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  2. Floating-point error mitigation - Wikipedia

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

    This page was last edited on 1 December 2024, at 18:58 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.

  3. Round-off error - Wikipedia

    en.wikipedia.org/wiki/Round-off_error

    The IEEE standard stores the sign, exponent, and significand in separate fields of a floating point word, each of which has a fixed width (number of bits). The two most commonly used levels of precision for floating-point numbers are single precision and double precision.

  4. Truncation error - Wikipedia

    en.wikipedia.org/wiki/Truncation_error

    Solution: The first derivative of () = is ′ =, and at =, ′ = The ... (the consequence of using finite precision floating point numbers on computers), is also ...

  5. Kahan summation algorithm - Wikipedia

    en.wikipedia.org/wiki/Kahan_summation_algorithm

    = -0.0415900 Because c is close to zero, normalization retains many digits after the floating point. sum = 10003.1 sum = t. The sum is so large that only the high-order digits of the input numbers are being accumulated. But on the next step, c, an approximation of the running error, counteracts the problem.

  6. Floating-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Floating-point_arithmetic

    Single-precision floating-point numbers on a number line: ... it is the exact solution to a nearby problem with slightly perturbed input data. If the perturbation ...

  7. IEEE 754 - Wikipedia

    en.wikipedia.org/wiki/IEEE_754

    The IEEE Standard for Floating-Point Arithmetic (IEEE 754) is a technical standard for floating-point arithmetic originally established in 1985 by the Institute of Electrical and Electronics Engineers (IEEE). The standard addressed many problems found in the diverse floating-point implementations that made them difficult to use reliably and ...

  8. Single-precision floating-point format - Wikipedia

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

    A floating-point variable can represent a wider range of numbers than a fixed-point variable of the same bit width at the cost of precision. A signed 32-bit integer variable has a maximum value of 2 31 − 1 = 2,147,483,647, whereas an IEEE 754 32-bit base-2 floating-point variable has a maximum value of (2 − 2 −23) × 2 127 ≈ 3.4028235 ...

  9. Parker–Sochacki method - Wikipedia

    en.wikipedia.org/wiki/Parker–Sochacki_method

    The order of the solution is only limited by the floating point representation on the machine running the program. And in some cases can be either extended by using arbitrary precision floating point numbers, or for special cases by finding solution with only integer or rational coefficients.