How to Install and Uninstall levmar.x86_64 Package on Red Hat Enterprise Linux 8 (RHEL 8)

Last updated: November 27,2024

1. Install "levmar.x86_64" package

Please follow the guidelines below to install levmar.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8)


    
        
            $
        

        
            
                sudo dnf update
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf install
            
                        
                levmar.x86_64
            
                    
    
    Copied

2. Uninstall "levmar.x86_64" package

Here is a brief guide to show you how to uninstall levmar.x86_64 on Red Hat Enterprise Linux 8 (RHEL 8):


    
        
            $
        

        
            
                sudo dnf remove
            
                        
                levmar.x86_64
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo dnf autoremove
            
                    
    
    Copied

3. Information about the levmar.x86_64 package on Red Hat Enterprise Linux 8 (RHEL 8)

Last metadata expiration check: 0:15:26 ago on Mon Feb 26 15:59:38 2024.
Available Packages
Name : levmar
Version : 2.6
Release : 3.el8
Architecture : x86_64
Size : 61 k
Source : levmar-2.6-3.el8.src.rpm
Repository : epel
Summary : Levenberg-Marquardt nonlinear least squares algorithm
URL : http://www.ics.forth.gr/~lourakis/levmar/
License : GPLv2+
Description : levmar is a native ANSI C implementation of the Levenberg-Marquardt
: optimization algorithm. Both unconstrained and constrained (under linear
: equations, inequality and box constraints) Levenberg-Marquardt variants are
: included. The LM algorithm is an iterative technique that finds a local
: minimum of a function that is expressed as the sum of squares of nonlinear
: functions. It has become a standard technique for nonlinear least-squares
: problems and can be thought of as a combination of steepest descent and the
: Gauss-Newton method. When the current solution is far from the correct on,
: the algorithm behaves like a steepest descent method: slow, but guaranteed
: to converge. When the current solution is close to the correct solution, it
: becomes a Gauss-Newton method.