Jmat.Real.dawson

Percentage Accurate: 54.9% → 100.0%

Time: 8.5s

Alternatives: 11

Speedup: 31.0×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x \cdot x\right) \cdot \left(x \cdot x\right)\\ t_1 := t\_0 \cdot \left(x \cdot x\right)\\ t_2 := t\_1 \cdot \left(x \cdot x\right)\\ t_3 := t\_2 \cdot \left(x \cdot x\right)\\ \frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot t\_0\right) + 0.0072644182 \cdot t\_1\right) + 0.0005064034 \cdot t\_2\right) + 0.0001789971 \cdot t\_3}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot t\_0\right) + 0.0694555761 \cdot t\_1\right) + 0.0140005442 \cdot t\_2\right) + 0.0008327945 \cdot t\_3\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(t\_3 \cdot \left(x \cdot x\right)\right)} \cdot x \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (* (* x x) (* x x)))
        (t_1 (* t_0 (* x x)))
        (t_2 (* t_1 (* x x)))
        (t_3 (* t_2 (* x x))))
   (*
    (/
     (+
      (+
       (+
        (+ (+ 1.0 (* 0.1049934947 (* x x))) (* 0.0424060604 t_0))
        (* 0.0072644182 t_1))
       (* 0.0005064034 t_2))
      (* 0.0001789971 t_3))
     (+
      (+
       (+
        (+
         (+ (+ 1.0 (* 0.7715471019 (* x x))) (* 0.2909738639 t_0))
         (* 0.0694555761 t_1))
        (* 0.0140005442 t_2))
       (* 0.0008327945 t_3))
      (* (* 2.0 0.0001789971) (* t_3 (* x x)))))
    x)))

C

double code(double x) {
	double t_0 = (x * x) * (x * x);
	double t_1 = t_0 * (x * x);
	double t_2 = t_1 * (x * x);
	double t_3 = t_2 * (x * x);
	return ((((((1.0 + (0.1049934947 * (x * x))) + (0.0424060604 * t_0)) + (0.0072644182 * t_1)) + (0.0005064034 * t_2)) + (0.0001789971 * t_3)) / ((((((1.0 + (0.7715471019 * (x * x))) + (0.2909738639 * t_0)) + (0.0694555761 * t_1)) + (0.0140005442 * t_2)) + (0.0008327945 * t_3)) + ((2.0 * 0.0001789971) * (t_3 * (x * x))))) * x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    t_0 = (x * x) * (x * x)
    t_1 = t_0 * (x * x)
    t_2 = t_1 * (x * x)
    t_3 = t_2 * (x * x)
    code = ((((((1.0d0 + (0.1049934947d0 * (x * x))) + (0.0424060604d0 * t_0)) + (0.0072644182d0 * t_1)) + (0.0005064034d0 * t_2)) + (0.0001789971d0 * t_3)) / ((((((1.0d0 + (0.7715471019d0 * (x * x))) + (0.2909738639d0 * t_0)) + (0.0694555761d0 * t_1)) + (0.0140005442d0 * t_2)) + (0.0008327945d0 * t_3)) + ((2.0d0 * 0.0001789971d0) * (t_3 * (x * x))))) * x
end function

Java

public static double code(double x) {
	double t_0 = (x * x) * (x * x);
	double t_1 = t_0 * (x * x);
	double t_2 = t_1 * (x * x);
	double t_3 = t_2 * (x * x);
	return ((((((1.0 + (0.1049934947 * (x * x))) + (0.0424060604 * t_0)) + (0.0072644182 * t_1)) + (0.0005064034 * t_2)) + (0.0001789971 * t_3)) / ((((((1.0 + (0.7715471019 * (x * x))) + (0.2909738639 * t_0)) + (0.0694555761 * t_1)) + (0.0140005442 * t_2)) + (0.0008327945 * t_3)) + ((2.0 * 0.0001789971) * (t_3 * (x * x))))) * x;
}

Python

def code(x):
	t_0 = (x * x) * (x * x)
	t_1 = t_0 * (x * x)
	t_2 = t_1 * (x * x)
	t_3 = t_2 * (x * x)
	return ((((((1.0 + (0.1049934947 * (x * x))) + (0.0424060604 * t_0)) + (0.0072644182 * t_1)) + (0.0005064034 * t_2)) + (0.0001789971 * t_3)) / ((((((1.0 + (0.7715471019 * (x * x))) + (0.2909738639 * t_0)) + (0.0694555761 * t_1)) + (0.0140005442 * t_2)) + (0.0008327945 * t_3)) + ((2.0 * 0.0001789971) * (t_3 * (x * x))))) * x

Julia

function code(x)
	t_0 = Float64(Float64(x * x) * Float64(x * x))
	t_1 = Float64(t_0 * Float64(x * x))
	t_2 = Float64(t_1 * Float64(x * x))
	t_3 = Float64(t_2 * Float64(x * x))
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(1.0 + Float64(0.1049934947 * Float64(x * x))) + Float64(0.0424060604 * t_0)) + Float64(0.0072644182 * t_1)) + Float64(0.0005064034 * t_2)) + Float64(0.0001789971 * t_3)) / Float64(Float64(Float64(Float64(Float64(Float64(1.0 + Float64(0.7715471019 * Float64(x * x))) + Float64(0.2909738639 * t_0)) + Float64(0.0694555761 * t_1)) + Float64(0.0140005442 * t_2)) + Float64(0.0008327945 * t_3)) + Float64(Float64(2.0 * 0.0001789971) * Float64(t_3 * Float64(x * x))))) * x)
end

MATLAB

function tmp = code(x)
	t_0 = (x * x) * (x * x);
	t_1 = t_0 * (x * x);
	t_2 = t_1 * (x * x);
	t_3 = t_2 * (x * x);
	tmp = ((((((1.0 + (0.1049934947 * (x * x))) + (0.0424060604 * t_0)) + (0.0072644182 * t_1)) + (0.0005064034 * t_2)) + (0.0001789971 * t_3)) / ((((((1.0 + (0.7715471019 * (x * x))) + (0.2909738639 * t_0)) + (0.0694555761 * t_1)) + (0.0140005442 * t_2)) + (0.0008327945 * t_3)) + ((2.0 * 0.0001789971) * (t_3 * (x * x))))) * x;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(x * x), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(x * x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * N[(x * x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 * N[(x * x), $MachinePrecision]), $MachinePrecision]}, N[(N[(N[(N[(N[(N[(N[(1.0 + N[(0.1049934947 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.0424060604 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[(0.0072644182 * t$95$1), $MachinePrecision]), $MachinePrecision] + N[(0.0005064034 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(0.0001789971 * t$95$3), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(N[(N[(1.0 + N[(0.7715471019 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.2909738639 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[(0.0694555761 * t$95$1), $MachinePrecision]), $MachinePrecision] + N[(0.0140005442 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(0.0008327945 * t$95$3), $MachinePrecision]), $MachinePrecision] + N[(N[(2.0 * 0.0001789971), $MachinePrecision] * N[(t$95$3 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]]]]]

Initial Program: 54.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(x \cdot x\right) \cdot \left(x \cdot x\right)\\ t_1 := t\_0 \cdot \left(x \cdot x\right)\\ t_2 := t\_1 \cdot \left(x \cdot x\right)\\ t_3 := t\_2 \cdot \left(x \cdot x\right)\\ \frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot t\_0\right) + 0.0072644182 \cdot t\_1\right) + 0.0005064034 \cdot t\_2\right) + 0.0001789971 \cdot t\_3}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot t\_0\right) + 0.0694555761 \cdot t\_1\right) + 0.0140005442 \cdot t\_2\right) + 0.0008327945 \cdot t\_3\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(t\_3 \cdot \left(x \cdot x\right)\right)} \cdot x \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (* (* x x) (* x x)))
        (t_1 (* t_0 (* x x)))
        (t_2 (* t_1 (* x x)))
        (t_3 (* t_2 (* x x))))
   (*
    (/
     (+
      (+
       (+
        (+ (+ 1.0 (* 0.1049934947 (* x x))) (* 0.0424060604 t_0))
        (* 0.0072644182 t_1))
       (* 0.0005064034 t_2))
      (* 0.0001789971 t_3))
     (+
      (+
       (+
        (+
         (+ (+ 1.0 (* 0.7715471019 (* x x))) (* 0.2909738639 t_0))
         (* 0.0694555761 t_1))
        (* 0.0140005442 t_2))
       (* 0.0008327945 t_3))
      (* (* 2.0 0.0001789971) (* t_3 (* x x)))))
    x)))

