Pre-normalizing Legendre Polynomials Addendum

Numerical Accuracy of the Spherical Harmonic \(\mu_\ell\) Recurrence Coefficient

The main Legendre.jl series contains a detailed article considering the numerical accuracy of the recurrence implementation as a whole. This short addendum presents a mathematical transformation which is used to improve the accuracy of one of the recurrence relation coefficients for the spherical harmonic normalization.


Introduction

In part III of the Legendre.jl series, I introduced how to compute pre-normalized associated Legendre polynomials by inserting the normalization condition into the recurrence relations that are iterated to calculate the polynomial values. Specifically, the standard associated Legendre polynomial recurrence relations define an unnormalized “unity” case (so called because $P_0^0(x) = 1$ for all $x$), and then we derived and added a “spherical harmonic” normalization which bakes in the appropriate factor required to combine the Legendre polynomials with a complex exponential in the definition of the spherical harmonics.

Then in part IV of the series, I concentrated on introducing a couple of algorithmic transformations that are independent of any specific normalization scheme to improve the numerical accuracy of the results in general. In contrast to that generic case, though, there are also numerical concerns specific to the normalization conditions themselves, and I specifically alluded to that fact in that article when I made the comment,

… (Of course, the normalization-specific constants should also be calculated in a numerically accurate way, but since that’s specific to each case, we’ll just assume that has been done expertly here.) …

At the time of writing that article, I already knew about an improvement that could be made to the spherical harmonic normalization’s recurrence coefficients, but I thought the digression to explain the modification was out of scope for both of those articles. This note is to simply introduce the transformation I have identified.

The Numerical Problem

The coefficient of concern is the $\mu_\ell$ term which is used to boost along the “diagonal”, taking $\lambda_\ell^\ell(x) \rightarrow \lambda_{\ell+1}^{\ell+1}(x)$. For the spherical harmonic normalization, the straight-forward derivation gives the simple expression \begin{align} \mu_\ell &= \sqrt{1 + \frac{1}{2\ell}} \end{align} which was quoted in Part III. This particular expression, though, is a victim of some numerical accuracy problems that are similar to those explored in Part IV.

First, the dynamic part of the expression (the $(2\ell)^{-1}$ factor) decreases in significance compared to the constant $1$ factor. This is a concern because the relative change from $\mu_{\ell-1}$ to $\mu_\ell$ is all that distinguishes each step of the recurrence from the previous, and the change $\mu_\ell - \mu_{\ell-1}$ is evocative of the catastrophic cancellation that was extensively discussed in Part IV. If the “identity” of each step of the recurrence fades as $\ell \rightarrow \infty$, we can surmise that this could decrease the numerical accuracy at high $\ell$.

The prior concern is exacerbated by another familiar numerical concern from Part IV — square roots of floating point quantities can introduce numerical error to the calculation. In this case, the problem arises because the $(2\ell)^{-1}$ factor is increasingly contained in the least significant digits of the quantity, and the square root operation progressively has fewer and fewer digits to work with (as they are truncated away in the finite representation of $1 + \epsilon$ for small $\epsilon$).

Before sharing the solution to these problems, let us first visualize the problem. The following figure shows the difference between the values of $\mu_\ell$ computed using both 32-bit and 64-bit floating point numbers versus a reference value computed using extended-precision BigFloat values.

The difference of values computed for the coefficient $\mu_\ell$ in the spherical harmonic normalization when computed at 32-bit (upper) and 64-bit (lower) floating point precision versus a reference value computed using extended-precision BigFloat numerics (and converted to the same floating point type). In both cases, the vertical scale has been scaled from absolute error to relative error in ulps. Note that in the upper panel, the error is systematically biased low for $\ell \gtrsim 1000$. With the greater digits of precision in the lower panel, there is no obvious sign of bias (with the error fluctuating upwards approximately as often as downwards), but we still systematically have incorrectly-rounded values being computed.

There are two obvious conclusions to be drawn from this figure. First, this particular expression is not able to produce correctly-rounded coefficients for either 32-bit or 64-bit floating point values, and instead the values fluctuation between $-1$, $0$, and $1$ ulps of error. Ideally the true error would be less than half an ulp of error so that during rounding, the finite floating point value would be correctly rounded to the nearest representable value.

