Is there any constant more misused in compsci than ieee epsilon? :)

It's defined as the difference between 1.0 and the smallest number larger than 1.0. More usefully, it's the spacing between adjacent representable float numbers in the range 1.0 to 2.0.

Because floats get less precise at every integer power of two, it's impossible for two numbers greater than or equal to 2.0 to be epsilon apart. The spacing between 2.0 and the next larger number is 2*epsilon.

That means `abs(a - b) <= epsilon` is equivalent to `a == b` for any a or b greater than or equal to 2.0. And if you use `<` then the limit will be 1.0 instead.

Epsilon is the wrong tool for the job in 99.9% of cases.

Everyone has already made several comments on the incorrect use of EPSILON here, but there's one more thing I want to add that hasn't yet been mentioned:

EPSILON = (1 ulp for numbers in the range [1, 2)). is a lousy choice for tolerance. Every operation whose result is in the range [1, 2) has a mathematical absolute error of ½ ulp. Doing just a few operations in a row has a chance to make the error term larger than your tolerance, simply because of the inherent inaccuracy of floating-point operations. Randomly generate a few doubles in the range [1, 10], then randomize the list and compute the sum of different random orders in the list, and your assertion should fail. I'd guess you haven't run into this issue because either very few people are using this particular assertion, or the people who do happen to be testing it in cases where the result is fully deterministic.

If you look at professional solvers for numerical algorithms, one of the things you'll notice is that not only is the (relative!) tolerance tunable, but there's actually several different tolerance values. The HiGHS linear solver for example uses 5 different tolerance values for its simplex algorithm. Furthermore, the default values for these tolerances tend to be in the region of 10^-6 - 10^-10... about the square root of f64::EPSILON. There's a basic rule of thumb in numerical analysis that you need your internal working precision to be roughly twice the number of digits as your output precision.

Your assertion code here doesn't make a ton of sense. The epsilon of choice here is the distance between 1 and the next number up, and it's completely separated from the scale of the numbers in question. 1e-50 will compare equal to 2e-50, for example.

I would suggest that "equals" actually is for "exactly equals" as in (a == b). In many pieces of floating point code this is the correct thing to test. Then also add a function for "within range of" so your users can specify an epsilon of interest, using the formula (abs(a - b) < eps). You may also want to support multidimensional quantities by allowing the user to specify a distance metric. You probably also want a relative version of the comparison in addition to an absolute version.

Auto-computing epsilons for an equality check is really hard and depends on the usage, as well as the numerics of the code that is upstream and downstream of the comparison. I don't see how you would do it in an assertion library.

Ignoring the misuse of epsilon, I'd also say that you'd be helping your users more by not providing a general `assert_f64_eq` macro, but rather force the user to decide the error model. Add a required "precision" parameter as an enum with different modes:

    // Precise matching:
    assert_f64_eq!(a, 0.1, Steps(2))
    // same as: assert!(a == 0.1.next_down().next_down())

    // Number of digits (after period) that are matching:
    assert_f64_eq!(a, 0.1, Digits(5))

    // Relative error:
    assert_f64_eq!(a, 0.1, Rel(0.5))

You generally want both relative and absolute tolerances. Relative handles scale, absolute handles values near zero (raw EPSILON isn’t a universal threshold per IEEE 754).

The usual pattern is abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) to avoid both large-value and near-zero pitfalls.

It depends on the use case, but do you consider NaN to be equal to NaN? For an assert macro, I would expect so. Also, your code works differently for very large and very small numbers, eg. 1.0000001, 1.0000002 vs 1e-100, 1.0000002e-100.

For my own soft-floating point math library, I expect the value is off by a some percentage, not just off by epsilon. And so I have my own almostSame method [1] which accounts for that and is quite a bit more complex. Actually multiple such methods. But well, that's just my own use case.

[1] https://github.com/thomasmueller/bau-lang/blob/main/src/test...

Machine eps provides the maximum rounding error for a single op. Let's say I write:

  let y = 2.0;
  let x = sqrt(y);

Note that in general we can only guarantee hitting this relative error for single ops. More elaborate computations may develop worse error as things compound. But it gets even worse. This error says nothing about errors that don't occur in the machine. For example, say I have a test that takes some experimental data, runs my whiz-bang algorithm, and checks if the result is close to elementary charge of an electron. Now I can't just worry about machine error but also a zillion different kinds of experimental error.

There are also cases where we want to enforce a contract on a number so we stay within acceptable domains. Author alluded to this. For example - if I compute some `x` s.t. I'm later going to take `acos(x)`, `x` had better be between `[-1, 1]`. `x >= -1 - EPS && x <= 1 + EPS` wouldn't be right because it would include two numbers, -1 - EPS and 1 + EPS, that are outside the acceptable domain.

- "I want to relax exact equality because my computation has errors" -> Make `assert_rel_tol` and `assert_abs_tol`.

- "I want to enforce determinism" -> exact equality.

- "I want to enforce a domain" -> exact comparison

Your code here is using eps for controlling absolute error, which is already not great since eps is about relative error. Unfortunately your assertion degenerates to `a == b` for large numbers but is extremely loose for small numbers.

Apart from what others have commented, IMO an “assertables” crate should not invent new predicates of its own, especially for domains (like math) that are orthogonal to assertability.

You should use two tolerances: absolute and relative. See for example numpy.allclose()

https://numpy.org/doc/stable/reference/generated/numpy.allcl...

Hyb error [1] might be what you want.

[1] https://arxiv.org/html/2403.07492v2

You should allow the user to supply the epsilon value, because the precision needed for the assertion will depend on the use case.

EQ should be exactly equal, I think. Although we often (incorrectly) model floats as a real plus some non-deterministic error, there are cases where you can expect an exact bit pattern, and that’s what EQ is for (the obvious example is, you could be writing a library and accept a scaling factor from the user—scaling factors of 1 or 0 allow you to optimize).

You probably also want an isclose and probably want to push most users toward using that.

Well, could you please describe a scenario where you think this assertion would be useful?

I suggest

if a.abs()+b.abs() >= (a-b).abs() * 2f64.powi(48)

It remains accurate for small and for big numbers. 48 is slightly less than 52.

You probably don't need the (in)accuracy.

Fix your precision so it matches.

You only need so many significant digits.

You want equality?

‘a.to_bits() == b.to_bits()’

Alternatively, use ‘partial_eq’ and fall back to bit equality if it returns None.

Author says in the article that for tests, and assertion is a test, it's ok to use epsilon.

I think a key you may want is ε which scales with the actual local floating point increment.

C++ implements this https://en.cppreference.com/cpp/numeric/math/nextafter

Rust does not https://rust-lang.github.io/rfcs/3173-float-next-up-down.htm... but people have in various places.

The use of epsilon is correct here. It's exactly what I was taught in comp sci over 20 years ago. You can call it's use here an "epsilon-delta".