LevenbergMarquardt class

The Levenberg–Marquardt algorithm, also known as the damped least-squares method, is used to solve non-linear least squares problems.

See https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm.

Inheritance

Constructors

LevenbergMarquardt(ParametrizedUnaryFunction<double> parametrizedFunction, {double? initialValue, dynamic initialValues, double? minValue, dynamic minValues, double? maxValue, dynamic maxValues, double gradientDifference = 1e-1, dynamic gradientDifferences, double damping = 1e-2, double dampingStepDown = 9.0, double dampingStepUp = 11.0, bool centralDifference = false, double improvementThreshold = 1e-3, double errorTolerance = 1e-7, int maxIterations = 100})

Properties

centralDifference → bool
If true the Jacobian matrix is approximated by central differences otherwise by forward differences.
final
damping → double
Small values of the damping factor result in a Gauss-Newton update and large values in a gradient descent update.
final
dampingStepDown → double
Factor to reduce the damping when there is not an improvement when updating parameters.
final
dampingStepUp → double
Factor to increase the damping when there is an improvement when updating parameters.
final
errorTolerance → double
Minimum uncertainty allowed for each point.
final
gradientDifferences → Vector<double>
The step size to approximate each parameter in the Jacobian matrix.
final
hashCode → int
The hash code for this object.
no setterinherited
improvementThreshold → double
The threshold to define an improvement through an update of parameters.
final
initialValues → Vector<double>
A vector of initial parameter values.
final
maxIterations → int
Maximum of allowed iterations
final
maxValues → Vector<double>
Maximum allowed values for parameters.
final
minValues → Vector<double>
Minimum allowed values for parameters.
final
parametrizedFunction → ParametrizedUnaryFunction<double>
A parametrized function
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited

Methods

fit({required Vector<double> xs, required Vector<double> ys, double weight = 1.0, Vector<double>? weights}) → LevenbergMarquardtResult
Fits a list of data points to the configured model.
override
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited