Henrywood and Agarwal, Equation (13)

Percentage Accurate: 24.3% → 44.9%

Time: 10.4s

Alternatives: 13

Speedup: 0.7×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \frac{c0}{2 \cdot w} \cdot \left(t\_0 + \sqrt{t\_0 \cdot t\_0 - M \cdot M}\right) \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (* (/ c0 (* 2.0 w)) (+ t_0 (sqrt (- (* t_0 t_0) (* M M)))))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	return (c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))));
}

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(c0, w, h, d, d_1, m)
use fmin_fmax_functions
    real(8), intent (in) :: c0
    real(8), intent (in) :: w
    real(8), intent (in) :: h
    real(8), intent (in) :: d
    real(8), intent (in) :: d_1
    real(8), intent (in) :: m
    real(8) :: t_0
    t_0 = (c0 * (d_1 * d_1)) / ((w * h) * (d * d))
    code = (c0 / (2.0d0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (m * m))))
end function

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	return (c0 / (2.0 * w)) * (t_0 + Math.sqrt(((t_0 * t_0) - (M * M))));
}

Python

def code(c0, w, h, D, d, M):
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D))
	return (c0 / (2.0 * w)) * (t_0 + math.sqrt(((t_0 * t_0) - (M * M))))

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	return Float64(Float64(c0 / Float64(2.0 * w)) * Float64(t_0 + sqrt(Float64(Float64(t_0 * t_0) - Float64(M * M)))))
end

MATLAB

function tmp = code(c0, w, h, D, d, M)
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	tmp = (c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))));
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision] * N[(t$95$0 + N[Sqrt[N[(N[(t$95$0 * t$95$0), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Initial Program: 24.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \frac{c0}{2 \cdot w} \cdot \left(t\_0 + \sqrt{t\_0 \cdot t\_0 - M \cdot M}\right) \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (* (/ c0 (* 2.0 w)) (+ t_0 (sqrt (- (* t_0 t_0) (* M M)))))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	return (c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))));
}

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(c0, w, h, d, d_1, m)
use fmin_fmax_functions
    real(8), intent (in) :: c0
    real(8), intent (in) :: w
    real(8), intent (in) :: h
    real(8), intent (in) :: d
    real(8), intent (in) :: d_1
    real(8), intent (in) :: m
    real(8) :: t_0
    t_0 = (c0 * (d_1 * d_1)) / ((w * h) * (d * d))
    code = (c0 / (2.0d0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (m * m))))
end function

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	return (c0 / (2.0 * w)) * (t_0 + Math.sqrt(((t_0 * t_0) - (M * M))));
}

Python

def code(c0, w, h, D, d, M):
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D))
	return (c0 / (2.0 * w)) * (t_0 + math.sqrt(((t_0 * t_0) - (M * M))))

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	return Float64(Float64(c0 / Float64(2.0 * w)) * Float64(t_0 + sqrt(Float64(Float64(t_0 * t_0) - Float64(M * M)))))
end

MATLAB

function tmp = code(c0, w, h, D, d, M)
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	tmp = (c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))));
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision] * N[(t$95$0 + N[Sqrt[N[(N[(t$95$0 * t$95$0), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Alternative 1: 44.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\\ t_1 := \frac{c0}{2 \cdot w}\\ t_2 := c0 \cdot t\_0\\ t_3 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \mathbf{if}\;t\_1 \cdot \left(t\_3 + \sqrt{t\_3 \cdot t\_3 - M \cdot M}\right) \leq \infty:\\ \;\;\;\;t\_1 \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, t\_0, M\right)\right)}^{0.5}, {\left(t\_2 - M\right)}^{0.5}, t\_2\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (* (/ d (* (* h w) D)) (/ d D)))
        (t_1 (/ c0 (* 2.0 w)))
        (t_2 (* c0 t_0))
        (t_3 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (if (<= (* t_1 (+ t_3 (sqrt (- (* t_3 t_3) (* M M))))) INFINITY)
     (* t_1 (fma (pow (fma c0 t_0 M) 0.5) (pow (- t_2 M) 0.5) t_2))
     (* (/ (* (pow (- (* M M)) 0.5) c0) w) 0.5))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (d / ((h * w) * D)) * (d / D);
	double t_1 = c0 / (2.0 * w);
	double t_2 = c0 * t_0;
	double t_3 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if ((t_1 * (t_3 + sqrt(((t_3 * t_3) - (M * M))))) <= ((double) INFINITY)) {
		tmp = t_1 * fma(pow(fma(c0, t_0, M), 0.5), pow((t_2 - M), 0.5), t_2);
	} else {
		tmp = ((pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(Float64(d / Float64(Float64(h * w) * D)) * Float64(d / D))
	t_1 = Float64(c0 / Float64(2.0 * w))
	t_2 = Float64(c0 * t_0)
	t_3 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	tmp = 0.0
	if (Float64(t_1 * Float64(t_3 + sqrt(Float64(Float64(t_3 * t_3) - Float64(M * M))))) <= Inf)
		tmp = Float64(t_1 * fma((fma(c0, t_0, M) ^ 0.5), (Float64(t_2 - M) ^ 0.5), t_2));
	else
		tmp = Float64(Float64(Float64((Float64(-Float64(M * M)) ^ 0.5) * c0) / w) * 0.5);
	end
	return tmp
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(N[(d / N[(N[(h * w), $MachinePrecision] * D), $MachinePrecision]), $MachinePrecision] * N[(d / D), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(c0 * t$95$0), $MachinePrecision]}, Block[{t$95$3 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(t$95$1 * N[(t$95$3 + N[Sqrt[N[(N[(t$95$3 * t$95$3), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(t$95$1 * N[(N[Power[N[(c0 * t$95$0 + M), $MachinePrecision], 0.5], $MachinePrecision] * N[Power[N[(t$95$2 - M), $MachinePrecision], 0.5], $MachinePrecision] + t$95$2), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Power[(-N[(M * M), $MachinePrecision]), 0.5], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]]]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M))))) < +inf.0

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 32.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)} \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \color{blue}{\frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{\color{blue}{d \cdot d}}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\color{blue}{\left(h \cdot w\right)} \cdot D\right) \cdot D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right)} \cdot D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      6. times-frac N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \color{blue}{\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \color{blue}{\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \color{blue}{\frac{d}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\color{blue}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\color{blue}{\left(h \cdot w\right)} \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      11. lower-/.f64 32.8

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \color{blue}{\frac{d}{D}}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

   5. Applied rewrites 32.8%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \color{blue}{\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]
   6. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \color{blue}{\frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{\color{blue}{d \cdot d}}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\left(\color{blue}{\left(h \cdot w\right)} \cdot D\right) \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right)} \cdot D} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      6. times-frac N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)} - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\color{blue}{\frac{d}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\color{blue}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\color{blue}{\left(h \cdot w\right)} \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      11. lower-/.f64 33.1

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \color{blue}{\frac{d}{D}}\right) - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

   7. Applied rewrites 33.1%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]
   8. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \color{blue}{\frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}}\right) \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{\color{blue}{d \cdot d}}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}}\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\left(\color{blue}{\left(h \cdot w\right)} \cdot D\right) \cdot D}\right) \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right)} \cdot D}\right) \]

