How to Install and Uninstall libdsdp-5.8gf Package on Ubuntu 20.10 (Groovy Gorilla)

Last updated: December 24,2024

1. Install "libdsdp-5.8gf" package

This tutorial shows how to install libdsdp-5.8gf on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libdsdp-5.8gf
            
                    
    
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2. Uninstall "libdsdp-5.8gf" package

Please follow the guidance below to uninstall libdsdp-5.8gf on Ubuntu 20.10 (Groovy Gorilla):


    
        
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                sudo apt remove
            
                        
                libdsdp-5.8gf
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the libdsdp-5.8gf package on Ubuntu 20.10 (Groovy Gorilla)

Package: libdsdp-5.8gf
Architecture: amd64
Version: 5.8-9.4build1
Priority: extra
Section: universe/libs
Source: dsdp
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Soeren Sonnenburg
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 498
Depends: libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3
Filename: pool/universe/d/dsdp/libdsdp-5.8gf_5.8-9.4build1_amd64.deb
Size: 184072
MD5sum: fcc3dc13fcda9484fa641567db04b699
SHA1: 7d1593ff4e44b81297b6f799f627826919751484
SHA256: db7d2cefe9958a2e8c56c3d2aecd93f65b3d19650ef032ce3b82fb1247cc688c
SHA512: f534034b917ddec824b138b8d19148658d0678d4f4fa66cd7fac234f599b312265619c703491749b60b12e6f94b732d0b9e2a87ab06367d10bb12176c2179c39
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 library files.
Description-md5: 0747ea6e16d480b70ce0d68d68364dd2