How to Install and Uninstall libcholmod3 Package on openSUSE Leap

Last updated: December 24,2024

1. Install "libcholmod3" package

Here is a brief guide to show you how to install libcholmod3 on openSUSE Leap


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                libcholmod3
            
                    
    
    Copied

2. Uninstall "libcholmod3" package

Please follow the steps below to uninstall libcholmod3 on openSUSE Leap:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                libcholmod3
            
                    
    
    Copied

3. Information about the libcholmod3 package on openSUSE Leap

Information for package libcholmod3:
------------------------------------
Repository : Update repository with updates from SUSE Linux Enterprise 15
Name : libcholmod3
Version : 3.0.12-150100.9.2.3
Arch : x86_64
Vendor : SUSE LLC
Installed Size : 1.6 MiB
Installed : No
Status : not installed
Source package : suitesparse-5.2.0-150100.9.2.3.src
Upstream URL : http://faculty.cse.tamu.edu/davis/SuiteSparse/
Summary : Supernodal Sparse Cholesky Factorization and Update/Downdate
Description :
CHOLMOD is a set of ANSI C routines for sparse Cholesky factorization
and update/downdate. A MATLAB interface is provided.
The performance of CHOLMOD was compared with 10 other codes in a
paper by Nick Gould, Yifan Hu, and Jennifer Scott. see also their raw
data. Comparing BCSLIB-EXT, CHOLMOD, MA57, MUMPS, Oblio, PARDISO,
SPOOLES, SPRSBLKLLT, TAUCS, UMFPACK, and WSMP, on 87 large symmetric
positive definite matrices, they found CHOLMOD to be fastest for 42
of the 87 matrices. Its run time is either fastest or within 10% of
the fastest for 73 out of 87 matrices. Considering just the larger
matrices, it is either the fastest or within 10% of the fastest for
40 out of 42 matrices. It uses the least amount of memory (or within
10% of the least) for 35 of the 42 larger matrices. Jennifer Scott
and Yifan Hu also discuss the design considerations for a sparse
direct code.
CHOLMOD is part of the SuiteSparse sparse matrix suite.