How to Install and Uninstall dsdp Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: November 07,2024

1. Install "dsdp" package

Learn how to install dsdp on Ubuntu 20.10 (Groovy Gorilla)


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

This is a short guide on how to uninstall dsdp on Ubuntu 20.10 (Groovy Gorilla):


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

Package: dsdp
Architecture: amd64
Version: 5.8-9.4build1
Priority: extra
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Soeren Sonnenburg
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 97
Depends: libc6 (>= 2.7), libdsdp-5.8gf
Filename: pool/universe/d/dsdp/dsdp_5.8-9.4build1_amd64.deb
Size: 27188
MD5sum: 1d7cf0c7164bdc3f9d7adccf792b36f4
SHA1: 888e2cc31e7a2e59f8f8ef884244ddb3d3eab6ba
SHA256: c0ef8e81848a9353b71f09e357bd1952d302266add6333071ece3678f6a6d28a
SHA512: 89af09d003e0d0a1d397f8736f725fb660502c69229d97092bc2f49ad4693e7b4401b367034b53eb3905cdd9131874ad49f9476e62252c663d28422b4df9a50a
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.
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This package contains the binaries.
Description-md5: f23b1ccc0454351abde108cef0799570