C

double code(double x) {
	double t_0 = (x * x) * (x * x);
	double t_1 = t_0 * (x * x);
	double t_2 = t_1 * (x * x);
	double t_3 = t_2 * (x * x);
	return ((((((1.0 + (0.1049934947 * (x * x))) + (0.0424060604 * t_0)) + (0.0072644182 * t_1)) + (0.0005064034 * t_2)) + (0.0001789971 * t_3)) / ((((((1.0 + (0.7715471019 * (x * x))) + (0.2909738639 * t_0)) + (0.0694555761 * t_1)) + (0.0140005442 * t_2)) + (0.0008327945 * t_3)) + ((2.0 * 0.0001789971) * (t_3 * (x * x))))) * x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    t_0 = (x * x) * (x * x)
    t_1 = t_0 * (x * x)
    t_2 = t_1 * (x * x)
    t_3 = t_2 * (x * x)
    code = ((((((1.0d0 + (0.1049934947d0 * (x * x))) + (0.0424060604d0 * t_0)) + (0.0072644182d0 * t_1)) + (0.0005064034d0 * t_2)) + (0.0001789971d0 * t_3)) / ((((((1.0d0 + (0.7715471019d0 * (x * x))) + (0.2909738639d0 * t_0)) + (0.0694555761d0 * t_1)) + (0.0140005442d0 * t_2)) + (0.0008327945d0 * t_3)) + ((2.0d0 * 0.0001789971d0) * (t_3 * (x * x))))) * x
end function

Java

public static double code(double x) {
	double t_0 = (x * x) * (x * x);
	double t_1 = t_0 * (x * x);
	double t_2 = t_1 * (x * x);
	double t_3 = t_2 * (x * x);
	return ((((((1.0 + (0.1049934947 * (x * x))) + (0.0424060604 * t_0)) + (0.0072644182 * t_1)) + (0.0005064034 * t_2)) + (0.0001789971 * t_3)) / ((((((1.0 + (0.7715471019 * (x * x))) + (0.2909738639 * t_0)) + (0.0694555761 * t_1)) + (0.0140005442 * t_2)) + (0.0008327945 * t_3)) + ((2.0 * 0.0001789971) * (t_3 * (x * x))))) * x;
}

Python

def code(x):
	t_0 = (x * x) * (x * x)
	t_1 = t_0 * (x * x)
	t_2 = t_1 * (x * x)
	t_3 = t_2 * (x * x)
	return ((((((1.0 + (0.1049934947 * (x * x))) + (0.0424060604 * t_0)) + (0.0072644182 * t_1)) + (0.0005064034 * t_2)) + (0.0001789971 * t_3)) / ((((((1.0 + (0.7715471019 * (x * x))) + (0.2909738639 * t_0)) + (0.0694555761 * t_1)) + (0.0140005442 * t_2)) + (0.0008327945 * t_3)) + ((2.0 * 0.0001789971) * (t_3 * (x * x))))) * x

Julia

function code(x)
	t_0 = Float64(Float64(x * x) * Float64(x * x))
	t_1 = Float64(t_0 * Float64(x * x))
	t_2 = Float64(t_1 * Float64(x * x))
	t_3 = Float64(t_2 * Float64(x * x))
	return Float64(Float64(Float64(Float64(Float64(Float64(Float64(1.0 + Float64(0.1049934947 * Float64(x * x))) + Float64(0.0424060604 * t_0)) + Float64(0.0072644182 * t_1)) + Float64(0.0005064034 * t_2)) + Float64(0.0001789971 * t_3)) / Float64(Float64(Float64(Float64(Float64(Float64(1.0 + Float64(0.7715471019 * Float64(x * x))) + Float64(0.2909738639 * t_0)) + Float64(0.0694555761 * t_1)) + Float64(0.0140005442 * t_2)) + Float64(0.0008327945 * t_3)) + Float64(Float64(2.0 * 0.0001789971) * Float64(t_3 * Float64(x * x))))) * x)
end

MATLAB

function tmp = code(x)
	t_0 = (x * x) * (x * x);
	t_1 = t_0 * (x * x);
	t_2 = t_1 * (x * x);
	t_3 = t_2 * (x * x);
	tmp = ((((((1.0 + (0.1049934947 * (x * x))) + (0.0424060604 * t_0)) + (0.0072644182 * t_1)) + (0.0005064034 * t_2)) + (0.0001789971 * t_3)) / ((((((1.0 + (0.7715471019 * (x * x))) + (0.2909738639 * t_0)) + (0.0694555761 * t_1)) + (0.0140005442 * t_2)) + (0.0008327945 * t_3)) + ((2.0 * 0.0001789971) * (t_3 * (x * x))))) * x;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[(x * x), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(x * x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * N[(x * x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 * N[(x * x), $MachinePrecision]), $MachinePrecision]}, N[(N[(N[(N[(N[(N[(N[(1.0 + N[(0.1049934947 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.0424060604 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[(0.0072644182 * t$95$1), $MachinePrecision]), $MachinePrecision] + N[(0.0005064034 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(0.0001789971 * t$95$3), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(N[(N[(1.0 + N[(0.7715471019 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.2909738639 * t$95$0), $MachinePrecision]), $MachinePrecision] + N[(0.0694555761 * t$95$1), $MachinePrecision]), $MachinePrecision] + N[(0.0140005442 * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(0.0008327945 * t$95$3), $MachinePrecision]), $MachinePrecision] + N[(N[(2.0 * 0.0001789971), $MachinePrecision] * N[(t$95$3 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]]]]]

