Jmat.Real.dawson

Percentage Accurate: 53.2% → 100.0%

Time: 13.8s

Alternatives: 11

Speedup: 23.0×

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

real(8) function code(x)
    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: 53.2% 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

real(8) function code(x)
    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, 0.3× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ \begin{array}{l} t_0 := \mathsf{fma}\left({x\_m}^{10}, 0.0001789971, \mathsf{fma}\left({x\_m}^{8}, 0.0005064034, 0.0072644182 \cdot {x\_m}^{6}\right)\right)\\ t_1 := \mathsf{fma}\left(\mathsf{fma}\left(x\_m \cdot x\_m, 0.0424060604, 0.1049934947\right), x\_m \cdot x\_m, 1\right)\\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 50000000:\\ \;\;\;\;\frac{\left({t\_1}^{2} - {t\_0}^{2}\right) \cdot x\_m}{\mathsf{fma}\left({x\_m}^{12}, 0.0003579942, \mathsf{fma}\left({x\_m}^{10}, 0.0008327945, \mathsf{fma}\left({x\_m}^{8}, 0.0140005442, \mathsf{fma}\left({x\_m}^{6}, 0.0694555761, \mathsf{fma}\left({x\_m}^{4}, 0.2909738639, \mathsf{fma}\left(0.7715471019, x\_m \cdot x\_m, 1\right)\right)\right)\right)\right)\right) \cdot \left(t\_1 - t\_0\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x\_m}\\ \end{array} \end{array} \end{array} \]

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
         (fma
          (pow x_m 10.0)
          0.0001789971
          (fma (pow x_m 8.0) 0.0005064034 (* 0.0072644182 (pow x_m 6.0)))))
        (t_1
         (fma (fma (* x_m x_m) 0.0424060604 0.1049934947) (* x_m x_m) 1.0)))
   (*
    x_s
    (if (<= x_m 50000000.0)
      (/
       (* (- (pow t_1 2.0) (pow t_0 2.0)) x_m)
       (*
        (fma
         (pow x_m 12.0)
         0.0003579942
         (fma
          (pow x_m 10.0)
          0.0008327945
          (fma
           (pow x_m 8.0)
           0.0140005442
           (fma
            (pow x_m 6.0)
            0.0694555761
            (fma
             (pow x_m 4.0)
             0.2909738639
             (fma 0.7715471019 (* x_m x_m) 1.0))))))
        (- t_1 t_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 = fma(pow(x_m, 10.0), 0.0001789971, fma(pow(x_m, 8.0), 0.0005064034, (0.0072644182 * pow(x_m, 6.0))));
	double t_1 = fma(fma((x_m * x_m), 0.0424060604, 0.1049934947), (x_m * x_m), 1.0);
	double tmp;
	if (x_m <= 50000000.0) {
		tmp = ((pow(t_1, 2.0) - pow(t_0, 2.0)) * x_m) / (fma(pow(x_m, 12.0), 0.0003579942, fma(pow(x_m, 10.0), 0.0008327945, fma(pow(x_m, 8.0), 0.0140005442, fma(pow(x_m, 6.0), 0.0694555761, fma(pow(x_m, 4.0), 0.2909738639, fma(0.7715471019, (x_m * x_m), 1.0)))))) * (t_1 - t_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 = fma((x_m ^ 10.0), 0.0001789971, fma((x_m ^ 8.0), 0.0005064034, Float64(0.0072644182 * (x_m ^ 6.0))))
	t_1 = fma(fma(Float64(x_m * x_m), 0.0424060604, 0.1049934947), Float64(x_m * x_m), 1.0)
	tmp = 0.0
	if (x_m <= 50000000.0)
		tmp = Float64(Float64(Float64((t_1 ^ 2.0) - (t_0 ^ 2.0)) * x_m) / Float64(fma((x_m ^ 12.0), 0.0003579942, fma((x_m ^ 10.0), 0.0008327945, fma((x_m ^ 8.0), 0.0140005442, fma((x_m ^ 6.0), 0.0694555761, fma((x_m ^ 4.0), 0.2909738639, fma(0.7715471019, Float64(x_m * x_m), 1.0)))))) * Float64(t_1 - t_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[Power[x$95$m, 10.0], $MachinePrecision] * 0.0001789971 + N[(N[Power[x$95$m, 8.0], $MachinePrecision] * 0.0005064034 + N[(0.0072644182 * N[Power[x$95$m, 6.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = 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]}, N[(x$95$s * If[LessEqual[x$95$m, 50000000.0], N[(N[(N[(N[Power[t$95$1, 2.0], $MachinePrecision] - N[Power[t$95$0, 2.0], $MachinePrecision]), $MachinePrecision] * x$95$m), $MachinePrecision] / N[(N[(N[Power[x$95$m, 12.0], $MachinePrecision] * 0.0003579942 + N[(N[Power[x$95$m, 10.0], $MachinePrecision] * 0.0008327945 + N[(N[Power[x$95$m, 8.0], $MachinePrecision] * 0.0140005442 + N[(N[Power[x$95$m, 6.0], $MachinePrecision] * 0.0694555761 + N[(N[Power[x$95$m, 4.0], $MachinePrecision] * 0.2909738639 + N[(0.7715471019 * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(t$95$1 - t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(0.5 / x$95$m), $MachinePrecision]]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < 5e7

   1. Initial program 70.0%

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

   4. Applied rewrites 69.8%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(0.0003579942, {x}^{12}, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(0.0140005442, {x}^{8}, \mathsf{fma}\left(0.0694555761, {x}^{6}, \mathsf{fma}\left(0.2909738639, {x}^{4}, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}{\mathsf{fma}\left(0.0001789971, {x}^{10}, \mathsf{fma}\left(0.0005064034, {x}^{8}, \mathsf{fma}\left(0.0072644182, {x}^{6}, \mathsf{fma}\left(0.0424060604, {x}^{4}, \mathsf{fma}\left(0.1049934947, x \cdot x, 1\right)\right)\right)\right)\right) \cdot x}}} \]

   6. Applied rewrites 69.9%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left({x}^{8}, 0.0005064034, \mathsf{fma}\left({x}^{6}, 0.0072644182, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), 1\right)\right)\right)\right) \cdot x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), 1\right)\right)\right)\right)\right)}} \]

   8. Applied rewrites 70.0%

      \[\leadsto \frac{\color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.0424060604, x \cdot x, 0.1049934947\right), x \cdot x, 1\right) + \mathsf{fma}\left({x}^{6}, 0.0072644182, \mathsf{fma}\left({x}^{10}, 0.0001789971, {x}^{8} \cdot 0.0005064034\right)\right)\right)} \cdot x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), 1\right)\right)\right)\right)\right)} \]

