2 Flonums🔗ℹ

For convenience, math/flonum re-exports racket/flonum as well as providing the functions document below.

2.1 Additional Flonum Functions🔗ℹ

Worst-case time complexity is O(n2), though the pathological inputs needed to observe quadratic time are exponentially improbable and are hard to generate purposely. Expected time complexity is O(n log(n)).

See flvector-sums for a variant that computes all the partial sums in xs.

Maximum observed error is 2 ulps, making these functions (currently) much more accurate than their racket/math counterparts. They also return sensible values on the largest possible domain.

These functions are as robust and accurate as their corresponding inverses.

For factorial-like functions that return sensible values for non-integers, see gamma and beta.

For log-factorial-like functions that return sensible values for non-integers, see log-gamma and log-beta.

Many flonum functions defined in terms of fllog and flexp become much more accurate when their defining expressions are put in terms of fllog1p and flexpm1. The functions exported by this module and by math/special-functions use them extensively.

Maximum observed error is 2.1 ulps, but is usually less than 0.7 (i.e. near rounding error).

For example, consider (fllogb 0.0 0.0). Taking an interated limit, we get ∞ if the outer limit is with respect to x, or 0 if the outer limit is with respect to b. This would normally mean (fllogb 0.0 0.0) = +nan.0.

2.2 Log-Space Arithmetic🔗ℹ

It is often useful, especially when working with probabilities and probability densities, to represent nonnegative numbers in log space, or as the natural logs of their true values. Generally, the reason is that the smallest positive flonum is too large.

In log space, exponentiation becomes multiplication, multiplication becomes addition, and addition becomes tricky. See lg+ and lgsum for solutions.

When logy > logx, lg- returns +nan.0. Both functions correctly treat -inf.0 as log-space 0.0.

To add more than two log-space numbers with the same guarantees, use lgsum.

Though more accurate than a naive implementation, both functions are prone to catastrophic cancellation in regions where they output a value close to 0.0 (or log-space 1.0). While these outputs have high relative error, their absolute error is very low, and when exponentiated, nearly have just rounding error. Further, catastrophic cancellation is unavoidable when logx and logy themselves have error, which is by far the common case.

These are, of course, excuses—but for floating-point research generally. There are currently no reasonably fast algorithms for computing lg+ and lg- with low relative error. For now, if you need that kind of accuracy, use math/bigfloat.

2.3 Debugging Flonum Functions🔗ℹ

The following functions and constants are useful in authoring and debugging flonum functions that must be accurate on the largest possible domain.

To get a ground-truth function such as exp to test against, compute the outputs as accurately as possible using exact rationals or high-precision bigfloats.

2.3.1 Measuring Floating-Point Error🔗ℹ

For non-rational arguments such as +nan.0, flulp-error returns 0.0 if (eqv? x r); otherwise it returns +inf.0.

A flonum function with maximum error 0.5 ulps exhibits only rounding error; it is correct. A flonum function with maximum error no greater than a few ulps is accurate. Most moderately complicated flonum functions, when implemented directly, seem to have over a hundred thousand ulps maximum error.

See relative-error for a similar way to measure approximation error when the approximation is not necessarily represented by a flonum.

2.3.2 Flonum Constants🔗ℹ

Approximation error is often understood in terms of relative error in epsilons. Number of epsilons relative error roughly corresponds with error in ulps, except when the approximation is subnormal.

2.3.3 Low-Level Flonum Operations🔗ℹ

Though flonum operations on subnormal numbers are still often implemented by software exception handling, the situation is improving. Robust flonum functions should handle subnormal inputs correctly, and reduce error in outputs as close to zero ulps as possible.

2.4 Double-Double Operations🔗ℹ

For extra precision, floating-point computations may use two nonoverlapping flonums to represent a single number. Such pairs are often called double-double numbers. The exact sum of the pair is the number it represents. (Because they are nonoverlapping, the floating-point sum is equal to the largest.)

For speed, especially with arithmetic operations, there is no data type for double-double numbers. They are always unboxed: given as two arguments, and received as two values. In both cases, the number with higher magnitude is first.

Inputs are never checked to ensure they are sorted and nonoverlapping, but outputs are guaranteed to be sorted and nonoverlapping if inputs are.