How to Install and Uninstall liblapack3 Package on openSUSE Leap
Last updated: January 26,2025
1. Install "liblapack3" package
Please follow the step by step instructions below to install liblapack3 on openSUSE Leap
$
sudo zypper refresh
Copied
$
sudo zypper install
liblapack3
Copied
2. Uninstall "liblapack3" package
This is a short guide on how to uninstall liblapack3 on openSUSE Leap:
$
sudo zypper remove
liblapack3
Copied
3. Information about the liblapack3 package on openSUSE Leap
Information for package liblapack3:
-----------------------------------
Repository : Main Repository
Name : liblapack3
Version : 3.9.0-150000.4.13.2
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 7.2 MiB
Installed : No
Status : not installed
Source package : lapack-3.9.0-150000.4.13.2.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 : Main Repository
Name : liblapack3
Version : 3.9.0-150000.4.13.2
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 7.2 MiB
Installed : No
Status : not installed
Source package : lapack-3.9.0-150000.4.13.2.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.