W = diag(w_1,...,w_n)[/itex]. It takes a very long time to build this normal equations matrix due to the large number of observations. However, I also want to iteratively re-weight the observations to reduce the effect of outliers, and so at the next iteration, the weight matrix will be the product of the original weights [itex]W[/itex] with some new weights [itex]V = diag(v_1,...,v_n)[/itex]. Therefore I need to construct the matrix:

    A^T W' A

    where [itex]W' = diag(w_1 v_1, w_2 v_2, ..., w_n v_n)[/itex].

    Does anyone know of a slick (fast) way to determine the matrix [itex]A^T W' A[/itex] given the matrix [itex]A^T W A[/itex] and the new weights [itex]V[/itex]? I am hoping I don't need to start over from scratch to build [itex]A^T W' A[/itex] for each iteration, since it takes so long.

    I did search the literature and didn't find much on this topic.
  2. jcsd
  3. Apr 17, 2015 #2


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    Yeah, forget forming the normal equations altogether. You should use a more modern LS algorithm which incorporates either the QR or the SVD algorithm.

    If the matrices are too large to store in memory at one time, then use an out-of-core solver.
  4. Apr 17, 2015 #3
    My matrix [itex]A[/itex] has 8 millions rows (observations) and 2000 columns (parameters). The normal equations matrix [itex]A^T W A[/itex] is therefore 2000-by-2000, which can easily be handled by LAPACK to solve for the unknown parameters.

    I can't even store the full matrix A in memory to attempt to do a QR or SVD on it. How do you propose I use these decompositions? Do you have a reference?
  5. Apr 17, 2015 #4


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    If you can't store it in memory, then don't. If you're familiar with a mapreduce rent an amazon cluster and use a hadoop system to run a QR decomposition in map reduce land. It'll generally compute faster and memory won't be an issue.

    Also relating to SteamKing's comment. Here's a paper on how to use out of core.


    It's specifically used in cases that a matrix cannot be stored in memory but only on disk.

    (Although personally for me, a mapreduce on a cluster is easier to handle)
  6. Apr 17, 2015 #5


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    It's not clear that solving that many normal equations will produce a useful result. Round off error in solving that large a system of normal equations could produce a garbage regression.

    Not at the moment. With such a large problem to solve, QR or SVD may not be practical. You may have to resort to a solution by iteration.

    The point is, the numerical techniques which may work well for finding a regression line thru a handful of data points may not be ideally suited when you have millions of data points and thousands of parameters.
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