   10. Applied rewrites 69.5%

       \[\leadsto \color{blue}{\frac{\left({\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right)\right)}^{2} - {\left(\mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left({x}^{8}, 0.0005064034, {x}^{6} \cdot 0.0072644182\right)\right)\right)}^{2}\right) \cdot x}{\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right) - \mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left({x}^{8}, 0.0005064034, {x}^{6} \cdot 0.0072644182\right)\right)\right) \cdot \mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left({x}^{4}, 0.2909738639, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}} \]

   if 5e7 < x

   1. Initial program 2.2%

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

   3. Taylor expanded in x around inf

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

      1. lower-/.f64 100.0

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

   5. Applied rewrites 100.0%

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

4. Final simplification 75.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 50000000:\\ \;\;\;\;\frac{\left({\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right)\right)}^{2} - {\left(\mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left({x}^{8}, 0.0005064034, 0.0072644182 \cdot {x}^{6}\right)\right)\right)}^{2}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left({x}^{4}, 0.2909738639, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), x \cdot x, 1\right) - \mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left({x}^{8}, 0.0005064034, 0.0072644182 \cdot {x}^{6}\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 100:\\ \;\;\;\;\frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(x\_m \cdot x\_m, 0.0001789971, 0.0005064034\right), x\_m \cdot x\_m, 0.0072644182\right) \cdot {x\_m}^{6} + \mathsf{fma}\left(\mathsf{fma}\left(0.0424060604, x\_m \cdot x\_m, 0.1049934947\right), x\_m \cdot x\_m, 1\right)\right) \cdot x\_m}{\mathsf{fma}\left({x\_m}^{12}, 0.0003579942, \mathsf{fma}\left({x\_m}^{10}, 0.0008327945, \mathsf{fma}\left({x\_m}^{8}, 0.0140005442, \mathsf{fma}\left({x\_m}^{6}, 0.0694555761, \mathsf{fma}\left(x\_m \cdot x\_m, \mathsf{fma}\left(x\_m \cdot x\_m, 0.2909738639, 0.7715471019\right), 1\right)\right)\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{-0.10624017004622396 - \frac{44.67740114490945}{x\_m \cdot x\_m}}{{x\_m}^{4}} - -2, x\_m, \frac{-1.0056716002661497}{x\_m}\right)}\\ \end{array} \end{array} \]

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 100.0)
    (/
     (*
      (+
       (*
        (fma
         (fma (* x_m x_m) 0.0001789971 0.0005064034)
         (* x_m x_m)
         0.0072644182)
        (pow x_m 6.0))
       (fma (fma 0.0424060604 (* x_m x_m) 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
        (pow x_m 8.0)
        0.0140005442
        (fma
         (pow x_m 6.0)
         0.0694555761
         (fma (* x_m x_m) (fma (* x_m x_m) 0.2909738639 0.7715471019) 1.0))))))
    (/
     1.0
     (fma
      (-
       (/
        (- -0.10624017004622396 (/ 44.67740114490945 (* x_m x_m)))
        (pow x_m 4.0))
       -2.0)
      x_m
      (/ -1.0056716002661497 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 <= 100.0) {
		tmp = (((fma(fma((x_m * x_m), 0.0001789971, 0.0005064034), (x_m * x_m), 0.0072644182) * pow(x_m, 6.0)) + fma(fma(0.0424060604, (x_m * x_m), 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(pow(x_m, 8.0), 0.0140005442, fma(pow(x_m, 6.0), 0.0694555761, fma((x_m * x_m), fma((x_m * x_m), 0.2909738639, 0.7715471019), 1.0)))));
	} else {
		tmp = 1.0 / fma((((-0.10624017004622396 - (44.67740114490945 / (x_m * x_m))) / pow(x_m, 4.0)) - -2.0), x_m, (-1.0056716002661497 / 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 <= 100.0)
		tmp = Float64(Float64(Float64(Float64(fma(fma(Float64(x_m * x_m), 0.0001789971, 0.0005064034), Float64(x_m * x_m), 0.0072644182) * (x_m ^ 6.0)) + fma(fma(0.0424060604, Float64(x_m * x_m), 0.1049934947), Float64(x_m * x_m), 1.0)) * x_m) / fma((x_m ^ 12.0), 0.0003579942, fma((x_m ^ 10.0), 0.0008327945, fma((x_m ^ 8.0), 0.0140005442, fma((x_m ^ 6.0), 0.0694555761, fma(Float64(x_m * x_m), fma(Float64(x_m * x_m), 0.2909738639, 0.7715471019), 1.0))))));
	else
		tmp = Float64(1.0 / fma(Float64(Float64(Float64(-0.10624017004622396 - Float64(44.67740114490945 / Float64(x_m * x_m))) / (x_m ^ 4.0)) - -2.0), x_m, Float64(-1.0056716002661497 / 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, 100.0], N[(N[(N[(N[(N[(N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.0001789971 + 0.0005064034), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 0.0072644182), $MachinePrecision] * N[Power[x$95$m, 6.0], $MachinePrecision]), $MachinePrecision] + N[(N[(0.0424060604 * N[(x$95$m * x$95$m), $MachinePrecision] + 0.1049934947), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] * x$95$m), $MachinePrecision] / N[(N[Power[x$95$m, 12.0], $MachinePrecision] * 0.0003579942 + N[(N[Power[x$95$m, 10.0], $MachinePrecision] * 0.0008327945 + N[(N[Power[x$95$m, 8.0], $MachinePrecision] * 0.0140005442 + N[(N[Power[x$95$m, 6.0], $MachinePrecision] * 0.0694555761 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.2909738639 + 0.7715471019), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(N[(N[(-0.10624017004622396 - N[(44.67740114490945 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[Power[x$95$m, 4.0], $MachinePrecision]), $MachinePrecision] - -2.0), $MachinePrecision] * x$95$m + N[(-1.0056716002661497 / x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 100

   1. Initial program 69.7%

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

   4. Applied rewrites 69.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(0.0003579942, {x}^{12}, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(0.0140005442, {x}^{8}, \mathsf{fma}\left(0.0694555761, {x}^{6}, \mathsf{fma}\left(0.2909738639, {x}^{4}, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}{\mathsf{fma}\left(0.0001789971, {x}^{10}, \mathsf{fma}\left(0.0005064034, {x}^{8}, \mathsf{fma}\left(0.0072644182, {x}^{6}, \mathsf{fma}\left(0.0424060604, {x}^{4}, \mathsf{fma}\left(0.1049934947, x \cdot x, 1\right)\right)\right)\right)\right) \cdot x}}} \]

   6. Applied rewrites 69.7%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left({x}^{10}, 0.0001789971, \mathsf{fma}\left({x}^{8}, 0.0005064034, \mathsf{fma}\left({x}^{6}, 0.0072644182, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.0424060604, 0.1049934947\right), 1\right)\right)\right)\right) \cdot x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), 1\right)\right)\right)\right)\right)}} \]

   8. Applied rewrites 69.7%

      \[\leadsto \frac{\color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.0424060604, x \cdot x, 0.1049934947\right), x \cdot x, 1\right) + \mathsf{fma}\left({x}^{6}, 0.0072644182, \mathsf{fma}\left({x}^{10}, 0.0001789971, {x}^{8} \cdot 0.0005064034\right)\right)\right)} \cdot x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), 1\right)\right)\right)\right)\right)} \]

   9. Taylor expanded in x around 0

      \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \color{blue}{{x}^{6} \cdot \left(\frac{36322091}{5000000000} + {x}^{2} \cdot \left(\frac{2532017}{5000000000} + \frac{1789971}{10000000000} \cdot {x}^{2}\right)\right)}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]
   10. Step-by-step derivation