Alternative 1: 100.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ \begin{array}{l} t_0 := \left(x\_m \cdot x\_m\right) \cdot x\_m\\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 220:\\ \;\;\;\;\mathsf{fma}\left({x\_m}^{10}, 0.0001789971, \mathsf{fma}\left(\mathsf{fma}\left(t\_0 \cdot 0.0005064034, t\_0, \left(t\_0 \cdot 0.0072644182\right) \cdot x\_m\right), x\_m \cdot x\_m, \mathsf{fma}\left(\mathsf{fma}\left(x\_m \cdot x\_m, 0.0424060604, 0.1049934947\right), x\_m \cdot x\_m, 1\right)\right)\right) \cdot \frac{x\_m}{\mathsf{fma}\left({x\_m}^{12}, 0.0003579942, \mathsf{fma}\left({x\_m}^{10}, 0.0008327945, \mathsf{fma}\left(\mathsf{fma}\left(t\_0 \cdot 0.0140005442, t\_0, \left(t\_0 \cdot 0.0694555761\right) \cdot x\_m\right) \cdot x\_m, x\_m, \mathsf{fma}\left(\mathsf{fma}\left(x\_m \cdot x\_m, 0.2909738639, 0.7715471019\right), x\_m \cdot x\_m, 1\right)\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{\frac{0.15298196345929074}{x\_m \cdot x\_m} - -0.2514179000665374}{x\_m} - \frac{-11.259630434457211}{t\_0 \cdot \left(x\_m \cdot x\_m\right)}}{x\_m} - -0.5}{x\_m}\\ \end{array} \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (let* ((t_0 (* (* x_m x_m) x_m)))
   (*
    x_s
    (if (<= x_m 220.0)
      (*
       (fma
        (pow x_m 10.0)
        0.0001789971
        (fma
         (fma (* t_0 0.0005064034) t_0 (* (* t_0 0.0072644182) x_m))
         (* x_m x_m)
         (fma (fma (* x_m x_m) 0.0424060604 0.1049934947) (* x_m x_m) 1.0)))
       (/
        x_m
        (fma
         (pow x_m 12.0)
         0.0003579942
         (fma
          (pow x_m 10.0)
          0.0008327945
          (fma
           (* (fma (* t_0 0.0140005442) t_0 (* (* t_0 0.0694555761) x_m)) x_m)
           x_m
           (fma
            (fma (* x_m x_m) 0.2909738639 0.7715471019)
            (* x_m x_m)
            1.0))))))
      (/
       (-
        (/
         (-
          (/ (- (/ 0.15298196345929074 (* x_m x_m)) -0.2514179000665374) x_m)
          (/ -11.259630434457211 (* t_0 (* x_m x_m))))
         x_m)
        -0.5)
       x_m)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double t_0 = (x_m * x_m) * x_m;
	double tmp;
	if (x_m <= 220.0) {
		tmp = fma(pow(x_m, 10.0), 0.0001789971, fma(fma((t_0 * 0.0005064034), t_0, ((t_0 * 0.0072644182) * x_m)), (x_m * x_m), fma(fma((x_m * x_m), 0.0424060604, 0.1049934947), (x_m * x_m), 1.0))) * (x_m / fma(pow(x_m, 12.0), 0.0003579942, fma(pow(x_m, 10.0), 0.0008327945, fma((fma((t_0 * 0.0140005442), t_0, ((t_0 * 0.0694555761) * x_m)) * x_m), x_m, fma(fma((x_m * x_m), 0.2909738639, 0.7715471019), (x_m * x_m), 1.0)))));
	} else {
		tmp = ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (t_0 * (x_m * x_m)))) / x_m) - -0.5) / x_m;
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	t_0 = Float64(Float64(x_m * x_m) * x_m)
	tmp = 0.0
	if (x_m <= 220.0)
		tmp = Float64(fma((x_m ^ 10.0), 0.0001789971, fma(fma(Float64(t_0 * 0.0005064034), t_0, Float64(Float64(t_0 * 0.0072644182) * x_m)), Float64(x_m * x_m), fma(fma(Float64(x_m * x_m), 0.0424060604, 0.1049934947), Float64(x_m * x_m), 1.0))) * Float64(x_m / fma((x_m ^ 12.0), 0.0003579942, fma((x_m ^ 10.0), 0.0008327945, fma(Float64(fma(Float64(t_0 * 0.0140005442), t_0, Float64(Float64(t_0 * 0.0694555761) * x_m)) * x_m), x_m, fma(fma(Float64(x_m * x_m), 0.2909738639, 0.7715471019), Float64(x_m * x_m), 1.0))))));
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(Float64(0.15298196345929074 / Float64(x_m * x_m)) - -0.2514179000665374) / x_m) - Float64(-11.259630434457211 / Float64(t_0 * Float64(x_m * x_m)))) / x_m) - -0.5) / x_m);
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := Block[{t$95$0 = N[(N[(x$95$m * x$95$m), $MachinePrecision] * x$95$m), $MachinePrecision]}, N[(x$95$s * If[LessEqual[x$95$m, 220.0], N[(N[(N[Power[x$95$m, 10.0], $MachinePrecision] * 0.0001789971 + N[(N[(N[(t$95$0 * 0.0005064034), $MachinePrecision] * t$95$0 + N[(N[(t$95$0 * 0.0072644182), $MachinePrecision] * x$95$m), $MachinePrecision]), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + N[(N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.0424060604 + 0.1049934947), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(x$95$m / N[(N[Power[x$95$m, 12.0], $MachinePrecision] * 0.0003579942 + N[(N[Power[x$95$m, 10.0], $MachinePrecision] * 0.0008327945 + N[(N[(N[(N[(t$95$0 * 0.0140005442), $MachinePrecision] * t$95$0 + N[(N[(t$95$0 * 0.0694555761), $MachinePrecision] * x$95$m), $MachinePrecision]), $MachinePrecision] * x$95$m), $MachinePrecision] * x$95$m + N[(N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.2909738639 + 0.7715471019), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[(N[(N[(0.15298196345929074 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] - -0.2514179000665374), $MachinePrecision] / x$95$m), $MachinePrecision] - N[(-11.259630434457211 / N[(t$95$0 * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x$95$m), $MachinePrecision] - -0.5), $MachinePrecision] / x$95$m), $MachinePrecision]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 220

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   3. Applied rewrites 55.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left({\left(x \cdot x\right)}^{5}, 0.0001789971, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(0.0072644182 \cdot \left(\left(x \cdot x\right) \cdot x\right), x, \left(0.0005064034 \cdot \left(\left(x \cdot x\right) \cdot x\right)\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)\right), \mathsf{fma}\left(x \cdot x, 0.1049934947 + 0.0424060604 \cdot \left(x \cdot x\right), 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\left({\left(x \cdot x\right)}^{5} \cdot 0.0003579942\right) \cdot x, x, \mathsf{fma}\left(0.0008327945, {\left(x \cdot x\right)}^{5}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(0.0694555761 \cdot \left(\left(x \cdot x\right) \cdot x\right), x, \left(0.0140005442 \cdot \left(\left(x \cdot x\right) \cdot x\right)\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)\right), \mathsf{fma}\left(x \cdot x, 0.7715471019 + 0.2909738639 \cdot \left(x \cdot x\right), 1\right)\right)\right)\right)}} \]

   5. Applied rewrites 54.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0005064034, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0072644182\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\left({x}^{10} \cdot x\right) \cdot x, 0.0003579942, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0140005442, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0694555761\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), x \cdot x, 1\right)\right)\right)\right)}} \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\color{blue}{\left({x}^{10} \cdot x\right) \cdot x}, \frac{1789971}{5000000000}, \mathsf{fma}\left(\frac{1665589}{2000000000}, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)\right)\right)} \]

      2. lift-*.f64 N/A

         \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\color{blue}{\left({x}^{10} \cdot x\right)} \cdot x, \frac{1789971}{5000000000}, \mathsf{fma}\left(\frac{1665589}{2000000000}, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)\right)\right)} \]

      3. associate-*l* N/A

         \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\color{blue}{{x}^{10} \cdot \left(x \cdot x\right)}, \frac{1789971}{5000000000}, \mathsf{fma}\left(\frac{1665589}{2000000000}, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)\right)\right)} \]

      4. lift-pow.f64 N/A

         \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\color{blue}{{x}^{10}} \cdot \left(x \cdot x\right), \frac{1789971}{5000000000}, \mathsf{fma}\left(\frac{1665589}{2000000000}, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)\right)\right)} \]

      5. pow2 N/A

         \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left({x}^{10} \cdot \color{blue}{{x}^{2}}, \frac{1789971}{5000000000}, \mathsf{fma}\left(\frac{1665589}{2000000000}, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)\right)\right)} \]

      6. pow-prod-up N/A

         \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\color{blue}{{x}^{\left(10 + 2\right)}}, \frac{1789971}{5000000000}, \mathsf{fma}\left(\frac{1665589}{2000000000}, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)\right)\right)} \]

      7. lower-pow.f64 N/A

         \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\color{blue}{{x}^{\left(10 + 2\right)}}, \frac{1789971}{5000000000}, \mathsf{fma}\left(\frac{1665589}{2000000000}, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)\right)\right)} \]

      8. metadata-eval 55.0

         \[\leadsto \mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0005064034, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0072644182\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left({x}^{\color{blue}{12}}, 0.0003579942, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0140005442, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0694555761\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), x \cdot x, 1\right)\right)\right)\right)} \]

      9. lift-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \color{blue}{\frac{1665589}{2000000000} \cdot {x}^{10} + \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)}\right)} \]

      10. *-commutative N/A

          \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \color{blue}{{x}^{10} \cdot \frac{1665589}{2000000000}} + \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)\right)\right)} \]

      11. lower-fma.f64 55.0

          \[\leadsto \mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0005064034, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0072644182\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \color{blue}{\mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0140005442, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0694555761\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), x \cdot x, 1\right)\right)\right)}\right)} \]

      12. lift-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left({x}^{10}, \frac{1789971}{10000000000}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{2532017}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{36322091}{5000000000}\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{106015151}{2500000000}, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \color{blue}{\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{70002721}{5000000000}, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{694555761}{10000000000}\right) \cdot x\right) \cdot \left(x \cdot x\right) + \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), x \cdot x, 1\right)}\right)\right)} \]

   7. Applied rewrites 55.0%

      \[\leadsto \mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0005064034, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0072644182\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\color{blue}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0140005442, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0694555761\right) \cdot x\right) \cdot x, x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), x \cdot x, 1\right)\right)\right)\right)}} \]

   if 220 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \left(\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} + \frac{344398180852034095277}{30586987988352776592} \cdot \frac{1}{{x}^{6}}\right)\right)}{x}} \]
   3. Step-by-step derivation

   4. Applied rewrites 50.2%

      \[\leadsto \color{blue}{\frac{0.5 + \left(\frac{0.15298196345929074}{{x}^{4}} + \mathsf{fma}\left(0.2514179000665374, \frac{1}{{x}^{2}}, 11.259630434457211 \cdot \frac{1}{{x}^{6}}\right)\right)}{x}} \]

   6. Applied rewrites 50.2%

      \[\leadsto \frac{\left(\left(\frac{0.15298196345929074}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x \cdot x}\right) - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - -0.5}{x} \]
   7. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      5. lift-/.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      7. associate-/r* N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{\frac{-600041}{2386628}}{x}}{x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      8. sub-div N/A

         \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      10. lower--.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      12. lower-/.f64 50.2

          \[\leadsto \frac{\left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - -0.5}{x} \]

