How to Install and Uninstall libensmallen-dev Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: May 20,2024

1. Install "libensmallen-dev" package

Please follow the instructions below to install libensmallen-dev on Ubuntu 21.04 (Hirsute Hippo)


    
        
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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

This guide let you learn how to uninstall libensmallen-dev on Ubuntu 21.04 (Hirsute Hippo):


    
        
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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 21.04 (Hirsute Hippo)

Package: libensmallen-dev
Architecture: amd64
Version: 2.16.2-2
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: 1421
Depends: libarmadillo-dev (>= 1:6.5), liblapack-dev
Filename: pool/universe/e/ensmallen/libensmallen-dev_2.16.2-2_amd64.deb
Size: 180252
MD5sum: 1ccea429788963d9324e25e01589d188
SHA1: 5aa9d97fb6c2e670f665633f5a2955ab840308db
SHA256: ded2450e27e47b67fc264f2eb6b92582333197a1b8d5fc9cf13d4b952cd0bf32
SHA512: b67047b5a44f4df5632e75854e8f99f4604f79304345180efbfb0890564c4f6f92429aa2430568b13520e51382599c803d1b1521ae1ad607e46aec00a4d67528
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