Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2, C

Percentage Accurate: 58.8% → 98.8%

Time: 7.2s

Alternatives: 24

Speedup: 4.5×

Specification

Math

\[\begin{array}{l} \\ \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (/
  (*
   (- x 2.0)
   (+
    (*
     (+ (* (+ (* (+ (* x 4.16438922228) 78.6994924154) x) 137.519416416) x) y)
     x)
    z))
  (+
   (* (+ (* (+ (* (+ x 43.3400022514) x) 263.505074721) x) 313.399215894) x)
   47.066876606)))

C

double code(double x, double y, double z) {
	return ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((x - 2.0d0) * ((((((((x * 4.16438922228d0) + 78.6994924154d0) * x) + 137.519416416d0) * x) + y) * x) + z)) / (((((((x + 43.3400022514d0) * x) + 263.505074721d0) * x) + 313.399215894d0) * x) + 47.066876606d0)
end function

Java

public static double code(double x, double y, double z) {
	return ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606);
}

Python

def code(x, y, z):
	return ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606)

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606))
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606);
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(N[(N[(N[(N[(x * 4.16438922228), $MachinePrecision] + 78.6994924154), $MachinePrecision] * x), $MachinePrecision] + 137.519416416), $MachinePrecision] * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(x + 43.3400022514), $MachinePrecision] * x), $MachinePrecision] + 263.505074721), $MachinePrecision] * x), $MachinePrecision] + 313.399215894), $MachinePrecision] * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision]

Initial Program: 58.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (/
  (*
   (- x 2.0)
   (+
    (*
     (+ (* (+ (* (+ (* x 4.16438922228) 78.6994924154) x) 137.519416416) x) y)
     x)
    z))
  (+
   (* (+ (* (+ (* (+ x 43.3400022514) x) 263.505074721) x) 313.399215894) x)
   47.066876606)))

C

double code(double x, double y, double z) {
	return ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((x - 2.0d0) * ((((((((x * 4.16438922228d0) + 78.6994924154d0) * x) + 137.519416416d0) * x) + y) * x) + z)) / (((((((x + 43.3400022514d0) * x) + 263.505074721d0) * x) + 313.399215894d0) * x) + 47.066876606d0)
end function

Java

public static double code(double x, double y, double z) {
	return ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606);
}

Python

def code(x, y, z):
	return ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606)

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606))
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606);
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(N[(N[(N[(N[(x * 4.16438922228), $MachinePrecision] + 78.6994924154), $MachinePrecision] * x), $MachinePrecision] + 137.519416416), $MachinePrecision] * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(x + 43.3400022514), $MachinePrecision] * x), $MachinePrecision] + 263.505074721), $MachinePrecision] * x), $MachinePrecision] + 313.399215894), $MachinePrecision] * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision]

Alternative 1: 98.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)\\ \mathbf{if}\;\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \leq 2 \cdot 10^{+297}:\\ \;\;\;\;\mathsf{fma}\left(\left(x - 2\right) \cdot x, \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right)}{t\_0}, \left(x - 2\right) \cdot \frac{z}{t\_0}\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0
         (fma
          (fma (fma (- x -43.3400022514) x 263.505074721) x 313.399215894)
          x
          47.066876606)))
   (if (<=
        (/
         (*
          (- x 2.0)
          (+
           (*
            (+
             (*
              (+ (* (+ (* x 4.16438922228) 78.6994924154) x) 137.519416416)
              x)
             y)
            x)
           z))
         (+
          (*
           (+ (* (+ (* (+ x 43.3400022514) x) 263.505074721) x) 313.399215894)
           x)
          47.066876606))
        2e+297)
     (fma
      (* (- x 2.0) x)
      (/
       (fma (fma (fma 4.16438922228 x 78.6994924154) x 137.519416416) x y)
       t_0)
      (* (- x 2.0) (/ z t_0)))
     (*
      (-
       (/
        (-
         -110.1139242984811
         (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x))
        x)
       -4.16438922228)
      x))))

C

double code(double x, double y, double z) {
	double t_0 = fma(fma(fma((x - -43.3400022514), x, 263.505074721), x, 313.399215894), x, 47.066876606);
	double tmp;
	if ((((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606)) <= 2e+297) {
		tmp = fma(((x - 2.0) * x), (fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y) / t_0), ((x - 2.0) * (z / t_0)));
	} else {
		tmp = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = fma(fma(fma(Float64(x - -43.3400022514), x, 263.505074721), x, 313.399215894), x, 47.066876606)
	tmp = 0.0
	if (Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606)) <= 2e+297)
		tmp = fma(Float64(Float64(x - 2.0) * x), Float64(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y) / t_0), Float64(Float64(x - 2.0) * Float64(z / t_0)));
	else
		tmp = Float64(Float64(Float64(Float64(-110.1139242984811 - Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(x - -43.3400022514), $MachinePrecision] * x + 263.505074721), $MachinePrecision] * x + 313.399215894), $MachinePrecision] * x + 47.066876606), $MachinePrecision]}, If[LessEqual[N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(N[(N[(N[(N[(x * 4.16438922228), $MachinePrecision] + 78.6994924154), $MachinePrecision] * x), $MachinePrecision] + 137.519416416), $MachinePrecision] * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(x + 43.3400022514), $MachinePrecision] * x), $MachinePrecision] + 263.505074721), $MachinePrecision] * x), $MachinePrecision] + 313.399215894), $MachinePrecision] * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], 2e+297], N[(N[(N[(x - 2.0), $MachinePrecision] * x), $MachinePrecision] * N[(N[(N[(N[(4.16438922228 * x + 78.6994924154), $MachinePrecision] * x + 137.519416416), $MachinePrecision] * x + y), $MachinePrecision] / t$95$0), $MachinePrecision] + N[(N[(x - 2.0), $MachinePrecision] * N[(z / t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(-110.1139242984811 - N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 (-.f64 x #s(literal 2 binary64)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 x #s(literal 104109730557/25000000000 binary64)) #s(literal 393497462077/5000000000 binary64)) x) #s(literal 4297481763/31250000 binary64)) x) y) x) z)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 x #s(literal 216700011257/5000000000 binary64)) x) #s(literal 263505074721/1000000000 binary64)) x) #s(literal 156699607947/500000000 binary64)) x) #s(literal 23533438303/500000000 binary64))) < 2e297

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 62.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x - 2\right) \cdot x, \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}, \left(x - 2\right) \cdot \frac{z}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}\right)} \]

   if 2e297 < (/.f64 (*.f64 (-.f64 x #s(literal 2 binary64)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 x #s(literal 104109730557/25000000000 binary64)) #s(literal 393497462077/5000000000 binary64)) x) #s(literal 4297481763/31250000 binary64)) x) y) x) z)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 x #s(literal 216700011257/5000000000 binary64)) x) #s(literal 263505074721/1000000000 binary64)) x) #s(literal 156699607947/500000000 binary64)) x) #s(literal 23533438303/500000000 binary64)))

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 98.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \leq 2 \cdot 10^{+297}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)} \cdot \left(x - 2\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<=
      (/
       (*
        (- x 2.0)
        (+
         (*
          (+
           (* (+ (* (+ (* x 4.16438922228) 78.6994924154) x) 137.519416416) x)
           y)
          x)
         z))
       (+
        (*
         (+ (* (+ (* (+ x 43.3400022514) x) 263.505074721) x) 313.399215894)
         x)
        47.066876606))
      2e+297)
   (*
    (/
     (fma
      (fma (fma (fma 4.16438922228 x 78.6994924154) x 137.519416416) x y)
      x
      z)
     (fma
      (fma (fma (- x -43.3400022514) x 263.505074721) x 313.399215894)
      x
      47.066876606))
    (- x 2.0))
   (*
    (-
     (/
      (-
       -110.1139242984811
       (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x))
      x)
     -4.16438922228)
    x)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / (((((((x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606)) <= 2e+297) {
		tmp = (fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) / fma(fma(fma((x - -43.3400022514), x, 263.505074721), x, 313.399215894), x, 47.066876606)) * (x - 2.0);
	} else {
		tmp = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / Float64(Float64(Float64(Float64(Float64(Float64(Float64(x + 43.3400022514) * x) + 263.505074721) * x) + 313.399215894) * x) + 47.066876606)) <= 2e+297)
		tmp = Float64(Float64(fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) / fma(fma(fma(Float64(x - -43.3400022514), x, 263.505074721), x, 313.399215894), x, 47.066876606)) * Float64(x - 2.0));
	else
		tmp = Float64(Float64(Float64(Float64(-110.1139242984811 - Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(N[(N[(N[(N[(x * 4.16438922228), $MachinePrecision] + 78.6994924154), $MachinePrecision] * x), $MachinePrecision] + 137.519416416), $MachinePrecision] * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(N[(N[(N[(x + 43.3400022514), $MachinePrecision] * x), $MachinePrecision] + 263.505074721), $MachinePrecision] * x), $MachinePrecision] + 313.399215894), $MachinePrecision] * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], 2e+297], N[(N[(N[(N[(N[(N[(4.16438922228 * x + 78.6994924154), $MachinePrecision] * x + 137.519416416), $MachinePrecision] * x + y), $MachinePrecision] * x + z), $MachinePrecision] / N[(N[(N[(N[(x - -43.3400022514), $MachinePrecision] * x + 263.505074721), $MachinePrecision] * x + 313.399215894), $MachinePrecision] * x + 47.066876606), $MachinePrecision]), $MachinePrecision] * N[(x - 2.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(-110.1139242984811 - N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 (-.f64 x #s(literal 2 binary64)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 x #s(literal 104109730557/25000000000 binary64)) #s(literal 393497462077/5000000000 binary64)) x) #s(literal 4297481763/31250000 binary64)) x) y) x) z)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 x #s(literal 216700011257/5000000000 binary64)) x) #s(literal 263505074721/1000000000 binary64)) x) #s(literal 156699607947/500000000 binary64)) x) #s(literal 23533438303/500000000 binary64))) < 2e297

