How to Install and Uninstall libcxsparse3 Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: December 28,2024

1. Install "libcxsparse3" package

This guide covers the steps necessary to install libcxsparse3 on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                libcxsparse3
            
                    
    
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2. Uninstall "libcxsparse3" package

Please follow the guidelines below to uninstall libcxsparse3 on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                libcxsparse3
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libcxsparse3 package on Ubuntu 20.10 (Groovy Gorilla)

Package: libcxsparse3
Architecture: amd64
Version: 1:5.8.1+dfsg-2
Multi-Arch: same
Priority: optional
Section: libs
Source: suitesparse
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 217
Depends: libc6 (>= 2.7)
Filename: pool/main/s/suitesparse/libcxsparse3_5.8.1+dfsg-2_amd64.deb
Size: 64864
MD5sum: b597b0e23e18ac42c411a29103a737a2
SHA1: 2c6dc916af7c17780040540a0ce34a370b6322ee
SHA256: c4657c264157f36c290dd64976e616a8e122cbde2c8c1c82826aff799228be14
SHA512: 757542fa27da413144a136d4feb50d0984ec4dd60b6b2be3f7c50542e8a3b9754a0f7571b94ebb6401aac02bc216c856395653f7b700defee4d89c60c385e0dd
Homepage: http://suitesparse.com
Description-en: concise sparse matrix library
Suitesparse is a collection of libraries for computations involving
sparse matrices.
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The CXSparse library provides several matrix algorithms. The focus is on direct
methods; iterative methods and solvers for eigenvalue problems are beyond the
scope of this package.
.
The performance of the sparse factorization methods in CXSparse will not be
competitive with UMFPACK or CHOLMOD, but the codes are much more concise and
easy to understand. Other methods are competitive.
Description-md5: c7ec3e255980b79debe7e029568c19da