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  2. Floating-point error mitigation - Wikipedia

    en.wikipedia.org/wiki/Floating-point_error...

    Variable length arithmetic represents numbers as a string of digits of a variable's length limited only by the memory available. Variable-length arithmetic operations are considerably slower than fixed-length format floating-point instructions.

  3. 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.

  4. 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.

  5. Guard digit - Wikipedia

    en.wikipedia.org/wiki/Guard_digit

    Shifting the second operand into position, as , gives it a fourth digit after the binary point. This creates the need to add an extra digit to the first operand—a guard digit—putting the subtraction into the form 2 1 × 0.1000 2 − 2 1 × 0.0111 2 {\displaystyle 2^{1}\times 0.1000_{2}-2^{1}\times 0.0111_{2}} .

  6. 7 Python mistakes all beginner developers make - AOL

    www.aol.com/7-python-mistakes-beginner...

    In Python, objects are reference types. Since Python is not a purely functional language like Haskell, functions can make side effects. 7 Python mistakes all beginner developers make — and how ...

  7. Extended precision - Wikipedia

    en.wikipedia.org/wiki/Extended_precision

    Extended precision refers to floating-point number formats that provide greater precision than the basic floating-point formats. [1] Extended-precision formats support a basic format by minimizing roundoff and overflow errors in intermediate values of expressions on the base format.

  8. Kahan summation algorithm - Wikipedia

    en.wikipedia.org/wiki/Kahan_summation_algorithm

    The algorithm does not mandate any specific choice of radix, only for the arithmetic to "normalize floating-point sums before rounding or truncating". [3] Computers typically use binary arithmetic, but to make the example easier to read, it will be given in decimal.

  9. Catastrophic cancellation - Wikipedia

    en.wikipedia.org/wiki/Catastrophic_cancellation

    Subtracting nearby numbers in floating-point arithmetic does not always cause catastrophic cancellation, or even any error—by the Sterbenz lemma, if the numbers are close enough the floating-point difference is exact. But cancellation may amplify errors in the inputs that arose from rounding in other floating-point arithmetic.