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

Last updated: November 07,2024

1. Install "libdogleg-dev" package

This guide covers the steps necessary to install libdogleg-dev on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                libdogleg-dev
            
                    
    
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2. Uninstall "libdogleg-dev" package

Please follow the step by step instructions below to uninstall libdogleg-dev on Ubuntu 20.10 (Groovy Gorilla):


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

Package: libdogleg-dev
Architecture: amd64
Version: 0.14-1
Multi-Arch: same
Priority: extra
Section: universe/libdevel
Source: libdogleg
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 49
Depends: libdogleg2 (= 0.14-1), libdogleg-doc, libsuitesparse-dev | libsuitesparse-metis-dev
Filename: pool/universe/libd/libdogleg/libdogleg-dev_0.14-1_amd64.deb
Size: 12468
MD5sum: 8d9a7a8f7f39ffd22b6ef239dc0fe79d
SHA1: 02d42585236aa594fcc28282338fef1fdf744c26
SHA256: 9d8824ec1158d4382a912bf99b5feaae7ae0cb8ec892fc6df4a975c54fe94464
SHA512: d551d89d0d79f908676c8c73a883b7b7c8ec56f479f69cbfd34dcaf003d7699e7327ab34b92bbfebe46bca919e5006c63e61b7cbd27e6e5ea9829be5fc42a436
Homepage: https://github.com/dkogan/libdogleg
Description-en: Powell's dog-leg nonlinear least squares solver for sparse matrices
Solves unconstrained nonlinear least squares problems using Powell's dog-leg
method. The user specifies a callback C function that returns the value and
gradients of the model function at a particular operating point. This library
takes a series of dog-leg steps to find a local minimum of the residual
surface.
.
This library works with sparse matrices, which makes it suitable for
efficiently solving very large problems.
.
Development files
Description-md5: 1be800fdfe9996453e20c86839aed87c