How to Install and Uninstall libadolc2 Package on openSUSE Leap
Last updated: February 01,2025
1. Install "libadolc2" package
Please follow the steps below to install libadolc2 on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
libadolc2
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2. Uninstall "libadolc2" package
This tutorial shows how to uninstall libadolc2 on openSUSE Leap:
$
sudo zypper remove
libadolc2
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3. Information about the libadolc2 package on openSUSE Leap
Information for package libadolc2:
----------------------------------
Repository : Main Repository
Name : libadolc2
Version : 2.6.2-1.30
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 837.1 KiB
Installed : No
Status : not installed
Source package : adolc-2.6.2-1.30.src
Upstream URL : http://projects.coin-or.org/ADOL-C
Summary : Algorithmic Differentiation Library for C/C++
Description :
The package ADOL-C (Automatic Differentiation by OverLoading in C++)
facilitates the evaluation of first and higher derivatives of vector
functions that are defined by computer programs written in C or
C++. The resulting derivative evaluation routines may be called from
C/C++, Fortran, or any other language that can be linked with C.
The numerical values of derivative vectors are obtained free of
truncation errors at a small multiple of the run time and randomly
accessed memory of the given function evaluation program.
----------------------------------
Repository : Main Repository
Name : libadolc2
Version : 2.6.2-1.30
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 837.1 KiB
Installed : No
Status : not installed
Source package : adolc-2.6.2-1.30.src
Upstream URL : http://projects.coin-or.org/ADOL-C
Summary : Algorithmic Differentiation Library for C/C++
Description :
The package ADOL-C (Automatic Differentiation by OverLoading in C++)
facilitates the evaluation of first and higher derivatives of vector
functions that are defined by computer programs written in C or
C++. The resulting derivative evaluation routines may be called from
C/C++, Fortran, or any other language that can be linked with C.
The numerical values of derivative vectors are obtained free of
truncation errors at a small multiple of the run time and randomly
accessed memory of the given function evaluation program.