How to Install and Uninstall metslib-devel.noarch Package on Rocky Linux 8
Last updated: November 13,2024
1. Install "metslib-devel.noarch" package
Learn how to install metslib-devel.noarch on Rocky Linux 8
$
sudo dnf update
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$
sudo dnf install
metslib-devel.noarch
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2. Uninstall "metslib-devel.noarch" package
Please follow the step by step instructions below to uninstall metslib-devel.noarch on Rocky Linux 8:
$
sudo dnf remove
metslib-devel.noarch
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$
sudo dnf autoremove
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3. Information about the metslib-devel.noarch package on Rocky Linux 8
Last metadata expiration check: 1:38:27 ago on Mon Sep 12 10:27:18 2022.
Available Packages
Name : metslib-devel
Version : 0.5.3
Release : 22.el8
Architecture : noarch
Size : 43 k
Source : metslib-0.5.3-22.el8.src.rpm
Repository : epel
Summary : Metaheuristic modeling framework and optimization toolkit in modern C++
URL : https://projects.coin-or.org/metslib
License : GPLv3+ or CPL
Description : METSlib is a metaheuristic modeling framework and optimization toolkit in
: modern C++ released as Free/Libre/Open Source Software.
:
: Model and algorithms are modular: any search algorithm can be applied to the
: same model. On the other hand no assumption is made on the model, you can
: work on any problem type: timetabling, assignment problems, vehicle routing,
: bin-packing and so on.
:
: Once you have implemented your model in the problem framework, the library
: makes easy testing different Tabu Search strategies or even different
: algorithms (Simulated Annealing or other local search based algorithms) with
: a few lines of code.
Available Packages
Name : metslib-devel
Version : 0.5.3
Release : 22.el8
Architecture : noarch
Size : 43 k
Source : metslib-0.5.3-22.el8.src.rpm
Repository : epel
Summary : Metaheuristic modeling framework and optimization toolkit in modern C++
URL : https://projects.coin-or.org/metslib
License : GPLv3+ or CPL
Description : METSlib is a metaheuristic modeling framework and optimization toolkit in
: modern C++ released as Free/Libre/Open Source Software.
:
: Model and algorithms are modular: any search algorithm can be applied to the
: same model. On the other hand no assumption is made on the model, you can
: work on any problem type: timetabling, assignment problems, vehicle routing,
: bin-packing and so on.
:
: Once you have implemented your model in the problem framework, the library
: makes easy testing different Tabu Search strategies or even different
: algorithms (Simulated Annealing or other local search based algorithms) with
: a few lines of code.