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

Last updated: November 06,2024

1. Install "libdsdp-dev" package

In this section, we are going to explain the necessary steps to install libdsdp-dev on Ubuntu 20.10 (Groovy Gorilla)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libdsdp-dev
            
                    
    
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2. Uninstall "libdsdp-dev" package

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


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

Package: libdsdp-dev
Architecture: amd64
Version: 5.8-9.4build1
Priority: extra
Section: universe/libdevel
Source: dsdp
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Soeren Sonnenburg
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 955
Depends: libdsdp-5.8gf (= 5.8-9.4build1)
Filename: pool/universe/d/dsdp/libdsdp-dev_5.8-9.4build1_amd64.deb
Size: 207100
MD5sum: 41c3d2dc0bfac91e1af45bda449a73fa
SHA1: feca02854fabb3fc146f4a544c6d678dcd23e070
SHA256: f2412c99a58e900c4ac3b1d77c51468a7c9e0b32941f36e8d858a4995e39aec5
SHA512: 24fd3f1932040fa6532c1c6f010614afeb7b6099d9b39a4ca4e0f0ce36182907b622a6fcff54bf093b181d46031d10fbaa66532e939ae8ddddb33ee102c2a1a8
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 header files for developers.
Description-md5: c4ae5d8646ec667afe57aff2c90533ff