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  2. Round-off error - Wikipedia

    en.wikipedia.org/wiki/Round-off_error

    In computing, a roundoff error, [1] also called rounding error, [2] is the difference between the result produced by a given algorithm using exact arithmetic and the result produced by the same algorithm using finite-precision, rounded arithmetic. [3]

  3. Floating-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Floating-point_arithmetic

    So a fixed-point scheme might use a string of 8 decimal digits with the decimal point in the middle, whereby "00012345" would represent 0001.2345. In scientific notation, the given number is scaled by a power of 10, so that it lies within a specific range—typically between 1 and 10, with the radix point appearing immediately after the first ...

  4. Quinary - Wikipedia

    en.wikipedia.org/wiki/Quinary

    Quinary (base 5 or pental [1] [2] [3]) is a numeral system with five as the base. A possible origination of a quinary system is that there are five digits on either hand . In the quinary place system, five numerals, from 0 to 4 , are used to represent any real number .

  5. Decimal - Wikipedia

    en.wikipedia.org/wiki/Decimal

    A repeating decimal is an infinite decimal that, after some place, repeats indefinitely the same sequence of digits (e.g., 5.123144144144144... = 5.123 144). [4] An infinite decimal represents a rational number , the quotient of two integers, if and only if it is a repeating decimal or has a finite number of non-zero digits.

  6. Repeating decimal - Wikipedia

    en.wikipedia.org/wiki/Repeating_decimal

    A repeating decimal or recurring decimal is a decimal representation of a number whose digits are eventually periodic (that is, after some place, the same sequence of digits is repeated forever); if this sequence consists only of zeros (that is if there is only a finite number of nonzero digits), the decimal is said to be terminating, and is not considered as repeating.

  7. IEEE 754 - Wikipedia

    en.wikipedia.org/wiki/IEEE_754

    Decimal digits is the precision of the format expressed in terms of an equivalent number of decimal digits. It is computed as digits × log 10 base . E.g. binary128 has approximately the same precision as a 34 digit decimal number.