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# Numerical power series reversion

*July 11, 2013*

In some cases, fast algorithms are less numerically stable than slow algorithms (see the earlier posts about polynomial multiplication and fast multipoint evaluation). In other cases, the fast algorithms automatically turn out to be more numerically stable than the slow ones. It appears that reversion of power series (at least sometimes) falls into the latter category. I have at last implemented reversion for power series over the reals in Arb; as previously for the various exact FLINT types, the algorithms are:

- Lagrange inversion (the naive algorithm)
- Newton iteration + fast composition using the Brent-Kung matrix algorithm
- My fast Lagrange inversion algorithm

Here is a comparison for reversion of $-\log(1-x) = \sum_{k \ge 1} x^k / k$ to order $O(x^n)$ and with a working precision of $10n$ bits:

n algorithm time last coefficient (midpoint +/- radius) 10 Lagrange 130 us 2.75573192239859e-06 +/- 8.6736e-19 10 Newton+BK 160 us 2.75573192239859e-06 +/- 1.7347e-17 10 Fast Lagrange 85 us 2.75573192239859e-06 +/- 3.4121e-24 100 Lagrange 100 ms 1.07151028812547e-156 +/- 3.9827e-59 100 Newton+BK 40 ms 1.07151028812547e-156 +/- 5.9409e-211 100 Fast Lagrange 27 ms 1.07151028812547e-156 +/- 3.0194e-245 1000 Lagrange 203 s 2.48516814326678e-2565 +/- 1.1505e+310 1000 Newton+BK 39 s 2.48516814326678e-2565 +/- 2.1049e-2504 1000 Fast Lagrange 22 s 2.48516814326678e-2565 +/- 1.1864e-2670

Interestingly, the true numerical error (the difference between the computed midpoint and the mathematically exact result) is of roughly the same order of magnitude with all algorithms, and the instability occurs in the error bound calculation. This raises the question of whether the algorithms (even just the naive one) can be modified in such a way that the computed error bound more closely matches the true error.

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