| Introduction | Uniform Distribution | Classical Examples | Construction | Irreducible Polynomials | Numerical Integraion | Pricing Theory | References |
Let
an arbitrary integer and
the residue system modulo
. The radical inverse function in base
is defined as
,
,
where
is the
-adic expansion of the integer
. For every integer
, the Van der Corput sequence in base
is the one-dimensional sequence
defined as
The generalized Van der Corput sequence is obtained by permuting the
digits
in the
-adic expansion.
![[Graphics:../Images/QRDemonstration_gr_39.gif]](../Images/QRDemonstration_gr_39.gif)
For maximal performance, the Van der Corput sequence is implemented using dynamic programming.
![[Graphics:../Images/QRDemonstration_gr_41.gif]](../Images/QRDemonstration_gr_41.gif)
![[Graphics:../Images/QRDemonstration_gr_42.gif]](../Images/QRDemonstration_gr_42.gif)
The first 10 sequence elements of the Van der Corput sequence in base 7, as rational numbers.
![[Graphics:../Images/QRDemonstration_gr_44.gif]](../Images/QRDemonstration_gr_44.gif)
To evaluate the sequence numerically, just enter the basis as a real number.
![[Graphics:../Images/QRDemonstration_gr_46.gif]](../Images/QRDemonstration_gr_46.gif)
The uniformity (i.e. discrepancy) of the Van der Corput sequence can be reduced further by permuting the digits in the digits expansion.
![[Graphics:../Images/QRDemonstration_gr_48.gif]](../Images/QRDemonstration_gr_48.gif)
![[Graphics:../Images/QRDemonstration_gr_50.gif]](../Images/QRDemonstration_gr_50.gif)
The Halton sequence
and the permuted Halton sequence are the multi-dimensional analogons.
The bases must be relatively prime.
![[Graphics:../Images/QRDemonstration_gr_53.gif]](../Images/QRDemonstration_gr_53.gif)
![[Graphics:../Images/QRDemonstration_gr_55.gif]](../Images/QRDemonstration_gr_55.gif)
![[Graphics:../Images/QRDemonstration_gr_56.gif]](../Images/QRDemonstration_gr_56.gif)
![[Graphics:../Images/QRDemonstration_gr_57.gif]](../Images/QRDemonstration_gr_57.gif)
![[Graphics:../Images/QRDemonstration_gr_59.gif]](../Images/QRDemonstration_gr_59.gif)
![[Graphics:../Images/QRDemonstration_gr_61.gif]](../Images/QRDemonstration_gr_61.gif)
![[Graphics:../Images/QRDemonstration_gr_62.gif]](../Images/QRDemonstration_gr_62.gif)
The
-element Hammersley point set in the bases
is defined for all
as
.
![[Graphics:../Images/QRDemonstration_gr_67.gif]](../Images/QRDemonstration_gr_67.gif)
![[Graphics:../Images/QRDemonstration_gr_69.gif]](../Images/QRDemonstration_gr_69.gif)
![[Graphics:../Images/QRDemonstration_gr_70.gif]](../Images/QRDemonstration_gr_70.gif)
A prominent record holder is given by the
-sequence, which is a special type of a Weyl sequence. The
-th element of the Weyl sequence generated by
is given by
, where
denotes the fractional part. Again, Mathematica offers symbolic
as well as numeric calculation.
![[Graphics:../Images/QRDemonstration_gr_76.gif]](../Images/QRDemonstration_gr_76.gif)
![[Graphics:../Images/QRDemonstration_gr_78.gif]](../Images/QRDemonstration_gr_78.gif)
Low discrepancy sequences are designed to be as uniform as possible. We compare the Halton sequence to ordinary pseudo-random sequences.
![[Graphics:../Images/QRDemonstration_gr_80.gif]](../Images/QRDemonstration_gr_80.gif)
![[Graphics:../Images/QRDemonstration_gr_81.gif]](../Images/QRDemonstration_gr_81.gif)
On the other hand ordinary pseudo-random numbers are visually less uniformly distributed and show clustering.
![[Graphics:../Images/QRDemonstration_gr_82.gif]](../Images/QRDemonstration_gr_82.gif)
![[Graphics:../Images/QRDemonstration_gr_83.gif]](../Images/QRDemonstration_gr_83.gif)
The statistical distribution properties of the Halton sequence and pseudo-random sequences compared:
![[Graphics:../Images/QRDemonstration_gr_84.gif]](../Images/QRDemonstration_gr_84.gif)
![[Graphics:../Images/QRDemonstration_gr_86.gif]](../Images/QRDemonstration_gr_86.gif)
| Mean | Variance | Skewness | Kurtosis |
| 0.499877959489822387` | 0.0833536485867313992` | 6.18749758509849812`*^-7 | 1.79999900052561905` |
| 0.499537691948635842` | 0.0833101617030894736` | 0.000534926833284326974` | 1.79996214563109262` |
![[Graphics:../Images/QRDemonstration_gr_87.gif]](../Images/QRDemonstration_gr_87.gif)
![[Graphics:../Images/QRDemonstration_gr_89.gif]](../Images/QRDemonstration_gr_89.gif)
| Mean | Variance | Skewness | Kurtosis |
| 0.495443704909244697` | 0.0810716751953905934` | 0.00904459277300382957` | 1.82653224064654544` |
| 0.496645918442837341` | 0.0833644462749997572` | -0.00568457459742052861` | 1.79784633429413975` |
The exact values of the uniform distribution:
![[Graphics:../Images/QRDemonstration_gr_90.gif]](../Images/QRDemonstration_gr_90.gif)
| Mean | Variance | Skewness | Kurtosis |
| 0 | |||
| 0.5` | 0.0833333333333333214` | 0 | 1.8` |
Other classical low-discrepancy sequences are those of Faure and Sobol. We refer to the
literature. Also, there is the construction of low-discrepancy sequences based on so
called
-nets and
-sequences and the digital method for their construction. This approach
was manly developed by H. Niederreiter. We will investigate this method in the next
chapter.
Converted by Mathematica November 18, 1998
| Introduction | Uniform Distribution | Classical Examples | Construction | Irreducible Polynomials | Numerical Integraion | Pricing Theory | References |