A RQP Based Method for Estimating Parameter Sensitivity Derivatives

Abstract

Abstract Parameter sensitivity analysis is defined as the estimation of changes in the modeling functions and design point due to small changes in the fixed parameters of the formulation. There are currently several methods for estimating parameter sensitivities which either require difficult to obtain second order information, or do not return reliable estimates for the derivatives. This paper presents a new method, based on the use of the recursive quadratic programming method in conjunction with differencing formulas to estimate the parameter sensitivities without the need to calculate second order information. The method is compared to existing methods and is shown to be very competitive in terms of the number of function evaluations required. In terms of accuracy, the method is shown to be equivalent to a modified version of the Kuhn-Tucker method, where the Hessian of the Lagrangian is estimated using the BFS method employed by the RQP algorithm. Initial testing on a test set of known characteristics demonstrates that the method can accurately calculate parameter sensitivities.