Package : levmar > RPM : levmar-2.6-3.mga8.src.rpm
Basic items
Name | levmar |
Version | 2.6 |
Release | 3.mga8 |
URL | http://users.ics.forth.gr/~lourakis/levmar/ |
Group | Development/C |
Summary | Levenberg-Marquardt nonlinear least squares algorithm |
Size | 89KB |
Arch | armv7hl |
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.
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.
Media information
Distribution release | Mageia 8 |
Media name | core-release |
Media arch | armv7hl |
Advanced items
Source RPM | NOT IN DATABASE ?! |
Build time | 2020-02-13 17:33:26 |
Changelog | View in Sophie |
Files | View in Sophie |
Dependencies | View in Sophie |