      13. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      14. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      15. associate-*l* N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(x \cdot \left(\left(x \cdot x\right) \cdot x\right)\right)}\right) - \frac{-1}{2}}{x} \]

      16. *-commutative N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      17. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      18. *-commutative N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot \left(x \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

   8. Applied rewrites 50.2%

      \[\leadsto \frac{\left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - \frac{-11.259630434457211}{\left(\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot x\right) \cdot x}\right) - -0.5}{x} \]
   9. Step-by-step derivation

   10. Applied rewrites 50.2%

       \[\leadsto \frac{\frac{\frac{\frac{0.15298196345929074}{x \cdot x} - -0.2514179000665374}{x} - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(x \cdot x\right)}}{x} - -0.5}{\color{blue}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 100.0% accurate, 1.3× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ \begin{array}{l} t_0 := \left(x\_m \cdot x\_m\right) \cdot x\_m\\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 100000000:\\ \;\;\;\;\mathsf{fma}\left({x\_m}^{10}, 0.0001789971, \mathsf{fma}\left(\mathsf{fma}\left(t\_0 \cdot 0.0005064034, t\_0, \left(t\_0 \cdot 0.0072644182\right) \cdot x\_m\right), x\_m \cdot x\_m, \mathsf{fma}\left(\mathsf{fma}\left(x\_m \cdot x\_m, 0.0424060604, 0.1049934947\right), x\_m \cdot x\_m, 1\right)\right)\right) \cdot \frac{x\_m}{\mathsf{fma}\left(\left({x\_m}^{10} \cdot x\_m\right) \cdot x\_m, 0.0003579942, \mathsf{fma}\left(0.0008327945, {x\_m}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(t\_0 \cdot 0.0140005442, t\_0, \left(t\_0 \cdot 0.0694555761\right) \cdot x\_m\right), x\_m \cdot x\_m, \mathsf{fma}\left(\mathsf{fma}\left(x\_m \cdot x\_m, 0.2909738639, 0.7715471019\right), x\_m \cdot x\_m, 1\right)\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x\_m}\\ \end{array} \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (let* ((t_0 (* (* x_m x_m) x_m)))
   (*
    x_s
    (if (<= x_m 100000000.0)
      (*
       (fma
        (pow x_m 10.0)
        0.0001789971
        (fma
         (fma (* t_0 0.0005064034) t_0 (* (* t_0 0.0072644182) x_m))
         (* x_m x_m)
         (fma (fma (* x_m x_m) 0.0424060604 0.1049934947) (* x_m x_m) 1.0)))
       (/
        x_m
        (fma
         (* (* (pow x_m 10.0) x_m) x_m)
         0.0003579942
         (fma
          0.0008327945
          (pow x_m 10.0)
          (fma
           (fma (* t_0 0.0140005442) t_0 (* (* t_0 0.0694555761) x_m))
           (* x_m x_m)
           (fma
            (fma (* x_m x_m) 0.2909738639 0.7715471019)
            (* x_m x_m)
            1.0))))))
      (/ 0.5 x_m)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double t_0 = (x_m * x_m) * x_m;
	double tmp;
	if (x_m <= 100000000.0) {
		tmp = fma(pow(x_m, 10.0), 0.0001789971, fma(fma((t_0 * 0.0005064034), t_0, ((t_0 * 0.0072644182) * x_m)), (x_m * x_m), fma(fma((x_m * x_m), 0.0424060604, 0.1049934947), (x_m * x_m), 1.0))) * (x_m / fma(((pow(x_m, 10.0) * x_m) * x_m), 0.0003579942, fma(0.0008327945, pow(x_m, 10.0), fma(fma((t_0 * 0.0140005442), t_0, ((t_0 * 0.0694555761) * x_m)), (x_m * x_m), fma(fma((x_m * x_m), 0.2909738639, 0.7715471019), (x_m * x_m), 1.0)))));
	} else {
		tmp = 0.5 / x_m;
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	t_0 = Float64(Float64(x_m * x_m) * x_m)
	tmp = 0.0
	if (x_m <= 100000000.0)
		tmp = Float64(fma((x_m ^ 10.0), 0.0001789971, fma(fma(Float64(t_0 * 0.0005064034), t_0, Float64(Float64(t_0 * 0.0072644182) * x_m)), Float64(x_m * x_m), fma(fma(Float64(x_m * x_m), 0.0424060604, 0.1049934947), Float64(x_m * x_m), 1.0))) * Float64(x_m / fma(Float64(Float64((x_m ^ 10.0) * x_m) * x_m), 0.0003579942, fma(0.0008327945, (x_m ^ 10.0), fma(fma(Float64(t_0 * 0.0140005442), t_0, Float64(Float64(t_0 * 0.0694555761) * x_m)), Float64(x_m * x_m), fma(fma(Float64(x_m * x_m), 0.2909738639, 0.7715471019), Float64(x_m * x_m), 1.0))))));
	else
		tmp = Float64(0.5 / x_m);
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := Block[{t$95$0 = N[(N[(x$95$m * x$95$m), $MachinePrecision] * x$95$m), $MachinePrecision]}, N[(x$95$s * If[LessEqual[x$95$m, 100000000.0], N[(N[(N[Power[x$95$m, 10.0], $MachinePrecision] * 0.0001789971 + N[(N[(N[(t$95$0 * 0.0005064034), $MachinePrecision] * t$95$0 + N[(N[(t$95$0 * 0.0072644182), $MachinePrecision] * x$95$m), $MachinePrecision]), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + N[(N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.0424060604 + 0.1049934947), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(x$95$m / N[(N[(N[(N[Power[x$95$m, 10.0], $MachinePrecision] * x$95$m), $MachinePrecision] * x$95$m), $MachinePrecision] * 0.0003579942 + N[(0.0008327945 * N[Power[x$95$m, 10.0], $MachinePrecision] + N[(N[(N[(t$95$0 * 0.0140005442), $MachinePrecision] * t$95$0 + N[(N[(t$95$0 * 0.0694555761), $MachinePrecision] * x$95$m), $MachinePrecision]), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + N[(N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.2909738639 + 0.7715471019), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(0.5 / x$95$m), $MachinePrecision]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1e8

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   3. Applied rewrites 55.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left({\left(x \cdot x\right)}^{5}, 0.0001789971, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(0.0072644182 \cdot \left(\left(x \cdot x\right) \cdot x\right), x, \left(0.0005064034 \cdot \left(\left(x \cdot x\right) \cdot x\right)\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)\right), \mathsf{fma}\left(x \cdot x, 0.1049934947 + 0.0424060604 \cdot \left(x \cdot x\right), 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\left({\left(x \cdot x\right)}^{5} \cdot 0.0003579942\right) \cdot x, x, \mathsf{fma}\left(0.0008327945, {\left(x \cdot x\right)}^{5}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(0.0694555761 \cdot \left(\left(x \cdot x\right) \cdot x\right), x, \left(0.0140005442 \cdot \left(\left(x \cdot x\right) \cdot x\right)\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)\right), \mathsf{fma}\left(x \cdot x, 0.7715471019 + 0.2909738639 \cdot \left(x \cdot x\right), 1\right)\right)\right)\right)}} \]

   5. Applied rewrites 54.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0005064034, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0072644182\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right)\right)\right) \cdot \frac{x}{\mathsf{fma}\left(\left({x}^{10} \cdot x\right) \cdot x, 0.0003579942, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(\mathsf{fma}\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0140005442, \left(x \cdot x\right) \cdot x, \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot 0.0694555761\right) \cdot x\right), x \cdot x, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), x \cdot x, 1\right)\right)\right)\right)}} \]

   if 1e8 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2}}{x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 50.6

         \[\leadsto \frac{0.5}{\color{blue}{x}} \]

   4. Applied rewrites 50.6%

      \[\leadsto \color{blue}{\frac{0.5}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 99.4% accurate, 5.3× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 1.42:\\ \;\;\;\;x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{x\_m}{\frac{\frac{\frac{0.15298196345929074}{x\_m \cdot x\_m} - -0.2514179000665374}{x\_m} - \frac{-11.259630434457211}{\left(\left(x\_m \cdot x\_m\right) \cdot x\_m\right) \cdot \left(x\_m \cdot x\_m\right)}}{x\_m} - -0.5}}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 1.42)
    x_m
    (/
     1.0
     (/
      x_m
      (-
       (/
        (-
         (/ (- (/ 0.15298196345929074 (* x_m x_m)) -0.2514179000665374) x_m)
         (/ -11.259630434457211 (* (* (* x_m x_m) x_m) (* x_m x_m))))
        x_m)
       -0.5))))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 1.42) {
		tmp = x_m;
	} else {
		tmp = 1.0 / (x_m / ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - -0.5));
	}
	return x_s * tmp;
}

