That's not a useful way to think. Floating point numbers are just floating point numbers. You aren't approximating floating point numbers. You are approximating real numbers.

Floating point numbers are compared against fixed point numbers where the point (the part between the integer part and fractional part) is fixed. That is why they are called floating. The nature of the real numbers is such that they can in general only be approximated, regardless of whether you use fixed point numbers or floating point numbers or a fancier computable number representation.

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ogogmad 2 days ago | parent | next [-]

The real numbers can be "represented" and "computed with" without needing approximations. The result is that there are no rounding errors. But it's not popular, and not necessarily worth the tradeoffs. On the other hand, see the Android calculator.

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kccqzy 2 days ago | parent [-]

The Android calculator doesn't support all real numbers. It supports a specific subset of computable numbers that are computable using the operations presented in the calculator app. It's a good domain-specific number representation. It's far from real numbers.

We can't even represent all the natural numbers: that would require infinite memory. Now you want to represent the real numbers, equinumerous with the power set of natural numbers?

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ogogmad 2 days ago | parent [-]

> We can't even represent all the natural numbers

We can represent all real numbers to the same extent that we can represent all natural numbers. They're just infinite strings, with each additional letter in the string mattering less and less as it goes along. The "Type Two Effectivity" model for modelling computations with infinite strings doesn't limit you to using just the computable real numbers. An uncomputable real number can be produced by outputting an infinite string letter by letter with each letter getting picked at random. The TTE model does OTOH limit you to computing only with the computable functions on those real numbers.

The TTE model basically uses (Python-like) generators to output an endless sequence of interval approximations to a real number.

	▲	kccqzy 2 days ago \| parent \| next [-]
		Let's just assume we have infinite memory and can represent real numbers using infinitely long strings. How do you compare two such numbers for equality? You can check that the first 1000 digits are the same but what about the 1001st digit? Suddenly you need infinite time to determine equality. That's not an effective representation.
	▲	Y_Y 2 days ago \| parent \| prev \| next [-]
		Georg Cantor would like a word. Representable numbers are countable, they have measure zero in the reals.
	▲	zahlman 2 days ago \| parent \| prev [-]
		> An uncomputable real number can be produced by outputting an infinite string letter by letter with each letter getting picked at random. This certainly outputs an uncomputable real number, given that you have true random input and not a PRNG. But random numbers from a seed and an algorithm are by definition computable. And if you can't compute the number, you have no basis for the claim that it's the right number. Also, good luck computing with arbitrarily long, computed-on-demand digit strings. Consider: how do you know how many digits you need from the input in order to be sure of N correct digits in the output? This might be possible in general, but it certainly doesn't sound like my idea of fun.

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AtlasBarfed 2 days ago | parent | prev [-]

So what you're saying is I can use floating point math in most computer languages for financial calculations?

Because anyone who's had any experience knows you absolutely can't.

I am explicitly excluding big decimal type functionality here. What we are talking about explicitly are floats and doubles.

	▲	kccqzy a day ago \| parent [-]
		I'm not saying that at all. Where did I say financial calculations in the above comment? I'm just explaining the concept of floating point; I didn't say anything about its applications.