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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2509.00159 |
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| _version_ | 1866908516557520896 |
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| author | Boschini, Matteo Gerosa, Davide Crespi, Alessandro Falcone, Matteo |
| author_facet | Boschini, Matteo Gerosa, Davide Crespi, Alessandro Falcone, Matteo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design Boschini, Matteo Gerosa, Davide Crespi, Alessandro Falcone, Matteo Methodology High Energy Astrophysical Phenomena Data Structures and Algorithms General Relativity and Quantum Cosmology 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. |
| title | LHS in LHS: A new expansion strategy for Latin hypercube sampling in simulation design |
| topic | Methodology High Energy Astrophysical Phenomena Data Structures and Algorithms General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2509.00159 |