Levenberg–Marquardt algorithm
GPTKB entity
Statements (30)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
mathematical optimization
|
| gptkbp:advantage |
fast convergence for small residual problems
may be slow for large-scale problems requires computation of Jacobian matrix |
| gptkbp:alsoKnownAs |
damped least-squares method
|
| gptkbp:category |
gptkb:logic
numerical analysis mathematical optimization |
| gptkbp:combines |
gptkb:Gauss–Newton_algorithm
gradient descent |
| https://www.w3.org/2000/01/rdf-schema#label |
Levenberg–Marquardt algorithm
|
| gptkbp:implementedIn |
gptkb:C++
gptkb:MATLAB gptkb:Python_(SciPy) R |
| gptkbp:improves |
gptkb:Donald_Marquardt
|
| gptkbp:namedAfter |
gptkb:Donald_Marquardt
gptkb:Kenneth_Levenberg |
| gptkbp:proposedBy |
gptkb:Donald_Marquardt
gptkb:Kenneth_Levenberg |
| gptkbp:relatedTo |
least squares
nonlinear regression trust region methods |
| gptkbp:usedFor |
nonlinear least squares problems
|
| gptkbp:usedIn |
gptkb:machine_learning
curve fitting parameter estimation |
| gptkbp:yearProposed |
1944
|
| gptkbp:bfsParent |
gptkb:Gauss–Newton_algorithm
|
| gptkbp:bfsLayer |
7
|