       1. *-commutative N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \color{blue}{\left(\frac{36322091}{5000000000} + {x}^{2} \cdot \left(\frac{2532017}{5000000000} + \frac{1789971}{10000000000} \cdot {x}^{2}\right)\right) \cdot {x}^{6}}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       2. lower-*.f64 N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \color{blue}{\left(\frac{36322091}{5000000000} + {x}^{2} \cdot \left(\frac{2532017}{5000000000} + \frac{1789971}{10000000000} \cdot {x}^{2}\right)\right) \cdot {x}^{6}}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       3. +-commutative N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \color{blue}{\left({x}^{2} \cdot \left(\frac{2532017}{5000000000} + \frac{1789971}{10000000000} \cdot {x}^{2}\right) + \frac{36322091}{5000000000}\right)} \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       4. *-commutative N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \left(\color{blue}{\left(\frac{2532017}{5000000000} + \frac{1789971}{10000000000} \cdot {x}^{2}\right) \cdot {x}^{2}} + \frac{36322091}{5000000000}\right) \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       5. lower-fma.f64 N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \color{blue}{\mathsf{fma}\left(\frac{2532017}{5000000000} + \frac{1789971}{10000000000} \cdot {x}^{2}, {x}^{2}, \frac{36322091}{5000000000}\right)} \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       6. +-commutative N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \mathsf{fma}\left(\color{blue}{\frac{1789971}{10000000000} \cdot {x}^{2} + \frac{2532017}{5000000000}}, {x}^{2}, \frac{36322091}{5000000000}\right) \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       7. *-commutative N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{1789971}{10000000000}} + \frac{2532017}{5000000000}, {x}^{2}, \frac{36322091}{5000000000}\right) \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       8. lower-fma.f64 N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1789971}{10000000000}, \frac{2532017}{5000000000}\right)}, {x}^{2}, \frac{36322091}{5000000000}\right) \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       9. unpow2 N/A

          \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1789971}{10000000000}, \frac{2532017}{5000000000}\right), {x}^{2}, \frac{36322091}{5000000000}\right) \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       10. lower-*.f64 N/A

           \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1789971}{10000000000}, \frac{2532017}{5000000000}\right), {x}^{2}, \frac{36322091}{5000000000}\right) \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       11. unpow2 N/A

           \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1789971}{10000000000}, \frac{2532017}{5000000000}\right), \color{blue}{x \cdot x}, \frac{36322091}{5000000000}\right) \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       12. lower-*.f64 N/A

           \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{106015151}{2500000000}, x \cdot x, \frac{1049934947}{10000000000}\right), x \cdot x, 1\right) + \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1789971}{10000000000}, \frac{2532017}{5000000000}\right), \color{blue}{x \cdot x}, \frac{36322091}{5000000000}\right) \cdot {x}^{6}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, \frac{1789971}{5000000000}, \mathsf{fma}\left({x}^{10}, \frac{1665589}{2000000000}, \mathsf{fma}\left({x}^{8}, \frac{70002721}{5000000000}, \mathsf{fma}\left({x}^{6}, \frac{694555761}{10000000000}, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \frac{2909738639}{10000000000}, \frac{7715471019}{10000000000}\right), 1\right)\right)\right)\right)\right)} \]

       13. lower-pow.f64 69.7

           \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.0424060604, x \cdot x, 0.1049934947\right), x \cdot x, 1\right) + \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0001789971, 0.0005064034\right), x \cdot x, 0.0072644182\right) \cdot \color{blue}{{x}^{6}}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), 1\right)\right)\right)\right)\right)} \]

   11. Applied rewrites 69.7%

       \[\leadsto \frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.0424060604, x \cdot x, 0.1049934947\right), x \cdot x, 1\right) + \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0001789971, 0.0005064034\right), x \cdot x, 0.0072644182\right) \cdot {x}^{6}}\right) \cdot x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), 1\right)\right)\right)\right)\right)} \]

   if 100 < x

   1. Initial program 6.3%

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

   4. Applied rewrites 6.3%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(0.0003579942, {x}^{12}, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(0.0140005442, {x}^{8}, \mathsf{fma}\left(0.0694555761, {x}^{6}, \mathsf{fma}\left(0.2909738639, {x}^{4}, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}{\mathsf{fma}\left(0.0001789971, {x}^{10}, \mathsf{fma}\left(0.0005064034, {x}^{8}, \mathsf{fma}\left(0.0072644182, {x}^{6}, \mathsf{fma}\left(0.0424060604, {x}^{4}, \mathsf{fma}\left(0.1049934947, x \cdot x, 1\right)\right)\right)\right)\right) \cdot x}}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{x \cdot \left(\left(2 + -1 \cdot \frac{\frac{113464366360}{1067998726947} + \frac{85409195760635256467}{1911686749272048537} \cdot \frac{1}{{x}^{2}}}{{x}^{4}}\right) - \frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)}} \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \frac{1}{x \cdot \color{blue}{\left(\left(2 + -1 \cdot \frac{\frac{113464366360}{1067998726947} + \frac{85409195760635256467}{1911686749272048537} \cdot \frac{1}{{x}^{2}}}{{x}^{4}}\right) + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}} \]

      2. distribute-rgt-in N/A

         \[\leadsto \frac{1}{\color{blue}{\left(2 + -1 \cdot \frac{\frac{113464366360}{1067998726947} + \frac{85409195760635256467}{1911686749272048537} \cdot \frac{1}{{x}^{2}}}{{x}^{4}}\right) \cdot x + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x}} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2 + -1 \cdot \frac{\frac{113464366360}{1067998726947} + \frac{85409195760635256467}{1911686749272048537} \cdot \frac{1}{{x}^{2}}}{{x}^{4}}, x, \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x\right)}} \]

   7. Applied rewrites 100.0%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{-0.10624017004622396 - \frac{44.67740114490945}{x \cdot x}}{{x}^{4}} - -2, x, \frac{-1.0056716002661497}{x}\right)}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 75.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 100:\\ \;\;\;\;\frac{\left(\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.0001789971, 0.0005064034\right), x \cdot x, 0.0072644182\right) \cdot {x}^{6} + \mathsf{fma}\left(\mathsf{fma}\left(0.0424060604, x \cdot x, 0.1049934947\right), x \cdot x, 1\right)\right) \cdot x}{\mathsf{fma}\left({x}^{12}, 0.0003579942, \mathsf{fma}\left({x}^{10}, 0.0008327945, \mathsf{fma}\left({x}^{8}, 0.0140005442, \mathsf{fma}\left({x}^{6}, 0.0694555761, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.2909738639, 0.7715471019\right), 1\right)\right)\right)\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{-0.10624017004622396 - \frac{44.67740114490945}{x \cdot x}}{{x}^{4}} - -2, x, \frac{-1.0056716002661497}{x}\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ \begin{array}{l} t_0 := \left(x\_m \cdot x\_m\right) \cdot \left(x\_m \cdot x\_m\right)\\ t_1 := t\_0 \cdot \left(x\_m \cdot x\_m\right)\\ t_2 := t\_1 \cdot \left(x\_m \cdot x\_m\right)\\ t_3 := t\_2 \cdot \left(x\_m \cdot x\_m\right)\\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 5000:\\ \;\;\;\;\frac{t\_3 \cdot 0.0001789971 + \left(t\_2 \cdot 0.0005064034 + \left(t\_1 \cdot 0.0072644182 + \left(t\_0 \cdot 0.0424060604 + \left(0.1049934947 \cdot \left(x\_m \cdot x\_m\right) + 1\right)\right)\right)\right)}{\left(t\_3 \cdot \left(x\_m \cdot x\_m\right)\right) \cdot \left(0.0001789971 \cdot 2\right) + \left(t\_3 \cdot 0.0008327945 + \left(t\_2 \cdot 0.0140005442 + \left(t\_1 \cdot 0.0694555761 + \left(t\_0 \cdot 0.2909738639 + \left(0.7715471019 \cdot \left(x\_m \cdot x\_m\right) + 1\right)\right)\right)\right)\right)} \cdot x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(2, x\_m, \frac{-1.0056716002661497}{x\_m}\right)}\\ \end{array} \end{array} \end{array} \]