Fortran

x\_m =     private
x\_s =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_s, x_m)
use fmin_fmax_functions
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 1.42d0) then
        tmp = x_m
    else
        tmp = 1.0d0 / (x_m / ((((((0.15298196345929074d0 / (x_m * x_m)) - (-0.2514179000665374d0)) / x_m) - ((-11.259630434457211d0) / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - (-0.5d0)))
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 1.42) {
		tmp = x_m;
	} else {
		tmp = 1.0 / (x_m / ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - -0.5));
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 1.42:
		tmp = x_m
	else:
		tmp = 1.0 / (x_m / ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - -0.5))
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 1.42)
		tmp = x_m;
	else
		tmp = Float64(1.0 / Float64(x_m / Float64(Float64(Float64(Float64(Float64(Float64(0.15298196345929074 / Float64(x_m * x_m)) - -0.2514179000665374) / x_m) - Float64(-11.259630434457211 / Float64(Float64(Float64(x_m * x_m) * x_m) * Float64(x_m * x_m)))) / x_m) - -0.5)));
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 1.42)
		tmp = x_m;
	else
		tmp = 1.0 / (x_m / ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - -0.5));
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 1.42], x$95$m, N[(1.0 / N[(x$95$m / N[(N[(N[(N[(N[(N[(0.15298196345929074 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] - -0.2514179000665374), $MachinePrecision] / x$95$m), $MachinePrecision] - N[(-11.259630434457211 / N[(N[(N[(x$95$m * x$95$m), $MachinePrecision] * x$95$m), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x$95$m), $MachinePrecision] - -0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 1.4199999999999999

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   3. Step-by-step derivation

   4. Applied rewrites 52.3%

      \[\leadsto \color{blue}{x} \]

   if 1.4199999999999999 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \left(\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} + \frac{344398180852034095277}{30586987988352776592} \cdot \frac{1}{{x}^{6}}\right)\right)}{x}} \]
   3. Step-by-step derivation

   4. Applied rewrites 50.2%

      \[\leadsto \color{blue}{\frac{0.5 + \left(\frac{0.15298196345929074}{{x}^{4}} + \mathsf{fma}\left(0.2514179000665374, \frac{1}{{x}^{2}}, 11.259630434457211 \cdot \frac{1}{{x}^{6}}\right)\right)}{x}} \]

   6. Applied rewrites 50.2%

      \[\leadsto \frac{\left(\left(\frac{0.15298196345929074}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x \cdot x}\right) - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - -0.5}{x} \]
   7. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      5. lift-/.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      7. associate-/r* N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{\frac{-600041}{2386628}}{x}}{x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      8. sub-div N/A

         \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      10. lower--.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      12. lower-/.f64 50.2

          \[\leadsto \frac{\left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - -0.5}{x} \]

      13. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      14. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      15. associate-*l* N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(x \cdot \left(\left(x \cdot x\right) \cdot x\right)\right)}\right) - \frac{-1}{2}}{x} \]

      16. *-commutative N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      17. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      18. *-commutative N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot \left(x \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

   8. Applied rewrites 50.2%

      \[\leadsto \frac{\left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - \frac{-11.259630434457211}{\left(\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot x\right) \cdot x}\right) - -0.5}{x} \]
   9. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot x\right) \cdot x}\right) - \frac{-1}{2}}{\color{blue}{x}} \]

      2. div-flip N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{x}{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot x\right) \cdot x}\right) - \frac{-1}{2}}}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{x}{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot x\right) \cdot x}\right) - \frac{-1}{2}}}} \]

      4. lower-/.f64 50.2

         \[\leadsto \frac{1}{\frac{x}{\color{blue}{\left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - \frac{-11.259630434457211}{\left(\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot x\right) \cdot x}\right) - -0.5}}} \]

   10. Applied rewrites 50.2%

       \[\leadsto \frac{1}{\color{blue}{\frac{x}{\frac{\frac{\frac{0.15298196345929074}{x \cdot x} - -0.2514179000665374}{x} - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(x \cdot x\right)}}{x} - -0.5}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 99.4% accurate, 5.7× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 1.42:\\ \;\;\;\;x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{\frac{0.15298196345929074}{x\_m \cdot x\_m} - -0.2514179000665374}{x\_m} - \frac{-11.259630434457211}{\left(\left(x\_m \cdot x\_m\right) \cdot x\_m\right) \cdot \left(x\_m \cdot x\_m\right)}}{x\_m} - -0.5}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 1.42)
    x_m
    (/
     (-
      (/
       (-
        (/ (- (/ 0.15298196345929074 (* x_m x_m)) -0.2514179000665374) x_m)
        (/ -11.259630434457211 (* (* (* x_m x_m) x_m) (* x_m x_m))))
       x_m)
      -0.5)
     x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 1.42) {
		tmp = x_m;
	} else {
		tmp = ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - -0.5) / x_m;
	}
	return x_s * tmp;
}

Fortran

x\_m =     private
x\_s =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_s, x_m)
use fmin_fmax_functions
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 1.42d0) then
        tmp = x_m
    else
        tmp = ((((((0.15298196345929074d0 / (x_m * x_m)) - (-0.2514179000665374d0)) / x_m) - ((-11.259630434457211d0) / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - (-0.5d0)) / x_m
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 1.42) {
		tmp = x_m;
	} else {
		tmp = ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - -0.5) / x_m;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 1.42:
		tmp = x_m
	else:
		tmp = ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - -0.5) / x_m
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 1.42)
		tmp = x_m;
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(Float64(0.15298196345929074 / Float64(x_m * x_m)) - -0.2514179000665374) / x_m) - Float64(-11.259630434457211 / Float64(Float64(Float64(x_m * x_m) * x_m) * Float64(x_m * x_m)))) / x_m) - -0.5) / x_m);
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 1.42)
		tmp = x_m;
	else
		tmp = ((((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) - (-11.259630434457211 / (((x_m * x_m) * x_m) * (x_m * x_m)))) / x_m) - -0.5) / x_m;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 1.42], x$95$m, N[(N[(N[(N[(N[(N[(N[(0.15298196345929074 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] - -0.2514179000665374), $MachinePrecision] / x$95$m), $MachinePrecision] - N[(-11.259630434457211 / N[(N[(N[(x$95$m * x$95$m), $MachinePrecision] * x$95$m), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x$95$m), $MachinePrecision] - -0.5), $MachinePrecision] / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 1.4199999999999999

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   3. Step-by-step derivation

   4. Applied rewrites 52.3%

      \[\leadsto \color{blue}{x} \]

   if 1.4199999999999999 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \left(\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} + \frac{344398180852034095277}{30586987988352776592} \cdot \frac{1}{{x}^{6}}\right)\right)}{x}} \]
   3. Step-by-step derivation

   4. Applied rewrites 50.2%

      \[\leadsto \color{blue}{\frac{0.5 + \left(\frac{0.15298196345929074}{{x}^{4}} + \mathsf{fma}\left(0.2514179000665374, \frac{1}{{x}^{2}}, 11.259630434457211 \cdot \frac{1}{{x}^{6}}\right)\right)}{x}} \]

   6. Applied rewrites 50.2%

      \[\leadsto \frac{\left(\left(\frac{0.15298196345929074}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x \cdot x}\right) - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - -0.5}{x} \]
   7. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      4. associate-/r* N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      5. lift-/.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      7. associate-/r* N/A

         \[\leadsto \frac{\left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{\frac{-600041}{2386628}}{x}}{x}\right) - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      8. sub-div N/A

         \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      10. lower--.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      11. lower-/.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      12. lower-/.f64 50.2

          \[\leadsto \frac{\left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - -0.5}{x} \]

      13. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      14. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(\left(x \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      15. associate-*l* N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(x \cdot \left(\left(x \cdot x\right) \cdot x\right)\right)}\right) - \frac{-1}{2}}{x} \]

      16. *-commutative N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      17. lift-*.f64 N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(x \cdot x\right) \cdot \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

      18. *-commutative N/A

          \[\leadsto \frac{\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{\frac{-344398180852034095277}{30586987988352776592}}{\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot \left(x \cdot x\right)}\right) - \frac{-1}{2}}{x} \]

   8. Applied rewrites 50.2%

      \[\leadsto \frac{\left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - \frac{-11.259630434457211}{\left(\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot x\right) \cdot x\right) \cdot x}\right) - -0.5}{x} \]
   9. Step-by-step derivation

