Prof. Lorenz T. Biegler's Talk
Model discrimination parameter estimation and statistical inference require the formulation and efficient solution of nonlinear programming (NLP) problems. Frequently these problems are ill-posed due to over-parameterized models or data with incomplete information. These characteristics lead to failure of many popular NLP codes. This talk presents some basic parameter estimation methods that can also handle structured features of ill-posed parameter estimation problems. These are treated with composite step trust region methods.