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@@ -166,10 +166,10 @@ Before going further, let us make some notational simplifications. We
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will assume that the matrix :math:`\frac{1}{\sqrt{\mu}} D` has been concatenated
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at the bottom of the matrix :math:`J` and similarly a vector of zeros
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has been added to the bottom of the vector :math:`f` and the rest of
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-our discussion will be in terms of :math:`J` and :math:`f`, i.e, the
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+our discussion will be in terms of :math:`J` and :math:`F`, i.e, the
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linear least squares problem.
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-.. math:: \min_{\Delta x} \frac{1}{2} \|J(x)\Delta x + f(x)\|^2 .
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+.. math:: \min_{\Delta x} \frac{1}{2} \|J(x)\Delta x + F(x)\|^2 .
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:label: simple
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For all but the smallest problems the solution of :eq:`simple` in
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@@ -648,11 +648,11 @@ can be quite substantial.
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access to :math:`S` via its product with a vector, one way to
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evaluate :math:`Sx` is to observe that
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- .. math:: x_1 &= E^\top x
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- .. math:: x_2 &= C^{-1} x_1
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- .. math:: x_3 &= Ex_2\\
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- .. math:: x_4 &= Bx\\
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- .. math:: Sx &= x_4 - x_3
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+ .. math:: x_1 &= E^\top x\\
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+ x_2 &= C^{-1} x_1\\
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+ x_3 &= Ex_2\\
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+ x_4 &= Bx\\
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+ Sx &= x_4 - x_3
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:label: schurtrick1
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Thus, we can run PCG on :math:`S` with the same computational
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Carl Dehlin