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Journal Articles Journal of Statistical Theory and Practice Year : 2021

Incremental space-filling design based on coverings and spacings: improving upon low discrepancy sequences

Abstract

The paper addresses the problem of defining families of ordered sequences $\{\xb_i\}_{i\in \mathds{N}}$ of elements of a compact subset $\SX$ of $\mathds{R}^d$ whose prefixes $\Xb_n=\{\xb_i\}_{i=1}^{n}$, for all orders $n$, have good space-filling properties as measured by the dispersion (covering radius) criterion. Our ultimate aim is the definition of incremental algorithms that generate sequences $\Xb_n$ with small optimality gap, i.e., with a small increase in the maximum distance between points of $\SX$ and the elements of $\Xb_n$ with respect to the optimal solution $\Xb_n^\star$. The paper is a first step in this direction, presenting incremental design algorithms with proven optimality bound for one-parameter families of criteria based on coverings and spacings that both converge to dispersion for large values of their parameter. The examples presented show that the covering-based method outperforms state-of-the-art competitors, including coffee-house, suggesting that it inherits from its guaranteed 50\% optimality gap.
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Dates and versions

hal-02987983 , version 1 (04-11-2020)
hal-02987983 , version 2 (16-04-2021)
hal-02987983 , version 3 (26-05-2021)

Identifiers

  • HAL Id : hal-02987983 , version 3

Cite

Amaya Nogales Gómez, Luc Pronzato, Maria Joao Rendas. Incremental space-filling design based on coverings and spacings: improving upon low discrepancy sequences. Journal of Statistical Theory and Practice, 2021, 15 (4), pp.77. ⟨hal-02987983v3⟩
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