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  2. Arbitrary-precision arithmetic - Wikipedia

    en.wikipedia.org/wiki/Arbitrary-precision_arithmetic

    But even with the greatest common divisor divided out, arithmetic with rational numbers can become unwieldy very quickly: 1/99 − 1/100 = 1/9900, and if 1/101 is then added, the result is 10001/999900. The size of arbitrary-precision numbers is limited in practice by the total storage available, and computation time.

  3. Decimal floating point - Wikipedia

    en.wikipedia.org/wiki/Decimal_floating_point

    A simple method to add floating-point numbers is to first represent them with the same exponent. In the example below, the second number is shifted right by 3 digits. We proceed with the usual addition method: The following example is decimal, which simply means the base is 10. 123456.7 = 1.234567 × 10 5 101.7654 = 1.017654 × 10 2 = 0. ...

  4. Minifloat - Wikipedia

    en.wikipedia.org/wiki/Minifloat

    The 10-bit format has a 5-bit mantissa, and the 11-bit format has a 6-bit mantissa. [8] [9] IEEE SA Working Group P3109 is currently working on a standard for 8-bit minifloats optimized for machine learning. The current draft defines not one format, but a family of 7 different formats, named "binary8pP", where "P" is a number from 1 to 7.

  5. Computer number format - Wikipedia

    en.wikipedia.org/wiki/Computer_number_format

    A computer number format is the internal representation of numeric values in digital device hardware and software, such as in programmable computers and calculators. [1] Numerical values are stored as groupings of bits , such as bytes and words.

  6. Extended precision - Wikipedia

    en.wikipedia.org/wiki/Extended_precision

    A 64-bit significand provides sufficient precision to avoid loss of precision when the results are converted back to double-precision format in the vast number of cases. This format provides a mechanism for indicating precision loss due to underflow which can be carried through further operations. For example, the calculation 2 × 10 −4930 × ...

  7. Python syntax and semantics - Wikipedia

    en.wikipedia.org/wiki/Python_syntax_and_semantics

    Python supports normal floating point numbers, which are created when a dot is used in a literal (e.g. 1.1), when an integer and a floating point number are used in an expression, or as a result of some mathematical operations ("true division" via the / operator, or exponentiation with a negative exponent).

  8. Quadruple-precision floating-point format - Wikipedia

    en.wikipedia.org/wiki/Quadruple-precision...

    This gives from 33 to 36 significant decimal digits precision. If a decimal string with at most 33 significant digits is converted to the IEEE 754 quadruple-precision format, giving a normal number, and then converted back to a decimal string with the same number of digits, the final result should match the original string.

  9. Numeral system - Wikipedia

    en.wikipedia.org/wiki/Numeral_system

    For example, if the threshold value for the first digit is b (i.e. 1) then a (i.e. 0) marks the end of the number (it has just one digit), so in numbers of more than one digit, first-digit range is only b–9 (i.e. 1–35), therefore the weight b 1 is 35 instead of 36.