      6. times-frac N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \left(\color{blue}{\frac{d}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}\right)\right) \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \left(\frac{d}{\color{blue}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}\right)\right) \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{\frac{1}{2}}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{\frac{1}{2}}, c0 \cdot \left(\frac{d}{\color{blue}{\left(h \cdot w\right)} \cdot D} \cdot \frac{d}{D}\right)\right) \]

      11. lower-/.f64 39.1

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{0.5}, c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \color{blue}{\frac{d}{D}}\right)\right) \]

   9. Applied rewrites 39.1%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) - M\right)}^{0.5}, c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)}\right) \]

   if +inf.0 < (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M)))))

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      2. pow1/2 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-pow.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 22.2

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]

   6. Applied rewrites 22.2%

      \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 44.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)\\ t_1 := \frac{c0}{2 \cdot w}\\ t_2 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \mathbf{if}\;t\_1 \cdot \left(t\_2 + \sqrt{t\_2 \cdot t\_2 - M \cdot M}\right) \leq \infty:\\ \;\;\;\;t\_1 \cdot \left(t\_0 + \sqrt{{t\_0}^{2} - M \cdot M}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (* c0 (* (/ d (* (* h w) D)) (/ d D))))
        (t_1 (/ c0 (* 2.0 w)))
        (t_2 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (if (<= (* t_1 (+ t_2 (sqrt (- (* t_2 t_2) (* M M))))) INFINITY)
     (* t_1 (+ t_0 (sqrt (- (pow t_0 2.0) (* M M)))))
     (* (/ (* (pow (- (* M M)) 0.5) c0) w) 0.5))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = c0 * ((d / ((h * w) * D)) * (d / D));
	double t_1 = c0 / (2.0 * w);
	double t_2 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if ((t_1 * (t_2 + sqrt(((t_2 * t_2) - (M * M))))) <= ((double) INFINITY)) {
		tmp = t_1 * (t_0 + sqrt((pow(t_0, 2.0) - (M * M))));
	} else {
		tmp = ((pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = c0 * ((d / ((h * w) * D)) * (d / D));
	double t_1 = c0 / (2.0 * w);
	double t_2 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if ((t_1 * (t_2 + Math.sqrt(((t_2 * t_2) - (M * M))))) <= Double.POSITIVE_INFINITY) {
		tmp = t_1 * (t_0 + Math.sqrt((Math.pow(t_0, 2.0) - (M * M))));
	} else {
		tmp = ((Math.pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	t_0 = c0 * ((d / ((h * w) * D)) * (d / D))
	t_1 = c0 / (2.0 * w)
	t_2 = (c0 * (d * d)) / ((w * h) * (D * D))
	tmp = 0
	if (t_1 * (t_2 + math.sqrt(((t_2 * t_2) - (M * M))))) <= math.inf:
		tmp = t_1 * (t_0 + math.sqrt((math.pow(t_0, 2.0) - (M * M))))
	else:
		tmp = ((math.pow(-(M * M), 0.5) * c0) / w) * 0.5
	return tmp

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(c0 * Float64(Float64(d / Float64(Float64(h * w) * D)) * Float64(d / D)))
	t_1 = Float64(c0 / Float64(2.0 * w))
	t_2 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	tmp = 0.0
	if (Float64(t_1 * Float64(t_2 + sqrt(Float64(Float64(t_2 * t_2) - Float64(M * M))))) <= Inf)
		tmp = Float64(t_1 * Float64(t_0 + sqrt(Float64((t_0 ^ 2.0) - Float64(M * M)))));
	else
		tmp = Float64(Float64(Float64((Float64(-Float64(M * M)) ^ 0.5) * c0) / w) * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	t_0 = c0 * ((d / ((h * w) * D)) * (d / D));
	t_1 = c0 / (2.0 * w);
	t_2 = (c0 * (d * d)) / ((w * h) * (D * D));
	tmp = 0.0;
	if ((t_1 * (t_2 + sqrt(((t_2 * t_2) - (M * M))))) <= Inf)
		tmp = t_1 * (t_0 + sqrt(((t_0 ^ 2.0) - (M * M))));
	else
		tmp = (((-(M * M) ^ 0.5) * c0) / w) * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(c0 * N[(N[(d / N[(N[(h * w), $MachinePrecision] * D), $MachinePrecision]), $MachinePrecision] * N[(d / D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(t$95$1 * N[(t$95$2 + N[Sqrt[N[(N[(t$95$2 * t$95$2), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(t$95$1 * N[(t$95$0 + N[Sqrt[N[(N[Power[t$95$0, 2.0], $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Power[(-N[(M * M), $MachinePrecision]), 0.5], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M))))) < +inf.0

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]
   2. Step-by-step derivation

   3. Applied rewrites 27.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right)} \]
   4. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \color{blue}{\frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \frac{\color{blue}{d \cdot d}}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \frac{d \cdot d}{\left(\color{blue}{\left(h \cdot w\right)} \cdot D\right) \cdot D} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right)} \cdot D} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      6. times-frac N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\color{blue}{\frac{d}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\color{blue}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\color{blue}{\left(h \cdot w\right)} \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      11. lower-/.f64 27.2

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \color{blue}{\frac{d}{D}}\right) + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

   5. Applied rewrites 27.2%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]
   6. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \color{blue}{\frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}}\right)}^{2} - M \cdot M}\right) \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \frac{\color{blue}{d \cdot d}}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}}\right)}^{2} - M \cdot M}\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\color{blue}{\left(h \cdot w\right)} \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\color{blue}{\left(\left(h \cdot w\right) \cdot D\right)} \cdot D}\right)}^{2} - M \cdot M}\right) \]

      6. times-frac N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)}\right)}^{2} - M \cdot M}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)}\right)}^{2} - M \cdot M}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \left(\color{blue}{\frac{d}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}\right)\right)}^{2} - M \cdot M}\right) \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \left(\frac{d}{\color{blue}{\left(h \cdot w\right) \cdot D}} \cdot \frac{d}{D}\right)\right)}^{2} - M \cdot M}\right) \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \left(\frac{d}{\color{blue}{\left(h \cdot w\right)} \cdot D} \cdot \frac{d}{D}\right)\right)}^{2} - M \cdot M}\right) \]

      11. lower-/.f64 34.6

          \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \color{blue}{\frac{d}{D}}\right)\right)}^{2} - M \cdot M}\right) \]

   7. Applied rewrites 34.6%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(c0 \cdot \left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right) + \sqrt{{\left(c0 \cdot \color{blue}{\left(\frac{d}{\left(h \cdot w\right) \cdot D} \cdot \frac{d}{D}\right)}\right)}^{2} - M \cdot M}\right) \]

   if +inf.0 < (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M)))))

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      2. pow1/2 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-pow.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 22.2

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]