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_m)))
        (t_1 (* t_0 (* x_m x_m)))
        (t_2 (* t_1 (* x_m x_m)))
        (t_3 (* t_2 (* x_m x_m))))
   (*
    x_s
    (if (<= x_m 5000.0)
      (*
       (/
        (+
         (* t_3 0.0001789971)
         (+
          (* t_2 0.0005064034)
          (+
           (* t_1 0.0072644182)
           (+ (* t_0 0.0424060604) (+ (* 0.1049934947 (* x_m x_m)) 1.0)))))
        (+
         (* (* t_3 (* x_m x_m)) (* 0.0001789971 2.0))
         (+
          (* t_3 0.0008327945)
          (+
           (* t_2 0.0140005442)
           (+
            (* t_1 0.0694555761)
            (+ (* t_0 0.2909738639) (+ (* 0.7715471019 (* x_m x_m)) 1.0)))))))
       x_m)
      (/ 1.0 (fma 2.0 x_m (/ -1.0056716002661497 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 * x_m);
	double t_1 = t_0 * (x_m * x_m);
	double t_2 = t_1 * (x_m * x_m);
	double t_3 = t_2 * (x_m * x_m);
	double tmp;
	if (x_m <= 5000.0) {
		tmp = (((t_3 * 0.0001789971) + ((t_2 * 0.0005064034) + ((t_1 * 0.0072644182) + ((t_0 * 0.0424060604) + ((0.1049934947 * (x_m * x_m)) + 1.0))))) / (((t_3 * (x_m * x_m)) * (0.0001789971 * 2.0)) + ((t_3 * 0.0008327945) + ((t_2 * 0.0140005442) + ((t_1 * 0.0694555761) + ((t_0 * 0.2909738639) + ((0.7715471019 * (x_m * x_m)) + 1.0))))))) * x_m;
	} else {
		tmp = 1.0 / fma(2.0, x_m, (-1.0056716002661497 / 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) * Float64(x_m * x_m))
	t_1 = Float64(t_0 * Float64(x_m * x_m))
	t_2 = Float64(t_1 * Float64(x_m * x_m))
	t_3 = Float64(t_2 * Float64(x_m * x_m))
	tmp = 0.0
	if (x_m <= 5000.0)
		tmp = Float64(Float64(Float64(Float64(t_3 * 0.0001789971) + Float64(Float64(t_2 * 0.0005064034) + Float64(Float64(t_1 * 0.0072644182) + Float64(Float64(t_0 * 0.0424060604) + Float64(Float64(0.1049934947 * Float64(x_m * x_m)) + 1.0))))) / Float64(Float64(Float64(t_3 * Float64(x_m * x_m)) * Float64(0.0001789971 * 2.0)) + Float64(Float64(t_3 * 0.0008327945) + Float64(Float64(t_2 * 0.0140005442) + Float64(Float64(t_1 * 0.0694555761) + Float64(Float64(t_0 * 0.2909738639) + Float64(Float64(0.7715471019 * Float64(x_m * x_m)) + 1.0))))))) * x_m);
	else
		tmp = Float64(1.0 / fma(2.0, x_m, Float64(-1.0056716002661497 / 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] * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(t$95$2 * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]}, N[(x$95$s * If[LessEqual[x$95$m, 5000.0], N[(N[(N[(N[(t$95$3 * 0.0001789971), $MachinePrecision] + N[(N[(t$95$2 * 0.0005064034), $MachinePrecision] + N[(N[(t$95$1 * 0.0072644182), $MachinePrecision] + N[(N[(t$95$0 * 0.0424060604), $MachinePrecision] + N[(N[(0.1049934947 * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(t$95$3 * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] * N[(0.0001789971 * 2.0), $MachinePrecision]), $MachinePrecision] + N[(N[(t$95$3 * 0.0008327945), $MachinePrecision] + N[(N[(t$95$2 * 0.0140005442), $MachinePrecision] + N[(N[(t$95$1 * 0.0694555761), $MachinePrecision] + N[(N[(t$95$0 * 0.2909738639), $MachinePrecision] + N[(N[(0.7715471019 * N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x$95$m), $MachinePrecision], N[(1.0 / N[(2.0 * x$95$m + N[(-1.0056716002661497 / x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if x < 5e3

   1. Initial program 69.8%

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

   if 5e3 < x

   1. Initial program 4.3%

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

   4. Applied rewrites 4.3%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(0.0003579942, {x}^{12}, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(0.0140005442, {x}^{8}, \mathsf{fma}\left(0.0694555761, {x}^{6}, \mathsf{fma}\left(0.2909738639, {x}^{4}, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}{\mathsf{fma}\left(0.0001789971, {x}^{10}, \mathsf{fma}\left(0.0005064034, {x}^{8}, \mathsf{fma}\left(0.0072644182, {x}^{6}, \mathsf{fma}\left(0.0424060604, {x}^{4}, \mathsf{fma}\left(0.1049934947, x \cdot x, 1\right)\right)\right)\right)\right) \cdot x}}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{x \cdot \left(2 - \frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)}} \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \frac{1}{x \cdot \color{blue}{\left(2 + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}} \]

      2. distribute-rgt-in N/A

         \[\leadsto \frac{1}{\color{blue}{2 \cdot x + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x}} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2, x, \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x\right)}} \]

      4. distribute-lft-neg-out N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\mathsf{neg}\left(\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right) \cdot x\right)}\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{x \cdot \left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)}\right)\right)} \]

      6. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(x \cdot \color{blue}{\left(\frac{1}{{x}^{2}} \cdot \frac{600041}{596657}\right)}\right)\right)} \]

      7. associate-*r* N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\left(x \cdot \frac{1}{{x}^{2}}\right) \cdot \frac{600041}{596657}}\right)\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\left(x \cdot \frac{1}{\color{blue}{x \cdot x}}\right) \cdot \frac{600041}{596657}\right)\right)} \]

      9. associate-/r* N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\left(x \cdot \color{blue}{\frac{\frac{1}{x}}{x}}\right) \cdot \frac{600041}{596657}\right)\right)} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\frac{x \cdot \frac{1}{x}}{x}} \cdot \frac{600041}{596657}\right)\right)} \]

      11. rgt-mult-inverse N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\frac{\color{blue}{1}}{x} \cdot \frac{600041}{596657}\right)\right)} \]

      12. associate-*l/ N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\frac{1 \cdot \frac{600041}{596657}}{x}}\right)\right)} \]

      13. metadata-eval N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\frac{\color{blue}{\frac{600041}{596657}}}{x}\right)\right)} \]

      14. distribute-neg-frac N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\frac{\mathsf{neg}\left(\frac{600041}{596657}\right)}{x}}\right)} \]

      15. lower-/.f64 N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\frac{\mathsf{neg}\left(\frac{600041}{596657}\right)}{x}}\right)} \]

      16. metadata-eval 100.0

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \frac{\color{blue}{-1.0056716002661497}}{x}\right)} \]

