How to Install and Uninstall liblevmar2 Package on openSUSE Leap
Last updated: November 23,2024
1. Install "liblevmar2" package
Please follow the steps below to install liblevmar2 on openSUSE Leap
$
sudo zypper refresh
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$
sudo zypper install
liblevmar2
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2. Uninstall "liblevmar2" package
Please follow the instructions below to uninstall liblevmar2 on openSUSE Leap:
$
sudo zypper remove
liblevmar2
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3. Information about the liblevmar2 package on openSUSE Leap
Information for package liblevmar2:
-----------------------------------
Repository : Main Repository
Name : liblevmar2
Version : 2.6-bp155.1.6
Arch : x86_64
Vendor : openSUSE
Installed Size : 139.5 KiB
Installed : No
Status : not installed
Source package : levmar-2.6-bp155.1.6.src
Upstream URL : http://users.ics.forth.gr/~lourakis/levmar/
Summary : Levenberg-Marquardt nonlinear least squares algorithm
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
-----------------------------------
Repository : Main Repository
Name : liblevmar2
Version : 2.6-bp155.1.6
Arch : x86_64
Vendor : openSUSE
Installed Size : 139.5 KiB
Installed : No
Status : not installed
Source package : levmar-2.6-bp155.1.6.src
Upstream URL : http://users.ics.forth.gr/~lourakis/levmar/
Summary : Levenberg-Marquardt nonlinear least squares algorithm
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