A0485
Title: A column generation approach to exact experimental design
Authors: Selin Ahipasaoglu - University of Southampton (United Kingdom) [presenting]
Stefano Cipolla - University of Southampton (United Kingdom)
Jacek Gondzio - University of Edinburgh (UK)
Abstract: The aim is to address the exact D-optimal experimental design problem by proposing an efficient algorithm that rapidly identifies the support of its continuous relaxation. The method leverages a column generation framework to solve such a continuous relaxation, where each restricted master problem is tackled using a primal-dual interior-point-based semidefinite programming solver. This enables fast and reliable detection of the design's support. The identified support is subsequently used to construct a feasible exact design that is provably close to optimal. It is shown that, for large-scale instances in which the number of regression points exceeds by far the number of experiments, the approach achieves superior performance compared to existing branch-and-bound-based algorithms in both computational efficiency and solution quality.
