LevenbergMarquardt Properties

Learning rate adjustment.

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

Gets or sets the convergence verification method.

Gets the current iteration number.

Gets or sets a parameterized model function mapping input vectors into output values, whose optimum parameters must be found.

Gets or sets a function that computes the gradient vector in respect to the function parameters, given a set of input and output values.

Gets whether the algorithm has converged.

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.

Please use MaxIterations instead.

Levenberg's damping factor, also known as lambda.

Gets or sets the maximum number of iterations performed by the iterative algorithm. Default is 100.

Gets the number of variables (free parameters) in the optimization problem.

Gets the number of variables (free parameters) in the optimization problem.

Gets or sets the parallelization options for this algorithm.

Gets the solution found, the values of the parameters which optimizes the function, in a least squares sense.

Gets standard error for each parameter in the solution.

Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.

Gets or sets the maximum relative change in the watched value after an iteration of the algorithm used to detect convergence. Default is zero.

Gets the value at the solution found. This should be the minimum value found for the objective function.