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

Last updated: May 17,2024

1. Install "libdogleg-doc" package

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


    
        
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                sudo apt update
            
                    
    
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            $
        

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

This guide let you learn how to uninstall libdogleg-doc on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                libdogleg-doc
            
                    
    
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            $
        

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

Package: libdogleg-doc
Architecture: all
Version: 0.14-1
Multi-Arch: foreign
Priority: optional
Section: universe/doc
Source: libdogleg
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Science Maintainers
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 50
Breaks: libdogleg-dev (<< 0.08-3)
Replaces: libdogleg-dev (<< 0.08-3)
Filename: pool/universe/libd/libdogleg/libdogleg-doc_0.14-1_all.deb
Size: 20340
MD5sum: a574f3f4f3826094021cafa74b48c4fa
SHA1: 0bee94a7c9ccacff78dc75cda370213cd44cf9f3
SHA256: e584ff1cb18cd86d47cc6ec99815a13c88737c64001283db88c9334e413539ac
SHA512: 8d96cd2b4b836a9f29c21b1cadd2ab517a27af4027718ef6ad95b67b8086305932b65534cfc8bca9134490e88929651122a0fc159cff14b1d243d310536d2f66
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.
.
This package contains the documentation
Description-md5: ea7352bc0bb27a70151a272954bb8641