How to Install and Uninstall levmar.i686 Package on Fedora 39
Last updated: January 16,2025
1. Install "levmar.i686" package
Please follow the instructions below to install levmar.i686 on Fedora 39
$
sudo dnf update
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$
sudo dnf install
levmar.i686
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2. Uninstall "levmar.i686" package
Learn how to uninstall levmar.i686 on Fedora 39:
$
sudo dnf remove
levmar.i686
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$
sudo dnf autoremove
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3. Information about the levmar.i686 package on Fedora 39
Last metadata expiration check: 4:04:07 ago on Thu Mar 7 17:44:52 2024.
Available Packages
Name : levmar
Version : 2.6
Release : 13.fc39
Architecture : i686
Size : 65 k
Source : levmar-2.6-13.fc39.src.rpm
Repository : fedora
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.
Available Packages
Name : levmar
Version : 2.6
Release : 13.fc39
Architecture : i686
Size : 65 k
Source : levmar-2.6-13.fc39.src.rpm
Repository : fedora
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.