Numerical recipes

7.7 Quasi- (that is, Sub-) Random Sequences 315
Note that we have not provided the routine sobseq with a means of starting the
sequence at a point other than the beginning, but this feature would be easy to add. Once
the initialization of the direction numbers iv has been done, the jth point can be obtained
directly by XORing together those direction numbers corresponding to nonzero bits in the
Gray code of j, as described above.
The Latin Hypercube
We might here give passing mention the unrelated technique of Latin square or
Latin hypercube sampling, which is useful when you must sample an N-dimensional
space exceedingly sparsely, at M points. For example, you may want to test the
crashworthiness of cars as a simultaneous function of 4 different design parameters,
but with a budget of only three expendable cars. (The issue is not whether this is a
good plan — it isn’t — but rather how to make the best of the situation!)
The idea is to partition each design parameter (dimension) into M segments, so
that the whole space is partitioned into M N cells. (You can choose the segments in
each dimension to be equal or unequal, according to taste.) With 4 parameters and 3
cars, for example, you end up with 3 × 3 × 3 × 3=81cells.
Next, choose M cells to contain the sample points by the following algorithm:
Randomly choose one of the M N cells for the first point. Now eliminate all cells
that agree with this point on any of its parameters (that is, cross out all cells in the
same row, column, etc.), leaving (M − 1) N candidates. Randomly choose one of
these, eliminate new rows and columns, and continue the process until there is only
one cell left, which then contains the final sample point.
The result of this construction is that each design parameter will have been
tested in every one of its subranges. If the response of the system under test is
dominated by one of the design parameters, that parameter will be found with this
sampling technique. On the other hand, if there is an important interaction among
different design parameters, then the Latin hypercube gives no particular advantage.
Use with care.
CITED REFERENCES AND FURTHER READING:
Halton, J.H. 1960, Numerische Mathematik, vol. 2, pp. 84–90. [1]
Bratley P., and Fox, B.L. 1988, ACM Transactions on Mathematical Software, vol. 14, pp. 88–
100. [2]
Lambert, J.P. 1988, in Numerical Mathematics – Singapore 1988, ISNM vol. 86, R.P. Agarwal,
Y.M. Chow, and S.J. Wilson, eds. (Basel: Birkhaüser), pp. 273–284.
Niederreiter, H. 1988, in Numerical Integration III, ISNM vol. 85, H. Brass and G. Hämmerlin,
eds. (Basel: Birkhaüser), pp. 157–171.
Sobol’, I.M. 1967, USSR Computational Mathematics and Mathematical Physics, vol. 7, no. 4,
pp. 86–112. [3]
Antonov, I.A., and Saleev, V.M 1979, USSR Computational Mathematics and Mathematical
Physics, vol. 19, no. 1, pp. 252–256. [4]
Dunn, O.J., and Clark, V.A. 1974, Applied Statistics: Analysis of Variance and Regression (New
York, Wiley) [discusses Latin Square].
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