How to Install and Uninstall lapack-devel Package on openSuSE Tumbleweed
Last updated: January 12,2025
1. Install "lapack-devel" package
Please follow the instructions below to install lapack-devel on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
lapack-devel
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2. Uninstall "lapack-devel" package
Learn how to uninstall lapack-devel on openSuSE Tumbleweed:
$
sudo zypper remove
lapack-devel
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3. Information about the lapack-devel package on openSuSE Tumbleweed
Information for package lapack-devel:
-------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : lapack-devel
Version : 3.9.0-9.5
Arch : x86_64
Vendor : openSUSE
Installed Size : 25 B
Installed : No
Status : not installed
Source package : lapack-3.9.0-9.5.src
Upstream URL : https://www.netlib.org/lapack/
Summary : Linear Algebra Package
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 : lapack-devel
Version : 3.9.0-9.5
Arch : x86_64
Vendor : openSUSE
Installed Size : 25 B
Installed : No
Status : not installed
Source package : lapack-3.9.0-9.5.src
Upstream URL : https://www.netlib.org/lapack/
Summary : Linear Algebra Package
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.