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  2. Rounding - Wikipedia

    en.wikipedia.org/wiki/Rounding

    If x is negative, round-down is the same as round-away-from-zero, and round-up is the same as round-toward-zero. In any case, if x is an integer, y is just x . Where many calculations are done in sequence, the choice of rounding method can have a very significant effect on the result.

  3. Round-off error - Wikipedia

    en.wikipedia.org/wiki/Round-off_error

    This rounding rule is more accurate but more computationally expensive. Rounding so that the last stored digit is even when there is a tie ensures that it is not rounded up or down systematically. This is to try to avoid the possibility of an unwanted slow drift in long calculations due simply to a biased rounding.

  4. Numeric precision in Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Numeric_precision_in...

    Excel maintains 15 figures in its numbers, but they are not always accurate; mathematically, the bottom line should be the same as the top line, in 'fp-math' the step '1 + 1/9000' leads to a rounding up as the first bit of the 14 bit tail '10111000110010' of the mantissa falling off the table when adding 1 is a '1', this up-rounding is not undone when subtracting the 1 again, since there is no ...

  5. Interval arithmetic - Wikipedia

    en.wikipedia.org/wiki/Interval_arithmetic

    Interval arithmetic is suitable for a variety of purposes; the most common use is in scientific works, particularly when the calculations are handled by software, where it is used to keep track of rounding errors in calculations and of uncertainties in the knowledge of the exact values of physical and technical parameters. The latter often ...

  6. Significant figures - Wikipedia

    en.wikipedia.org/wiki/Significant_figures

    Then, round the final result, for example, to the fewest number of significant figures (for multiplication or division) or leftmost last significant digit position (for addition or subtraction) among the inputs in the final calculation. [14] (2.3494 + 1.345) × 1.2 = 3.69 4 4 × 1.2 = 4. 4 3328 ≈ 4.4.

  7. Machine epsilon - Wikipedia

    en.wikipedia.org/wiki/Machine_epsilon

    This alternative definition is significantly more widespread: machine epsilon is the difference between 1 and the next larger floating point number.This definition is used in language constants in Ada, C, C++, Fortran, MATLAB, Mathematica, Octave, Pascal, Python and Rust etc., and defined in textbooks like «Numerical Recipes» by Press et al.

  8. Approximation - Wikipedia

    en.wikipedia.org/wiki/Approximation

    Calculations are likely to involve rounding errors and other approximation errors. Log tables , slide rules and calculators produce approximate answers to all but the simplest calculations. The results of computer calculations are normally an approximation expressed in a limited number of significant digits, although they can be programmed to ...

  9. Numerical analysis - Wikipedia

    en.wikipedia.org/wiki/Numerical_analysis

    The field of numerical analysis predates the invention of modern computers by many centuries. Linear interpolation was already in use more than 2000 years ago. Many great mathematicians of the past were preoccupied by numerical analysis, [5] as is obvious from the names of important algorithms like Newton's method, Lagrange interpolation polynomial, Gaussian elimination, or Euler's method.