  We develop adaptive discretization algorithms for locally optimal
experimental design of nonlinear prediction models. With these algorithms, we
refine and improve a pertinent state-of-the-art algorithm in various respects.
We establish novel termination, convergence, and convergence rate results for
the proposed algorithms. In particular, we prove a sublinear convergence rate
result under very general assumptions on the design criterion and, most
notably, a linear convergence result under the additional assumption that the
design criterion is strongly convex and the design space is finite.
Additionally, we prove the finite termination at approximately optimal designs,
including upper bounds on the number of iterations until termination. And
finally, we illustrate the practical use of the proposed algorithms by means of
two application examples from chemical engineering: one with a stationary model
and one with a dynamic model.