The second obvious (and potentially more serious) problem is that for $\ell \gtrsim 1000$, the error in the 32-bit floating point values is biased low (i.e. the error is always either $0$ or $-1$ ulps). We might expect this to introduce a systematic bias in the Legendre polynomials at high $\ell$ as well due to this fact.1

Stabilizing the recurrence coefficient

With an understanding of the two underlying causes, we can employ a bit of algebra to solve the problem.

The first issue wouldn’t be a problem if the constant bias by $1$ didn’t exist. Floating point numbers actually have variable density across the real number line, and the density increases logarithmically approaching $0$. Therefore, let us consider the modified coefficient \begin{align*} \mu'_\ell &= \mu_\ell - 1 = \sqrt{1 + \frac{1}{2\ell}} - 1 \end{align*} If we ignore the problem of actually numerically computing the intermediate steps without rounding, the modified coefficient $\mu'_\ell$ has better numerical behavior because $\displaystyle\lim_{\ell\rightarrow0} \mu'_\ell = 0$ and therefore is able to take advantage of the logarithmic density of floating point values. Said differently, $\mu_\ell$ mostly changes in the least-significant bits of the significand because there’s always the constant $1$ which must be represented by the exponent $2^0$. In contrast, $\mu'_\ell$ can “reset” its scale with changes to the exponent, so the significand will on average have all of its bits available to actually store the value’s binary representation.

In reality, we do have to worry about the intermediate calculations, and in this form we have actually added a catastrophic cancellation problem between the factors of $\sqrt{1 + \frac{1}{2\ell}}$ and $1$. We can eliminate the cancellation, though, by completing the square so that the $1$ inside the square root and outside cancel out. Including some simplifications, we have: \begin{align*} \left( \sqrt{1 + \frac{1}{2\ell}} - 1 \right) \times \frac{\sqrt{1 + \frac{1}{2\ell}} + 1}{\sqrt{1 + \frac{1}{2\ell}} + 1} &= \frac{\left(1 + \frac{1}{2\ell}\right) - 1}{\sqrt{1 + \frac{1}{2\ell}} + 1} \\ {} &= \frac{\frac{1}{2\ell}}{\sqrt{1 + \frac{1}{2\ell}} + 1} \\ \mu'_\ell &= \frac{1}{\sqrt{2\ell(2\ell + 1)} + 2\ell} \end{align*} What we’ve gained by this transformation is (a) eliminating the catastrophic cancellation we just created, and (b) the square root is now taken on a quantity which grows quadratically rather than the asymptoting quantity (which translates to always having more bits of significance to work with).

To finish, we just rewrite $\mu_\ell$ in terms of $\mu'_\ell$ to arrive at the modified expression \begin{align} \mu_\ell &= 1 + \left[ 2\ell + \sqrt{2\ell(2\ell + 1)} \right]^{-1} \end{align} There is still an unavoidable numerical problem with the second term always decreasing in significance with respect to the constant factor of unity, but the growth of the error is slower due to moving the constant outside of the square root.

Substituting this new form into the exercise shown in Fig. 2.1, we again plot the relative error (in ulps) of 32-bit and 64-bit floating point calculations against the extended BigFloat calculation.

The difference of values computed for the coefficient $\mu_\ell$ in the spherical harmonic normalization when computed at 32-bit (upper) and 64-bit (lower) floating point precision versus a reference value computed using extended-precision BigFloat numerics (and converted to the same floating point type). In both cases, the vertical scale has been scaled from absolute error to relative error in ulps.
Both panels show identically zero value everywhere, which means the modified calculation is always correctly rounded to the nearest representable value in floating point.


  1. The same biasing of error terms should also appear in the 64-bit floating point case as well, but at much higher $\ell$. 32-bit floats have only 23 bits in the significand, and the representation of $1 + \frac{1}{2\ell}$ changes only in the least-signficant 12 bits or so after $\ell \approx 1000$ (i.e. $23 - \left[\log_2 1.0 - \log_2 \frac{1}{2\ell}\right] \lesssim 12$ for $\ell \gtrsim 1000$). To push the factor of $(2\ell)^{-1}$ into the bottom 12 or so bits of a 64-bit floating point number with 52 bits in the significand requires pushing $\ell$ out to values on the order of $2\ell \approx 2^{52 - 12}$, e.g. $\ell \gtrsim 5.5\times 10^{11}$↩︎