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + \frac{216700011257}{5000000000}\right) \cdot x + \frac{263505074721}{1000000000}\right) \cdot x + \frac{156699607947}{500000000}\right) \cdot x + \frac{23533438303}{500000000}}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}}{\left(\left(\left(x + \frac{216700011257}{5000000000}\right) \cdot x + \frac{263505074721}{1000000000}\right) \cdot x + \frac{156699607947}{500000000}\right) \cdot x + \frac{23533438303}{500000000}} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{\left(x - 2\right) \cdot \frac{\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z}{\left(\left(\left(x + \frac{216700011257}{5000000000}\right) \cdot x + \frac{263505074721}{1000000000}\right) \cdot x + \frac{156699607947}{500000000}\right) \cdot x + \frac{23533438303}{500000000}}} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z}{\left(\left(\left(x + \frac{216700011257}{5000000000}\right) \cdot x + \frac{263505074721}{1000000000}\right) \cdot x + \frac{156699607947}{500000000}\right) \cdot x + \frac{23533438303}{500000000}} \cdot \left(x - 2\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z}{\left(\left(\left(x + \frac{216700011257}{5000000000}\right) \cdot x + \frac{263505074721}{1000000000}\right) \cdot x + \frac{156699607947}{500000000}\right) \cdot x + \frac{23533438303}{500000000}} \cdot \left(x - 2\right)} \]

   3. Applied rewrites 61.6%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)} \cdot \left(x - 2\right)} \]

   if 2e297 < (/.f64 (*.f64 (-.f64 x #s(literal 2 binary64)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 x #s(literal 104109730557/25000000000 binary64)) #s(literal 393497462077/5000000000 binary64)) x) #s(literal 4297481763/31250000 binary64)) x) y) x) z)) (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 (*.f64 (+.f64 x #s(literal 216700011257/5000000000 binary64)) x) #s(literal 263505074721/1000000000 binary64)) x) #s(literal 156699607947/500000000 binary64)) x) #s(literal 23533438303/500000000 binary64)))

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 96.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot x\\ \mathbf{if}\;x \leq -5.5:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 17:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(263.505074721 \cdot x + 313.399215894\right) \cdot x + 47.066876606}\\ \mathbf{elif}\;x \leq 5 \cdot 10^{+58}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right)}{{x}^{4}} \cdot \left(x - 2\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0
         (*
          (-
           (/
            (-
             -110.1139242984811
             (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x))
            x)
           -4.16438922228)
          x)))
   (if (<= x -5.5)
     t_0
     (if (<= x 17.0)
       (/
        (*
         (- x 2.0)
         (+
          (*
           (+
            (* (+ (* (+ (* x 4.16438922228) 78.6994924154) x) 137.519416416) x)
            y)
           x)
          z))
        (+ (* (+ (* 263.505074721 x) 313.399215894) x) 47.066876606))
       (if (<= x 5e+58)
         (*
          (/
           (fma
            (fma (fma (fma 4.16438922228 x 78.6994924154) x 137.519416416) x y)
            x
            z)
           (pow x 4.0))
          (- x 2.0))
         t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	double tmp;
	if (x <= -5.5) {
		tmp = t_0;
	} else if (x <= 17.0) {
		tmp = ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / ((((263.505074721 * x) + 313.399215894) * x) + 47.066876606);
	} else if (x <= 5e+58) {
		tmp = (fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) / pow(x, 4.0)) * (x - 2.0);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(-110.1139242984811 - Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x)
	tmp = 0.0
	if (x <= -5.5)
		tmp = t_0;
	elseif (x <= 17.0)
		tmp = Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / Float64(Float64(Float64(Float64(263.505074721 * x) + 313.399215894) * x) + 47.066876606));
	elseif (x <= 5e+58)
		tmp = Float64(Float64(fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) / (x ^ 4.0)) * Float64(x - 2.0));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(-110.1139242984811 - N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -5.5], t$95$0, If[LessEqual[x, 17.0], N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(N[(N[(N[(N[(x * 4.16438922228), $MachinePrecision] + 78.6994924154), $MachinePrecision] * x), $MachinePrecision] + 137.519416416), $MachinePrecision] * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(263.505074721 * x), $MachinePrecision] + 313.399215894), $MachinePrecision] * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 5e+58], N[(N[(N[(N[(N[(N[(4.16438922228 * x + 78.6994924154), $MachinePrecision] * x + 137.519416416), $MachinePrecision] * x + y), $MachinePrecision] * x + z), $MachinePrecision] / N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision] * N[(x - 2.0), $MachinePrecision]), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.5 or 4.99999999999999986e58 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -5.5 < x < 17

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\left(\color{blue}{\frac{263505074721}{1000000000}} \cdot x + \frac{156699607947}{500000000}\right) \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\color{blue}{263.505074721} \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   if 17 < x < 4.99999999999999986e58

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around inf

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{{x}^{4}}} \]
   3. Step-by-step derivation

      1. lower-pow.f64 9.9

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{{x}^{\color{blue}{4}}} \]

   4. Applied rewrites 9.9%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{{x}^{4}}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{{x}^{4}}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}}{{x}^{4}} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{\left(x - 2\right) \cdot \frac{\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z}{{x}^{4}}} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z}{{x}^{4}} \cdot \left(x - 2\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z}{{x}^{4}} \cdot \left(x - 2\right)} \]

   6. Applied rewrites 12.7%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right)}{{x}^{4}} \cdot \left(x - 2\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 96.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot x\\ \mathbf{if}\;x \leq -5.5:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 18:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(263.505074721 \cdot x + 313.399215894\right) \cdot x + 47.066876606}\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{+62}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{1}{{x}^{3}}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0
         (*
          (-
           (/
            (-
             -110.1139242984811
             (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x))
            x)
           -4.16438922228)
          x)))
   (if (<= x -5.5)
     t_0
     (if (<= x 18.0)
       (/
        (*
         (- x 2.0)
         (+
          (*
           (+
            (* (+ (* (+ (* x 4.16438922228) 78.6994924154) x) 137.519416416) x)
            y)
           x)
          z))
        (+ (* (+ (* 263.505074721 x) 313.399215894) x) 47.066876606))
       (if (<= x 3.9e+62)
         (*
          (fma
           (fma (fma (fma 4.16438922228 x 78.6994924154) x 137.519416416) x y)
           x
           z)
          (/ 1.0 (pow x 3.0)))
         t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	double tmp;
	if (x <= -5.5) {
		tmp = t_0;
	} else if (x <= 18.0) {
		tmp = ((x - 2.0) * ((((((((x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / ((((263.505074721 * x) + 313.399215894) * x) + 47.066876606);
	} else if (x <= 3.9e+62) {
		tmp = fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) * (1.0 / pow(x, 3.0));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(-110.1139242984811 - Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x)
	tmp = 0.0
	if (x <= -5.5)
		tmp = t_0;
	elseif (x <= 18.0)
		tmp = Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(Float64(Float64(Float64(Float64(x * 4.16438922228) + 78.6994924154) * x) + 137.519416416) * x) + y) * x) + z)) / Float64(Float64(Float64(Float64(263.505074721 * x) + 313.399215894) * x) + 47.066876606));
	elseif (x <= 3.9e+62)
		tmp = Float64(fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) * Float64(1.0 / (x ^ 3.0)));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(-110.1139242984811 - N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -5.5], t$95$0, If[LessEqual[x, 18.0], N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(N[(N[(N[(N[(x * 4.16438922228), $MachinePrecision] + 78.6994924154), $MachinePrecision] * x), $MachinePrecision] + 137.519416416), $MachinePrecision] * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(N[(263.505074721 * x), $MachinePrecision] + 313.399215894), $MachinePrecision] * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.9e+62], N[(N[(N[(N[(N[(4.16438922228 * x + 78.6994924154), $MachinePrecision] * x + 137.519416416), $MachinePrecision] * x + y), $MachinePrecision] * x + z), $MachinePrecision] * N[(1.0 / N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.5 or 3.9e62 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -5.5 < x < 18

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\left(\color{blue}{\frac{263505074721}{1000000000}} \cdot x + \frac{156699607947}{500000000}\right) \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\color{blue}{263.505074721} \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   if 18 < x < 3.9e62

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 61.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \color{blue}{\frac{1}{{x}^{3}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \frac{1}{\color{blue}{{x}^{3}}} \]

      2. lower-pow.f64 11.9

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{1}{{x}^{\color{blue}{3}}} \]

   6. Applied rewrites 11.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \color{blue}{\frac{1}{{x}^{3}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 96.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot x\\ t_1 := \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right)\\ \mathbf{if}\;x \leq -5.5:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 18:\\ \;\;\;\;t\_1 \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(263.505074721, x, 313.399215894\right), x, 47.066876606\right)}\\ \mathbf{elif}\;x \leq 3.9 \cdot 10^{+62}:\\ \;\;\;\;t\_1 \cdot \frac{1}{{x}^{3}}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0
         (*
          (-
           (/
            (-
             -110.1139242984811
             (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x))
            x)
           -4.16438922228)
          x))
        (t_1
         (fma
          (fma (fma (fma 4.16438922228 x 78.6994924154) x 137.519416416) x y)
          x
          z)))
   (if (<= x -5.5)
     t_0
     (if (<= x 18.0)
       (*
        t_1
        (/ (- x 2.0) (fma (fma 263.505074721 x 313.399215894) x 47.066876606)))
       (if (<= x 3.9e+62) (* t_1 (/ 1.0 (pow x 3.0))) t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	double t_1 = fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z);
	double tmp;
	if (x <= -5.5) {
		tmp = t_0;
	} else if (x <= 18.0) {
		tmp = t_1 * ((x - 2.0) / fma(fma(263.505074721, x, 313.399215894), x, 47.066876606));
	} else if (x <= 3.9e+62) {
		tmp = t_1 * (1.0 / pow(x, 3.0));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(-110.1139242984811 - Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x)
	t_1 = fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z)
	tmp = 0.0
	if (x <= -5.5)
		tmp = t_0;
	elseif (x <= 18.0)
		tmp = Float64(t_1 * Float64(Float64(x - 2.0) / fma(fma(263.505074721, x, 313.399215894), x, 47.066876606)));
	elseif (x <= 3.9e+62)
		tmp = Float64(t_1 * Float64(1.0 / (x ^ 3.0)));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(-110.1139242984811 - N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[(N[(4.16438922228 * x + 78.6994924154), $MachinePrecision] * x + 137.519416416), $MachinePrecision] * x + y), $MachinePrecision] * x + z), $MachinePrecision]}, If[LessEqual[x, -5.5], t$95$0, If[LessEqual[x, 18.0], N[(t$95$1 * N[(N[(x - 2.0), $MachinePrecision] / N[(N[(263.505074721 * x + 313.399215894), $MachinePrecision] * x + 47.066876606), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 3.9e+62], N[(t$95$1 * N[(1.0 / N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.5 or 3.9e62 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -5.5 < x < 18

