Algorithm 247: Radical-inverse quasi-random point sequence

By J. Halton, G. B. Smith. 1964

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An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, methodology): “Among them, Full Factorial, Fractional Factorial (Box et al., 2005), Central Composite (Myers, 1971), BoxeBehnken (Box and Behnken, 1960), Monte Carlo, Latin Hypercube (McKay et al., 1979) and QuasiMonte Carlo (QMC) (Sobol',1967; Halton,1964) designs are themost widely used.”
A Polynomial Chaos-Based Kalman Filter Approach for Parameter Estimation of Mechanical Systems (2010, unknown): “The collocation points used in this study are obtained using an algorithm based on the Halton algorithm [69], which is similar to the Hammersley algorithm [70].”
Critical Hyper-Parameters: No Random, No Cry (2017, background): “Then the Halton sequence [7] is deﬁned as { ( γ q 1 ( k ) , . . . , γ q d ( k )) : 1 ≤ k ≤ n } and the Hammersley sequence is deﬁned as { (( k − ) /n, γ q 1 ( k ) , . . . , γ q d − 1 ( k )) : 1 ≤ k ≤ n } One can also deﬁne scrambled versions of those sequences by randomly permuting the digits in…”
DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 3.0 Reference Manual (2001, unknown): “For more information about these sequences, see[43], [44], and [1].”