We will study efficient incremental solutions to combinatorial optimisation problems occurring in design of
computer experiments. Modern industrial processes often resort to complex simulation models whose
computational cost requires substitution by a surrogate of much lesser complexity. The surrogate quality
depends on the set of simulation inputs (the design) used for its construction. Quality increases with design size,
which can often only be decided online, during the sequential integration of simulation results. The objective is to
propose an ordered design (a sequence of simulation runs) which is nearly optimal (for the corresponding size)
when stopped at any point. Many variants of this constrained subset-selection problem are NP-hard and
algorithms with approximation guarantees have been proposed in the computer science community. We believe
that more efficient approximation bounds and algorithms can be constructed by taking the specificity of the
design problem into account.
Forschungs-