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Table 3 Results of using various ellipsoid surface samplers to generate \(10^8\) points on a triaxial ellipsoid, using two different random number generators. Each column shows (from left to right): algorithm name, run-time (in seconds), speed relative to naive rejection, acceptance rate, relative standard deviation (as a percentage of the mean), the \(\chi ^2\) statistic and whether \(\chi ^2\) is smaller than the critical value, \(\chi _\textrm{crit}^2 = 65\,033.6\)

From: Patch area and uniform sampling on the surface of any ellipsoid

Triaxial Ellipsoid: \((a,b,c) = (3,2,1)\)

RNG

Lagged Fibonacci (lagfib4xor)

Algorithm

\(t_\textrm{run}\) (s)

speed

r

RSD (%)

\(\chi ^2\)

\(\chi ^2 < \chi _\textrm{crit}^2\)

Naive Scale

1.488

1.0

1.0

33.063

8716777.8

No

Grad Rej

4.669

0.319

0.64832

3.222

63735.1

Yes

Grad (Trig)

6.237

0.239

0.64838

3.263

64731.0

Yes

Grad (Pol)

9.692

0.154

0.64830

3.27

64894.7

Yes

Area Rej

5.956

0.25

0.71481

3.252

64109.0

Yes

Area (Pol)

5.281

0.282

0.71489

3.234

64490.2

Yes

Area (Merc)

15.64

0.095

0.20620

3.24

65870.9

No

Ray Method

14.205

0.105

0.43220

3.269

64564.7

Yes

RNG

YARN5s (yarn5s)

Algorithm

\(t_\textrm{run}\) (s)

speed

r

RSD (%)

\(\chi ^2\)

\(\chi ^2 < \chi _\textrm{crit}^2\)

Naive Scale

3.137

1.0

1.0

33.087

8724015.4

No

Grad Rej

8.592

0.365

0.64833

3.29

64737.6

Yes

Grad (Trig)

10.03

0.313

0.64832

3.234

64219.2

Yes

Grad (Pol)

13.074

0.24

0.64830

3.276

64459.8

Yes

Area Rej

8.521

0.368

0.71483

3.243

64377.8

Yes

Area (Pol)

8.217

0.382

0.71489

3.261

64086.4

Yes

Area (Merc)

28.426

0.11

0.20619

3.227

65387.4

No

Ray Method

21.785

0.144

0.43221

3.276

64486.1

Yes