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  2. Regula falsi - Wikipedia

    en.wikipedia.org/wiki/Regula_falsi

    The method of false position provides an exact solution for linear functions, but more direct algebraic techniques have supplanted its use for these functions. However, in numerical analysis, double false position became a root-finding algorithm used in iterative numerical approximation techniques.

  3. Ridders' method - Wikipedia

    en.wikipedia.org/wiki/Ridders'_method

    In numerical analysis, Ridders' method is a root-finding algorithm based on the false position method and the use of an exponential function to successively approximate a root of a continuous function (). The method is due to C. Ridders.

  4. Root-finding algorithm - Wikipedia

    en.wikipedia.org/wiki/Root-finding_algorithm

    The false position method, also called the regula falsi method, is similar to the bisection method, but instead of using bisection search's middle of the interval it uses the x-intercept of the line that connects the plotted function values at the endpoints of the interval, that is

  5. Secant method - Wikipedia

    en.wikipedia.org/wiki/Secant_method

    This means that the false position method always converges; however, only with a linear order of convergence. Bracketing with a super-linear order of convergence as the secant method can be attained with improvements to the false position method (see Regula falsi § Improvements in regula falsi) such as the ITP method or the Illinois method.

  6. Rule of false position - Wikipedia

    en.wikipedia.org/?title=Rule_of_false_position&...

    Retrieved from "https://en.wikipedia.org/w/index.php?title=Rule_of_false_position&oldid=906963438"

  7. Method of false position - Wikipedia

    en.wikipedia.org/?title=Method_of_false_position&...

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  8. File:Regula falsi.svg - Wikipedia

    en.wikipedia.org/wiki/File:Regula_falsi.svg

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  9. Array programming - Wikipedia

    en.wikipedia.org/wiki/Array_programming

    Matrix multiplication is an example of a 2-rank function, because it operates on 2-dimensional objects (matrices). Collapse operators reduce the dimensionality of an input data array by one or more dimensions. For example, summing over elements collapses the input array by 1 dimension.