   6. Applied rewrites 22.2%

      \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 44.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\\ t_1 := \frac{c0}{2 \cdot w}\\ t_2 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \mathbf{if}\;t\_1 \cdot \left(t\_2 + \sqrt{t\_2 \cdot t\_2 - M \cdot M}\right) \leq \infty:\\ \;\;\;\;t\_1 \cdot \left(t\_0 + \sqrt{{t\_0}^{2} - M \cdot M}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (* c0 (/ (* d d) (* (* (* h w) D) D))))
        (t_1 (/ c0 (* 2.0 w)))
        (t_2 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (if (<= (* t_1 (+ t_2 (sqrt (- (* t_2 t_2) (* M M))))) INFINITY)
     (* t_1 (+ t_0 (sqrt (- (pow t_0 2.0) (* M M)))))
     (* (/ (* (pow (- (* M M)) 0.5) c0) w) 0.5))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = c0 * ((d * d) / (((h * w) * D) * D));
	double t_1 = c0 / (2.0 * w);
	double t_2 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if ((t_1 * (t_2 + sqrt(((t_2 * t_2) - (M * M))))) <= ((double) INFINITY)) {
		tmp = t_1 * (t_0 + sqrt((pow(t_0, 2.0) - (M * M))));
	} else {
		tmp = ((pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = c0 * ((d * d) / (((h * w) * D) * D));
	double t_1 = c0 / (2.0 * w);
	double t_2 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if ((t_1 * (t_2 + Math.sqrt(((t_2 * t_2) - (M * M))))) <= Double.POSITIVE_INFINITY) {
		tmp = t_1 * (t_0 + Math.sqrt((Math.pow(t_0, 2.0) - (M * M))));
	} else {
		tmp = ((Math.pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	t_0 = c0 * ((d * d) / (((h * w) * D) * D))
	t_1 = c0 / (2.0 * w)
	t_2 = (c0 * (d * d)) / ((w * h) * (D * D))
	tmp = 0
	if (t_1 * (t_2 + math.sqrt(((t_2 * t_2) - (M * M))))) <= math.inf:
		tmp = t_1 * (t_0 + math.sqrt((math.pow(t_0, 2.0) - (M * M))))
	else:
		tmp = ((math.pow(-(M * M), 0.5) * c0) / w) * 0.5
	return tmp

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(c0 * Float64(Float64(d * d) / Float64(Float64(Float64(h * w) * D) * D)))
	t_1 = Float64(c0 / Float64(2.0 * w))
	t_2 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	tmp = 0.0
	if (Float64(t_1 * Float64(t_2 + sqrt(Float64(Float64(t_2 * t_2) - Float64(M * M))))) <= Inf)
		tmp = Float64(t_1 * Float64(t_0 + sqrt(Float64((t_0 ^ 2.0) - Float64(M * M)))));
	else
		tmp = Float64(Float64(Float64((Float64(-Float64(M * M)) ^ 0.5) * c0) / w) * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	t_0 = c0 * ((d * d) / (((h * w) * D) * D));
	t_1 = c0 / (2.0 * w);
	t_2 = (c0 * (d * d)) / ((w * h) * (D * D));
	tmp = 0.0;
	if ((t_1 * (t_2 + sqrt(((t_2 * t_2) - (M * M))))) <= Inf)
		tmp = t_1 * (t_0 + sqrt(((t_0 ^ 2.0) - (M * M))));
	else
		tmp = (((-(M * M) ^ 0.5) * c0) / w) * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(c0 * N[(N[(d * d), $MachinePrecision] / N[(N[(N[(h * w), $MachinePrecision] * D), $MachinePrecision] * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(t$95$1 * N[(t$95$2 + N[Sqrt[N[(N[(t$95$2 * t$95$2), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(t$95$1 * N[(t$95$0 + N[Sqrt[N[(N[Power[t$95$0, 2.0], $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Power[(-N[(M * M), $MachinePrecision]), 0.5], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M))))) < +inf.0

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]
   2. Step-by-step derivation

   3. Applied rewrites 27.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} + \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right)} \]

   if +inf.0 < (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M)))))

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      2. pow1/2 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-pow.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 22.2

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]

   6. Applied rewrites 22.2%

      \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 43.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\\ t_1 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \mathbf{if}\;\frac{c0}{2 \cdot w} \cdot \left(t\_1 + \sqrt{t\_1 \cdot t\_1 - M \cdot M}\right) \leq \infty:\\ \;\;\;\;\mathsf{fma}\left(c0, t\_0, \sqrt{{\left(c0 \cdot t\_0\right)}^{2} - M \cdot M}\right) \cdot \frac{c0}{w + w}\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (/ (* d d) (* (* (* h w) D) D)))
        (t_1 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (if (<=
        (* (/ c0 (* 2.0 w)) (+ t_1 (sqrt (- (* t_1 t_1) (* M M)))))
        INFINITY)
     (* (fma c0 t_0 (sqrt (- (pow (* c0 t_0) 2.0) (* M M)))) (/ c0 (+ w w)))
     (* (/ (* (pow (- (* M M)) 0.5) c0) w) 0.5))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (d * d) / (((h * w) * D) * D);
	double t_1 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_1 + sqrt(((t_1 * t_1) - (M * M))))) <= ((double) INFINITY)) {
		tmp = fma(c0, t_0, sqrt((pow((c0 * t_0), 2.0) - (M * M)))) * (c0 / (w + w));
	} else {
		tmp = ((pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(Float64(d * d) / Float64(Float64(Float64(h * w) * D) * D))
	t_1 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	tmp = 0.0
	if (Float64(Float64(c0 / Float64(2.0 * w)) * Float64(t_1 + sqrt(Float64(Float64(t_1 * t_1) - Float64(M * M))))) <= Inf)
		tmp = Float64(fma(c0, t_0, sqrt(Float64((Float64(c0 * t_0) ^ 2.0) - Float64(M * M)))) * Float64(c0 / Float64(w + w)));
	else
		tmp = Float64(Float64(Float64((Float64(-Float64(M * M)) ^ 0.5) * c0) / w) * 0.5);
	end
	return tmp
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(N[(d * d), $MachinePrecision] / N[(N[(N[(h * w), $MachinePrecision] * D), $MachinePrecision] * D), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision] * N[(t$95$1 + N[Sqrt[N[(N[(t$95$1 * t$95$1), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(N[(c0 * t$95$0 + N[Sqrt[N[(N[Power[N[(c0 * t$95$0), $MachinePrecision], 2.0], $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(c0 / N[(w + w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Power[(-N[(M * M), $MachinePrecision]), 0.5], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M))))) < +inf.0

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 26.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, \sqrt{{\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)}^{2} - M \cdot M}\right) \cdot \frac{c0}{w + w}} \]

   if +inf.0 < (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M)))))

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      2. pow1/2 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-pow.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 22.2

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]

   6. Applied rewrites 22.2%

      \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 43.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := c0 \cdot \left(d \cdot d\right)\\ t_1 := \frac{t\_0}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \mathbf{if}\;\frac{c0}{2 \cdot w} \cdot \left(t\_1 + \sqrt{t\_1 \cdot t\_1 - M \cdot M}\right) \leq \infty:\\ \;\;\;\;0.5 \cdot \frac{c0 \cdot \left(2 \cdot \frac{t\_0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}{w}\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (* c0 (* d d))) (t_1 (/ t_0 (* (* w h) (* D D)))))
   (if (<=
        (* (/ c0 (* 2.0 w)) (+ t_1 (sqrt (- (* t_1 t_1) (* M M)))))
        INFINITY)
     (* 0.5 (/ (* c0 (* 2.0 (/ t_0 (* (* D D) (* h w))))) w))
     (* (/ (* (pow (- (* M M)) 0.5) c0) w) 0.5))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = c0 * (d * d);
	double t_1 = t_0 / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_1 + sqrt(((t_1 * t_1) - (M * M))))) <= ((double) INFINITY)) {
		tmp = 0.5 * ((c0 * (2.0 * (t_0 / ((D * D) * (h * w))))) / w);
	} else {
		tmp = ((pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = c0 * (d * d);
	double t_1 = t_0 / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_1 + Math.sqrt(((t_1 * t_1) - (M * M))))) <= Double.POSITIVE_INFINITY) {
		tmp = 0.5 * ((c0 * (2.0 * (t_0 / ((D * D) * (h * w))))) / w);
	} else {
		tmp = ((Math.pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	t_0 = c0 * (d * d)
	t_1 = t_0 / ((w * h) * (D * D))
	tmp = 0
	if ((c0 / (2.0 * w)) * (t_1 + math.sqrt(((t_1 * t_1) - (M * M))))) <= math.inf:
		tmp = 0.5 * ((c0 * (2.0 * (t_0 / ((D * D) * (h * w))))) / w)
	else:
		tmp = ((math.pow(-(M * M), 0.5) * c0) / w) * 0.5
	return tmp

