All of the algorithms used in the RSM software package rely on the use of a function approximator that can provide estimates of: the value of parameter settings, their gradients, the noise level, and confidence intervals on those estimates. RSM is integrated with the GMBL (General Memory Based Learning) package and uses it to provide these estimates from historical data and new data from experiments. GMBL is described in another document and its inner workings will not be discussed here. We only need to know that it can provide the estimates listed above. The rest of this section describes how each of the different algorithms for optimization works.