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

Last updated: November 28,2024

1. Install "coin-or-FlopC++.x86_64" package

This is a short guide on how to install coin-or-FlopC++.x86_64 on Fedora 34


    
        
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                sudo dnf update
            
                    
    
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                sudo dnf install
            
                        
                coin-or-FlopC++.x86_64
            
                    
    
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2. Uninstall "coin-or-FlopC++.x86_64" package

This tutorial shows how to uninstall coin-or-FlopC++.x86_64 on Fedora 34:


    
        
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                sudo dnf remove
            
                        
                coin-or-FlopC++.x86_64
            
                    
    
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            $
        

        
            
                sudo dnf autoremove
            
                    
    
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3. Information about the coin-or-FlopC++.x86_64 package on Fedora 34

Last metadata expiration check: 1:11:19 ago on Tue Sep 6 08:10:37 2022.
Available Packages
Name : coin-or-FlopC++
Version : 1.2.5
Release : 5.fc34
Architecture : x86_64
Size : 65 k
Source : coin-or-FlopC++-1.2.5-5.fc34.src.rpm
Repository : fedora
Summary : Algebraic modeling language
URL : https://projects.coin-or.org/FlopC++
License : EPL-1.0
Description : An open source algebraic modeling language implemented as a C++ class
: library.
:
: Using FLOPC++, linear optimization models can be specified in a declarative
: style, similar to algebraic modeling languages such as GAMS and AMPL,
: within a C++ program. As a result the traditional strengths of algebraic
: modeling languages are preserved, while embedding linear optimization
: models in software applications is facilitated.
:
: FLOPC++ can be used as a substitute for traditional modeling languages,
: when modeling linear optimization problems, but its principal strength
: lies in the fact that the modeling facilities are combined with a
: powerful general purpose programming language. This combination is
: essential for implementing efficient algorithms (using linear optimization
: for subproblems), integrating optimization models in user applications, etc.