How to Install and Uninstall lapack-devel-static Package on openSuSE Tumbleweed
Last updated: December 25,2024
1. Install "lapack-devel-static" package
This is a short guide on how to install lapack-devel-static on openSuSE Tumbleweed
$
sudo zypper refresh
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$
sudo zypper install
lapack-devel-static
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2. Uninstall "lapack-devel-static" package
Please follow the steps below to uninstall lapack-devel-static on openSuSE Tumbleweed:
$
sudo zypper remove
lapack-devel-static
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3. Information about the lapack-devel-static package on openSuSE Tumbleweed
Information for package lapack-devel-static:
--------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : lapack-devel-static
Version : 3.9.0-9.5
Arch : x86_64
Vendor : openSUSE
Installed Size : 25.9 MiB
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 - static libraries
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-static
Version : 3.9.0-9.5
Arch : x86_64
Vendor : openSUSE
Installed Size : 25.9 MiB
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 - static libraries
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.