How to Install and Uninstall metslib-devel.noarch Package on Fedora 35
Last updated: November 16,2024
1. Install "metslib-devel.noarch" package
Here is a brief guide to show you how to install metslib-devel.noarch on Fedora 35
$
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 instructions below to uninstall metslib-devel.noarch on Fedora 35:
$
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 Fedora 35
Last metadata expiration check: 2:45:28 ago on Wed Sep 7 08:25:01 2022.
Available Packages
Name : metslib-devel
Version : 0.5.3
Release : 23.fc35
Architecture : noarch
Size : 39 k
Source : metslib-0.5.3-23.fc35.src.rpm
Repository : fedora
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 : 23.fc35
Architecture : noarch
Size : 39 k
Source : metslib-0.5.3-23.fc35.src.rpm
Repository : fedora
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.