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(c0 * Float64(d * d))
	t_1 = Float64(t_0 / Float64(Float64(w * h) * Float64(D * D)))
	tmp = 0.0
	if (Float64(Float64(c0 / Float64(2.0 * w)) * Float64(t_1 + sqrt(Float64(Float64(t_1 * t_1) - Float64(M * M))))) <= Inf)
		tmp = Float64(0.5 * Float64(Float64(c0 * Float64(2.0 * Float64(t_0 / Float64(Float64(D * D) * Float64(h * w))))) / w));
	else
		tmp = Float64(Float64(Float64((Float64(-Float64(M * M)) ^ 0.5) * c0) / w) * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	t_0 = c0 * (d * d);
	t_1 = t_0 / ((w * h) * (D * D));
	tmp = 0.0;
	if (((c0 / (2.0 * w)) * (t_1 + sqrt(((t_1 * t_1) - (M * M))))) <= Inf)
		tmp = 0.5 * ((c0 * (2.0 * (t_0 / ((D * D) * (h * w))))) / w);
	else
		tmp = (((-(M * M) ^ 0.5) * c0) / w) * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision] * N[(t$95$1 + N[Sqrt[N[(N[(t$95$1 * t$95$1), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(0.5 * N[(N[(c0 * N[(2.0 * N[(t$95$0 / N[(N[(D * D), $MachinePrecision] * N[(h * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / w), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Power[(-N[(M * M), $MachinePrecision]), 0.5], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M))))) < +inf.0

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 32.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)} \]

   4. Taylor expanded in d around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{w}} \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{w}} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{\color{blue}{w}} \]

   6. Applied rewrites 33.2%

      \[\leadsto \color{blue}{0.5 \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(\frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)} + {\left(\frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}^{1}\right)\right)}{w}} \]

   7. Taylor expanded in c0 around 0

      \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}\right)}{w} \]
   8. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}\right)}{w} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}\right)}{w} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}\right)}{w} \]

      4. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}\right)}{w} \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}\right)}{w} \]

      6. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}{w} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}{w} \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}{w} \]

      9. lift-*.f64 32.5

         \[\leadsto 0.5 \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}{w} \]

   9. Applied rewrites 32.5%

      \[\leadsto 0.5 \cdot \frac{c0 \cdot \left(2 \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}{w} \]

   if +inf.0 < (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M)))))

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      2. pow1/2 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-pow.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 22.2

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]

   6. Applied rewrites 22.2%

      \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 43.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \mathbf{if}\;\frac{c0}{2 \cdot w} \cdot \left(t\_0 + \sqrt{t\_0 \cdot t\_0 - M \cdot M}\right) \leq \infty:\\ \;\;\;\;0.5 \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)\right)}{w}\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (if (<=
        (* (/ c0 (* 2.0 w)) (+ t_0 (sqrt (- (* t_0 t_0) (* M M)))))
        INFINITY)
     (* 0.5 (/ (* c0 (* (* d d) (* 2.0 (/ c0 (* (* D D) (* h w)))))) w))
     (* (/ (* (pow (- (* M M)) 0.5) c0) w) 0.5))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))))) <= ((double) INFINITY)) {
		tmp = 0.5 * ((c0 * ((d * d) * (2.0 * (c0 / ((D * D) * (h * w)))))) / w);
	} else {
		tmp = ((pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_0 + Math.sqrt(((t_0 * t_0) - (M * M))))) <= Double.POSITIVE_INFINITY) {
		tmp = 0.5 * ((c0 * ((d * d) * (2.0 * (c0 / ((D * D) * (h * w)))))) / w);
	} else {
		tmp = ((Math.pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D))
	tmp = 0
	if ((c0 / (2.0 * w)) * (t_0 + math.sqrt(((t_0 * t_0) - (M * M))))) <= math.inf:
		tmp = 0.5 * ((c0 * ((d * d) * (2.0 * (c0 / ((D * D) * (h * w)))))) / w)
	else:
		tmp = ((math.pow(-(M * M), 0.5) * c0) / w) * 0.5
	return tmp

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	tmp = 0.0
	if (Float64(Float64(c0 / Float64(2.0 * w)) * Float64(t_0 + sqrt(Float64(Float64(t_0 * t_0) - Float64(M * M))))) <= Inf)
		tmp = Float64(0.5 * Float64(Float64(c0 * Float64(Float64(d * d) * Float64(2.0 * Float64(c0 / Float64(Float64(D * D) * Float64(h * w)))))) / w));
	else
		tmp = Float64(Float64(Float64((Float64(-Float64(M * M)) ^ 0.5) * c0) / w) * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	tmp = 0.0;
	if (((c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))))) <= Inf)
		tmp = 0.5 * ((c0 * ((d * d) * (2.0 * (c0 / ((D * D) * (h * w)))))) / w);
	else
		tmp = (((-(M * M) ^ 0.5) * c0) / w) * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision] * N[(t$95$0 + N[Sqrt[N[(N[(t$95$0 * t$95$0), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(0.5 * N[(N[(c0 * N[(N[(d * d), $MachinePrecision] * N[(2.0 * N[(c0 / N[(N[(D * D), $MachinePrecision] * N[(h * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / w), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Power[(-N[(M * M), $MachinePrecision]), 0.5], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M))))) < +inf.0

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 32.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)} \]

   4. Taylor expanded in d around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{w}} \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{w}} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{\color{blue}{w}} \]

   6. Applied rewrites 33.2%

      \[\leadsto \color{blue}{0.5 \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(\frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)} + {\left(\frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}^{1}\right)\right)}{w}} \]

   7. Taylor expanded in c0 around 0

      \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}\right)\right)}{w} \]
   8. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}\right)\right)}{w} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}\right)\right)}{w} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)\right)}{w} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)\right)}{w} \]

      5. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)\right)}{w} \]

      6. lift-*.f64 33.2

         \[\leadsto 0.5 \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)\right)}{w} \]

   9. Applied rewrites 33.2%

      \[\leadsto 0.5 \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(2 \cdot \frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)\right)}{w} \]

   if +inf.0 < (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M)))))

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      2. pow1/2 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-pow.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 22.2

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]