   7. Applied rewrites 100.0%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2, x, \frac{-1.0056716002661497}{x}\right)}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 75.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 5000:\\ \;\;\;\;\frac{\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot 0.0001789971 + \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot 0.0005064034 + \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot 0.0072644182 + \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot 0.0424060604 + \left(0.1049934947 \cdot \left(x \cdot x\right) + 1\right)\right)\right)\right)}{\left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(0.0001789971 \cdot 2\right) + \left(\left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot 0.0008327945 + \left(\left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot 0.0140005442 + \left(\left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot x\right)\right) \cdot 0.0694555761 + \left(\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot 0.2909738639 + \left(0.7715471019 \cdot \left(x \cdot x\right) + 1\right)\right)\right)\right)\right)} \cdot x\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(2, x, \frac{-1.0056716002661497}{x}\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.8% accurate, 2.5× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 1.75:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x\_m \cdot x\_m, 0.265709700396151\right), x\_m \cdot x\_m, -0.6665536072\right), x\_m \cdot x\_m, 1\right) \cdot x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\frac{-0.10624017004622396 - \frac{44.67740114490945}{x\_m \cdot x\_m}}{{x\_m}^{4}} - -2, x\_m, \frac{-1.0056716002661497}{x\_m}\right)}\\ \end{array} \end{array} \]

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 1.75)
    (*
     (fma
      (fma
       (fma -0.0732490286039007 (* x_m x_m) 0.265709700396151)
       (* x_m x_m)
       -0.6665536072)
      (* x_m x_m)
      1.0)
     x_m)
    (/
     1.0
     (fma
      (-
       (/
        (- -0.10624017004622396 (/ 44.67740114490945 (* x_m x_m)))
        (pow x_m 4.0))
       -2.0)
      x_m
      (/ -1.0056716002661497 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 <= 1.75) {
		tmp = fma(fma(fma(-0.0732490286039007, (x_m * x_m), 0.265709700396151), (x_m * x_m), -0.6665536072), (x_m * x_m), 1.0) * x_m;
	} else {
		tmp = 1.0 / fma((((-0.10624017004622396 - (44.67740114490945 / (x_m * x_m))) / pow(x_m, 4.0)) - -2.0), x_m, (-1.0056716002661497 / 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 <= 1.75)
		tmp = Float64(fma(fma(fma(-0.0732490286039007, Float64(x_m * x_m), 0.265709700396151), Float64(x_m * x_m), -0.6665536072), Float64(x_m * x_m), 1.0) * x_m);
	else
		tmp = Float64(1.0 / fma(Float64(Float64(Float64(-0.10624017004622396 - Float64(44.67740114490945 / Float64(x_m * x_m))) / (x_m ^ 4.0)) - -2.0), x_m, Float64(-1.0056716002661497 / 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, 1.75], N[(N[(N[(N[(-0.0732490286039007 * N[(x$95$m * x$95$m), $MachinePrecision] + 0.265709700396151), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + -0.6665536072), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision] * x$95$m), $MachinePrecision], N[(1.0 / N[(N[(N[(N[(-0.10624017004622396 - N[(44.67740114490945 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[Power[x$95$m, 4.0], $MachinePrecision]), $MachinePrecision] - -2.0), $MachinePrecision] * x$95$m + N[(-1.0056716002661497 / x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 1.75

   1. Initial program 69.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right)\right)} \cdot x \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right) + 1\right)} \cdot x \]

      2. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}, {x}^{2}, 1\right)} \cdot x \]

      4. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) + \left(\mathsf{neg}\left(\frac{833192009}{1250000000}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) \cdot {x}^{2}} + \left(\mathsf{neg}\left(\frac{833192009}{1250000000}\right)\right), {x}^{2}, 1\right) \cdot x \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) \cdot {x}^{2} + \color{blue}{\frac{-833192009}{1250000000}}, {x}^{2}, 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}, {x}^{2}, \frac{-833192009}{1250000000}\right)}, {x}^{2}, 1\right) \cdot x \]

      8. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2} + \frac{3321371254951887171}{12500000000000000000}}, {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, {x}^{2}, \frac{3321371254951887171}{12500000000000000000}\right)}, {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      10. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, \color{blue}{x \cdot x}, \frac{3321371254951887171}{12500000000000000000}\right), {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      11. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, \color{blue}{x \cdot x}, \frac{3321371254951887171}{12500000000000000000}\right), {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      14. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), x \cdot x, \frac{-833192009}{1250000000}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      15. lower-*.f64 66.0

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x \cdot x, 0.265709700396151\right), x \cdot x, -0.6665536072\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 66.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x \cdot x, 0.265709700396151\right), x \cdot x, -0.6665536072\right), x \cdot x, 1\right)} \cdot x \]

   if 1.75 < x

   1. Initial program 6.3%

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

   4. Applied rewrites 6.3%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(0.0003579942, {x}^{12}, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(0.0140005442, {x}^{8}, \mathsf{fma}\left(0.0694555761, {x}^{6}, \mathsf{fma}\left(0.2909738639, {x}^{4}, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}{\mathsf{fma}\left(0.0001789971, {x}^{10}, \mathsf{fma}\left(0.0005064034, {x}^{8}, \mathsf{fma}\left(0.0072644182, {x}^{6}, \mathsf{fma}\left(0.0424060604, {x}^{4}, \mathsf{fma}\left(0.1049934947, x \cdot x, 1\right)\right)\right)\right)\right) \cdot x}}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{x \cdot \left(\left(2 + -1 \cdot \frac{\frac{113464366360}{1067998726947} + \frac{85409195760635256467}{1911686749272048537} \cdot \frac{1}{{x}^{2}}}{{x}^{4}}\right) - \frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)}} \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \frac{1}{x \cdot \color{blue}{\left(\left(2 + -1 \cdot \frac{\frac{113464366360}{1067998726947} + \frac{85409195760635256467}{1911686749272048537} \cdot \frac{1}{{x}^{2}}}{{x}^{4}}\right) + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}} \]

      2. distribute-rgt-in N/A

         \[\leadsto \frac{1}{\color{blue}{\left(2 + -1 \cdot \frac{\frac{113464366360}{1067998726947} + \frac{85409195760635256467}{1911686749272048537} \cdot \frac{1}{{x}^{2}}}{{x}^{4}}\right) \cdot x + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x}} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2 + -1 \cdot \frac{\frac{113464366360}{1067998726947} + \frac{85409195760635256467}{1911686749272048537} \cdot \frac{1}{{x}^{2}}}{{x}^{4}}, x, \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x\right)}} \]

   7. Applied rewrites 100.0%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(\frac{-0.10624017004622396 - \frac{44.67740114490945}{x \cdot x}}{{x}^{4}} - -2, x, \frac{-1.0056716002661497}{x}\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 99.8% accurate, 7.7× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 1.25:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x\_m \cdot x\_m, 0.265709700396151\right), x\_m \cdot x\_m, -0.6665536072\right), x\_m \cdot x\_m, 1\right) \cdot x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(2, x\_m, \frac{-1.0056716002661497 - \frac{0.10624017004622396}{x\_m \cdot x\_m}}{x\_m}\right)}\\ \end{array} \end{array} \]

