How to Install and Uninstall coin-or-Couenne.x86_64 Package on Fedora 34

Last updated: October 05,2024

1. Install "coin-or-Couenne.x86_64" package

This guide covers the steps necessary to install coin-or-Couenne.x86_64 on Fedora 34


    
        
            $
        

        
            
                sudo dnf update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf install
            
                        
                coin-or-Couenne.x86_64
            
                    
    
    Copied

2. Uninstall "coin-or-Couenne.x86_64" package

This guide let you learn how to uninstall coin-or-Couenne.x86_64 on Fedora 34:


    
        
            $
        

        
            
                sudo dnf remove
            
                        
                coin-or-Couenne.x86_64
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the coin-or-Couenne.x86_64 package on Fedora 34

Last metadata expiration check: 1:47:26 ago on Tue Sep 6 08:10:37 2022.
Available Packages
Name : coin-or-Couenne
Version : 0.5.8
Release : 5.fc34
Architecture : x86_64
Size : 578 k
Source : coin-or-Couenne-0.5.8-5.fc34.src.rpm
Repository : fedora
Summary : An exact solver for nonconvex MINLPs
URL : https://projects.coin-or.org/Couenne
License : EPL-1.0
Description : Couenne (Convex Over and Under ENvelopes for Nonlinear Estimation) is a
: branch&bound algorithm to solve Mixed-Integer Nonlinear Programming (MINLP)
: problems of the form:
: min f0(x,y)
: fi(x,y) <= 0 i=1,2..., m
: x in Rn, y in Zp
:
: where all fi(x,y) are, in general, nonlinear functions.
:
: Couenne aims at finding global optima of nonconvex MINLPs. It implements
: linearization, bound reduction, and branching methods within a
: branch-and-bound framework. Its main components are:
:
: * an expression library;
: * separation of linearization cuts;
: * branching rules;
: * bound tightening methods.