[1] Similarly to the Manhattan update rule, Rprop takes into account only the sign of the partial derivative over all patterns (not the magnitude), and acts independently on each "weight".
For each weight, if there was a sign change of the partial derivative of the total error function compared to the last iteration, the update value for that weight is multiplied by a factor η−, where η− < 1.
If the last iteration produced the same sign, the update value is multiplied by a factor of η+, where η+ > 1.
[citation needed] Rprop can result in very large weight increments or decrements if the gradients are large, which is a problem when using mini-batches as opposed to full batches.
[citation needed] Martin Riedmiller developed three algorithms, all named RPROP.