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 61.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}} \]

   4. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{263505074721}{1000000000}}, x, \frac{156699607947}{500000000}\right), x, \frac{23533438303}{500000000}\right)} \]
   5. Step-by-step derivation

   6. Applied rewrites 50.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{263.505074721}, x, 313.399215894\right), x, 47.066876606\right)} \]

   if 18 < x < 3.9e62

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 61.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \color{blue}{\frac{1}{{x}^{3}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \frac{1}{\color{blue}{{x}^{3}}} \]

      2. lower-pow.f64 11.9

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{1}{{x}^{\color{blue}{3}}} \]

   6. Applied rewrites 11.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \color{blue}{\frac{1}{{x}^{3}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 95.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot x\\ \mathbf{if}\;x \leq -5.5:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 0.003:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(263.505074721, x, 313.399215894\right), x, 47.066876606\right)}\\ \mathbf{elif}\;x \leq 5.2 \cdot 10^{+60}:\\ \;\;\;\;\mathsf{fma}\left(\left(x - 2\right) \cdot x, \frac{4.16438922228}{x}, \left(x - 2\right) \cdot \frac{z}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0
         (*
          (-
           (/
            (-
             -110.1139242984811
             (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x))
            x)
           -4.16438922228)
          x)))
   (if (<= x -5.5)
     t_0
     (if (<= x 0.003)
       (*
        (fma
         (fma (fma (fma 4.16438922228 x 78.6994924154) x 137.519416416) x y)
         x
         z)
        (/ (- x 2.0) (fma (fma 263.505074721 x 313.399215894) x 47.066876606)))
       (if (<= x 5.2e+60)
         (fma
          (* (- x 2.0) x)
          (/ 4.16438922228 x)
          (*
           (- x 2.0)
           (/
            z
            (fma
             (fma (fma (- x -43.3400022514) x 263.505074721) x 313.399215894)
             x
             47.066876606))))
         t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	double tmp;
	if (x <= -5.5) {
		tmp = t_0;
	} else if (x <= 0.003) {
		tmp = fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) * ((x - 2.0) / fma(fma(263.505074721, x, 313.399215894), x, 47.066876606));
	} else if (x <= 5.2e+60) {
		tmp = fma(((x - 2.0) * x), (4.16438922228 / x), ((x - 2.0) * (z / fma(fma(fma((x - -43.3400022514), x, 263.505074721), x, 313.399215894), x, 47.066876606))));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(-110.1139242984811 - Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x)
	tmp = 0.0
	if (x <= -5.5)
		tmp = t_0;
	elseif (x <= 0.003)
		tmp = Float64(fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) * Float64(Float64(x - 2.0) / fma(fma(263.505074721, x, 313.399215894), x, 47.066876606)));
	elseif (x <= 5.2e+60)
		tmp = fma(Float64(Float64(x - 2.0) * x), Float64(4.16438922228 / x), Float64(Float64(x - 2.0) * Float64(z / fma(fma(fma(Float64(x - -43.3400022514), x, 263.505074721), x, 313.399215894), x, 47.066876606))));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(-110.1139242984811 - N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -5.5], t$95$0, If[LessEqual[x, 0.003], N[(N[(N[(N[(N[(4.16438922228 * x + 78.6994924154), $MachinePrecision] * x + 137.519416416), $MachinePrecision] * x + y), $MachinePrecision] * x + z), $MachinePrecision] * N[(N[(x - 2.0), $MachinePrecision] / N[(N[(263.505074721 * x + 313.399215894), $MachinePrecision] * x + 47.066876606), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 5.2e+60], N[(N[(N[(x - 2.0), $MachinePrecision] * x), $MachinePrecision] * N[(4.16438922228 / x), $MachinePrecision] + N[(N[(x - 2.0), $MachinePrecision] * N[(z / N[(N[(N[(N[(x - -43.3400022514), $MachinePrecision] * x + 263.505074721), $MachinePrecision] * x + 313.399215894), $MachinePrecision] * x + 47.066876606), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.5 or 5.20000000000000016e60 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -5.5 < x < 0.0030000000000000001

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 61.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}} \]

   4. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{263505074721}{1000000000}}, x, \frac{156699607947}{500000000}\right), x, \frac{23533438303}{500000000}\right)} \]
   5. Step-by-step derivation

   6. Applied rewrites 50.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{263.505074721}, x, 313.399215894\right), x, 47.066876606\right)} \]

   if 0.0030000000000000001 < x < 5.20000000000000016e60

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 62.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x - 2\right) \cdot x, \frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}, \left(x - 2\right) \cdot \frac{z}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}\right)} \]

   4. Taylor expanded in x around inf

      \[\leadsto \mathsf{fma}\left(\left(x - 2\right) \cdot x, \color{blue}{\frac{\frac{104109730557}{25000000000}}{x}}, \left(x - 2\right) \cdot \frac{z}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - \frac{-216700011257}{5000000000}, x, \frac{263505074721}{1000000000}\right), x, \frac{156699607947}{500000000}\right), x, \frac{23533438303}{500000000}\right)}\right) \]
   5. Step-by-step derivation

      1. lower-/.f64 46.5

         \[\leadsto \mathsf{fma}\left(\left(x - 2\right) \cdot x, \frac{4.16438922228}{\color{blue}{x}}, \left(x - 2\right) \cdot \frac{z}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}\right) \]

   6. Applied rewrites 46.5%

      \[\leadsto \mathsf{fma}\left(\left(x - 2\right) \cdot x, \color{blue}{\frac{4.16438922228}{x}}, \left(x - 2\right) \cdot \frac{z}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 95.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}\\ \mathbf{if}\;x \leq -5.5:\\ \;\;\;\;\left(\frac{-110.1139242984811 - t\_0}{x} - -4.16438922228\right) \cdot x\\ \mathbf{elif}\;x \leq 130:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(263.505074721, x, 313.399215894\right), x, 47.066876606\right)}\\ \mathbf{else}:\\ \;\;\;\;\left(-x\right) \cdot \frac{t\_0 - -110.1139242984811}{x} - x \cdot -4.16438922228\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x)))
   (if (<= x -5.5)
     (* (- (/ (- -110.1139242984811 t_0) x) -4.16438922228) x)
     (if (<= x 130.0)
       (*
        (fma
         (fma (fma (fma 4.16438922228 x 78.6994924154) x 137.519416416) x y)
         x
         z)
        (/ (- x 2.0) (fma (fma 263.505074721 x 313.399215894) x 47.066876606)))
       (- (* (- x) (/ (- t_0 -110.1139242984811) x)) (* x -4.16438922228))))))

C

double code(double x, double y, double z) {
	double t_0 = (((130977.50649958357 - y) / x) - 3655.1204654076414) / x;
	double tmp;
	if (x <= -5.5) {
		tmp = (((-110.1139242984811 - t_0) / x) - -4.16438922228) * x;
	} else if (x <= 130.0) {
		tmp = fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) * ((x - 2.0) / fma(fma(263.505074721, x, 313.399215894), x, 47.066876606));
	} else {
		tmp = (-x * ((t_0 - -110.1139242984811) / x)) - (x * -4.16438922228);
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)
	tmp = 0.0
	if (x <= -5.5)
		tmp = Float64(Float64(Float64(Float64(-110.1139242984811 - t_0) / x) - -4.16438922228) * x);
	elseif (x <= 130.0)
		tmp = Float64(fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) * Float64(Float64(x - 2.0) / fma(fma(263.505074721, x, 313.399215894), x, 47.066876606)));
	else
		tmp = Float64(Float64(Float64(-x) * Float64(Float64(t_0 - -110.1139242984811) / x)) - Float64(x * -4.16438922228));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[x, -5.5], N[(N[(N[(N[(-110.1139242984811 - t$95$0), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[x, 130.0], N[(N[(N[(N[(N[(4.16438922228 * x + 78.6994924154), $MachinePrecision] * x + 137.519416416), $MachinePrecision] * x + y), $MachinePrecision] * x + z), $MachinePrecision] * N[(N[(x - 2.0), $MachinePrecision] / N[(N[(263.505074721 * x + 313.399215894), $MachinePrecision] * x + 47.066876606), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[((-x) * N[(N[(t$95$0 - -110.1139242984811), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] - N[(x * -4.16438922228), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -5.5

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -5.5 < x < 130

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 61.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}} \]

   4. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{263505074721}{1000000000}}, x, \frac{156699607947}{500000000}\right), x, \frac{23533438303}{500000000}\right)} \]
   5. Step-by-step derivation

   6. Applied rewrites 50.9%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{263.505074721}, x, 313.399215894\right), x, 47.066876606\right)} \]

   if 130 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(-x\right) \cdot \frac{\frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x} - -110.1139242984811}{x} - \color{blue}{x \cdot -4.16438922228} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 95.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}\\ \mathbf{if}\;x \leq -0.17:\\ \;\;\;\;\left(\frac{-110.1139242984811 - t\_0}{x} - -4.16438922228\right) \cdot x\\ \mathbf{elif}\;x \leq 2:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x \cdot \left(0.3041881842569256 + -1.787568985856513 \cdot x\right) - 0.0424927283095952\right)\\ \mathbf{else}:\\ \;\;\;\;\left(-x\right) \cdot \frac{t\_0 - -110.1139242984811}{x} - x \cdot -4.16438922228\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x)))
   (if (<= x -0.17)
     (* (- (/ (- -110.1139242984811 t_0) x) -4.16438922228) x)
     (if (<= x 2.0)
       (*
        (fma
         (fma (fma (fma 4.16438922228 x 78.6994924154) x 137.519416416) x y)
         x
         z)
        (-
         (* x (+ 0.3041881842569256 (* -1.787568985856513 x)))
         0.0424927283095952))
       (- (* (- x) (/ (- t_0 -110.1139242984811) x)) (* x -4.16438922228))))))

