rprop {CaDENCE} R Documentation

## Resilient backpropagation (Rprop) optimization algorithm

### Description

From Riedmiller (1994): Rprop stands for 'Resilient backpropagation' and is a local adaptive learning scheme. The basic principle of Rprop is to eliminate the harmful influence of the size of the partial derivative on the weight step. As a consequence, only the sign of the derivative is considered to indicate the direction of the weight update. The size of the weight change is exclusively determined by a weight-specific, so called 'update-value'.

This function implements the iRprop+ algorithm from Igel and Huesken (2003).

### Usage

```rprop(w, f, iterlim = 100, print.level = 1, delta.0 = 0.1,
delta.min = 1e-06, delta.max = 50, epsilon = 1e-08,
step.tol = 1e-06, f.target = -Inf, ...)
```

### Arguments

 `w` the starting parameters for the minimization. `f` the function to be minimized. If the function value has an attribute called `gradient`, this will be used in the calculation of updated parameter values. Otherwise, numerical derivatives will be used. `iterlim` the maximum number of iterations before the optimization is stopped. `print.level` the level of printing which is done during optimization. A value of `0` suppresses any progress reporting, whereas positive values report the value of `f` and the mean change in `f` over the previous three iterations. `delta.0` size of the initial Rprop update-value. `delta.min` minimum value for the adaptive Rprop update-value. `delta.max` maximum value for the adaptive Rprop update-value. `epsilon` step-size used in the finite difference calculation of the gradient. `step.tol` convergence criterion. Optimization will stop if the change in `f` over the previous three iterations falls below this value. `f.target` target value of `f`. Optimization will stop if `f` falls below this value. `...` further arguments to be passed to `f`.

### Value

A list with elements:

 `par` The best set of parameters found. `value` The value of `f` corresponding to `par`. `gradient` An estimate of the gradient at the solution found.

### References

Igel, C. and M. Huesken, 2003. Empirical evaluation of the improved Rprop learning algorithms. Neurocomputing 50: 105-123.

Riedmiller, M., 1994. Advanced supervised learning in multilayer perceptrons - from backpropagation to adaptive learning techniques. Computer Standards and Interfaces 16(3): 265-278.

[Package CaDENCE version 1.2.5 Index]