[crazygringo]

Well, that was trivially easy to disprove with a little jiggling of numbers in the console:

  Math.abs(1.8 - (0.1 + 0.2 + 0.9 + 0.6)) < Number.EPSILON

returns false.

Also, you generally really shouldn't be implementing any currency logic using floating point numbers, yikes. Stick to integers that represent the value in cents, or tenths of cents, or similar. Or, even better, a DECIMAL data type if your platform supports it.

I genuinely hope you've never written financial software that judges if the results of two calculations are equal via the method you've described.

[29athrowaway]

I never said you should use floating-point numbers for currency. I said that if you are not aware of the shortcomings of floating-point numbers (the only numeric type in JavaScript, not counting workarounds like typed arrays) you should not be working with values representing currencies until you do.

Fixed-point numbers (aka "decimal" type in Java, C# and others) are the preferred way to deal with this problem as I mentioned in this same discussion hours ago.

Now, in the example you presented, you have a round-off error amplication problem where the round-off error grows larger than the epsilon. You can avoid that using the Kahan summation algorithm.

https://en.wikipedia.org/wiki/Kahan_summation_algorithm

    const arr = [0.1, 0.2, 0.9, 0.6];
    let sum = 0.0;
    let c = 0.0;
    for(let i = 0, l = arr.length; i < l; i++) {
        y = arr[i] - c;
        t = sum + y;
        c = (t - sum) - y;
        sum = t;
    }

Now you evaluate sum, and it's 1.8 as expected.

[29athrowaway]

^ And of course a comment mentioning the correct way of doing things got downvoted.