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Hello,

I'm looking for a way to create an approximate row-orthonormal matrix with the number of rows (m) > the number of columns (n); i.e., finding A(mxn) so that A(mxn) . A^T(nxm) = I(mxm). I used singular value decomposition (e.g., DGESVD in mkl mathlib), but what I actually got was an orthonormal square eigenvector matrix.

Thank you!

I'm looking for a way to create an approximate row-orthonormal matrix with the number of rows (m) > the number of columns (n); i.e., finding A(mxn) so that A(mxn) . A^T(nxm) = I(mxm). I used singular value decomposition (e.g., DGESVD in mkl mathlib), but what I actually got was an orthonormal square eigenvector matrix.

Thank you!

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