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  2. Floating-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Floating-point_arithmetic

    The nearest floating-point number with only five digits is 12.346. And 1/3 = 0.3333… is not a floating-point number in base ten with any finite number of digits. In practice, most floating-point systems use base two, though base ten (decimal floating point) is also common.

  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. Double-precision floating-point format - Wikipedia

    en.wikipedia.org/wiki/Double-precision_floating...

    Double-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide range of numeric values by using a floating radix point. Double precision may be chosen when the range or precision of single precision would be insufficient.

  5. Single-precision floating-point format - Wikipedia

    en.wikipedia.org/wiki/Single-precision_floating...

    A floating-point variable can represent a wider range of numbers than a fixed-point variable of the same bit width at the cost of precision. A signed 32-bit integer variable has a maximum value of 2 31 − 1 = 2,147,483,647, whereas an IEEE 754 32-bit base-2 floating-point variable has a maximum value of (2 − 2 −23) × 2 127 ≈ 3.4028235 ...

  6. Arbitrary-precision arithmetic - Wikipedia

    en.wikipedia.org/wiki/Arbitrary-precision_arithmetic

    Even floating-point numbers are soon outranged, so it may help to recast the calculations in terms of the logarithm of the number. But if exact values for large factorials are desired, then special software is required, as in the pseudocode that follows, which implements the classic algorithm to calculate 1, 1×2, 1×2×3, 1×2×3×4, etc. the ...

  7. IEEE 754 - Wikipedia

    en.wikipedia.org/wiki/IEEE_754

    The predicate agrees with the comparison predicates (see section § Comparison predicates) when one floating-point number is less than the other. The main differences are: [34] NaN is sortable. NaN is treated as if it had a larger absolute value than Infinity (or any other floating-point numbers). (−NaN < −Infinity; +Infinity < +NaN.)

  8. IEEE 854-1987 - Wikipedia

    en.wikipedia.org/wiki/IEEE_854-1987

    IEEE 754-2008 also had many other updates to the IEEE floating-point standardisation. IEEE 854 arithmetic was first commercially implemented in the HP-71B handheld computer, which used decimal floating point with 12 digits of significand, and an exponent range of ±499, with a 15 digit significand used for intermediate results.

  9. IEEE 754-1985 - Wikipedia

    en.wikipedia.org/wiki/IEEE_754-1985

    Floating-point numbers in IEEE 754 format consist of three fields: a sign bit, a biased exponent, and a fraction. The following example illustrates the meaning of each. The decimal number 0.15625 10 represented in binary is 0.00101 2 (that is, 1/8 + 1/32). (Subscripts indicate the number base.)