triangulate-spade: small optimization, potentially up to a factor of 1/2 #1207
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Benchmarking results after the optimization:
```
TriangulateSpade (unconstrained) - small polys
time: [7.7546 ms 7.7579 ms 7.7615 ms]
change: [-0.1240% -0.0645% -0.0048%] (p = 0.04 < 0.05)
Change within noise threshold.
Found 9 outliers among 100 measurements (9.00%)
5 (5.00%) high mild
4 (4.00%) high severe
Benchmarking TriangulateSpade (constrained) - small polys: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 51.6s, or reduce sample count to 10.
TriangulateSpade (constrained) - small polys
time: [512.64 ms 513.03 ms 513.44 ms]
change: [-10.101% -10.023% -9.9359%] (p = 0.00 < 0.05)
Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
1 (1.00%) high mild
TriangulateEarcut - small polys
time: [4.2893 ms 4.2922 ms 4.2949 ms]
change: [+0.5207% +0.6343% +0.7477%] (p = 0.00 < 0.05)
Change within noise threshold.
Found 16 outliers among 100 measurements (16.00%)
6 (6.00%) low severe
7 (7.00%) low mild
1 (1.00%) high mild
2 (2.00%) high severe
TriangulateSpade (unconstrained) - large_poly
time: [3.4148 ms 3.4174 ms 3.4204 ms]
change: [+0.7127% +0.8072% +0.9017%] (p = 0.00 < 0.05)
Change within noise threshold.
Found 3 outliers among 100 measurements (3.00%)
1 (1.00%) high mild
2 (2.00%) high severe
Benchmarking TriangulateSpade (constrained) - large_poly: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 91.7s, or reduce sample count to 10.
TriangulateSpade (constrained) - large_poly
time: [916.72 ms 917.95 ms 919.33 ms]
change: [-10.563% -10.367% -10.184%] (p = 0.00 < 0.05)
Performance has improved.
Found 3 outliers among 100 measurements (3.00%)
3 (3.00%) high severe
Benchmarking TriangulateEarcut - large_poly: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 7.3s, enable flat sampling, or reduce sample count to 50.
TriangulateEarcut - large_poly
time: [1.4410 ms 1.4438 ms 1.4474 ms]
change: [-0.7569% -0.5342% -0.3114%] (p = 0.00 < 0.05)
Change within noise threshold.
Found 12 outliers among 100 measurements (12.00%)
7 (7.00%) high mild
5 (5.00%) high severe
```
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Just found this when randomly inspecting the code.
Benchmarking results after the optimization:
I added an entry toCHANGES.mdif knowledge of this change could be valuable to users.