How to Install and Uninstall libspqr4 Package on openSuSE Tumbleweed
Last updated: December 27,2024
1. Install "libspqr4" package
Please follow the guidance below to install libspqr4 on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
libspqr4
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2. Uninstall "libspqr4" package
In this section, we are going to explain the necessary steps to uninstall libspqr4 on openSuSE Tumbleweed:
$
sudo zypper remove
libspqr4
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3. Information about the libspqr4 package on openSuSE Tumbleweed
Information for package libspqr4:
---------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libspqr4
Version : 7.5.1-50.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 551.8 KiB
Installed : No
Status : not installed
Source package : suitesparse-7.5.1-50.1.src
Upstream URL : https://people.engr.tamu.edu/davis/suitesparse.html
Summary : Multifrontal Sparse QR
Description :
SuiteSparseQR is an implementation of the multifrontal sparse QR
factorization method. Parallelism is exploited both in the BLAS and
across different frontal matrices using Intel's Threading Building
Blocks, a shared-memory programming model for modern multicore
architectures. It can obtain a substantial fraction of the
theoretical peak performance of a multicore computer. The package is
written in C++ with user interfaces for MATLAB, C, and C++.
SuiteSparseQR is part of the SuiteSparse sparse matrix suite.
---------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : libspqr4
Version : 7.5.1-50.1
Arch : x86_64
Vendor : openSUSE
Installed Size : 551.8 KiB
Installed : No
Status : not installed
Source package : suitesparse-7.5.1-50.1.src
Upstream URL : https://people.engr.tamu.edu/davis/suitesparse.html
Summary : Multifrontal Sparse QR
Description :
SuiteSparseQR is an implementation of the multifrontal sparse QR
factorization method. Parallelism is exploited both in the BLAS and
across different frontal matrices using Intel's Threading Building
Blocks, a shared-memory programming model for modern multicore
architectures. It can obtain a substantial fraction of the
theoretical peak performance of a multicore computer. The package is
written in C++ with user interfaces for MATLAB, C, and C++.
SuiteSparseQR is part of the SuiteSparse sparse matrix suite.