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 1.25)
    (*
     (fma
      (fma
       (fma -0.0732490286039007 (* x_m x_m) 0.265709700396151)
       (* x_m x_m)
       -0.6665536072)
      (* x_m x_m)
      1.0)
     x_m)
    (/
     1.0
     (fma
      2.0
      x_m
      (/ (- -1.0056716002661497 (/ 0.10624017004622396 (* 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 <= 1.25) {
		tmp = fma(fma(fma(-0.0732490286039007, (x_m * x_m), 0.265709700396151), (x_m * x_m), -0.6665536072), (x_m * x_m), 1.0) * x_m;
	} else {
		tmp = 1.0 / fma(2.0, x_m, ((-1.0056716002661497 - (0.10624017004622396 / (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 <= 1.25)
		tmp = Float64(fma(fma(fma(-0.0732490286039007, Float64(x_m * x_m), 0.265709700396151), Float64(x_m * x_m), -0.6665536072), Float64(x_m * x_m), 1.0) * x_m);
	else
		tmp = Float64(1.0 / fma(2.0, x_m, Float64(Float64(-1.0056716002661497 - Float64(0.10624017004622396 / Float64(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, 1.25], N[(N[(N[(N[(-0.0732490286039007 * N[(x$95$m * x$95$m), $MachinePrecision] + 0.265709700396151), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + -0.6665536072), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision] * x$95$m), $MachinePrecision], N[(1.0 / N[(2.0 * x$95$m + N[(N[(-1.0056716002661497 - N[(0.10624017004622396 / N[(x$95$m * x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 1.25

   1. Initial program 69.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right)\right)} \cdot x \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right) + 1\right)} \cdot x \]

      2. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}, {x}^{2}, 1\right)} \cdot x \]

      4. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) + \left(\mathsf{neg}\left(\frac{833192009}{1250000000}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) \cdot {x}^{2}} + \left(\mathsf{neg}\left(\frac{833192009}{1250000000}\right)\right), {x}^{2}, 1\right) \cdot x \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) \cdot {x}^{2} + \color{blue}{\frac{-833192009}{1250000000}}, {x}^{2}, 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}, {x}^{2}, \frac{-833192009}{1250000000}\right)}, {x}^{2}, 1\right) \cdot x \]

      8. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2} + \frac{3321371254951887171}{12500000000000000000}}, {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, {x}^{2}, \frac{3321371254951887171}{12500000000000000000}\right)}, {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      10. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, \color{blue}{x \cdot x}, \frac{3321371254951887171}{12500000000000000000}\right), {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      11. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, \color{blue}{x \cdot x}, \frac{3321371254951887171}{12500000000000000000}\right), {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      14. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), x \cdot x, \frac{-833192009}{1250000000}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      15. lower-*.f64 66.0

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x \cdot x, 0.265709700396151\right), x \cdot x, -0.6665536072\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 66.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x \cdot x, 0.265709700396151\right), x \cdot x, -0.6665536072\right), x \cdot x, 1\right)} \cdot x \]

   if 1.25 < x

   1. Initial program 6.3%

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

   4. Applied rewrites 6.3%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(0.0003579942, {x}^{12}, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(0.0140005442, {x}^{8}, \mathsf{fma}\left(0.0694555761, {x}^{6}, \mathsf{fma}\left(0.2909738639, {x}^{4}, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}{\mathsf{fma}\left(0.0001789971, {x}^{10}, \mathsf{fma}\left(0.0005064034, {x}^{8}, \mathsf{fma}\left(0.0072644182, {x}^{6}, \mathsf{fma}\left(0.0424060604, {x}^{4}, \mathsf{fma}\left(0.1049934947, x \cdot x, 1\right)\right)\right)\right)\right) \cdot x}}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{x \cdot \left(2 + -1 \cdot \frac{\frac{600041}{596657} + \frac{113464366360}{1067998726947} \cdot \frac{1}{{x}^{2}}}{{x}^{2}}\right)}} \]

   7. Applied rewrites 99.9%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2, x, \frac{-1.0056716002661497 - \frac{0.10624017004622396}{x \cdot x}}{x}\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 99.7% accurate, 9.2× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 1.25:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x\_m \cdot x\_m, 0.265709700396151\right), x\_m \cdot x\_m, -0.6665536072\right), x\_m \cdot x\_m, 1\right) \cdot x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(2, x\_m, \frac{-1.0056716002661497}{x\_m}\right)}\\ \end{array} \end{array} \]

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 1.25)
    (*
     (fma
      (fma
       (fma -0.0732490286039007 (* x_m x_m) 0.265709700396151)
       (* x_m x_m)
       -0.6665536072)
      (* x_m x_m)
      1.0)
     x_m)
    (/ 1.0 (fma 2.0 x_m (/ -1.0056716002661497 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 <= 1.25) {
		tmp = fma(fma(fma(-0.0732490286039007, (x_m * x_m), 0.265709700396151), (x_m * x_m), -0.6665536072), (x_m * x_m), 1.0) * x_m;
	} else {
		tmp = 1.0 / fma(2.0, x_m, (-1.0056716002661497 / 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 <= 1.25)
		tmp = Float64(fma(fma(fma(-0.0732490286039007, Float64(x_m * x_m), 0.265709700396151), Float64(x_m * x_m), -0.6665536072), Float64(x_m * x_m), 1.0) * x_m);
	else
		tmp = Float64(1.0 / fma(2.0, x_m, Float64(-1.0056716002661497 / 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, 1.25], N[(N[(N[(N[(-0.0732490286039007 * N[(x$95$m * x$95$m), $MachinePrecision] + 0.265709700396151), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + -0.6665536072), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision] * x$95$m), $MachinePrecision], N[(1.0 / N[(2.0 * x$95$m + N[(-1.0056716002661497 / x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 1.25

   1. Initial program 69.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right)\right)} \cdot x \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right) + 1\right)} \cdot x \]

      2. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) - \frac{833192009}{1250000000}, {x}^{2}, 1\right)} \cdot x \]

      4. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) + \left(\mathsf{neg}\left(\frac{833192009}{1250000000}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) \cdot {x}^{2}} + \left(\mathsf{neg}\left(\frac{833192009}{1250000000}\right)\right), {x}^{2}, 1\right) \cdot x \]

      6. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}\right) \cdot {x}^{2} + \color{blue}{\frac{-833192009}{1250000000}}, {x}^{2}, 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000} + \frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2}, {x}^{2}, \frac{-833192009}{1250000000}\right)}, {x}^{2}, 1\right) \cdot x \]

      8. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{-9156128575487588197208397249}{125000000000000000000000000000} \cdot {x}^{2} + \frac{3321371254951887171}{12500000000000000000}}, {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      9. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, {x}^{2}, \frac{3321371254951887171}{12500000000000000000}\right)}, {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      10. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, \color{blue}{x \cdot x}, \frac{3321371254951887171}{12500000000000000000}\right), {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      11. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, \color{blue}{x \cdot x}, \frac{3321371254951887171}{12500000000000000000}\right), {x}^{2}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      14. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-9156128575487588197208397249}{125000000000000000000000000000}, x \cdot x, \frac{3321371254951887171}{12500000000000000000}\right), x \cdot x, \frac{-833192009}{1250000000}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      15. lower-*.f64 66.0