C

double code(double x, double y, double z) {
	double t_0 = (((130977.50649958357 - y) / x) - 3655.1204654076414) / x;
	double tmp;
	if (x <= -0.17) {
		tmp = (((-110.1139242984811 - t_0) / x) - -4.16438922228) * x;
	} else if (x <= 2.0) {
		tmp = fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) * ((x * (0.3041881842569256 + (-1.787568985856513 * x))) - 0.0424927283095952);
	} else {
		tmp = (-x * ((t_0 - -110.1139242984811) / x)) - (x * -4.16438922228);
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)
	tmp = 0.0
	if (x <= -0.17)
		tmp = Float64(Float64(Float64(Float64(-110.1139242984811 - t_0) / x) - -4.16438922228) * x);
	elseif (x <= 2.0)
		tmp = Float64(fma(fma(fma(fma(4.16438922228, x, 78.6994924154), x, 137.519416416), x, y), x, z) * Float64(Float64(x * Float64(0.3041881842569256 + Float64(-1.787568985856513 * x))) - 0.0424927283095952));
	else
		tmp = Float64(Float64(Float64(-x) * Float64(Float64(t_0 - -110.1139242984811) / x)) - Float64(x * -4.16438922228));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[x, -0.17], N[(N[(N[(N[(-110.1139242984811 - t$95$0), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[x, 2.0], N[(N[(N[(N[(N[(4.16438922228 * x + 78.6994924154), $MachinePrecision] * x + 137.519416416), $MachinePrecision] * x + y), $MachinePrecision] * x + z), $MachinePrecision] * N[(N[(x * N[(0.3041881842569256 + N[(-1.787568985856513 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 0.0424927283095952), $MachinePrecision]), $MachinePrecision], N[(N[((-x) * N[(N[(t$95$0 - -110.1139242984811), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] - N[(x * -4.16438922228), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -0.170000000000000012

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -0.170000000000000012 < x < 2

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 61.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \frac{x - 2}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}} \]

   4. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \color{blue}{\left(x \cdot \left(\frac{168466327098500000000}{553822718361107519809} + \frac{-23298017199368982832548000000000}{13033352773350869092174451844127} \cdot x\right) - \frac{1000000000}{23533438303}\right)} \]
   5. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \left(x \cdot \left(\frac{168466327098500000000}{553822718361107519809} + \frac{-23298017199368982832548000000000}{13033352773350869092174451844127} \cdot x\right) - \color{blue}{\frac{1000000000}{23533438303}}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \left(x \cdot \left(\frac{168466327098500000000}{553822718361107519809} + \frac{-23298017199368982832548000000000}{13033352773350869092174451844127} \cdot x\right) - \frac{1000000000}{23533438303}\right) \]

      3. lower-+.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{104109730557}{25000000000}, x, \frac{393497462077}{5000000000}\right), x, \frac{4297481763}{31250000}\right), x, y\right), x, z\right) \cdot \left(x \cdot \left(\frac{168466327098500000000}{553822718361107519809} + \frac{-23298017199368982832548000000000}{13033352773350869092174451844127} \cdot x\right) - \frac{1000000000}{23533438303}\right) \]

      4. lower-*.f64 51.0

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x \cdot \left(0.3041881842569256 + -1.787568985856513 \cdot x\right) - 0.0424927283095952\right) \]

   6. Applied rewrites 51.0%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \color{blue}{\left(x \cdot \left(0.3041881842569256 + -1.787568985856513 \cdot x\right) - 0.0424927283095952\right)} \]

   if 2 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(-x\right) \cdot \frac{\frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x} - -110.1139242984811}{x} - \color{blue}{x \cdot -4.16438922228} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 95.7% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}\\ \mathbf{if}\;x \leq -37:\\ \;\;\;\;\left(\frac{-110.1139242984811 - t\_0}{x} - -4.16438922228\right) \cdot x\\ \mathbf{elif}\;x \leq 75:\\ \;\;\;\;\frac{\mathsf{fma}\left(\mathsf{fma}\left(78.6994924154 \cdot x - -137.519416416, x, y\right), x, z\right) \cdot \left(x - 2\right)}{\mathsf{fma}\left(313.399215894, x, 47.066876606\right)}\\ \mathbf{else}:\\ \;\;\;\;\left(-x\right) \cdot \frac{t\_0 - -110.1139242984811}{x} - x \cdot -4.16438922228\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x)))
   (if (<= x -37.0)
     (* (- (/ (- -110.1139242984811 t_0) x) -4.16438922228) x)
     (if (<= x 75.0)
       (/
        (*
         (fma (fma (- (* 78.6994924154 x) -137.519416416) x y) x z)
         (- x 2.0))
        (fma 313.399215894 x 47.066876606))
       (- (* (- x) (/ (- t_0 -110.1139242984811) x)) (* x -4.16438922228))))))

C

double code(double x, double y, double z) {
	double t_0 = (((130977.50649958357 - y) / x) - 3655.1204654076414) / x;
	double tmp;
	if (x <= -37.0) {
		tmp = (((-110.1139242984811 - t_0) / x) - -4.16438922228) * x;
	} else if (x <= 75.0) {
		tmp = (fma(fma(((78.6994924154 * x) - -137.519416416), x, y), x, z) * (x - 2.0)) / fma(313.399215894, x, 47.066876606);
	} else {
		tmp = (-x * ((t_0 - -110.1139242984811) / x)) - (x * -4.16438922228);
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)
	tmp = 0.0
	if (x <= -37.0)
		tmp = Float64(Float64(Float64(Float64(-110.1139242984811 - t_0) / x) - -4.16438922228) * x);
	elseif (x <= 75.0)
		tmp = Float64(Float64(fma(fma(Float64(Float64(78.6994924154 * x) - -137.519416416), x, y), x, z) * Float64(x - 2.0)) / fma(313.399215894, x, 47.066876606));
	else
		tmp = Float64(Float64(Float64(-x) * Float64(Float64(t_0 - -110.1139242984811) / x)) - Float64(x * -4.16438922228));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[x, -37.0], N[(N[(N[(N[(-110.1139242984811 - t$95$0), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[x, 75.0], N[(N[(N[(N[(N[(N[(78.6994924154 * x), $MachinePrecision] - -137.519416416), $MachinePrecision] * x + y), $MachinePrecision] * x + z), $MachinePrecision] * N[(x - 2.0), $MachinePrecision]), $MachinePrecision] / N[(313.399215894 * x + 47.066876606), $MachinePrecision]), $MachinePrecision], N[(N[((-x) * N[(N[(t$95$0 - -110.1139242984811), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] - N[(x * -4.16438922228), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -37

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -37 < x < 75

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\color{blue}{\frac{393497462077}{5000000000} \cdot x} + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 52.2

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(78.6994924154 \cdot \color{blue}{x} + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 52.2%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\color{blue}{78.6994924154 \cdot x} + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]
   8. Step-by-step derivation

   9. Applied rewrites 52.2%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(78.6994924154 \cdot x - -137.519416416, x, y\right), x, z\right) \cdot \left(x - 2\right)}{\mathsf{fma}\left(313.399215894, x, 47.066876606\right)}} \]

   if 75 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(-x\right) \cdot \frac{\frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x} - -110.1139242984811}{x} - \color{blue}{x \cdot -4.16438922228} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 95.6% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}\\ \mathbf{if}\;x \leq -37:\\ \;\;\;\;\left(\frac{-110.1139242984811 - t\_0}{x} - -4.16438922228\right) \cdot x\\ \mathbf{elif}\;x \leq 75:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606}\\ \mathbf{else}:\\ \;\;\;\;\left(-x\right) \cdot \frac{t\_0 - -110.1139242984811}{x} - x \cdot -4.16438922228\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x)))
   (if (<= x -37.0)
     (* (- (/ (- -110.1139242984811 t_0) x) -4.16438922228) x)
     (if (<= x 75.0)
       (/
        (* (- x 2.0) (+ (* (+ (* 137.519416416 x) y) x) z))
        (+ (* 313.399215894 x) 47.066876606))
       (- (* (- x) (/ (- t_0 -110.1139242984811) x)) (* x -4.16438922228))))))

C

double code(double x, double y, double z) {
	double t_0 = (((130977.50649958357 - y) / x) - 3655.1204654076414) / x;
	double tmp;
	if (x <= -37.0) {
		tmp = (((-110.1139242984811 - t_0) / x) - -4.16438922228) * x;
	} else if (x <= 75.0) {
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = (-x * ((t_0 - -110.1139242984811) / x)) - (x * -4.16438922228);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (((130977.50649958357d0 - y) / x) - 3655.1204654076414d0) / x
    if (x <= (-37.0d0)) then
        tmp = ((((-110.1139242984811d0) - t_0) / x) - (-4.16438922228d0)) * x
    else if (x <= 75.0d0) then
        tmp = ((x - 2.0d0) * ((((137.519416416d0 * x) + y) * x) + z)) / ((313.399215894d0 * x) + 47.066876606d0)
    else
        tmp = (-x * ((t_0 - (-110.1139242984811d0)) / x)) - (x * (-4.16438922228d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (((130977.50649958357 - y) / x) - 3655.1204654076414) / x;
	double tmp;
	if (x <= -37.0) {
		tmp = (((-110.1139242984811 - t_0) / x) - -4.16438922228) * x;
	} else if (x <= 75.0) {
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = (-x * ((t_0 - -110.1139242984811) / x)) - (x * -4.16438922228);
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (((130977.50649958357 - y) / x) - 3655.1204654076414) / x
	tmp = 0
	if x <= -37.0:
		tmp = (((-110.1139242984811 - t_0) / x) - -4.16438922228) * x
	elif x <= 75.0:
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606)
	else:
		tmp = (-x * ((t_0 - -110.1139242984811) / x)) - (x * -4.16438922228)
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)
	tmp = 0.0
	if (x <= -37.0)
		tmp = Float64(Float64(Float64(Float64(-110.1139242984811 - t_0) / x) - -4.16438922228) * x);
	elseif (x <= 75.0)
		tmp = Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(137.519416416 * x) + y) * x) + z)) / Float64(Float64(313.399215894 * x) + 47.066876606));
	else
		tmp = Float64(Float64(Float64(-x) * Float64(Float64(t_0 - -110.1139242984811) / x)) - Float64(x * -4.16438922228));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (((130977.50649958357 - y) / x) - 3655.1204654076414) / x;
	tmp = 0.0;
	if (x <= -37.0)
		tmp = (((-110.1139242984811 - t_0) / x) - -4.16438922228) * x;
	elseif (x <= 75.0)
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	else
		tmp = (-x * ((t_0 - -110.1139242984811) / x)) - (x * -4.16438922228);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]}, If[LessEqual[x, -37.0], N[(N[(N[(N[(-110.1139242984811 - t$95$0), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[x, 75.0], N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(137.519416416 * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(313.399215894 * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], N[(N[((-x) * N[(N[(t$95$0 - -110.1139242984811), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] - N[(x * -4.16438922228), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -37

