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  2. Round-off error - Wikipedia

    en.wikipedia.org/wiki/Round-off_error

    Round-to-nearest: () is set to the nearest floating-point number to . When there is a tie, the floating-point number whose last stored digit is even (also, the last digit, in binary form, is equal to 0) is used.

  3. Guard digit - Wikipedia

    en.wikipedia.org/wiki/Guard_digit

    As an example, consider the subtraction . Here, the product notation indicates a binary floating point representation with the exponent of the representation given as a power of two and with the significand given with three bits after the binary point. To compute the subtraction it is necessary to change the forms of these numbers so that they ...

  4. 2Sum - Wikipedia

    en.wikipedia.org/wiki/2Sum

    Provided the floating-point arithmetic is correctly rounded to nearest (with ties resolved any way), as is the default in IEEE 754, and provided the sum does not overflow and, if it underflows, underflows gradually, it can be proven that + = +.

  5. Kahan summation algorithm - Wikipedia

    en.wikipedia.org/wiki/Kahan_summation_algorithm

    The second result would be 10005.81828 before rounding and 10005.8 after rounding. This is not correct. However, with compensated summation, we get the correctly rounded result of 10005.9. Assume that c has the initial value zero. Trailing zeros shown where they are significant for the six-digit floating-point number.

  6. Machine epsilon - Wikipedia

    en.wikipedia.org/wiki/Machine_epsilon

    The following examples compute interval machine epsilon in the sense of the spacing of the floating point numbers at 1 rather than in the sense of the unit roundoff. Note that results depend on the particular floating-point format used, such as float , double , long double , or similar as supported by the programming language, the compiler, and ...

  7. Catastrophic cancellation - Wikipedia

    en.wikipedia.org/wiki/Catastrophic_cancellation

    Although the radix conversion from decimal floating-point to binary floating-point only incurs a small relative error, catastrophic cancellation may amplify it into a much larger one: double x = 1.000000000000001 ; // rounded to 1 + 5*2^{-52} double y = 1.000000000000002 ; // rounded to 1 + 9*2^{-52} double z = y - x ; // difference is exactly ...

  8. Unit in the last place - Wikipedia

    en.wikipedia.org/wiki/Unit_in_the_last_place

    The IEEE 754 specification—followed by all modern floating-point hardware—requires that the result of an elementary arithmetic operation (addition, subtraction, multiplication, division, and square root since 1985, and FMA since 2008) be correctly rounded, which implies that in rounding to nearest, the rounded result is within 0.5 ulp of ...

  9. Rounding - Wikipedia

    en.wikipedia.org/wiki/Rounding

    In floating-point arithmetic, rounding aims to turn a given value x into a value y with a specified number of significant digits. In other words, y should be a multiple of a number m that depends on the magnitude of x. The number m is a power of the base (usually 2 or 10) of the floating-point representation.