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- Why floating-point values do not represent exact value
The floating-point numbers serve as rough approximations of mathematical real numbers They do not represent the exact value For this reason, we compare the arithmetic results of float variables with a minimum tolerance value Example:
- Why are floating point numbers inaccurate? - Stack Overflow
These are slightly more difficult to work with than binary floating point, but the biggest problem is that most computers don't offer hardware support for them
- Why are floating point numbers used often in Science Engineering?
What sets floating point apart from fixed point is that you don't have to commit to a certain number of decimal places - you can have really small quantities with a lot of decimal places or really large quantities with limited precision
- How Floating Point Numbers Work and When You Should or Shouldnt Use . . .
When building a financial application, don't use floating point numbers unless you can guarantee that you're rounding to the appropriate precision every step of the way
- Floating-point arithmetic - Wikipedia
In computing, floating-point arithmetic (FP) is arithmetic on subsets of real numbers formed by a significand (a signed sequence of a fixed number of digits in some base) multiplied by an integer power of that base Numbers of this form are called floating-point numbers [1]: 3 [2]: 10
- What every physicist should know about floating-point arithmetic, PHYS . . .
Because floating-point numbers have a limited number of digits, they cannot represent all real numbers accurately: when there are more digits than the format allows, the leftover ones are omitted - the number is rounded
- Why Are Floating Point Numbers Inaccurate? - Baeldung
In this tutorial, we’ll go over the basic ideas of floating-point representation and learn the limits of floating-point accuracy, when doing practical numerical computing
- Floating-point Numbers Arent Real - 97-things-every-x-should-know . . .
Real numbers have infinite precision and are therefore continuous and non-lossy; floating-point numbers have limited precision, so they are finite, and they resemble "badly-behaved" integers, because they're not evenly spaced throughout their range
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