AI engineering glossary · Calculus optimisation
What is Levenberg-Marquardt algorithm?
Levenberg-Marquardt algorithm: A non-linear least-squares method that blends Gauss-Newton with gradient descent by adding a damping term to J^T J. Large damping behaves like cautious gradient descent; small damping behaves like Gauss-Newton. It is a standard curve-fitting routine.
Levenberg-Marquardt algorithm: an example
scipy.optimize.curve_fit uses it by default for unbounded problems when fitting a model to measurements.
Related terms
More Calculus optimisation terms
- Limit
- Line search
- Linear approximation
- Linear programming
- Lipschitz continuity
- Local minimum
- Loss landscape
- Mini-batch gradient descent
Where you learn it
Levenberg-Marquardt algorithm is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.