{"ID":2876868,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00159","arxiv_id":"2509.00159","title":"LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design","abstract":"Latin Hypercube Sampling (LHS) is a prominent tool in simulation design, with a variety of applications in high-dimensional and computationally expensive problems. LHS allows for various optimization strategies, most notably to ensure space-filling properties. However, LHS is a single-stage algorithm that requires a priori knowledge of the targeted sample size. In this work, we present LHS in LHS, a new expansion algorithm for LHS that enables the addition of new samples to an existing LHS-distributed set while (approximately) preserving its properties. In summary, the algorithm identifies regions of the parameter space that are far from the initial set, draws a new LHS within those regions, and then merges it with the original samples. As a by-product, we introduce a new metric, the LHS degree, which quantifies the deviation of a given design from an LHS distribution. Our public implementation is distributed via the Python package expandLHS.","short_abstract":"Latin Hypercube Sampling (LHS) is a prominent tool in simulation design, with a variety of applications in high-dimensional and computationally expensive problems. LHS allows for various optimization strategies, most notably to ensure space-filling properties. However, LHS is a single-stage algorithm that requires a pr...","url_abs":"https://arxiv.org/abs/2509.00159","url_pdf":"https://arxiv.org/pdf/2509.00159v1","authors":"[\"Matteo Boschini\",\"Davide Gerosa\",\"Alessandro Crespi\",\"Matteo Falcone\"]","published":"2025-08-29T18:00:10Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"astro-ph.HE\",\"cs.DS\",\"gr-qc\"]","methods":"[]","has_code":false}