1. A method of optimization of a process having an associated metric and comprising a plurality of sub-processes, the method comprising the steps of: (a) providing a target metric value for the process; (b) providing one or more ranges of acceptable values for the sub-process metrics to define a constraint set; (c) providing a nonlinear regression model that has been trained in the relationship between the sub-process metrics and the process metric such that the nonlinear regression model can determine a predicted process metric value from the measured sub-process metric values; and (d) using the nonlinear regression model and an optimizer to determine values for the sub-process metrics within the constraint set that produce at a substantially lowest cost a predicted process metric value substantially as close as possible to the target process metric value.