   6. Applied rewrites 22.2%

      \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 41.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \mathbf{if}\;\frac{c0}{2 \cdot w} \cdot \left(t\_0 + \sqrt{t\_0 \cdot t\_0 - M \cdot M}\right) \leq \infty:\\ \;\;\;\;0.5 \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w}\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (if (<=
        (* (/ c0 (* 2.0 w)) (+ t_0 (sqrt (- (* t_0 t_0) (* M M)))))
        INFINITY)
     (* 0.5 (/ (* 2.0 (/ (* (* c0 c0) (* d d)) (* (* D D) (* h w)))) w))
     (* (/ (* (pow (- (* M M)) 0.5) c0) w) 0.5))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))))) <= ((double) INFINITY)) {
		tmp = 0.5 * ((2.0 * (((c0 * c0) * (d * d)) / ((D * D) * (h * w)))) / w);
	} else {
		tmp = ((pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_0 + Math.sqrt(((t_0 * t_0) - (M * M))))) <= Double.POSITIVE_INFINITY) {
		tmp = 0.5 * ((2.0 * (((c0 * c0) * (d * d)) / ((D * D) * (h * w)))) / w);
	} else {
		tmp = ((Math.pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D))
	tmp = 0
	if ((c0 / (2.0 * w)) * (t_0 + math.sqrt(((t_0 * t_0) - (M * M))))) <= math.inf:
		tmp = 0.5 * ((2.0 * (((c0 * c0) * (d * d)) / ((D * D) * (h * w)))) / w)
	else:
		tmp = ((math.pow(-(M * M), 0.5) * c0) / w) * 0.5
	return tmp

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	tmp = 0.0
	if (Float64(Float64(c0 / Float64(2.0 * w)) * Float64(t_0 + sqrt(Float64(Float64(t_0 * t_0) - Float64(M * M))))) <= Inf)
		tmp = Float64(0.5 * Float64(Float64(2.0 * Float64(Float64(Float64(c0 * c0) * Float64(d * d)) / Float64(Float64(D * D) * Float64(h * w)))) / w));
	else
		tmp = Float64(Float64(Float64((Float64(-Float64(M * M)) ^ 0.5) * c0) / w) * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	tmp = 0.0;
	if (((c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))))) <= Inf)
		tmp = 0.5 * ((2.0 * (((c0 * c0) * (d * d)) / ((D * D) * (h * w)))) / w);
	else
		tmp = (((-(M * M) ^ 0.5) * c0) / w) * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision] * N[(t$95$0 + N[Sqrt[N[(N[(t$95$0 * t$95$0), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(0.5 * N[(N[(2.0 * N[(N[(N[(c0 * c0), $MachinePrecision] * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(D * D), $MachinePrecision] * N[(h * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / w), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Power[(-N[(M * M), $MachinePrecision]), 0.5], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M))))) < +inf.0

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 32.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)} \]

   4. Taylor expanded in d around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{w}} \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{w}} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{c0 \cdot \left({d}^{2} \cdot \left(\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)} + {\left(\sqrt{\frac{c0}{{D}^{2} \cdot \left(h \cdot w\right)}}\right)}^{2}\right)\right)}{\color{blue}{w}} \]

   6. Applied rewrites 33.2%

      \[\leadsto \color{blue}{0.5 \cdot \frac{c0 \cdot \left(\left(d \cdot d\right) \cdot \left(\frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)} + {\left(\frac{c0}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}\right)}^{1}\right)\right)}{w}} \]

   7. Taylor expanded in c0 around 0

      \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]
   8. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      5. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      8. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

      10. lift-*.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

      11. lift-*.f64 28.8

          \[\leadsto 0.5 \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

   9. Applied rewrites 28.8%

      \[\leadsto 0.5 \cdot \frac{2 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

   if +inf.0 < (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M)))))

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      2. pow1/2 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-pow.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 22.2

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]

   6. Applied rewrites 22.2%

      \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 39.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)}\\ \mathbf{if}\;\frac{c0}{2 \cdot w} \cdot \left(t\_0 + \sqrt{t\_0 \cdot t\_0 - M \cdot M}\right) \leq \infty:\\ \;\;\;\;0.5 \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w}\\ \mathbf{else}:\\ \;\;\;\;\frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (let* ((t_0 (/ (* c0 (* d d)) (* (* w h) (* D D)))))
   (if (<=
        (* (/ c0 (* 2.0 w)) (+ t_0 (sqrt (- (* t_0 t_0) (* M M)))))
        INFINITY)
     (* 0.5 (/ (/ (* (* c0 c0) (* d d)) (* (* D D) (* h w))) w))
     (* (/ (* (pow (- (* M M)) 0.5) c0) w) 0.5))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))))) <= ((double) INFINITY)) {
		tmp = 0.5 * ((((c0 * c0) * (d * d)) / ((D * D) * (h * w))) / w);
	} else {
		tmp = ((pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	double tmp;
	if (((c0 / (2.0 * w)) * (t_0 + Math.sqrt(((t_0 * t_0) - (M * M))))) <= Double.POSITIVE_INFINITY) {
		tmp = 0.5 * ((((c0 * c0) * (d * d)) / ((D * D) * (h * w))) / w);
	} else {
		tmp = ((Math.pow(-(M * M), 0.5) * c0) / w) * 0.5;
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D))
	tmp = 0
	if ((c0 / (2.0 * w)) * (t_0 + math.sqrt(((t_0 * t_0) - (M * M))))) <= math.inf:
		tmp = 0.5 * ((((c0 * c0) * (d * d)) / ((D * D) * (h * w))) / w)
	else:
		tmp = ((math.pow(-(M * M), 0.5) * c0) / w) * 0.5
	return tmp

Julia

function code(c0, w, h, D, d, M)
	t_0 = Float64(Float64(c0 * Float64(d * d)) / Float64(Float64(w * h) * Float64(D * D)))
	tmp = 0.0
	if (Float64(Float64(c0 / Float64(2.0 * w)) * Float64(t_0 + sqrt(Float64(Float64(t_0 * t_0) - Float64(M * M))))) <= Inf)
		tmp = Float64(0.5 * Float64(Float64(Float64(Float64(c0 * c0) * Float64(d * d)) / Float64(Float64(D * D) * Float64(h * w))) / w));
	else
		tmp = Float64(Float64(Float64((Float64(-Float64(M * M)) ^ 0.5) * c0) / w) * 0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	t_0 = (c0 * (d * d)) / ((w * h) * (D * D));
	tmp = 0.0;
	if (((c0 / (2.0 * w)) * (t_0 + sqrt(((t_0 * t_0) - (M * M))))) <= Inf)
		tmp = 0.5 * ((((c0 * c0) * (d * d)) / ((D * D) * (h * w))) / w);
	else
		tmp = (((-(M * M) ^ 0.5) * c0) / w) * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := Block[{t$95$0 = N[(N[(c0 * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(w * h), $MachinePrecision] * N[(D * D), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision] * N[(t$95$0 + N[Sqrt[N[(N[(t$95$0 * t$95$0), $MachinePrecision] - N[(M * M), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], Infinity], N[(0.5 * N[(N[(N[(N[(c0 * c0), $MachinePrecision] * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(D * D), $MachinePrecision] * N[(h * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / w), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[Power[(-N[(M * M), $MachinePrecision]), 0.5], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M))))) < +inf.0

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 32.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)} \]

   4. Taylor expanded in c0 around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{w}} \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{w}} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{\color{blue}{w}} \]

   6. Applied rewrites 21.4%

      \[\leadsto \color{blue}{0.5 \cdot \frac{\left(\left(c0 \cdot c0\right) \cdot c0\right) \cdot {\left(\frac{d \cdot d}{\left(D \cdot D\right) \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}\right)}^{1}}{w}} \]

