How to Install and Uninstall DSDP.x86_64 Package on Fedora 38

Last updated: November 30,2024

1. Install "DSDP.x86_64" package

Please follow the guidelines below to install DSDP.x86_64 on Fedora 38


    
        
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                sudo dnf update
            
                    
    
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            $
        

        
            
                sudo dnf install
            
                        
                DSDP.x86_64
            
                    
    
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2. Uninstall "DSDP.x86_64" package

Please follow the guidelines below to uninstall DSDP.x86_64 on Fedora 38:


    
        
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                sudo dnf remove
            
                        
                DSDP.x86_64
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the DSDP.x86_64 package on Fedora 38

Last metadata expiration check: 4:32:34 ago on Sat Mar 16 22:59:57 2024.
Available Packages
Name : DSDP
Version : 5.8
Release : 32.fc38
Architecture : x86_64
Size : 640 k
Source : DSDP-5.8-32.fc38.src.rpm
Repository : fedora
Summary : Software for semidefinite programming
URL : https://www.mcs.anl.gov/hs/software/DSDP/
License : DSDP AND OFL-1.1-RFN AND Knuth-CTAN
Description : DSDP 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. The software can be used as a set of subroutines, through Matlab,
: or by reading and writing to data files. 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.