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# This file is distributed under the same license as the mars package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2020.
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"Project-Id-Version: mars 0.5.0a2\n"
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#: ../../source/reference/tensor/generated/mars.tensor.linalg.qr.rst:2
msgid "mars.tensor.linalg.qr"
msgstr ""

#: mars.tensor.linalg.qr:1 of
msgid "Compute the qr factorization of a matrix."
msgstr ""

#: mars.tensor.linalg.qr:3 of
msgid ""
"Factor the matrix `a` as *qr*, where `q` is orthonormal and `r` is upper-"
"triangular."
msgstr ""

#: mars.tensor.linalg.qr of
msgid "Parameters"
msgstr ""

#: mars.tensor.linalg.qr:6 of
msgid "Matrix to be factored."
msgstr ""

#: mars.tensor.linalg.qr:8 of
msgid ""
"method to calculate qr factorization, tsqr as default  TSQR is presented "
"in:      A. Benson, D. Gleich, and J. Demmel.     Direct QR "
"factorizations for tall-and-skinny matrices in     MapReduce "
"architectures.     IEEE International Conference on Big Data, 2013.     "
"http://arxiv.org/abs/1301.1071  FSQR is a QR decomposition for fat and "
"short matrix:     A = [A1, A2, A3, ...], A1 may be decomposed as A1 = Q1 "
"* R1,     for A = Q * R, Q = Q1, R = [R1, R2, R3, ...] where A2 = Q1 * "
"R2, A3 = Q1 * R3, ..."
msgstr ""

#: mars.tensor.linalg.qr:8 of
msgid "method to calculate qr factorization, tsqr as default"
msgstr ""

#: mars.tensor.linalg.qr:10 of
msgid "TSQR is presented in:"
msgstr ""

#: mars.tensor.linalg.qr:12 of
msgid ""
"A. Benson, D. Gleich, and J. Demmel. Direct QR factorizations for tall-"
"and-skinny matrices in MapReduce architectures. IEEE International "
"Conference on Big Data, 2013. http://arxiv.org/abs/1301.1071"
msgstr ""

#: mars.tensor.linalg.qr:19 of
msgid "FSQR is a QR decomposition for fat and short matrix:"
msgstr ""

#: mars.tensor.linalg.qr:19 of
msgid ""
"A = [A1, A2, A3, ...], A1 may be decomposed as A1 = Q1 * R1, for A = Q * "
"R, Q = Q1, R = [R1, R2, R3, ...] where A2 = Q1 * R2, A3 = Q1 * R3, ..."
msgstr ""

#: mars.tensor.linalg.qr of
msgid "Returns"
msgstr ""

#: mars.tensor.linalg.qr:23 of
msgid ""
"* **q** (*Tensor of float or complex, optional*) -- A matrix with "
"orthonormal columns. When mode = 'complete' the   result is an "
"orthogonal/unitary matrix depending on whether or not   a is "
"real/complex. The determinant may be either +/- 1 in that   case. * **r**"
" (*Tensor of float or complex, optional*) -- The upper-triangular matrix."
msgstr ""

#: mars.tensor.linalg.qr:23 of
msgid ""
"**q** (*Tensor of float or complex, optional*) -- A matrix with "
"orthonormal columns. When mode = 'complete' the result is an "
"orthogonal/unitary matrix depending on whether or not a is real/complex. "
"The determinant may be either +/- 1 in that case."
msgstr ""

#: mars.tensor.linalg.qr:27 of
msgid ""
"**r** (*Tensor of float or complex, optional*) -- The upper-triangular "
"matrix."
msgstr ""

#: mars.tensor.linalg.qr of
msgid "Raises"
msgstr ""

#: mars.tensor.linalg.qr:29 of
msgid "If factoring fails."
msgstr ""

#: mars.tensor.linalg.qr:32 of
msgid "Notes"
msgstr ""

#: mars.tensor.linalg.qr:33 of
msgid ""
"For more information on the qr factorization, see for example: "
"http://en.wikipedia.org/wiki/QR_factorization"
msgstr ""

#: mars.tensor.linalg.qr:37 of
msgid "Examples"
msgstr ""

