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doc typo
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@ -234,7 +234,7 @@ struct ei_qr_preconditioner_impl<MatrixType, HouseholderQRPreconditioner, Precon
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* \include JacobiSVD_basic.cpp
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* Output: \verbinclude JacobiSVD_basic.out
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*
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* This %JacobiSVD class a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than
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* This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than
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* bidiagonalizing SVD algorithms for large square matrices; however its complexity is still \f$ O(n^2p) \f$ where \a n is the smaller dimension and
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* \a p is the greater dimension, meaning that it is still of the same order of complexity as the faster bidiagonalizing R-SVD algorithms.
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* In particular, like any R-SVD, it takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
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