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -37 < x < 75

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{\frac{4297481763}{31250000} \cdot x} + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 51.0

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot \color{blue}{x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{137.519416416 \cdot x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   if 75 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(-x\right) \cdot \frac{\frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x} - -110.1139242984811}{x} - \color{blue}{x \cdot -4.16438922228} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 11: 95.6% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot x\\ \mathbf{if}\;x \leq -37:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 75:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0
         (*
          (-
           (/
            (-
             -110.1139242984811
             (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x))
            x)
           -4.16438922228)
          x)))
   (if (<= x -37.0)
     t_0
     (if (<= x 75.0)
       (/
        (* (- x 2.0) (+ (* (+ (* 137.519416416 x) y) x) z))
        (+ (* 313.399215894 x) 47.066876606))
       t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	double tmp;
	if (x <= -37.0) {
		tmp = t_0;
	} else if (x <= 75.0) {
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((((-110.1139242984811d0) - ((((130977.50649958357d0 - y) / x) - 3655.1204654076414d0) / x)) / x) - (-4.16438922228d0)) * x
    if (x <= (-37.0d0)) then
        tmp = t_0
    else if (x <= 75.0d0) then
        tmp = ((x - 2.0d0) * ((((137.519416416d0 * x) + y) * x) + z)) / ((313.399215894d0 * x) + 47.066876606d0)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	double tmp;
	if (x <= -37.0) {
		tmp = t_0;
	} else if (x <= 75.0) {
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x
	tmp = 0
	if x <= -37.0:
		tmp = t_0
	elif x <= 75.0:
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(-110.1139242984811 - Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x)
	tmp = 0.0
	if (x <= -37.0)
		tmp = t_0;
	elseif (x <= 75.0)
		tmp = Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(137.519416416 * x) + y) * x) + z)) / Float64(Float64(313.399215894 * x) + 47.066876606));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	tmp = 0.0;
	if (x <= -37.0)
		tmp = t_0;
	elseif (x <= 75.0)
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(-110.1139242984811 - N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -37.0], t$95$0, If[LessEqual[x, 75.0], N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(137.519416416 * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(313.399215894 * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -37 or 75 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -37 < x < 75

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{\frac{4297481763}{31250000} \cdot x} + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 51.0

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot \color{blue}{x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{137.519416416 \cdot x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 95.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot x\\ \mathbf{if}\;x \leq -37:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 2:\\ \;\;\;\;\frac{-2 \cdot \left(\left(137.519416416 \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0
         (*
          (-
           (/
            (-
             -110.1139242984811
             (/ (- (/ (- 130977.50649958357 y) x) 3655.1204654076414) x))
            x)
           -4.16438922228)
          x)))
   (if (<= x -37.0)
     t_0
     (if (<= x 2.0)
       (/
        (* -2.0 (+ (* (+ (* 137.519416416 x) y) x) z))
        (+ (* 313.399215894 x) 47.066876606))
       t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	double tmp;
	if (x <= -37.0) {
		tmp = t_0;
	} else if (x <= 2.0) {
		tmp = (-2.0 * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((((-110.1139242984811d0) - ((((130977.50649958357d0 - y) / x) - 3655.1204654076414d0) / x)) / x) - (-4.16438922228d0)) * x
    if (x <= (-37.0d0)) then
        tmp = t_0
    else if (x <= 2.0d0) then
        tmp = ((-2.0d0) * ((((137.519416416d0 * x) + y) * x) + z)) / ((313.399215894d0 * x) + 47.066876606d0)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	double tmp;
	if (x <= -37.0) {
		tmp = t_0;
	} else if (x <= 2.0) {
		tmp = (-2.0 * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x
	tmp = 0
	if x <= -37.0:
		tmp = t_0
	elif x <= 2.0:
		tmp = (-2.0 * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(Float64(Float64(-110.1139242984811 - Float64(Float64(Float64(Float64(130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x)
	tmp = 0.0
	if (x <= -37.0)
		tmp = t_0;
	elseif (x <= 2.0)
		tmp = Float64(Float64(-2.0 * Float64(Float64(Float64(Float64(137.519416416 * x) + y) * x) + z)) / Float64(Float64(313.399215894 * x) + 47.066876606));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (((-110.1139242984811 - ((((130977.50649958357 - y) / x) - 3655.1204654076414) / x)) / x) - -4.16438922228) * x;
	tmp = 0.0;
	if (x <= -37.0)
		tmp = t_0;
	elseif (x <= 2.0)
		tmp = (-2.0 * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[(N[(-110.1139242984811 - N[(N[(N[(N[(130977.50649958357 - y), $MachinePrecision] / x), $MachinePrecision] - 3655.1204654076414), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision] - -4.16438922228), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[x, -37.0], t$95$0, If[LessEqual[x, 2.0], N[(N[(-2.0 * N[(N[(N[(N[(137.519416416 * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(313.399215894 * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -37 or 2 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   6. Applied rewrites 47.6%

      \[\leadsto \left(\frac{-110.1139242984811 - \frac{\frac{130977.50649958357 - y}{x} - 3655.1204654076414}{x}}{x} - -4.16438922228\right) \cdot \color{blue}{x} \]

   if -37 < x < 2

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{\frac{4297481763}{31250000} \cdot x} + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 51.0

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot \color{blue}{x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{137.519416416 \cdot x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{-2} \cdot \left(\left(\frac{4297481763}{31250000} \cdot x + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   9. Step-by-step derivation

   10. Applied rewrites 49.6%

       \[\leadsto \frac{\color{blue}{-2} \cdot \left(\left(137.519416416 \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 92.6% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -37:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 2:\\ \;\;\;\;\frac{-2 \cdot \left(\left(137.519416416 \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -37.0)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 2.0)
     (/
      (* -2.0 (+ (* (+ (* 137.519416416 x) y) x) z))
      (+ (* 313.399215894 x) 47.066876606))
     (/ 1.0 (/ (+ 0.24013125253755718 (* 6.349501247902845 (/ 1.0 x))) x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -37.0) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 2.0) {
		tmp = (-2.0 * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-37.0d0)) then
        tmp = 1.0d0 / (0.24013125253755718d0 / x)
    else if (x <= 2.0d0) then
        tmp = ((-2.0d0) * ((((137.519416416d0 * x) + y) * x) + z)) / ((313.399215894d0 * x) + 47.066876606d0)
    else
        tmp = 1.0d0 / ((0.24013125253755718d0 + (6.349501247902845d0 * (1.0d0 / x))) / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -37.0) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 2.0) {
		tmp = (-2.0 * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -37.0:
		tmp = 1.0 / (0.24013125253755718 / x)
	elif x <= 2.0:
		tmp = (-2.0 * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606)
	else:
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -37.0)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 2.0)
		tmp = Float64(Float64(-2.0 * Float64(Float64(Float64(Float64(137.519416416 * x) + y) * x) + z)) / Float64(Float64(313.399215894 * x) + 47.066876606));
	else
		tmp = Float64(1.0 / Float64(Float64(0.24013125253755718 + Float64(6.349501247902845 * Float64(1.0 / x))) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -37.0)
		tmp = 1.0 / (0.24013125253755718 / x);
	elseif (x <= 2.0)
		tmp = (-2.0 * ((((137.519416416 * x) + y) * x) + z)) / ((313.399215894 * x) + 47.066876606);
	else
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -37.0], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 2.0], N[(N[(-2.0 * N[(N[(N[(N[(137.519416416 * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(313.399215894 * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(0.24013125253755718 + N[(6.349501247902845 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -37

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -37 < x < 2

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{\frac{4297481763}{31250000} \cdot x} + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 51.0

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot \color{blue}{x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{137.519416416 \cdot x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{-2} \cdot \left(\left(\frac{4297481763}{31250000} \cdot x + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   9. Step-by-step derivation

