How to Install and Uninstall lapack-devel Package on openSUSE Leap
Last updated: December 25,2024
1. Install "lapack-devel" package
Please follow the steps below to install lapack-devel on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
lapack-devel
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2. Uninstall "lapack-devel" package
Here is a brief guide to show you how to uninstall lapack-devel on openSUSE Leap:
$
sudo zypper remove
lapack-devel
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3. Information about the lapack-devel package on openSUSE Leap
Information for package lapack-devel:
-------------------------------------
Repository : Main Repository
Name : lapack-devel
Version : 3.9.0-150000.4.13.2
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 0 B
Installed : No
Status : not installed
Source package : lapack-3.9.0-150000.4.13.2.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 : Main Repository
Name : lapack-devel
Version : 3.9.0-150000.4.13.2
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 0 B
Installed : No
Status : not installed
Source package : lapack-3.9.0-150000.4.13.2.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.