   7. Taylor expanded in c0 around 0

      \[\leadsto \frac{1}{2} \cdot \frac{\frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]
   8. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      5. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      8. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

      10. lift-*.f64 27.3

          \[\leadsto 0.5 \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

   9. Applied rewrites 27.3%

      \[\leadsto 0.5 \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

   if +inf.0 < (*.f64 (/.f64 c0 (*.f64 #s(literal 2 binary64) w)) (+.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (sqrt.f64 (-.f64 (*.f64 (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D))) (/.f64 (*.f64 c0 (*.f64 d d)) (*.f64 (*.f64 w h) (*.f64 D D)))) (*.f64 M M)))))

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
   5. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      2. pow1/2 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      4. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-pow.f64 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left({M}^{2}\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. pow2 N/A

         \[\leadsto \frac{{\left(\mathsf{neg}\left(M \cdot M\right)\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. lift-neg.f64 N/A

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{\frac{1}{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 22.2

         \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]

   6. Applied rewrites 22.2%

      \[\leadsto \frac{{\left(-M \cdot M\right)}^{0.5} \cdot c0}{w} \cdot 0.5 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 24.2% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;M \leq 3.05 \cdot 10^{-164}:\\ \;\;\;\;\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w}\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (if (<= M 3.05e-164)
   (* (/ (* (sqrt (- (* M M))) c0) w) 0.5)
   (* 0.5 (/ (/ (* (* c0 c0) (* d d)) (* (* D D) (* h w))) w))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double tmp;
	if (M <= 3.05e-164) {
		tmp = ((sqrt(-(M * M)) * c0) / w) * 0.5;
	} else {
		tmp = 0.5 * ((((c0 * c0) * (d * d)) / ((D * D) * (h * w))) / w);
	}
	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(c0, w, h, d, d_1, m)
use fmin_fmax_functions
    real(8), intent (in) :: c0
    real(8), intent (in) :: w
    real(8), intent (in) :: h
    real(8), intent (in) :: d
    real(8), intent (in) :: d_1
    real(8), intent (in) :: m
    real(8) :: tmp
    if (m <= 3.05d-164) then
        tmp = ((sqrt(-(m * m)) * c0) / w) * 0.5d0
    else
        tmp = 0.5d0 * ((((c0 * c0) * (d_1 * d_1)) / ((d * d) * (h * w))) / w)
    end if
    code = tmp
end function

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double tmp;
	if (M <= 3.05e-164) {
		tmp = ((Math.sqrt(-(M * M)) * c0) / w) * 0.5;
	} else {
		tmp = 0.5 * ((((c0 * c0) * (d * d)) / ((D * D) * (h * w))) / w);
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	tmp = 0
	if M <= 3.05e-164:
		tmp = ((math.sqrt(-(M * M)) * c0) / w) * 0.5
	else:
		tmp = 0.5 * ((((c0 * c0) * (d * d)) / ((D * D) * (h * w))) / w)
	return tmp

Julia

function code(c0, w, h, D, d, M)
	tmp = 0.0
	if (M <= 3.05e-164)
		tmp = Float64(Float64(Float64(sqrt(Float64(-Float64(M * M))) * c0) / w) * 0.5);
	else
		tmp = Float64(0.5 * Float64(Float64(Float64(Float64(c0 * c0) * Float64(d * d)) / Float64(Float64(D * D) * Float64(h * w))) / w));
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	tmp = 0.0;
	if (M <= 3.05e-164)
		tmp = ((sqrt(-(M * M)) * c0) / w) * 0.5;
	else
		tmp = 0.5 * ((((c0 * c0) * (d * d)) / ((D * D) * (h * w))) / w);
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := If[LessEqual[M, 3.05e-164], N[(N[(N[(N[Sqrt[(-N[(M * M), $MachinePrecision])], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision], N[(0.5 * N[(N[(N[(N[(c0 * c0), $MachinePrecision] * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(D * D), $MachinePrecision] * N[(h * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / w), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if M < 3.05000000000000007e-164

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]

   if 3.05000000000000007e-164 < M

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 32.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)} \]

   4. Taylor expanded in c0 around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{w}} \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{w}} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{\color{blue}{w}} \]

   6. Applied rewrites 21.4%

      \[\leadsto \color{blue}{0.5 \cdot \frac{\left(\left(c0 \cdot c0\right) \cdot c0\right) \cdot {\left(\frac{d \cdot d}{\left(D \cdot D\right) \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}\right)}^{1}}{w}} \]

   7. Taylor expanded in c0 around 0

      \[\leadsto \frac{1}{2} \cdot \frac{\frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]
   8. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      5. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot w\right)}}{w} \]

      8. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

      10. lift-*.f64 27.3

          \[\leadsto 0.5 \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]

   9. Applied rewrites 27.3%

      \[\leadsto 0.5 \cdot \frac{\frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot w\right)}}{w} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 23.0% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;M \leq 3.05 \cdot 10^{-164}:\\ \;\;\;\;\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \left(\frac{c0 \cdot c0}{D \cdot D} \cdot \frac{d \cdot d}{h \cdot \left(w \cdot w\right)}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (if (<= M 3.05e-164)
   (* (/ (* (sqrt (- (* M M))) c0) w) 0.5)
   (* 0.5 (* (/ (* c0 c0) (* D D)) (/ (* d d) (* h (* w w)))))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double tmp;
	if (M <= 3.05e-164) {
		tmp = ((sqrt(-(M * M)) * c0) / w) * 0.5;
	} else {
		tmp = 0.5 * (((c0 * c0) / (D * D)) * ((d * d) / (h * (w * w))));
	}
	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(c0, w, h, d, d_1, m)
use fmin_fmax_functions
    real(8), intent (in) :: c0
    real(8), intent (in) :: w
    real(8), intent (in) :: h
    real(8), intent (in) :: d
    real(8), intent (in) :: d_1
    real(8), intent (in) :: m
    real(8) :: tmp
    if (m <= 3.05d-164) then
        tmp = ((sqrt(-(m * m)) * c0) / w) * 0.5d0
    else
        tmp = 0.5d0 * (((c0 * c0) / (d * d)) * ((d_1 * d_1) / (h * (w * w))))
    end if
    code = tmp
end function

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double tmp;
	if (M <= 3.05e-164) {
		tmp = ((Math.sqrt(-(M * M)) * c0) / w) * 0.5;
	} else {
		tmp = 0.5 * (((c0 * c0) / (D * D)) * ((d * d) / (h * (w * w))));
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	tmp = 0
	if M <= 3.05e-164:
		tmp = ((math.sqrt(-(M * M)) * c0) / w) * 0.5
	else:
		tmp = 0.5 * (((c0 * c0) / (D * D)) * ((d * d) / (h * (w * w))))
	return tmp

Julia

function code(c0, w, h, D, d, M)
	tmp = 0.0
	if (M <= 3.05e-164)
		tmp = Float64(Float64(Float64(sqrt(Float64(-Float64(M * M))) * c0) / w) * 0.5);
	else
		tmp = Float64(0.5 * Float64(Float64(Float64(c0 * c0) / Float64(D * D)) * Float64(Float64(d * d) / Float64(h * Float64(w * w)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	tmp = 0.0;
	if (M <= 3.05e-164)
		tmp = ((sqrt(-(M * M)) * c0) / w) * 0.5;
	else
		tmp = 0.5 * (((c0 * c0) / (D * D)) * ((d * d) / (h * (w * w))));
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := If[LessEqual[M, 3.05e-164], N[(N[(N[(N[Sqrt[(-N[(M * M), $MachinePrecision])], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision], N[(0.5 * N[(N[(N[(c0 * c0), $MachinePrecision] / N[(D * D), $MachinePrecision]), $MachinePrecision] * N[(N[(d * d), $MachinePrecision] / N[(h * N[(w * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if M < 3.05000000000000007e-164