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x \cdot x, 0.265709700396151\right), x \cdot x, -0.6665536072\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 66.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.0732490286039007, x \cdot x, 0.265709700396151\right), x \cdot x, -0.6665536072\right), x \cdot x, 1\right)} \cdot x \]

   if 1.25 < x

   1. Initial program 6.3%

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

   4. Applied rewrites 6.3%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(0.0003579942, {x}^{12}, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(0.0140005442, {x}^{8}, \mathsf{fma}\left(0.0694555761, {x}^{6}, \mathsf{fma}\left(0.2909738639, {x}^{4}, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}{\mathsf{fma}\left(0.0001789971, {x}^{10}, \mathsf{fma}\left(0.0005064034, {x}^{8}, \mathsf{fma}\left(0.0072644182, {x}^{6}, \mathsf{fma}\left(0.0424060604, {x}^{4}, \mathsf{fma}\left(0.1049934947, x \cdot x, 1\right)\right)\right)\right)\right) \cdot x}}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{x \cdot \left(2 - \frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)}} \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \frac{1}{x \cdot \color{blue}{\left(2 + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}} \]

      2. distribute-rgt-in N/A

         \[\leadsto \frac{1}{\color{blue}{2 \cdot x + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x}} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2, x, \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x\right)}} \]

      4. distribute-lft-neg-out N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\mathsf{neg}\left(\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right) \cdot x\right)}\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{x \cdot \left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)}\right)\right)} \]

      6. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(x \cdot \color{blue}{\left(\frac{1}{{x}^{2}} \cdot \frac{600041}{596657}\right)}\right)\right)} \]

      7. associate-*r* N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\left(x \cdot \frac{1}{{x}^{2}}\right) \cdot \frac{600041}{596657}}\right)\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\left(x \cdot \frac{1}{\color{blue}{x \cdot x}}\right) \cdot \frac{600041}{596657}\right)\right)} \]

      9. associate-/r* N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\left(x \cdot \color{blue}{\frac{\frac{1}{x}}{x}}\right) \cdot \frac{600041}{596657}\right)\right)} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\frac{x \cdot \frac{1}{x}}{x}} \cdot \frac{600041}{596657}\right)\right)} \]

      11. rgt-mult-inverse N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\frac{\color{blue}{1}}{x} \cdot \frac{600041}{596657}\right)\right)} \]

      12. associate-*l/ N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\frac{1 \cdot \frac{600041}{596657}}{x}}\right)\right)} \]

      13. metadata-eval N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\frac{\color{blue}{\frac{600041}{596657}}}{x}\right)\right)} \]

      14. distribute-neg-frac N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\frac{\mathsf{neg}\left(\frac{600041}{596657}\right)}{x}}\right)} \]

      15. lower-/.f64 N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\frac{\mathsf{neg}\left(\frac{600041}{596657}\right)}{x}}\right)} \]

      16. metadata-eval 99.6

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \frac{\color{blue}{-1.0056716002661497}}{x}\right)} \]

   7. Applied rewrites 99.6%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2, x, \frac{-1.0056716002661497}{x}\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 99.7% accurate, 11.9× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 1.15:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(0.265709700396151, x\_m \cdot x\_m, -0.6665536072\right), x\_m \cdot x\_m, 1\right) \cdot x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(2, x\_m, \frac{-1.0056716002661497}{x\_m}\right)}\\ \end{array} \end{array} \]

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 1.15)
    (*
     (fma (fma 0.265709700396151 (* x_m x_m) -0.6665536072) (* x_m x_m) 1.0)
     x_m)
    (/ 1.0 (fma 2.0 x_m (/ -1.0056716002661497 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 <= 1.15) {
		tmp = fma(fma(0.265709700396151, (x_m * x_m), -0.6665536072), (x_m * x_m), 1.0) * x_m;
	} else {
		tmp = 1.0 / fma(2.0, x_m, (-1.0056716002661497 / 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 <= 1.15)
		tmp = Float64(fma(fma(0.265709700396151, Float64(x_m * x_m), -0.6665536072), Float64(x_m * x_m), 1.0) * x_m);
	else
		tmp = Float64(1.0 / fma(2.0, x_m, Float64(-1.0056716002661497 / 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, 1.15], N[(N[(N[(0.265709700396151 * N[(x$95$m * x$95$m), $MachinePrecision] + -0.6665536072), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision] * x$95$m), $MachinePrecision], N[(1.0 / N[(2.0 * x$95$m + N[(-1.0056716002661497 / x$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 1.1499999999999999

   1. Initial program 69.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} - \frac{833192009}{1250000000}\right)\right)} \cdot x \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} - \frac{833192009}{1250000000}\right) + 1\right)} \cdot x \]

      2. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} - \frac{833192009}{1250000000}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} - \frac{833192009}{1250000000}, {x}^{2}, 1\right)} \cdot x \]

      4. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{833192009}{1250000000}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      5. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} + \color{blue}{\frac{-833192009}{1250000000}}, {x}^{2}, 1\right) \cdot x \]

      6. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000}, {x}^{2}, \frac{-833192009}{1250000000}\right)}, {x}^{2}, 1\right) \cdot x \]

      7. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000}, \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000}, \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000}, x \cdot x, \frac{-833192009}{1250000000}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      10. lower-*.f64 66.2

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.265709700396151, x \cdot x, -0.6665536072\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 66.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.265709700396151, x \cdot x, -0.6665536072\right), x \cdot x, 1\right)} \cdot x \]

   if 1.1499999999999999 < x

   1. Initial program 6.3%

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

   4. Applied rewrites 6.3%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(0.0003579942, {x}^{12}, \mathsf{fma}\left(0.0008327945, {x}^{10}, \mathsf{fma}\left(0.0140005442, {x}^{8}, \mathsf{fma}\left(0.0694555761, {x}^{6}, \mathsf{fma}\left(0.2909738639, {x}^{4}, \mathsf{fma}\left(0.7715471019, x \cdot x, 1\right)\right)\right)\right)\right)\right)}{\mathsf{fma}\left(0.0001789971, {x}^{10}, \mathsf{fma}\left(0.0005064034, {x}^{8}, \mathsf{fma}\left(0.0072644182, {x}^{6}, \mathsf{fma}\left(0.0424060604, {x}^{4}, \mathsf{fma}\left(0.1049934947, x \cdot x, 1\right)\right)\right)\right)\right) \cdot x}}} \]

   5. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{x \cdot \left(2 - \frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)}} \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \frac{1}{x \cdot \color{blue}{\left(2 + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}} \]

      2. distribute-rgt-in N/A

         \[\leadsto \frac{1}{\color{blue}{2 \cdot x + \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x}} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2, x, \left(\mathsf{neg}\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot x\right)}} \]

      4. distribute-lft-neg-out N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\mathsf{neg}\left(\left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right) \cdot x\right)}\right)} \]

      5. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{x \cdot \left(\frac{600041}{596657} \cdot \frac{1}{{x}^{2}}\right)}\right)\right)} \]

      6. *-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(x \cdot \color{blue}{\left(\frac{1}{{x}^{2}} \cdot \frac{600041}{596657}\right)}\right)\right)} \]

      7. associate-*r* N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\left(x \cdot \frac{1}{{x}^{2}}\right) \cdot \frac{600041}{596657}}\right)\right)} \]

      8. unpow2 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\left(x \cdot \frac{1}{\color{blue}{x \cdot x}}\right) \cdot \frac{600041}{596657}\right)\right)} \]

      9. associate-/r* N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\left(x \cdot \color{blue}{\frac{\frac{1}{x}}{x}}\right) \cdot \frac{600041}{596657}\right)\right)} \]

