One-Dimensional Nested Maximin Designs

The design of computer experiments is an important step in black box evaluation and optimization processes.When dealing with multiple black box functions the need often arises to construct designs for all black boxes jointly, instead of individually.These so-called nested designs are used to deal with linking parameters and sequential evaluations.In this paper we discuss one-dimensional nested maximin designs.We show how to nest two designs optimally and develop a heuristic to nest three and four designs.Furthermore, it is proven that the loss in space-fillingness, with respect to traditional maximin designs, is at most 14:64 percent and 19:21 percent, when nesting two and three designs, respectively.simulation;computers;integer programming

Nested Maximin Latin Hypercube Designs in Two Dimensions

In black box evaluation and optimization Latin hypercube designs play an important role.When dealing with multiple black box functions the need often arises to construct designs for all black boxes jointly, instead of individually.These so-called nested designs consist of two separate designs, one being a subset of the other, and are used to deal with linking parameters and sequential evaluations.In this paper we construct nested maximin designs in two dimensions.We show that different types of grids should be considered when constructing nested designs and discuss how to determine which grid to use best for a specifc computer experiment.In the appendix to this paper maximin distances for different numbers of points are provided; the corresponding nested maximin designs can be found on the website http://www.spacefillingdesigns.nl.

Bounds for Maximin Latin Hypercube Designs

Latin hypercube designs (LHDs) play an important role when approximating computer simula- tion models. To obtain good space-filling properties, the maximin criterion is frequently used. Unfortunately, constructing maximin LHDs can be quite time-consuming when the number of dimensions and design points increase. In these cases, we can use approximate maximin LHDs. In this paper, we construct bounds for the separation distance of certain classes of maximin LHDs. These bounds are useful for assessing the quality of approximate maximin LHDs. Until now only upper bounds are known for the separation distance of certain classes of unrestricted maximin designs, i.e. for maximin designs without a Latin hypercube struc- ture. The separation distance of maximin LHDs also satisfies these “unrestricted” bounds. By using some of the special properties of LHDs, we are able to find new and tighter bounds for maximin LHDs. Within the different methods used to determine the upper bounds, a vari- ety of combinatorial optimization techniques are employed. Mixed Integer Programming, the Travelling Salesman Problem, and the Graph Covering Problem are among the formulations used to obtain the bounds. Besides these bounds, also a construction method is described for generating LHDs that meet Baer’s bound for the ℓ1 distance measure for certain values of n.Latin hypercube design;maximin;space-filling;mixed integer programming;trav- elling salesman problem;graph covering.

Safe Approximations of Chance Constraints Using Historical Data

This paper proposes a new way to construct uncertainty sets for robust optimization. Our approach uses the available historical data for the uncertain parameters and is based on goodness-of-fit statistics. It guarantees that the probability that the uncertain constraint holds is at least the prescribed value. Compared to existing safe approximation methods for chance constraints, our approach directly uses the historical-data information and leads to tighter uncertainty sets and therefore to better objective values. This improvement is significant especially when the number of uncertain parameters is low. Other advantages of our approach are that it can handle joint chance constraints easily, it can deal with uncertain parameters that are dependent, and it can be extended to nonlinear inequalities. Several numerical examples illustrate the validity of our approach.robust optimization;chance constraint;phi-divergence;goodness-of-fit statistics

Robust Optimization Using Computer Experiments

During metamodel-based optimization three types of implicit errors are typically made.The first error is the simulation-model error, which is defined by the difference between reality and the computer model.The second error is the metamodel error, which is defined by the difference between the computer model and the metamodel.The third is the implementation error.This paper presents new ideas on how to cope with these errors during optimization, in such a way that the final solution is robust with respect to these errors.We apply the robust counterpart theory of Ben-Tal and Nemirovsky to the most frequently used metamodels: linear regression and Kriging models.The methods proposed are applied to the design of two parts of the TV tube.The simulationmodel errors receive little attention in the literature, while in practice these errors may have a significant impact due to propagation of such errors