   10. Applied rewrites 50.2%

       \[\leadsto \frac{\frac{\frac{\frac{0.15298196345929074}{x \cdot x} - -0.2514179000665374}{x} - \frac{-11.259630434457211}{\left(\left(x \cdot x\right) \cdot x\right) \cdot \left(x \cdot x\right)}}{x} - -0.5}{\color{blue}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 99.4% accurate, 8.6× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.98:\\ \;\;\;\;x\_m\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{\frac{\frac{0.15298196345929074}{x\_m \cdot x\_m} - -0.2514179000665374}{x\_m}}{x\_m} - -0.5\right) \cdot \frac{1}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 0.98)
    x_m
    (*
     (-
      (/
       (/ (- (/ 0.15298196345929074 (* x_m x_m)) -0.2514179000665374) x_m)
       x_m)
      -0.5)
     (/ 1.0 x_m)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.98) {
		tmp = x_m;
	} else {
		tmp = (((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) * (1.0 / x_m);
	}
	return x_s * tmp;
}

Fortran

x\_m =     private
x\_s =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_s, x_m)
use fmin_fmax_functions
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 0.98d0) then
        tmp = x_m
    else
        tmp = (((((0.15298196345929074d0 / (x_m * x_m)) - (-0.2514179000665374d0)) / x_m) / x_m) - (-0.5d0)) * (1.0d0 / x_m)
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.98) {
		tmp = x_m;
	} else {
		tmp = (((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) * (1.0 / x_m);
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 0.98:
		tmp = x_m
	else:
		tmp = (((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) * (1.0 / x_m)
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 0.98)
		tmp = x_m;
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(0.15298196345929074 / Float64(x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) * Float64(1.0 / x_m));
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 0.98)
		tmp = x_m;
	else
		tmp = (((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) * (1.0 / x_m);
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 0.98], x$95$m, N[(N[(N[(N[(N[(N[(0.15298196345929074 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] - -0.2514179000665374), $MachinePrecision] / x$95$m), $MachinePrecision] / x$95$m), $MachinePrecision] - -0.5), $MachinePrecision] * N[(1.0 / x$95$m), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.97999999999999998

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   3. Step-by-step derivation

   4. Applied rewrites 52.3%

      \[\leadsto \color{blue}{x} \]

   if 0.97999999999999998 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)}{x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)}{\color{blue}{x}} \]

   4. Applied rewrites 50.3%

      \[\leadsto \color{blue}{\frac{0.5 + \left(\frac{0.15298196345929074}{{x}^{4}} + 0.2514179000665374 \cdot \frac{1}{{x}^{2}}\right)}{x}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)}{\color{blue}{x}} \]

      2. mult-flip N/A

         \[\leadsto \left(\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot \color{blue}{\frac{1}{x}} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot \color{blue}{\frac{1}{x}} \]

   6. Applied rewrites 50.3%

      \[\leadsto \left(\left(\frac{0.15298196345929074}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x \cdot x}\right) - -0.5\right) \cdot \color{blue}{\frac{1}{x}} \]
   7. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      3. lift-*.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      4. associate-/r* N/A

         \[\leadsto \left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      5. lift-/.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      6. lift-*.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      7. associate-/r* N/A

         \[\leadsto \left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{\frac{-600041}{2386628}}{x}}{x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      8. sub-div N/A

         \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      10. lower--.f64 N/A

          \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      11. lower-/.f64 N/A

          \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      12. lower-/.f64 50.3

          \[\leadsto \left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - -0.5\right) \cdot \frac{1}{x} \]

   8. Applied rewrites 50.3%

      \[\leadsto \left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - -0.5\right) \cdot \frac{1}{x} \]
   9. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      3. lift-*.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      4. associate-/r* N/A

         \[\leadsto \left(\frac{\frac{\frac{\frac{1307076337763}{8543989815576}}{x \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      5. lift-/.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{\frac{1307076337763}{8543989815576}}{x \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      6. sub-div N/A

         \[\leadsto \left(\frac{\frac{\frac{\frac{1307076337763}{8543989815576}}{x \cdot x} - \frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      7. lower-/.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{\frac{1307076337763}{8543989815576}}{x \cdot x} - \frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      8. lower--.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{\frac{1307076337763}{8543989815576}}{x \cdot x} - \frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      9. lower-/.f64 50.3

         \[\leadsto \left(\frac{\frac{\frac{0.15298196345929074}{x \cdot x} - -0.2514179000665374}{x}}{x} - -0.5\right) \cdot \frac{1}{x} \]

   10. Applied rewrites 50.3%

       \[\leadsto \left(\frac{\frac{\frac{0.15298196345929074}{x \cdot x} - -0.2514179000665374}{x}}{x} - -0.5\right) \cdot \frac{\color{blue}{1}}{x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 99.4% accurate, 9.6× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.98:\\ \;\;\;\;x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\frac{\frac{0.15298196345929074}{x\_m \cdot x\_m} - -0.2514179000665374}{x\_m}}{x\_m} - -0.5}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 0.98)
    x_m
    (/
     (-
      (/
       (/ (- (/ 0.15298196345929074 (* x_m x_m)) -0.2514179000665374) x_m)
       x_m)
      -0.5)
     x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.98) {
		tmp = x_m;
	} else {
		tmp = (((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) / x_m;
	}
	return x_s * tmp;
}

Fortran

x\_m =     private
x\_s =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_s, x_m)
use fmin_fmax_functions
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 0.98d0) then
        tmp = x_m
    else
        tmp = (((((0.15298196345929074d0 / (x_m * x_m)) - (-0.2514179000665374d0)) / x_m) / x_m) - (-0.5d0)) / x_m
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.98) {
		tmp = x_m;
	} else {
		tmp = (((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) / x_m;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 0.98:
		tmp = x_m
	else:
		tmp = (((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) / x_m
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 0.98)
		tmp = x_m;
	else
		tmp = Float64(Float64(Float64(Float64(Float64(Float64(0.15298196345929074 / Float64(x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) / x_m);
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 0.98)
		tmp = x_m;
	else
		tmp = (((((0.15298196345929074 / (x_m * x_m)) - -0.2514179000665374) / x_m) / x_m) - -0.5) / x_m;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 0.98], x$95$m, N[(N[(N[(N[(N[(N[(0.15298196345929074 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] - -0.2514179000665374), $MachinePrecision] / x$95$m), $MachinePrecision] / x$95$m), $MachinePrecision] - -0.5), $MachinePrecision] / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.97999999999999998

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   3. Step-by-step derivation

   4. Applied rewrites 52.3%

      \[\leadsto \color{blue}{x} \]

   if 0.97999999999999998 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)}{x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)}{\color{blue}{x}} \]

   4. Applied rewrites 50.3%

      \[\leadsto \color{blue}{\frac{0.5 + \left(\frac{0.15298196345929074}{{x}^{4}} + 0.2514179000665374 \cdot \frac{1}{{x}^{2}}\right)}{x}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)}{\color{blue}{x}} \]

      2. mult-flip N/A

         \[\leadsto \left(\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot \color{blue}{\frac{1}{x}} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} + \left(\frac{\frac{1307076337763}{8543989815576}}{{x}^{4}} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot \color{blue}{\frac{1}{x}} \]

   6. Applied rewrites 50.3%

      \[\leadsto \left(\left(\frac{0.15298196345929074}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x \cdot x}\right) - -0.5\right) \cdot \color{blue}{\frac{1}{x}} \]
   7. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      3. lift-*.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{1307076337763}{8543989815576}}{\left(\left(x \cdot x\right) \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      4. associate-/r* N/A

         \[\leadsto \left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      5. lift-/.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      6. lift-*.f64 N/A

         \[\leadsto \left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{-600041}{2386628}}{x \cdot x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      7. associate-/r* N/A

         \[\leadsto \left(\left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x}}{x} - \frac{\frac{\frac{-600041}{2386628}}{x}}{x}\right) - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      8. sub-div N/A

         \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      9. lower-/.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      10. lower--.f64 N/A

          \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      11. lower-/.f64 N/A

          \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \frac{1}{x} \]

      12. lower-/.f64 50.3

          \[\leadsto \left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - -0.5\right) \cdot \frac{1}{x} \]

   8. Applied rewrites 50.3%

      \[\leadsto \left(\frac{\frac{0.15298196345929074}{\left(x \cdot x\right) \cdot x} - \frac{-0.2514179000665374}{x}}{x} - -0.5\right) \cdot \frac{1}{x} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(\frac{\frac{\frac{1307076337763}{8543989815576}}{\left(x \cdot x\right) \cdot x} - \frac{\frac{-600041}{2386628}}{x}}{x} - \frac{-1}{2}\right) \cdot \color{blue}{\frac{1}{x}} \]

