How to Install and Uninstall libdsdp-dev Package on Ubuntu 16.04 LTS (Xenial Xerus)

Last updated: May 18,2024

1. Install "libdsdp-dev" package

Please follow the guidelines below to install libdsdp-dev on Ubuntu 16.04 LTS (Xenial Xerus)


    
        
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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 is a short guide on how to uninstall libdsdp-dev on Ubuntu 16.04 LTS (Xenial Xerus):


    
        
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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 16.04 LTS (Xenial Xerus)

Package: libdsdp-dev
Priority: extra
Section: universe/libdevel
Installed-Size: 992
Maintainer: Ubuntu Developers
Original-Maintainer: Soeren Sonnenburg
Architecture: amd64
Source: dsdp
Version: 5.8-9.1ubuntu2
Depends: libdsdp-5.8gf (= 5.8-9.1ubuntu2)
Filename: pool/universe/d/dsdp/libdsdp-dev_5.8-9.1ubuntu2_amd64.deb
Size: 215134
MD5sum: 92e0f767a26173e88415e9bc8ba62ccb
SHA1: 78629e400b7bbcf26fd812930f5b9d5c3ef664d5
SHA256: ce0029696e97f83a177f78bacda09b0b00da04adcfde49ede69910a65655b106
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
Homepage: http://www-unix.mcs.anl.gov/DSDP/
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Origin: Ubuntu