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]

   if 3.05000000000000007e-164 < M

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 32.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)} \]

   4. Taylor expanded in c0 around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{w}} \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{w}} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{\color{blue}{w}} \]

   6. Applied rewrites 21.4%

      \[\leadsto \color{blue}{0.5 \cdot \frac{\left(\left(c0 \cdot c0\right) \cdot c0\right) \cdot {\left(\frac{d \cdot d}{\left(D \cdot D\right) \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}\right)}^{1}}{w}} \]

   7. Taylor expanded in c0 around 0

      \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{\color{blue}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)}} \]
   8. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \color{blue}{\left(h \cdot {w}^{2}\right)}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(\color{blue}{h} \cdot {w}^{2}\right)} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)} \]

      5. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot \color{blue}{{w}^{2}}\right)} \]

      8. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot {\color{blue}{w}}^{2}\right)} \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot {\color{blue}{w}}^{2}\right)} \]

      10. lower-*.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot {w}^{\color{blue}{2}}\right)} \]

      11. pow2 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)} \]

      12. lift-*.f64 24.3

          \[\leadsto 0.5 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)} \]

   9. Applied rewrites 24.3%

      \[\leadsto 0.5 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\color{blue}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)}} \]
   10. Step-by-step derivation

       1. lift-/.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \color{blue}{\left(h \cdot \left(w \cdot w\right)\right)}} \]

       2. lift-*.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(\color{blue}{h} \cdot \left(w \cdot w\right)\right)} \]

       3. lift-*.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)} \]

       4. pow2 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)} \]

       5. lift-*.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)} \]

       6. pow2 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)} \]

       7. lift-*.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{\left(D \cdot D\right) \cdot \left(h \cdot \color{blue}{\left(w \cdot w\right)}\right)} \]

       8. lift-*.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{\left(D \cdot D\right) \cdot \left(h \cdot \left(\color{blue}{w} \cdot w\right)\right)} \]

       9. pow2 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot \left(\color{blue}{w} \cdot w\right)\right)} \]

       10. times-frac N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{{c0}^{2}}{{D}^{2}} \cdot \frac{{d}^{2}}{\color{blue}{h \cdot \left(w \cdot w\right)}}\right) \]

       11. lift-*.f64 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{{c0}^{2}}{{D}^{2}} \cdot \frac{{d}^{2}}{h \cdot \left(w \cdot \color{blue}{w}\right)}\right) \]

       12. lift-*.f64 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{{c0}^{2}}{{D}^{2}} \cdot \frac{{d}^{2}}{h \cdot \left(w \cdot w\right)}\right) \]

       13. pow2 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{{c0}^{2}}{{D}^{2}} \cdot \frac{{d}^{2}}{h \cdot {w}^{2}}\right) \]

       14. lower-*.f64 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{{c0}^{2}}{{D}^{2}} \cdot \frac{{d}^{2}}{\color{blue}{h \cdot {w}^{2}}}\right) \]

       15. lower-/.f64 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{{c0}^{2}}{{D}^{2}} \cdot \frac{{d}^{2}}{\color{blue}{h} \cdot {w}^{2}}\right) \]

       16. pow2 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{c0 \cdot c0}{{D}^{2}} \cdot \frac{{d}^{2}}{h \cdot {w}^{2}}\right) \]

       17. lift-*.f64 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{c0 \cdot c0}{{D}^{2}} \cdot \frac{{d}^{2}}{h \cdot {w}^{2}}\right) \]

       18. pow2 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{c0 \cdot c0}{D \cdot D} \cdot \frac{{d}^{2}}{h \cdot {w}^{2}}\right) \]

       19. lift-*.f64 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{c0 \cdot c0}{D \cdot D} \cdot \frac{{d}^{2}}{h \cdot {w}^{2}}\right) \]

       20. pow2 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{c0 \cdot c0}{D \cdot D} \cdot \frac{{d}^{2}}{h \cdot \left(w \cdot w\right)}\right) \]

       21. lift-*.f64 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{c0 \cdot c0}{D \cdot D} \cdot \frac{{d}^{2}}{h \cdot \left(w \cdot w\right)}\right) \]

       22. lift-*.f64 N/A

           \[\leadsto \frac{1}{2} \cdot \left(\frac{c0 \cdot c0}{D \cdot D} \cdot \frac{{d}^{2}}{h \cdot \left(w \cdot \color{blue}{w}\right)}\right) \]

   11. Applied rewrites 24.6%

       \[\leadsto 0.5 \cdot \left(\frac{c0 \cdot c0}{D \cdot D} \cdot \frac{d \cdot d}{\color{blue}{h \cdot \left(w \cdot w\right)}}\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 22.9% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;M \leq 1.38 \cdot 10^{-163}:\\ \;\;\;\;\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (if (<= M 1.38e-163)
   (* (/ (* (sqrt (- (* M M))) c0) w) 0.5)
   (* 0.5 (/ (* (* c0 c0) (* d d)) (* (* D D) (* h (* w w)))))))

C

double code(double c0, double w, double h, double D, double d, double M) {
	double tmp;
	if (M <= 1.38e-163) {
		tmp = ((sqrt(-(M * M)) * c0) / w) * 0.5;
	} else {
		tmp = 0.5 * (((c0 * c0) * (d * d)) / ((D * D) * (h * (w * w))));
	}
	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(c0, w, h, d, d_1, m)
use fmin_fmax_functions
    real(8), intent (in) :: c0
    real(8), intent (in) :: w
    real(8), intent (in) :: h
    real(8), intent (in) :: d
    real(8), intent (in) :: d_1
    real(8), intent (in) :: m
    real(8) :: tmp
    if (m <= 1.38d-163) then
        tmp = ((sqrt(-(m * m)) * c0) / w) * 0.5d0
    else
        tmp = 0.5d0 * (((c0 * c0) * (d_1 * d_1)) / ((d * d) * (h * (w * w))))
    end if
    code = tmp
end function

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	double tmp;
	if (M <= 1.38e-163) {
		tmp = ((Math.sqrt(-(M * M)) * c0) / w) * 0.5;
	} else {
		tmp = 0.5 * (((c0 * c0) * (d * d)) / ((D * D) * (h * (w * w))));
	}
	return tmp;
}

Python

def code(c0, w, h, D, d, M):
	tmp = 0
	if M <= 1.38e-163:
		tmp = ((math.sqrt(-(M * M)) * c0) / w) * 0.5
	else:
		tmp = 0.5 * (((c0 * c0) * (d * d)) / ((D * D) * (h * (w * w))))
	return tmp