   10. Applied rewrites 50.3%

       \[\leadsto \color{blue}{\frac{\frac{\frac{\frac{0.15298196345929074}{x \cdot x} - -0.2514179000665374}{x}}{x} - -0.5}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 99.4% accurate, 11.1× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.9:\\ \;\;\;\;x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{x\_m}{\mathsf{fma}\left(0.5, x\_m, \frac{0.2514179000665374}{x\_m}\right)} \cdot x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 0.9)
    x_m
    (/ 1.0 (* (/ x_m (fma 0.5 x_m (/ 0.2514179000665374 x_m))) x_m)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.9) {
		tmp = x_m;
	} else {
		tmp = 1.0 / ((x_m / fma(0.5, x_m, (0.2514179000665374 / x_m))) * x_m);
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 0.9)
		tmp = x_m;
	else
		tmp = Float64(1.0 / Float64(Float64(x_m / fma(0.5, x_m, Float64(0.2514179000665374 / x_m))) * x_m));
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 0.9], x$95$m, N[(1.0 / N[(N[(x$95$m / N[(0.5 * x$95$m + N[(0.2514179000665374 / x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x$95$m), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.900000000000000022

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   3. Step-by-step derivation

   4. Applied rewrites 52.3%

      \[\leadsto \color{blue}{x} \]

   if 0.900000000000000022 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{\color{blue}{x}} \]

   4. Applied rewrites 50.4%

      \[\leadsto \color{blue}{\frac{0.5 + 0.2514179000665374 \cdot \frac{1}{{x}^{2}}}{x}} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      4. lift-pow.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      5. pow2 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{x \cdot x}}{x} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{\frac{600041}{2386628}}{x \cdot x}}{x} \]

      7. add-to-fraction N/A

         \[\leadsto \frac{\frac{\frac{1}{2} \cdot \left(x \cdot x\right) + \frac{600041}{2386628}}{x \cdot x}}{x} \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{\frac{\frac{1}{2} \cdot \left(x \cdot x\right) + \frac{600041}{2386628}}{x \cdot x}}{x} \]

      9. lower-fma.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x}}{x} \]

      10. lower-*.f64 N/A

          \[\leadsto \frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x}}{x} \]

      11. lower-*.f64 25.8

          \[\leadsto \frac{\frac{\mathsf{fma}\left(0.5, x \cdot x, 0.2514179000665374\right)}{x \cdot x}}{x} \]

   6. Applied rewrites 25.8%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(0.5, x \cdot x, 0.2514179000665374\right)}{x \cdot x}}{x} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x}}{\color{blue}{x}} \]

      2. mult-flip N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x} \cdot \color{blue}{\frac{1}{x}} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x} \cdot \frac{\color{blue}{1}}{x} \]

      4. div-flip N/A

         \[\leadsto \frac{1}{\frac{x \cdot x}{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}} \cdot \frac{\color{blue}{1}}{x} \]

      5. frac-times N/A

         \[\leadsto \frac{1 \cdot 1}{\color{blue}{\frac{x \cdot x}{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)} \cdot x}} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{x \cdot x}{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}} \cdot x} \]

      7. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\frac{x \cdot x}{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)} \cdot x}} \]

      8. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{x \cdot x}{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)} \cdot \color{blue}{x}} \]

      9. lower-/.f64 25.8

         \[\leadsto \frac{1}{\frac{x \cdot x}{\mathsf{fma}\left(0.5, x \cdot x, 0.2514179000665374\right)} \cdot x} \]

   8. Applied rewrites 25.8%

      \[\leadsto \frac{1}{\color{blue}{\frac{x \cdot x}{\mathsf{fma}\left(0.5, x \cdot x, 0.2514179000665374\right)} \cdot x}} \]
   9. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{1}{\frac{x \cdot x}{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)} \cdot x} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{1}{\frac{x \cdot x}{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)} \cdot x} \]

      3. associate-/l* N/A

         \[\leadsto \frac{1}{\left(x \cdot \frac{x}{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}\right) \cdot x} \]

      4. div-flip N/A

         \[\leadsto \frac{1}{\left(x \cdot \frac{1}{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x}}\right) \cdot x} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{1}{\left(x \cdot \frac{1}{\frac{\frac{1}{2} \cdot \left(x \cdot x\right) + \frac{600041}{2386628}}{x}}\right) \cdot x} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{1}{\left(x \cdot \frac{1}{\frac{\frac{1}{2} \cdot \left(x \cdot x\right) + \frac{600041}{2386628}}{x}}\right) \cdot x} \]

      7. associate-*r* N/A

         \[\leadsto \frac{1}{\left(x \cdot \frac{1}{\frac{\left(\frac{1}{2} \cdot x\right) \cdot x + \frac{600041}{2386628}}{x}}\right) \cdot x} \]

      8. add-to-fraction N/A

         \[\leadsto \frac{1}{\left(x \cdot \frac{1}{\frac{1}{2} \cdot x + \frac{\frac{600041}{2386628}}{x}}\right) \cdot x} \]

      9. lift-/.f64 N/A

         \[\leadsto \frac{1}{\left(x \cdot \frac{1}{\frac{1}{2} \cdot x + \frac{\frac{600041}{2386628}}{x}}\right) \cdot x} \]

      10. lift-fma.f64 N/A

          \[\leadsto \frac{1}{\left(x \cdot \frac{1}{\mathsf{fma}\left(\frac{1}{2}, x, \frac{\frac{600041}{2386628}}{x}\right)}\right) \cdot x} \]

      11. mult-flip N/A

          \[\leadsto \frac{1}{\frac{x}{\mathsf{fma}\left(\frac{1}{2}, x, \frac{\frac{600041}{2386628}}{x}\right)} \cdot x} \]

      12. lower-/.f64 50.3

          \[\leadsto \frac{1}{\frac{x}{\mathsf{fma}\left(0.5, x, \frac{0.2514179000665374}{x}\right)} \cdot x} \]

   10. Applied rewrites 50.3%

       \[\leadsto \frac{1}{\frac{x}{\mathsf{fma}\left(0.5, x, \frac{0.2514179000665374}{x}\right)} \cdot \color{blue}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 99.4% accurate, 12.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.9:\\ \;\;\;\;x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(0.5, x\_m, \frac{0.2514179000665374}{x\_m}\right)}{x\_m}}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 0.9)
    x_m
    (/ (/ (fma 0.5 x_m (/ 0.2514179000665374 x_m)) x_m) x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.9) {
		tmp = x_m;
	} else {
		tmp = (fma(0.5, x_m, (0.2514179000665374 / x_m)) / x_m) / x_m;
	}
	return x_s * tmp;
}

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 0.9)
		tmp = x_m;
	else
		tmp = Float64(Float64(fma(0.5, x_m, Float64(0.2514179000665374 / x_m)) / x_m) / x_m);
	end
	return Float64(x_s * tmp)
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 0.9], x$95$m, N[(N[(N[(0.5 * x$95$m + N[(0.2514179000665374 / x$95$m), $MachinePrecision]), $MachinePrecision] / x$95$m), $MachinePrecision] / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.900000000000000022

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   3. Step-by-step derivation

   4. Applied rewrites 52.3%

      \[\leadsto \color{blue}{x} \]

   if 0.900000000000000022 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{\color{blue}{x}} \]

   4. Applied rewrites 50.4%

      \[\leadsto \color{blue}{\frac{0.5 + 0.2514179000665374 \cdot \frac{1}{{x}^{2}}}{x}} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      3. lift-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      4. lift-pow.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      5. pow2 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{x \cdot x}}{x} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{\frac{600041}{2386628}}{x \cdot x}}{x} \]

      7. add-to-fraction N/A

         \[\leadsto \frac{\frac{\frac{1}{2} \cdot \left(x \cdot x\right) + \frac{600041}{2386628}}{x \cdot x}}{x} \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{\frac{\frac{1}{2} \cdot \left(x \cdot x\right) + \frac{600041}{2386628}}{x \cdot x}}{x} \]

      9. lower-fma.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x}}{x} \]

      10. lower-*.f64 N/A

          \[\leadsto \frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x}}{x} \]

      11. lower-*.f64 25.8

          \[\leadsto \frac{\frac{\mathsf{fma}\left(0.5, x \cdot x, 0.2514179000665374\right)}{x \cdot x}}{x} \]

