AMDG Justinas V. D. wrote:
I have uploaded Niederreiter's base 2 (NB2) low discrepancy sequence generator (as described in Bratley, Fox, Niederreiter, ACM Trans. Model. Comp. Sim. 2, 195 (1992)) to Boost Vault. Low discrepancy sequences are particularly important for quasi random Monte Carlo integration approximations, because they enable algorithms to converge approx. the order of magnitude faster.
The implementation is tested against GSL (GNU Scientific Library), and generates identical uniform [0, 1) output for all dimensions up to 12 (GSL does not support more dimensions). The implementation, however, supports up to 20 dimensions.
For validation, Boost.Random uses the 10,000th value produced by a default constructed generator.
Some notes: 1) Initialization is pretty fast. To initialize NB2 state is a lot of work, and some of it is alleviated by (I hope) judicious use of metaprogramming. I did not measure really rigorously, but the impression is that initialization is about twice as fast.
This is fine. Generators should not generally be initialized very often.
2) I am not exactly a Boost.Random guru, so I probably missed some conceptual requirements, but it works just fine with uniform_01 template (see dim2demo.cpp).
A few things that you could add a) non-default seeding. b) stream operators.
IMO Boost.Random (and a lot of other Boost libraries) is one of the best things after the sliced bread, but it sorely lacks some low discrepancy sequence generator, be it Niederreiter's, Sobel's or something else equally effective.
Would you be willing to write documentation for this as well? In Christ, Steven Watanabe