How to Install and Uninstall libensmallen-dev Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: May 18,2024

1. Install "libensmallen-dev" package

This is a short guide on how to install libensmallen-dev on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libensmallen-dev
            
                    
    
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2. Uninstall "libensmallen-dev" package

Please follow the guidance below to uninstall libensmallen-dev on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                libensmallen-dev
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libensmallen-dev package on Ubuntu 20.10 (Groovy Gorilla)

Package: libensmallen-dev
Architecture: amd64
Version: 2.14.1-1
Multi-Arch: same
Priority: optional
Section: universe/libdevel
Source: ensmallen
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1393
Depends: libarmadillo-dev (>= 1:6.5), liblapack-dev
Filename: pool/universe/e/ensmallen/libensmallen-dev_2.14.1-1_amd64.deb
Size: 176152
MD5sum: 0595095f287b9d83b251ed6111e9349c
SHA1: 0d3aa2906490d5b7f1ce25523a59d999d89fffc9
SHA256: 856b07373b87b1b8ea1ca00c4331f39ddb119dc16223f621ceae4ce8b6d29784
SHA512: a3bc4607af8370100de2c95191ec3372a0a495e9281a5ffeb720fba46aca23ed3724459d444e5a58358f13a7f595b5241fe7489332f3e3c6ab2a8c2a853bd601
Homepage: https://www.ensmallen.org/
Description-en: C++ header-only library for mathematical optimization
Ensmallen provides a simple set of abstractions for writing an objective
function to optimize. It also provides a large set of standard and cutting-edge
optimizers that can be used for virtually any mathematical optimization task.
These include full-batch gradient descent techniques, small-batch techniques,
gradient-free optimizers, and constrained optimization.
Description-md5: 53786bc3db68afada07edf24cfdd11fb