   10. Applied rewrites 49.6%

       \[\leadsto \frac{\color{blue}{-2} \cdot \left(\left(137.519416416 \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   if 2 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{\color{blue}{x}}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      4. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 14: 91.7% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.4 \cdot 10^{+34}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 180:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot x + y\right) \cdot x + z\right)}{47.066876606}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -2.4e+34)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 180.0)
     (/ (* (- x 2.0) (+ (* (+ (* 137.519416416 x) y) x) z)) 47.066876606)
     (/ 1.0 (/ (+ 0.24013125253755718 (* 6.349501247902845 (/ 1.0 x))) x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -2.4e+34) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 180.0) {
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / 47.066876606;
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-2.4d+34)) then
        tmp = 1.0d0 / (0.24013125253755718d0 / x)
    else if (x <= 180.0d0) then
        tmp = ((x - 2.0d0) * ((((137.519416416d0 * x) + y) * x) + z)) / 47.066876606d0
    else
        tmp = 1.0d0 / ((0.24013125253755718d0 + (6.349501247902845d0 * (1.0d0 / x))) / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -2.4e+34) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 180.0) {
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / 47.066876606;
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -2.4e+34:
		tmp = 1.0 / (0.24013125253755718 / x)
	elif x <= 180.0:
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / 47.066876606
	else:
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -2.4e+34)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 180.0)
		tmp = Float64(Float64(Float64(x - 2.0) * Float64(Float64(Float64(Float64(137.519416416 * x) + y) * x) + z)) / 47.066876606);
	else
		tmp = Float64(1.0 / Float64(Float64(0.24013125253755718 + Float64(6.349501247902845 * Float64(1.0 / x))) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -2.4e+34)
		tmp = 1.0 / (0.24013125253755718 / x);
	elseif (x <= 180.0)
		tmp = ((x - 2.0) * ((((137.519416416 * x) + y) * x) + z)) / 47.066876606;
	else
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -2.4e+34], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 180.0], N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(N[(N[(137.519416416 * x), $MachinePrecision] + y), $MachinePrecision] * x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / 47.066876606), $MachinePrecision], N[(1.0 / N[(N[(0.24013125253755718 + N[(6.349501247902845 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.39999999999999987e34

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -2.39999999999999987e34 < x < 180

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{\frac{4297481763}{31250000} \cdot x} + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 51.0

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot \color{blue}{x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\color{blue}{137.519416416 \cdot x} + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\frac{4297481763}{31250000} \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{23533438303}{500000000}}} \]
   9. Step-by-step derivation

   10. Applied rewrites 51.9%

       \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(137.519416416 \cdot x + y\right) \cdot x + z\right)}{\color{blue}{47.066876606}} \]

   if 180 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{\color{blue}{x}}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      4. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 15: 90.2% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 135:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}{313.399215894 \cdot x + 47.066876606}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.2e-8)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 135.0)
     (/ (* (- x 2.0) (+ (* x y) z)) (+ (* 313.399215894 x) 47.066876606))
     (/ 1.0 (/ (+ 0.24013125253755718 (* 6.349501247902845 (/ 1.0 x))) x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 135.0) {
		tmp = ((x - 2.0) * ((x * y) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-7.2d-8)) then
        tmp = 1.0d0 / (0.24013125253755718d0 / x)
    else if (x <= 135.0d0) then
        tmp = ((x - 2.0d0) * ((x * y) + z)) / ((313.399215894d0 * x) + 47.066876606d0)
    else
        tmp = 1.0d0 / ((0.24013125253755718d0 + (6.349501247902845d0 * (1.0d0 / x))) / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 135.0) {
		tmp = ((x - 2.0) * ((x * y) + z)) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -7.2e-8:
		tmp = 1.0 / (0.24013125253755718 / x)
	elif x <= 135.0:
		tmp = ((x - 2.0) * ((x * y) + z)) / ((313.399215894 * x) + 47.066876606)
	else:
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 135.0)
		tmp = Float64(Float64(Float64(x - 2.0) * Float64(Float64(x * y) + z)) / Float64(Float64(313.399215894 * x) + 47.066876606));
	else
		tmp = Float64(1.0 / Float64(Float64(0.24013125253755718 + Float64(6.349501247902845 * Float64(1.0 / x))) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -7.2e-8)
		tmp = 1.0 / (0.24013125253755718 / x);
	elseif (x <= 135.0)
		tmp = ((x - 2.0) * ((x * y) + z)) / ((313.399215894 * x) + 47.066876606);
	else
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.2e-8], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 135.0], N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / N[(N[(313.399215894 * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(0.24013125253755718 + N[(6.349501247902845 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.19999999999999962e-8

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -7.19999999999999962e-8 < x < 135

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\color{blue}{x \cdot y} + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 48.7

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(x \cdot \color{blue}{y} + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 48.7%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\color{blue}{x \cdot y} + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   if 135 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{\color{blue}{x}}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      4. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 16: 90.2% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 135:\\ \;\;\;\;\frac{y \cdot x + z}{\mathsf{fma}\left(313.399215894, x, 47.066876606\right)} \cdot \left(x - 2\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.2e-8)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 135.0)
     (* (/ (+ (* y x) z) (fma 313.399215894 x 47.066876606)) (- x 2.0))
     (/ 1.0 (/ (+ 0.24013125253755718 (* 6.349501247902845 (/ 1.0 x))) x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 135.0) {
		tmp = (((y * x) + z) / fma(313.399215894, x, 47.066876606)) * (x - 2.0);
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 135.0)
		tmp = Float64(Float64(Float64(Float64(y * x) + z) / fma(313.399215894, x, 47.066876606)) * Float64(x - 2.0));
	else
		tmp = Float64(1.0 / Float64(Float64(0.24013125253755718 + Float64(6.349501247902845 * Float64(1.0 / x))) / x));
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.2e-8], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 135.0], N[(N[(N[(N[(y * x), $MachinePrecision] + z), $MachinePrecision] / N[(313.399215894 * x + 47.066876606), $MachinePrecision]), $MachinePrecision] * N[(x - 2.0), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(0.24013125253755718 + N[(6.349501247902845 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.19999999999999962e-8

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -7.19999999999999962e-8 < x < 135

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\color{blue}{x \cdot y} + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 48.7

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(x \cdot \color{blue}{y} + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 48.7%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\color{blue}{x \cdot y} + z\right)}{313.399215894 \cdot x + 47.066876606} \]
   8. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]

      3. associate-/l* N/A

         \[\leadsto \color{blue}{\left(x - 2\right) \cdot \frac{x \cdot y + z}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}}} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\frac{x \cdot y + z}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \cdot \left(x - 2\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{x \cdot y + z}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \cdot \left(x - 2\right)} \]

   9. Applied rewrites 48.7%

      \[\leadsto \color{blue}{\frac{y \cdot x + z}{\mathsf{fma}\left(313.399215894, x, 47.066876606\right)} \cdot \left(x - 2\right)} \]

   if 135 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{\color{blue}{x}}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      4. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 17: 89.9% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 135:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}{47.066876606}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.2e-8)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 135.0)
     (/ (* (- x 2.0) (+ (* x y) z)) 47.066876606)
     (/ 1.0 (/ (+ 0.24013125253755718 (* 6.349501247902845 (/ 1.0 x))) x)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 135.0) {
		tmp = ((x - 2.0) * ((x * y) + z)) / 47.066876606;
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-7.2d-8)) then
        tmp = 1.0d0 / (0.24013125253755718d0 / x)
    else if (x <= 135.0d0) then
        tmp = ((x - 2.0d0) * ((x * y) + z)) / 47.066876606d0
    else
        tmp = 1.0d0 / ((0.24013125253755718d0 + (6.349501247902845d0 * (1.0d0 / x))) / x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 135.0) {
		tmp = ((x - 2.0) * ((x * y) + z)) / 47.066876606;
	} else {
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -7.2e-8:
		tmp = 1.0 / (0.24013125253755718 / x)
	elif x <= 135.0:
		tmp = ((x - 2.0) * ((x * y) + z)) / 47.066876606
	else:
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 135.0)
		tmp = Float64(Float64(Float64(x - 2.0) * Float64(Float64(x * y) + z)) / 47.066876606);
	else
		tmp = Float64(1.0 / Float64(Float64(0.24013125253755718 + Float64(6.349501247902845 * Float64(1.0 / x))) / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -7.2e-8)
		tmp = 1.0 / (0.24013125253755718 / x);
	elseif (x <= 135.0)
		tmp = ((x - 2.0) * ((x * y) + z)) / 47.066876606;
	else
		tmp = 1.0 / ((0.24013125253755718 + (6.349501247902845 * (1.0 / x))) / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.2e-8], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 135.0], N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / 47.066876606), $MachinePrecision], N[(1.0 / N[(N[(0.24013125253755718 + N[(6.349501247902845 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.19999999999999962e-8

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -7.19999999999999962e-8 < x < 135

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\color{blue}{x \cdot y} + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 48.7

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(x \cdot \color{blue}{y} + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 48.7%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\color{blue}{x \cdot y} + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}{\color{blue}{\frac{23533438303}{500000000}}} \]
   9. Step-by-step derivation

   10. Applied rewrites 48.6%

       \[\leadsto \frac{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}{\color{blue}{47.066876606}} \]

   if 135 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{\color{blue}{x}}} \]

      2. lower-+.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{1}{\frac{\frac{25000000000}{104109730557} + \frac{68821202686550684400745}{10838835996651139530249} \cdot \frac{1}{x}}{x}} \]

      4. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718 + 6.349501247902845 \cdot \frac{1}{x}}{x}}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 18: 89.8% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 135:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}{47.066876606}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.2e-8)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 135.0)
     (/ (* (- x 2.0) (+ (* x y) z)) 47.066876606)
     (* x (- 4.16438922228 (* 110.1139242984811 (/ 1.0 x)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 135.0) {
		tmp = ((x - 2.0) * ((x * y) + z)) / 47.066876606;
	} else {
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-7.2d-8)) then
        tmp = 1.0d0 / (0.24013125253755718d0 / x)
    else if (x <= 135.0d0) then
        tmp = ((x - 2.0d0) * ((x * y) + z)) / 47.066876606d0
    else
        tmp = x * (4.16438922228d0 - (110.1139242984811d0 * (1.0d0 / x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 135.0) {
		tmp = ((x - 2.0) * ((x * y) + z)) / 47.066876606;
	} else {
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -7.2e-8:
		tmp = 1.0 / (0.24013125253755718 / x)
	elif x <= 135.0:
		tmp = ((x - 2.0) * ((x * y) + z)) / 47.066876606
	else:
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 135.0)
		tmp = Float64(Float64(Float64(x - 2.0) * Float64(Float64(x * y) + z)) / 47.066876606);
	else
		tmp = Float64(x * Float64(4.16438922228 - Float64(110.1139242984811 * Float64(1.0 / x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -7.2e-8)
		tmp = 1.0 / (0.24013125253755718 / x);
	elseif (x <= 135.0)
		tmp = ((x - 2.0) * ((x * y) + z)) / 47.066876606;
	else
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.2e-8], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 135.0], N[(N[(N[(x - 2.0), $MachinePrecision] * N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] / 47.066876606), $MachinePrecision], N[(x * N[(4.16438922228 - N[(110.1139242984811 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.19999999999999962e-8