      10. associate-*r/ N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\frac{x \cdot \frac{1}{x}}{x}} \cdot \frac{600041}{596657}\right)\right)} \]

      11. rgt-mult-inverse N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\frac{\color{blue}{1}}{x} \cdot \frac{600041}{596657}\right)\right)} \]

      12. associate-*l/ N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\color{blue}{\frac{1 \cdot \frac{600041}{596657}}{x}}\right)\right)} \]

      13. metadata-eval N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \mathsf{neg}\left(\frac{\color{blue}{\frac{600041}{596657}}}{x}\right)\right)} \]

      14. distribute-neg-frac N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\frac{\mathsf{neg}\left(\frac{600041}{596657}\right)}{x}}\right)} \]

      15. lower-/.f64 N/A

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \color{blue}{\frac{\mathsf{neg}\left(\frac{600041}{596657}\right)}{x}}\right)} \]

      16. metadata-eval 99.6

          \[\leadsto \frac{1}{\mathsf{fma}\left(2, x, \frac{\color{blue}{-1.0056716002661497}}{x}\right)} \]

   7. Applied rewrites 99.6%

      \[\leadsto \frac{1}{\color{blue}{\mathsf{fma}\left(2, x, \frac{-1.0056716002661497}{x}\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 99.5% accurate, 12.2× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.88:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(0.265709700396151, x\_m \cdot x\_m, -0.6665536072\right), x\_m \cdot x\_m, 1\right) \cdot x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x\_m}\\ \end{array} \end{array} \]

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 0.88)
    (*
     (fma (fma 0.265709700396151 (* x_m x_m) -0.6665536072) (* x_m x_m) 1.0)
     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 <= 0.88) {
		tmp = fma(fma(0.265709700396151, (x_m * x_m), -0.6665536072), (x_m * x_m), 1.0) * 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 <= 0.88)
		tmp = Float64(fma(fma(0.265709700396151, Float64(x_m * x_m), -0.6665536072), Float64(x_m * x_m), 1.0) * x_m);
	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_] := N[(x$95$s * If[LessEqual[x$95$m, 0.88], N[(N[(N[(0.265709700396151 * N[(x$95$m * x$95$m), $MachinePrecision] + -0.6665536072), $MachinePrecision] * N[(x$95$m * x$95$m), $MachinePrecision] + 1.0), $MachinePrecision] * x$95$m), $MachinePrecision], N[(0.5 / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.880000000000000004

   1. Initial program 69.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} - \frac{833192009}{1250000000}\right)\right)} \cdot x \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} - \frac{833192009}{1250000000}\right) + 1\right)} \cdot x \]

      2. *-commutative N/A

         \[\leadsto \left(\color{blue}{\left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} - \frac{833192009}{1250000000}\right) \cdot {x}^{2}} + 1\right) \cdot x \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} - \frac{833192009}{1250000000}, {x}^{2}, 1\right)} \cdot x \]

      4. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{833192009}{1250000000}\right)\right)}, {x}^{2}, 1\right) \cdot x \]

      5. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000} \cdot {x}^{2} + \color{blue}{\frac{-833192009}{1250000000}}, {x}^{2}, 1\right) \cdot x \]

      6. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000}, {x}^{2}, \frac{-833192009}{1250000000}\right)}, {x}^{2}, 1\right) \cdot x \]

      7. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000}, \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000}, \color{blue}{x \cdot x}, \frac{-833192009}{1250000000}\right), {x}^{2}, 1\right) \cdot x \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{3321371254951887171}{12500000000000000000}, x \cdot x, \frac{-833192009}{1250000000}\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

      10. lower-*.f64 66.2

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.265709700396151, x \cdot x, -0.6665536072\right), \color{blue}{x \cdot x}, 1\right) \cdot x \]

   5. Applied rewrites 66.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.265709700396151, x \cdot x, -0.6665536072\right), x \cdot x, 1\right)} \cdot x \]

   if 0.880000000000000004 < x

   1. Initial program 6.3%

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

   3. Taylor expanded in x around inf

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

      1. lower-/.f64 98.8

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

   5. Applied rewrites 98.8%

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

Alternative 9: 99.4% accurate, 18.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 0.78:\\ \;\;\;\;\mathsf{fma}\left(x\_m \cdot x\_m, -0.6665536072, 1\right) \cdot x\_m\\ \mathbf{else}:\\ \;\;\;\;\frac{0.5}{x\_m}\\ \end{array} \end{array} \]

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 0.78) (* (fma (* x_m x_m) -0.6665536072 1.0) 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 <= 0.78) {
		tmp = fma((x_m * x_m), -0.6665536072, 1.0) * 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 <= 0.78)
		tmp = Float64(fma(Float64(x_m * x_m), -0.6665536072, 1.0) * x_m);
	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_] := N[(x$95$s * If[LessEqual[x$95$m, 0.78], N[(N[(N[(x$95$m * x$95$m), $MachinePrecision] * -0.6665536072 + 1.0), $MachinePrecision] * x$95$m), $MachinePrecision], N[(0.5 / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.78000000000000003

   1. Initial program 69.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(1 + \frac{-833192009}{1250000000} \cdot {x}^{2}\right)} \cdot x \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{-833192009}{1250000000} \cdot {x}^{2} + 1\right)} \cdot x \]

      2. *-commutative N/A

         \[\leadsto \left(\color{blue}{{x}^{2} \cdot \frac{-833192009}{1250000000}} + 1\right) \cdot x \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{-833192009}{1250000000}, 1\right)} \cdot x \]

      4. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{-833192009}{1250000000}, 1\right) \cdot x \]

      5. lower-*.f64 65.6

         \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x}, -0.6665536072, 1\right) \cdot x \]

   5. Applied rewrites 65.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, -0.6665536072, 1\right)} \cdot x \]

   if 0.78000000000000003 < x

   1. Initial program 6.3%

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

   3. Taylor expanded in x around inf

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

      1. lower-/.f64 98.8

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

   5. Applied rewrites 98.8%

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

Alternative 10: 99.1% accurate, 23.0× speedup

Math

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

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) (* 1.0 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 = 1.0 * x_m;
	} else {
		tmp = 0.5 / x_m;
	}
	return x_s * tmp;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if (x_m <= 0.7d0) then
        tmp = 1.0d0 * 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 = 1.0 * 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 = 1.0 * 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 = Float64(1.0 * 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 = 1.0 * 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], N[(1.0 * x$95$m), $MachinePrecision], N[(0.5 / x$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 0.69999999999999996

   1. Initial program 69.7%

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

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1} \cdot x \]
   4. Step-by-step derivation

   5. Applied rewrites 66.2%

      \[\leadsto \color{blue}{1} \cdot x \]

   if 0.69999999999999996 < x

   1. Initial program 6.3%

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

   3. Taylor expanded in x around inf

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

      1. lower-/.f64 98.8

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

   5. Applied rewrites 98.8%

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

Alternative 11: 50.5% accurate, 69.2× speedup

Math

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

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m) :precision binary64 (* x_s (* 1.0 x_m)))

C

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

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * (1.0d0 * 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 * (1.0 * x_m);
}

Python

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

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * Float64(1.0 * 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 * (1.0 * 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 * N[(1.0 * x$95$m), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 57.8%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1} \cdot x \]
4. Step-by-step derivation

5. Applied rewrites 54.5%

   \[\leadsto \color{blue}{1} \cdot x \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024295 
(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))