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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. Fixed-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Fixed-point_arithmetic

    A fixed-point representation of a fractional number is essentially an integer that is to be implicitly multiplied by a fixed scaling factor. For example, the value 1.23 can be stored in a variable as the integer value 1230 with implicit scaling factor of 1/1000 (meaning that the last 3 decimal digits are implicitly assumed to be a decimal fraction), and the value 1 230 000 can be represented ...

  4. Decimal data type - Wikipedia

    en.wikipedia.org/wiki/Decimal_data_type

    C# has a built-in data type decimal consisting of 128 bits resulting in 28–29 significant digits. It has an approximate range of ±1.0 × 10 −28 to ±7.9228 × 10 28. [1] Starting with Python 2.4, Python's standard library includes a Decimal class in the module decimal. [2] Ruby's standard library includes a BigDecimal class in the module ...

  5. Arithmetic shift - Wikipedia

    en.wikipedia.org/wiki/Arithmetic_shift

    The formal definition of an arithmetic shift, from Federal Standard 1037C is that it is: . A shift, applied to the representation of a number in a fixed radix numeration system and in a fixed-point representation system, and in which only the characters representing the fixed-point part of the number are moved.

  6. Floating-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Floating-point_arithmetic

    For example, the decimal numbers 0.1 and 0.01 cannot be represented exactly as binary floating-point numbers. In the IEEE 754 binary32 format with its 24-bit significand, the result of attempting to square the approximation to 0.1 is neither 0.01 nor the representable number closest to it.

  7. Double-precision floating-point format - Wikipedia

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

    Given the hexadecimal representation 3FD5 5555 5555 5555 16, Sign = 0 Exponent = 3FD 16 = 1021 Exponent Bias = 1023 (constant value; see above) Fraction = 5 5555 5555 5555 16 Value = 2 (Exponent − Exponent Bias) × 1.Fraction – Note that Fraction must not be converted to decimal here = 22 × (15 5555 5555 5555 16 × 2 −52) = 2 −54 ...

  8. Bit numbering - Wikipedia

    en.wikipedia.org/wiki/Bit_numbering

    When the bit numbering starts at zero for the least significant bit (LSb) the numbering scheme is called LSb 0. [1] This bit numbering method has the advantage that for any unsigned number the value of the number can be calculated by using exponentiation with the bit number and a base of 2. [ 2 ]

  9. Bitwise operation - Wikipedia

    en.wikipedia.org/wiki/Bitwise_operation

    2.5.3 JavaScript. 2.5.4 ... OR with the pattern with only the fourth bit set: 0010 (decimal 2) ... to evaluate each of two or more units place to 1 or 0. [12 ...