   6. Applied rewrites 25.8%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(0.5, x \cdot x, 0.2514179000665374\right)}{x \cdot x}}{x} \]
   7. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x}}{x} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x \cdot x}}{x} \]

      3. associate-/r* N/A

         \[\leadsto \frac{\frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x}}{x}}{x} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x \cdot x, \frac{600041}{2386628}\right)}{x}}{x}}{x} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\frac{\frac{\frac{1}{2} \cdot \left(x \cdot x\right) + \frac{600041}{2386628}}{x}}{x}}{x} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\frac{\frac{\frac{1}{2} \cdot \left(x \cdot x\right) + \frac{600041}{2386628}}{x}}{x}}{x} \]

      7. associate-*r* N/A

         \[\leadsto \frac{\frac{\frac{\left(\frac{1}{2} \cdot x\right) \cdot x + \frac{600041}{2386628}}{x}}{x}}{x} \]

      8. add-to-fraction-rev N/A

         \[\leadsto \frac{\frac{\frac{1}{2} \cdot x + \frac{\frac{600041}{2386628}}{x}}{x}}{x} \]

      9. lower-fma.f64 N/A

         \[\leadsto \frac{\frac{\mathsf{fma}\left(\frac{1}{2}, x, \frac{\frac{600041}{2386628}}{x}\right)}{x}}{x} \]

      10. lower-/.f64 50.4

          \[\leadsto \frac{\frac{\mathsf{fma}\left(0.5, x, \frac{0.2514179000665374}{x}\right)}{x}}{x} \]

   8. Applied rewrites 50.4%

      \[\leadsto \frac{\frac{\mathsf{fma}\left(0.5, x, \frac{0.2514179000665374}{x}\right)}{x}}{x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 99.3% accurate, 14.8× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.9:\\ \;\;\;\;x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{0.2514179000665374}{x\_m \cdot x\_m} - -0.5}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 0.9) x_m (/ (- (/ 0.2514179000665374 (* x_m x_m)) -0.5) x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.9) {
		tmp = x_m;
	} else {
		tmp = ((0.2514179000665374 / (x_m * x_m)) - -0.5) / x_m;
	}
	return x_s * tmp;
}

Fortran

x\_m =     private
x\_s =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_s, x_m)
use fmin_fmax_functions
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 0.9d0) then
        tmp = x_m
    else
        tmp = ((0.2514179000665374d0 / (x_m * x_m)) - (-0.5d0)) / x_m
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.9) {
		tmp = x_m;
	} else {
		tmp = ((0.2514179000665374 / (x_m * x_m)) - -0.5) / x_m;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 0.9:
		tmp = x_m
	else:
		tmp = ((0.2514179000665374 / (x_m * x_m)) - -0.5) / x_m
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 0.9)
		tmp = x_m;
	else
		tmp = Float64(Float64(Float64(0.2514179000665374 / Float64(x_m * x_m)) - -0.5) / x_m);
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 0.9)
		tmp = x_m;
	else
		tmp = ((0.2514179000665374 / (x_m * x_m)) - -0.5) / x_m;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 0.9], x$95$m, N[(N[(N[(0.2514179000665374 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] - -0.5), $MachinePrecision] / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.900000000000000022

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   3. Step-by-step derivation

   4. Applied rewrites 52.3%

      \[\leadsto \color{blue}{x} \]

   if 0.900000000000000022 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{\color{blue}{x}} \]

   4. Applied rewrites 50.4%

      \[\leadsto \color{blue}{\frac{0.5 + 0.2514179000665374 \cdot \frac{1}{{x}^{2}}}{x}} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \frac{\frac{1}{2} + \frac{600041}{2386628} \cdot \frac{1}{{x}^{2}}}{x} \]

      2. +-commutative N/A

         \[\leadsto \frac{\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} + \frac{1}{2}}{x} \]

      3. add-flip N/A

         \[\leadsto \frac{\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      4. lower--.f64 N/A

         \[\leadsto \frac{\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      6. lift-/.f64 N/A

         \[\leadsto \frac{\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      7. lift-pow.f64 N/A

         \[\leadsto \frac{\frac{600041}{2386628} \cdot \frac{1}{{x}^{2}} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      8. pow2 N/A

         \[\leadsto \frac{\frac{600041}{2386628} \cdot \frac{1}{x \cdot x} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      9. mult-flip-rev N/A

         \[\leadsto \frac{\frac{\frac{600041}{2386628}}{x \cdot x} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      10. lower-/.f64 N/A

          \[\leadsto \frac{\frac{\frac{600041}{2386628}}{x \cdot x} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\frac{\frac{600041}{2386628}}{x \cdot x} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}{x} \]

      12. metadata-eval 50.4

          \[\leadsto \frac{\frac{0.2514179000665374}{x \cdot x} - -0.5}{x} \]

   6. Applied rewrites 50.4%

      \[\leadsto \frac{\frac{0.2514179000665374}{x \cdot x} - -0.5}{\color{blue}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 99.2% accurate, 31.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.7:\\ \;\;\;\;x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x\_m}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (* x_s (if (<= x_m 0.7) x_m (/ 0.5 x_m))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.7) {
		tmp = x_m;
	} else {
		tmp = 0.5 / x_m;
	}
	return x_s * tmp;
}

Fortran

x\_m =     private
x\_s =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_s, x_m)
use fmin_fmax_functions
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 0.7d0) then
        tmp = x_m
    else
        tmp = 0.5d0 / x_m
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 0.7) {
		tmp = x_m;
	} else {
		tmp = 0.5 / x_m;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if x_m <= 0.7:
		tmp = x_m
	else:
		tmp = 0.5 / x_m
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (x_m <= 0.7)
		tmp = x_m;
	else
		tmp = Float64(0.5 / x_m);
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if (x_m <= 0.7)
		tmp = x_m;
	else
		tmp = 0.5 / x_m;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[x$95$m, 0.7], x$95$m, N[(0.5 / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.69999999999999996

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x} \]
   3. Step-by-step derivation

   4. Applied rewrites 52.3%

      \[\leadsto \color{blue}{x} \]

   if 0.69999999999999996 < x

   1. Initial program 54.9%

      \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{\frac{1}{2}}{x}} \]
   3. Step-by-step derivation

      1. lower-/.f64 50.6

         \[\leadsto \frac{0.5}{\color{blue}{x}} \]

   4. Applied rewrites 50.6%

      \[\leadsto \color{blue}{\frac{0.5}{x}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 52.3% accurate, 253.1× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot x\_m \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m) :precision binary64 (* x_s x_m))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * x_m;
}

Fortran

x\_m =     private
x\_s =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(x_s, x_m)
use fmin_fmax_functions
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * x_m
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	return x_s * x_m;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * x_m

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * x_m)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * x_m;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * x$95$m), $MachinePrecision]

Derivation

1. Initial program 54.9%

   \[\frac{\left(\left(\left(\left(1 + 0.1049934947 \cdot \left(x \cdot x\right)\right) + 0.0424060604 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0072644182 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0005064034 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0001789971 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)}{\left(\left(\left(\left(\left(1 + 0.7715471019 \cdot \left(x \cdot x\right)\right) + 0.2909738639 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0694555761 \cdot \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0140005442 \cdot \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + 0.0008327945 \cdot \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)\right) + \left(2 \cdot 0.0001789971\right) \cdot \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right)} \cdot x \]

2. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x} \]
3. Step-by-step derivation

4. Applied rewrites 52.3%

   \[\leadsto \color{blue}{x} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025149 
(FPCore (x)
  :name "Jmat.Real.dawson"
  :precision binary64
  (* (/ (+ (+ (+ (+ (+ 1.0 (* 0.1049934947 (* x x))) (* 0.0424060604 (* (* x x) (* x x)))) (* 0.0072644182 (* (* (* x x) (* x x)) (* x x)))) (* 0.0005064034 (* (* (* (* x x) (* x x)) (* x x)) (* x x)))) (* 0.0001789971 (* (* (* (* (* x x) (* x x)) (* x x)) (* x x)) (* x x)))) (+ (+ (+ (+ (+ (+ 1.0 (* 0.7715471019 (* x x))) (* 0.2909738639 (* (* x x) (* x x)))) (* 0.0694555761 (* (* (* x x) (* x x)) (* x x)))) (* 0.0140005442 (* (* (* (* x x) (* x x)) (* x x)) (* x x)))) (* 0.0008327945 (* (* (* (* (* x x) (* x x)) (* x x)) (* x x)) (* x x)))) (* (* 2.0 0.0001789971) (* (* (* (* (* (* x x) (* x x)) (* x x)) (* x x)) (* x x)) (* x x))))) x))