INcremental Design of EXperiments

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.

INDEX has studied 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 on-line, during the sequential integration of simulation results. The objective was 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 have constructed more efficient approximation bounds and algorithms by taking the specificity of the design problem into account.