Accord.NET Framework

## LevenbergMarquardt Properties |

The LevenbergMarquardt type exposes the following members.

Properties

Name | Description | |
---|---|---|

Adjustment |
Learning rate adjustment.
| |

Blocks |
Gets or sets the number of blocks to divide the
Jacobian matrix in the Hessian calculation to
preserve memory. Default is 1.
| |

Convergence |
Gets or sets the convergence verification method.
(Inherited from BaseLeastSquaresMethod.) | |

CurrentIteration |
Gets the current iteration number.
(Inherited from BaseLeastSquaresMethod.) | |

Function |
Gets or sets a parameterized model function mapping input vectors
into output values, whose optimum parameters must be found.
(Inherited from BaseLeastSquaresMethod.) | |

Gradient |
Gets or sets a function that computes the gradient vector in respect
to the function parameters, given a set of input and output values.
(Inherited from BaseLeastSquaresMethod.) | |

HasConverged |
Gets whether the algorithm has converged.
(Inherited from BaseLeastSquaresMethod.) | |

Hessian |
Gets the approximate Hessian matrix of second derivatives
generated in the last algorithm iteration. The Hessian is
stored in the upper triangular part of this matrix. See
remarks for details.
| |

Iterations | Obsolete.
Please use MaxIterations instead.
(Inherited from BaseLeastSquaresMethod.) | |

LearningRate |
Levenberg's damping factor, also known as lambda.
| |

MaxIterations |
Gets or sets the maximum number of iterations
performed by the iterative algorithm. Default
is 100.
(Inherited from BaseLeastSquaresMethod.) | |

NumberOfParameters |
Gets the number of variables (free parameters) in the optimization problem.
(Inherited from BaseLeastSquaresMethod.) | |

NumberOfVariables | Obsolete.
Gets the number of variables (free parameters) in the optimization problem.
(Inherited from BaseLeastSquaresMethod.) | |

ParallelOptions |
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.) | |

Solution |
Gets the solution found, the values of the parameters which
optimizes the function, in a least squares sense.
(Inherited from BaseLeastSquaresMethod.) | |

StandardErrors |
Gets standard error for each parameter in the solution.
| |

Token |
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.) | |

Tolerance |
Gets or sets the maximum relative change in the watched value
after an iteration of the algorithm used to detect convergence.
Default is zero.
(Inherited from BaseLeastSquaresMethod.) | |

Value |
Gets the value at the solution found. This should be
the minimum value found for the objective function.
(Inherited from BaseLeastSquaresMethod.) |

See Also