How to Install and Uninstall dsdp-doc Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: November 24,2024

1. Install "dsdp-doc" package

Please follow the guidelines below to install dsdp-doc on Ubuntu 21.04 (Hirsute Hippo)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                dsdp-doc
            
                    
    
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2. Uninstall "dsdp-doc" package

This guide covers the steps necessary to uninstall dsdp-doc on Ubuntu 21.04 (Hirsute Hippo):


    
        
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                sudo apt remove
            
                        
                dsdp-doc
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the dsdp-doc package on Ubuntu 21.04 (Hirsute Hippo)

Package: dsdp-doc
Architecture: all
Version: 5.8-9.4build1
Priority: extra
Section: universe/doc
Source: dsdp
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Soeren Sonnenburg
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 7995
Recommends: dsdp
Filename: pool/universe/d/dsdp/dsdp-doc_5.8-9.4build1_all.deb
Size: 534628
MD5sum: 094928e3787b2f0656c697459be6d240
SHA1: 55ed11f236e1f533755c4c7db85c1c7e10b1f074
SHA256: b9c8415b327ad239c624402c9a3e9d7d0e9909484c36cd377b66950b991a6526
SHA512: f4f044ca4b6e630cb47c96e41775264a75404dd762c10d520b6f294bde95f412d4df88a58bf36654bbcebf560ff293cd2e0e6ae4d2c41df6ed11e204f11a962a
Homepage: http://www-unix.mcs.anl.gov/DSDP/
Description-en: Software for Semidefinite Programming
The DSDP software is a free open source implementation of an interior-point
method for semidefinite programming. It provides primal and dual solutions,
exploits low-rank structure and sparsity in the data, and has relatively
low memory requirements for an interior-point method. It allows feasible
and infeasible starting points and provides approximate certificates of
infeasibility when no feasible solution exists. The dual-scaling
algorithm implemented in this package has a convergence proof and
worst-case polynomial complexity under mild assumptions on the
data. Furthermore, the solver offers scalable parallel performance for
large problems and a well documented interface. Some of the most popular
applications of semidefinite programming and linear matrix inequalities
(LMI) are model control, truss topology design, and semidefinite
relaxations of combinatorial and global optimization problems.
.
This package contains the documentation and examples.
Description-md5: 26082894d8d34e85cfb5511f23cc16cd