How to Install and Uninstall liblapack3 Package on openSuSE Tumbleweed
Last updated: December 25,2024
1. Install "liblapack3" package
Please follow the guidance below to install liblapack3 on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
liblapack3
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2. Uninstall "liblapack3" package
In this section, we are going to explain the necessary steps to uninstall liblapack3 on openSuSE Tumbleweed:
$
sudo zypper remove
liblapack3
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3. Information about the liblapack3 package on openSuSE Tumbleweed
Information for package liblapack3:
-----------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : liblapack3
Version : 3.9.0-9.5
Arch : x86_64
Vendor : openSUSE
Installed Size : 7.5 MiB
Installed : No
Status : not installed
Source package : lapack-3.9.0-9.5.src
Upstream URL : https://www.netlib.org/lapack/
Summary : LAPACK Shared Library
Description :
LAPACK provides routines for solving systems of simultaneous linear
equations, least-squares solutions of linear systems of equations,
eigenvalue problems, and singular value problems. The associated matrix
factorizations (LU, Cholesky, QR, SVD, Schur, generalized Schur) are
also provided, as are related computations such as reordering of the
Schur factorizations and estimating condition numbers. Dense and banded
matrices are handled, but not general sparse matrices. In all areas,
similar functionality is provided for real and complex matrices, in
both single and double precision.
-----------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : liblapack3
Version : 3.9.0-9.5
Arch : x86_64
Vendor : openSUSE
Installed Size : 7.5 MiB
Installed : No
Status : not installed
Source package : lapack-3.9.0-9.5.src
Upstream URL : https://www.netlib.org/lapack/
Summary : LAPACK Shared Library
Description :
LAPACK provides routines for solving systems of simultaneous linear
equations, least-squares solutions of linear systems of equations,
eigenvalue problems, and singular value problems. The associated matrix
factorizations (LU, Cholesky, QR, SVD, Schur, generalized Schur) are
also provided, as are related computations such as reordering of the
Schur factorizations and estimating condition numbers. Dense and banded
matrices are handled, but not general sparse matrices. In all areas,
similar functionality is provided for real and complex matrices, in
both single and double precision.