{"ID":2898944,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03210","arxiv_id":"2507.03210","title":"A column generation approach to exact experimental design","abstract":"In this work, we address the exact D-optimal experimental design problem by proposing an efficient algorithm that rapidly identifies the support of its continuous relaxation. Our 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. We show that, for large-scale instances in which the number of regression points exceeds by far the number of experiments, our approach achieves superior performance compared to existing branch-and-bound-based algorithms in both computational efficiency and solution quality.","short_abstract":"In this work, we address the exact D-optimal experimental design problem by proposing an efficient algorithm that rapidly identifies the support of its continuous relaxation. Our method leverages a column generation framework to solve such a continuous relaxation, where each restricted master problem is tackled using a...","url_abs":"https://arxiv.org/abs/2507.03210","url_pdf":"https://arxiv.org/pdf/2507.03210v2","authors":"[\"Selin Ahipasaoglu\",\"Stefano Cipolla\",\"Jacek Gondzio\"]","published":"2025-07-03T22:50:05Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}