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -7.19999999999999962e-8 < x < 135

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\color{blue}{x \cdot y} + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 48.7

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(x \cdot \color{blue}{y} + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 48.7%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\color{blue}{x \cdot y} + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}{\color{blue}{\frac{23533438303}{500000000}}} \]
   9. Step-by-step derivation

   10. Applied rewrites 48.6%

       \[\leadsto \frac{\left(x - 2\right) \cdot \left(x \cdot y + z\right)}{\color{blue}{47.066876606}} \]

   if 135 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto x \cdot \color{blue}{\left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}\right)} \]

      2. lower--.f64 N/A

         \[\leadsto x \cdot \left(\frac{104109730557}{25000000000} - \color{blue}{\frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto x \cdot \left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \color{blue}{\frac{1}{x}}\right) \]

      4. lower-/.f64 45.2

         \[\leadsto x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{\color{blue}{x}}\right) \]

   4. Applied rewrites 45.2%

      \[\leadsto \color{blue}{x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{x}\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 19: 77.5% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 130:\\ \;\;\;\;\frac{\left(x - 2\right) \cdot z}{313.399215894 \cdot x + 47.066876606}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.2e-8)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 130.0)
     (/ (* (- x 2.0) z) (+ (* 313.399215894 x) 47.066876606))
     (* x (- 4.16438922228 (* 110.1139242984811 (/ 1.0 x)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 130.0) {
		tmp = ((x - 2.0) * z) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-7.2d-8)) then
        tmp = 1.0d0 / (0.24013125253755718d0 / x)
    else if (x <= 130.0d0) then
        tmp = ((x - 2.0d0) * z) / ((313.399215894d0 * x) + 47.066876606d0)
    else
        tmp = x * (4.16438922228d0 - (110.1139242984811d0 * (1.0d0 / x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 130.0) {
		tmp = ((x - 2.0) * z) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -7.2e-8:
		tmp = 1.0 / (0.24013125253755718 / x)
	elif x <= 130.0:
		tmp = ((x - 2.0) * z) / ((313.399215894 * x) + 47.066876606)
	else:
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 130.0)
		tmp = Float64(Float64(Float64(x - 2.0) * z) / Float64(Float64(313.399215894 * x) + 47.066876606));
	else
		tmp = Float64(x * Float64(4.16438922228 - Float64(110.1139242984811 * Float64(1.0 / x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -7.2e-8)
		tmp = 1.0 / (0.24013125253755718 / x);
	elseif (x <= 130.0)
		tmp = ((x - 2.0) * z) / ((313.399215894 * x) + 47.066876606);
	else
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.2e-8], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 130.0], N[(N[(N[(x - 2.0), $MachinePrecision] * z), $MachinePrecision] / N[(N[(313.399215894 * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], N[(x * N[(4.16438922228 - N[(110.1139242984811 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.19999999999999962e-8

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -7.19999999999999962e-8 < x < 130

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \color{blue}{z}}{\left(\left(\left(x + \frac{216700011257}{5000000000}\right) \cdot x + \frac{263505074721}{1000000000}\right) \cdot x + \frac{156699607947}{500000000}\right) \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 37.3%

      \[\leadsto \frac{\left(x - 2\right) \cdot \color{blue}{z}}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot z}{\color{blue}{\frac{156699607947}{500000000} \cdot x} + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 35.8

         \[\leadsto \frac{\left(x - 2\right) \cdot z}{313.399215894 \cdot \color{blue}{x} + 47.066876606} \]

   7. Applied rewrites 35.8%

      \[\leadsto \frac{\left(x - 2\right) \cdot z}{\color{blue}{313.399215894 \cdot x} + 47.066876606} \]

   if 130 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto x \cdot \color{blue}{\left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}\right)} \]

      2. lower--.f64 N/A

         \[\leadsto x \cdot \left(\frac{104109730557}{25000000000} - \color{blue}{\frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto x \cdot \left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \color{blue}{\frac{1}{x}}\right) \]

      4. lower-/.f64 45.2

         \[\leadsto x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{\color{blue}{x}}\right) \]

   4. Applied rewrites 45.2%

      \[\leadsto \color{blue}{x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{x}\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 20: 77.4% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 1.7:\\ \;\;\;\;\frac{-2 \cdot z}{313.399215894 \cdot x + 47.066876606}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.2e-8)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 1.7)
     (/ (* -2.0 z) (+ (* 313.399215894 x) 47.066876606))
     (* x (- 4.16438922228 (* 110.1139242984811 (/ 1.0 x)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 1.7) {
		tmp = (-2.0 * z) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-7.2d-8)) then
        tmp = 1.0d0 / (0.24013125253755718d0 / x)
    else if (x <= 1.7d0) then
        tmp = ((-2.0d0) * z) / ((313.399215894d0 * x) + 47.066876606d0)
    else
        tmp = x * (4.16438922228d0 - (110.1139242984811d0 * (1.0d0 / x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 1.7) {
		tmp = (-2.0 * z) / ((313.399215894 * x) + 47.066876606);
	} else {
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -7.2e-8:
		tmp = 1.0 / (0.24013125253755718 / x)
	elif x <= 1.7:
		tmp = (-2.0 * z) / ((313.399215894 * x) + 47.066876606)
	else:
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 1.7)
		tmp = Float64(Float64(-2.0 * z) / Float64(Float64(313.399215894 * x) + 47.066876606));
	else
		tmp = Float64(x * Float64(4.16438922228 - Float64(110.1139242984811 * Float64(1.0 / x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -7.2e-8)
		tmp = 1.0 / (0.24013125253755718 / x);
	elseif (x <= 1.7)
		tmp = (-2.0 * z) / ((313.399215894 * x) + 47.066876606);
	else
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.2e-8], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 1.7], N[(N[(-2.0 * z), $MachinePrecision] / N[(N[(313.399215894 * x), $MachinePrecision] + 47.066876606), $MachinePrecision]), $MachinePrecision], N[(x * N[(4.16438922228 - N[(110.1139242984811 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.19999999999999962e-8

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -7.19999999999999962e-8 < x < 1.69999999999999996

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot \frac{104109730557}{25000000000} + \frac{393497462077}{5000000000}\right) \cdot x + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{\frac{156699607947}{500000000}} \cdot x + \frac{23533438303}{500000000}} \]
   3. Step-by-step derivation

   4. Applied rewrites 51.0%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\color{blue}{313.399215894} \cdot x + 47.066876606} \]

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\color{blue}{\frac{393497462077}{5000000000} \cdot x} + \frac{4297481763}{31250000}\right) \cdot x + y\right) \cdot x + z\right)}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   6. Step-by-step derivation

      1. lower-*.f64 52.2

         \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(78.6994924154 \cdot \color{blue}{x} + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   7. Applied rewrites 52.2%

      \[\leadsto \frac{\left(x - 2\right) \cdot \left(\left(\left(\color{blue}{78.6994924154 \cdot x} + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{313.399215894 \cdot x + 47.066876606} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{-2 \cdot z}}{\frac{156699607947}{500000000} \cdot x + \frac{23533438303}{500000000}} \]
   9. Step-by-step derivation

      1. lower-*.f64 35.4

         \[\leadsto \frac{-2 \cdot \color{blue}{z}}{313.399215894 \cdot x + 47.066876606} \]

   10. Applied rewrites 35.4%

       \[\leadsto \frac{\color{blue}{-2 \cdot z}}{313.399215894 \cdot x + 47.066876606} \]

   if 1.69999999999999996 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto x \cdot \color{blue}{\left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}\right)} \]

      2. lower--.f64 N/A

         \[\leadsto x \cdot \left(\frac{104109730557}{25000000000} - \color{blue}{\frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto x \cdot \left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \color{blue}{\frac{1}{x}}\right) \]

      4. lower-/.f64 45.2

         \[\leadsto x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{\color{blue}{x}}\right) \]

   4. Applied rewrites 45.2%

      \[\leadsto \color{blue}{x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{x}\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 21: 77.3% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;\frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{elif}\;x \leq 5:\\ \;\;\;\;-0.0424927283095952 \cdot z\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{x}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.2e-8)
   (/ 1.0 (/ 0.24013125253755718 x))
   (if (<= x 5.0)
     (* -0.0424927283095952 z)
     (* x (- 4.16438922228 (* 110.1139242984811 (/ 1.0 x)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 5.0) {
		tmp = -0.0424927283095952 * z;
	} else {
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-7.2d-8)) then
        tmp = 1.0d0 / (0.24013125253755718d0 / x)
    else if (x <= 5.0d0) then
        tmp = (-0.0424927283095952d0) * z
    else
        tmp = x * (4.16438922228d0 - (110.1139242984811d0 * (1.0d0 / x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 1.0 / (0.24013125253755718 / x);
	} else if (x <= 5.0) {
		tmp = -0.0424927283095952 * z;
	} else {
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -7.2e-8:
		tmp = 1.0 / (0.24013125253755718 / x)
	elif x <= 5.0:
		tmp = -0.0424927283095952 * z
	else:
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)))
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = Float64(1.0 / Float64(0.24013125253755718 / x));
	elseif (x <= 5.0)
		tmp = Float64(-0.0424927283095952 * z);
	else
		tmp = Float64(x * Float64(4.16438922228 - Float64(110.1139242984811 * Float64(1.0 / x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -7.2e-8)
		tmp = 1.0 / (0.24013125253755718 / x);
	elseif (x <= 5.0)
		tmp = -0.0424927283095952 * z;
	else
		tmp = x * (4.16438922228 - (110.1139242984811 * (1.0 / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.2e-8], N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 5.0], N[(-0.0424927283095952 * z), $MachinePrecision], N[(x * N[(4.16438922228 - N[(110.1139242984811 * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.19999999999999962e-8

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -7.19999999999999962e-8 < x < 5

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{-1000000000}{23533438303} \cdot z} \]
   3. Step-by-step derivation

