How to Install and Uninstall metslib-devel Package on openSUSE Leap
Last updated: November 08,2024
1. Install "metslib-devel" package
This tutorial shows how to install metslib-devel on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
metslib-devel
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2. Uninstall "metslib-devel" package
This guide let you learn how to uninstall metslib-devel on openSUSE Leap:
$
sudo zypper remove
metslib-devel
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3. Information about the metslib-devel package on openSUSE Leap
Information for package metslib-devel:
--------------------------------------
Repository : Main Repository
Name : metslib-devel
Version : 0.5.3-bp155.1.4
Arch : noarch
Vendor : openSUSE
Installed Size : 121.9 KiB
Installed : No
Status : not installed
Source package : metslib-0.5.3-bp155.1.4.src
Upstream URL : https://projects.coin-or.org/metslib
Summary : Metaheuristic modeling framework and optimization toolkit in modern C++
Description :
METSlib is a metaheuristic modeling framework and optimization
toolkit in C++.
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, any problem type can be worked on: timetabling, assignment
problems, vehicle routing, bin-packing and so on.
Once the model is implemented in the problem framework, the library
allows testing of different Taboo Search strategies or even different
algorithms (Simulated Annealing or other local search based
algorithms) with a few lines of code.
--------------------------------------
Repository : Main Repository
Name : metslib-devel
Version : 0.5.3-bp155.1.4
Arch : noarch
Vendor : openSUSE
Installed Size : 121.9 KiB
Installed : No
Status : not installed
Source package : metslib-0.5.3-bp155.1.4.src
Upstream URL : https://projects.coin-or.org/metslib
Summary : Metaheuristic modeling framework and optimization toolkit in modern C++
Description :
METSlib is a metaheuristic modeling framework and optimization
toolkit in C++.
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, any problem type can be worked on: timetabling, assignment
problems, vehicle routing, bin-packing and so on.
Once the model is implemented in the problem framework, the library
allows testing of different Taboo Search strategies or even different
algorithms (Simulated Annealing or other local search based
algorithms) with a few lines of code.