How to Install and Uninstall libensmallen-dev Package on Ubuntu 21.10 (Impish Indri)

Last updated: October 05,2024

1. Install "libensmallen-dev" package

Please follow the step by step instructions below to install libensmallen-dev on Ubuntu 21.10 (Impish Indri)


    
        
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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

Learn how to uninstall libensmallen-dev on Ubuntu 21.10 (Impish Indri):


    
        
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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 21.10 (Impish Indri)

Package: libensmallen-dev
Architecture: amd64
Version: 2.17.0-2ubuntu1
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: 1523
Depends: libarmadillo-dev (>= 1:6.5), liblapack-dev
Filename: pool/universe/e/ensmallen/libensmallen-dev_2.17.0-2ubuntu1_amd64.deb
Size: 195318
MD5sum: e47799a43a85a56df681d7565b47a871
SHA1: 9cf59716658207a9aeb01afefda984d89acca5ee
SHA256: a40a95f78f94e3b912b56cedc506d31007d57c90540c197250249dc1433e9278
SHA512: dda8e3bd4d0637c8a7c85f13b209215c509a8e919f1e51abb506f724506ebab8aceb707fb38e8ee8f0b1298919f8340409ea634234f148633aa6b83108a55664
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