      1. lower-*.f64 35.5

         \[\leadsto -0.0424927283095952 \cdot \color{blue}{z} \]

   4. Applied rewrites 35.5%

      \[\leadsto \color{blue}{-0.0424927283095952 \cdot z} \]

   if 5 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}\right)} \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto x \cdot \color{blue}{\left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}\right)} \]

      2. lower--.f64 N/A

         \[\leadsto x \cdot \left(\frac{104109730557}{25000000000} - \color{blue}{\frac{13764240537310136880149}{125000000000000000000} \cdot \frac{1}{x}}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto x \cdot \left(\frac{104109730557}{25000000000} - \frac{13764240537310136880149}{125000000000000000000} \cdot \color{blue}{\frac{1}{x}}\right) \]

      4. lower-/.f64 45.2

         \[\leadsto x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{\color{blue}{x}}\right) \]

   4. Applied rewrites 45.2%

      \[\leadsto \color{blue}{x \cdot \left(4.16438922228 - 110.1139242984811 \cdot \frac{1}{x}\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 22: 77.2% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{\frac{0.24013125253755718}{x}}\\ \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 2:\\ \;\;\;\;-0.0424927283095952 \cdot z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ 1.0 (/ 0.24013125253755718 x))))
   (if (<= x -7.2e-8) t_0 (if (<= x 2.0) (* -0.0424927283095952 z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = 1.0 / (0.24013125253755718 / x);
	double tmp;
	if (x <= -7.2e-8) {
		tmp = t_0;
	} else if (x <= 2.0) {
		tmp = -0.0424927283095952 * z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 / (0.24013125253755718d0 / x)
    if (x <= (-7.2d-8)) then
        tmp = t_0
    else if (x <= 2.0d0) then
        tmp = (-0.0424927283095952d0) * z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = 1.0 / (0.24013125253755718 / x);
	double tmp;
	if (x <= -7.2e-8) {
		tmp = t_0;
	} else if (x <= 2.0) {
		tmp = -0.0424927283095952 * z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = 1.0 / (0.24013125253755718 / x)
	tmp = 0
	if x <= -7.2e-8:
		tmp = t_0
	elif x <= 2.0:
		tmp = -0.0424927283095952 * z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(1.0 / Float64(0.24013125253755718 / x))
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = t_0;
	elseif (x <= 2.0)
		tmp = Float64(-0.0424927283095952 * z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = 1.0 / (0.24013125253755718 / x);
	tmp = 0.0;
	if (x <= -7.2e-8)
		tmp = t_0;
	elseif (x <= 2.0)
		tmp = -0.0424927283095952 * z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(1.0 / N[(0.24013125253755718 / x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -7.2e-8], t$95$0, If[LessEqual[x, 2.0], N[(-0.0424927283095952 * z), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x < -7.19999999999999962e-8 or 2 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   3. Applied rewrites 58.7%

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(x - -43.3400022514, x, 263.505074721\right), x, 313.399215894\right), x, 47.066876606\right)}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(4.16438922228, x, 78.6994924154\right), x, 137.519416416\right), x, y\right), x, z\right) \cdot \left(x - 2\right)}}} \]

   4. Taylor expanded in x around inf

      \[\leadsto \frac{1}{\color{blue}{\frac{\frac{25000000000}{104109730557}}{x}}} \]
   5. Step-by-step derivation

      1. lower-/.f64 45.1

         \[\leadsto \frac{1}{\frac{0.24013125253755718}{\color{blue}{x}}} \]

   6. Applied rewrites 45.1%

      \[\leadsto \frac{1}{\color{blue}{\frac{0.24013125253755718}{x}}} \]

   if -7.19999999999999962e-8 < x < 2

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{-1000000000}{23533438303} \cdot z} \]
   3. Step-by-step derivation

      1. lower-*.f64 35.5

         \[\leadsto -0.0424927283095952 \cdot \color{blue}{z} \]

   4. Applied rewrites 35.5%

      \[\leadsto \color{blue}{-0.0424927283095952 \cdot z} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 23: 77.1% accurate, 4.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -7.2 \cdot 10^{-8}:\\ \;\;\;\;4.16438922228 \cdot x\\ \mathbf{elif}\;x \leq 2:\\ \;\;\;\;-0.0424927283095952 \cdot z\\ \mathbf{else}:\\ \;\;\;\;4.16438922228 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -7.2e-8)
   (* 4.16438922228 x)
   (if (<= x 2.0) (* -0.0424927283095952 z) (* 4.16438922228 x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 4.16438922228 * x;
	} else if (x <= 2.0) {
		tmp = -0.0424927283095952 * z;
	} else {
		tmp = 4.16438922228 * x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-7.2d-8)) then
        tmp = 4.16438922228d0 * x
    else if (x <= 2.0d0) then
        tmp = (-0.0424927283095952d0) * z
    else
        tmp = 4.16438922228d0 * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -7.2e-8) {
		tmp = 4.16438922228 * x;
	} else if (x <= 2.0) {
		tmp = -0.0424927283095952 * z;
	} else {
		tmp = 4.16438922228 * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -7.2e-8:
		tmp = 4.16438922228 * x
	elif x <= 2.0:
		tmp = -0.0424927283095952 * z
	else:
		tmp = 4.16438922228 * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -7.2e-8)
		tmp = Float64(4.16438922228 * x);
	elseif (x <= 2.0)
		tmp = Float64(-0.0424927283095952 * z);
	else
		tmp = Float64(4.16438922228 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -7.2e-8)
		tmp = 4.16438922228 * x;
	elseif (x <= 2.0)
		tmp = -0.0424927283095952 * z;
	else
		tmp = 4.16438922228 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -7.2e-8], N[(4.16438922228 * x), $MachinePrecision], If[LessEqual[x, 2.0], N[(-0.0424927283095952 * z), $MachinePrecision], N[(4.16438922228 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -7.19999999999999962e-8 or 2 < x

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \frac{y}{x} + \frac{409304707811198655637810418659684985388407301}{3125000000000000000000000000000000000000} \cdot \frac{1}{x}\right) - \frac{2284450290879775841688574159837293}{625000000000000000000000000000}}{x} - \frac{13764240537310136880149}{125000000000000000000}}{x} - \frac{104109730557}{25000000000}\right)\right)} \]

   4. Applied rewrites 47.6%

      \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(-1 \cdot \frac{-1 \cdot \frac{\mathsf{fma}\left(-1, \frac{y}{x}, 130977.50649958357 \cdot \frac{1}{x}\right) - 3655.1204654076414}{x} - 110.1139242984811}{x} - 4.16438922228\right)\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto -1 \cdot \left(x \cdot \frac{-104109730557}{25000000000}\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 44.9%

      \[\leadsto -1 \cdot \left(x \cdot -4.16438922228\right) \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{\left(x \cdot \frac{-104109730557}{25000000000}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(x \cdot \frac{-104109730557}{25000000000}\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \mathsf{neg}\left(x \cdot \frac{-104109730557}{25000000000}\right) \]

      4. *-commutative N/A

         \[\leadsto \mathsf{neg}\left(\frac{-104109730557}{25000000000} \cdot x\right) \]

      5. distribute-lft-neg-in N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{-104109730557}{25000000000}\right)\right) \cdot \color{blue}{x} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\frac{-104109730557}{25000000000}\right)\right) \cdot \color{blue}{x} \]

      7. lower-neg.f64 44.9

         \[\leadsto \left(--4.16438922228\right) \cdot x \]

   9. Applied rewrites 44.9%

      \[\leadsto \left(--4.16438922228\right) \cdot \color{blue}{x} \]

   10. Taylor expanded in x around inf

       \[\leadsto \frac{104109730557}{25000000000} \cdot x \]
   11. Step-by-step derivation

   12. Applied rewrites 44.9%

       \[\leadsto 4.16438922228 \cdot x \]

   if -7.19999999999999962e-8 < x < 2

   1. Initial program 58.8%

      \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{-1000000000}{23533438303} \cdot z} \]
   3. Step-by-step derivation

      1. lower-*.f64 35.5

         \[\leadsto -0.0424927283095952 \cdot \color{blue}{z} \]

   4. Applied rewrites 35.5%

      \[\leadsto \color{blue}{-0.0424927283095952 \cdot z} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 24: 35.5% accurate, 13.3× speedup

Math

\[\begin{array}{l} \\ -0.0424927283095952 \cdot z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* -0.0424927283095952 z))

C

double code(double x, double y, double z) {
	return -0.0424927283095952 * z;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (-0.0424927283095952d0) * z
end function

Java

public static double code(double x, double y, double z) {
	return -0.0424927283095952 * z;
}

Python

def code(x, y, z):
	return -0.0424927283095952 * z

Julia

function code(x, y, z)
	return Float64(-0.0424927283095952 * z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = -0.0424927283095952 * z;
end

Wolfram

code[x_, y_, z_] := N[(-0.0424927283095952 * z), $MachinePrecision]

Derivation

1. Initial program 58.8%

   \[\frac{\left(x - 2\right) \cdot \left(\left(\left(\left(x \cdot 4.16438922228 + 78.6994924154\right) \cdot x + 137.519416416\right) \cdot x + y\right) \cdot x + z\right)}{\left(\left(\left(x + 43.3400022514\right) \cdot x + 263.505074721\right) \cdot x + 313.399215894\right) \cdot x + 47.066876606} \]

2. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{-1000000000}{23533438303} \cdot z} \]
3. Step-by-step derivation

   1. lower-*.f64 35.5

      \[\leadsto -0.0424927283095952 \cdot \color{blue}{z} \]

4. Applied rewrites 35.5%

   \[\leadsto \color{blue}{-0.0424927283095952 \cdot z} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025154 
(FPCore (x y z)
  :name "Numeric.SpecFunctions:logGamma from math-functions-0.1.5.2, C"
  :precision binary64
  (/ (* (- x 2.0) (+ (* (+ (* (+ (* (+ (* x 4.16438922228) 78.6994924154) x) 137.519416416) x) y) x) z)) (+ (* (+ (* (+ (* (+ x 43.3400022514) x) 263.505074721) x) 313.399215894) x) 47.066876606)))