Julia

function code(c0, w, h, D, d, M)
	tmp = 0.0
	if (M <= 1.38e-163)
		tmp = Float64(Float64(Float64(sqrt(Float64(-Float64(M * M))) * c0) / w) * 0.5);
	else
		tmp = Float64(0.5 * Float64(Float64(Float64(c0 * c0) * Float64(d * d)) / Float64(Float64(D * D) * Float64(h * Float64(w * w)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(c0, w, h, D, d, M)
	tmp = 0.0;
	if (M <= 1.38e-163)
		tmp = ((sqrt(-(M * M)) * c0) / w) * 0.5;
	else
		tmp = 0.5 * (((c0 * c0) * (d * d)) / ((D * D) * (h * (w * w))));
	end
	tmp_2 = tmp;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := If[LessEqual[M, 1.38e-163], N[(N[(N[(N[Sqrt[(-N[(M * M), $MachinePrecision])], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision], N[(0.5 * N[(N[(N[(c0 * c0), $MachinePrecision] * N[(d * d), $MachinePrecision]), $MachinePrecision] / N[(N[(D * D), $MachinePrecision] * N[(h * N[(w * w), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if M < 1.37999999999999999e-163

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   2. Taylor expanded in c0 around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-/.f64 N/A

         \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

      7. lower-neg.f64 N/A

         \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

      8. pow2 N/A

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

      9. lift-*.f64 14.2

         \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

   4. Applied rewrites 14.2%

      \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]

   if 1.37999999999999999e-163 < M

   1. Initial program 24.3%

      \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

   3. Applied rewrites 32.7%

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\mathsf{fma}\left({\left(\mathsf{fma}\left(c0, \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}, M\right)\right)}^{0.5}, {\left(c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D} - M\right)}^{0.5}, c0 \cdot \frac{d \cdot d}{\left(\left(h \cdot w\right) \cdot D\right) \cdot D}\right)} \]

   4. Taylor expanded in c0 around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{w}} \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{\frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{w}} \]

      2. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{3} \cdot {\left(\sqrt{\frac{{d}^{2}}{{D}^{2} \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}}\right)}^{2}}{\color{blue}{w}} \]

   6. Applied rewrites 21.4%

      \[\leadsto \color{blue}{0.5 \cdot \frac{\left(\left(c0 \cdot c0\right) \cdot c0\right) \cdot {\left(\frac{d \cdot d}{\left(D \cdot D\right) \cdot \left(c0 \cdot \left(h \cdot w\right)\right)}\right)}^{1}}{w}} \]

   7. Taylor expanded in c0 around 0

      \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{\color{blue}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)}} \]
   8. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \color{blue}{\left(h \cdot {w}^{2}\right)}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{{c0}^{2} \cdot {d}^{2}}{{D}^{2} \cdot \left(\color{blue}{h} \cdot {w}^{2}\right)} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot {d}^{2}}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)} \]

      5. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot {w}^{2}\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{{D}^{2} \cdot \left(h \cdot \color{blue}{{w}^{2}}\right)} \]

      8. pow2 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot {\color{blue}{w}}^{2}\right)} \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot {\color{blue}{w}}^{2}\right)} \]

      10. lower-*.f64 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot {w}^{\color{blue}{2}}\right)} \]

      11. pow2 N/A

          \[\leadsto \frac{1}{2} \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)} \]

      12. lift-*.f64 24.3

          \[\leadsto 0.5 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)} \]

   9. Applied rewrites 24.3%

      \[\leadsto 0.5 \cdot \frac{\left(c0 \cdot c0\right) \cdot \left(d \cdot d\right)}{\color{blue}{\left(D \cdot D\right) \cdot \left(h \cdot \left(w \cdot w\right)\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 14.2% accurate, 4.9× speedup

Math

\[\begin{array}{l} \\ \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (* (/ (* (sqrt (- (* M M))) c0) w) 0.5))

C

double code(double c0, double w, double h, double D, double d, double M) {
	return ((sqrt(-(M * M)) * c0) / w) * 0.5;
}

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(c0, w, h, d, d_1, m)
use fmin_fmax_functions
    real(8), intent (in) :: c0
    real(8), intent (in) :: w
    real(8), intent (in) :: h
    real(8), intent (in) :: d
    real(8), intent (in) :: d_1
    real(8), intent (in) :: m
    code = ((sqrt(-(m * m)) * c0) / w) * 0.5d0
end function

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	return ((Math.sqrt(-(M * M)) * c0) / w) * 0.5;
}

Python

def code(c0, w, h, D, d, M):
	return ((math.sqrt(-(M * M)) * c0) / w) * 0.5

Julia

function code(c0, w, h, D, d, M)
	return Float64(Float64(Float64(sqrt(Float64(-Float64(M * M))) * c0) / w) * 0.5)
end

MATLAB

function tmp = code(c0, w, h, D, d, M)
	tmp = ((sqrt(-(M * M)) * c0) / w) * 0.5;
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := N[(N[(N[(N[Sqrt[(-N[(M * M), $MachinePrecision])], $MachinePrecision] * c0), $MachinePrecision] / w), $MachinePrecision] * 0.5), $MachinePrecision]

Derivation

1. Initial program 24.3%

   \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

2. Taylor expanded in c0 around 0

   \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w}} \]
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \color{blue}{\frac{1}{2}} \]

   3. lower-/.f64 N/A

      \[\leadsto \frac{c0 \cdot \sqrt{\mathsf{neg}\left({M}^{2}\right)}}{w} \cdot \frac{1}{2} \]

   4. *-commutative N/A

      \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

   5. lower-*.f64 N/A

      \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

   6. lower-sqrt.f64 N/A

      \[\leadsto \frac{\sqrt{\mathsf{neg}\left({M}^{2}\right)} \cdot c0}{w} \cdot \frac{1}{2} \]

   7. lower-neg.f64 N/A

      \[\leadsto \frac{\sqrt{-{M}^{2}} \cdot c0}{w} \cdot \frac{1}{2} \]

   8. pow2 N/A

      \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot \frac{1}{2} \]

   9. lift-*.f64 14.2

      \[\leadsto \frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5 \]

4. Applied rewrites 14.2%

   \[\leadsto \color{blue}{\frac{\sqrt{-M \cdot M} \cdot c0}{w} \cdot 0.5} \]
5. Add Preprocessing

Alternative 13: 0.0% accurate, 5.2× speedup

Math

\[\begin{array}{l} \\ \frac{c0}{2 \cdot w} \cdot \left(\sqrt{-1} \cdot M\right) \end{array} \]

FPCore

(FPCore (c0 w h D d M)
 :precision binary64
 (* (/ c0 (* 2.0 w)) (* (sqrt -1.0) M)))

C

double code(double c0, double w, double h, double D, double d, double M) {
	return (c0 / (2.0 * w)) * (sqrt(-1.0) * M);
}

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(c0, w, h, d, d_1, m)
use fmin_fmax_functions
    real(8), intent (in) :: c0
    real(8), intent (in) :: w
    real(8), intent (in) :: h
    real(8), intent (in) :: d
    real(8), intent (in) :: d_1
    real(8), intent (in) :: m
    code = (c0 / (2.0d0 * w)) * (sqrt((-1.0d0)) * m)
end function

Java

public static double code(double c0, double w, double h, double D, double d, double M) {
	return (c0 / (2.0 * w)) * (Math.sqrt(-1.0) * M);
}

Python

def code(c0, w, h, D, d, M):
	return (c0 / (2.0 * w)) * (math.sqrt(-1.0) * M)

Julia

function code(c0, w, h, D, d, M)
	return Float64(Float64(c0 / Float64(2.0 * w)) * Float64(sqrt(-1.0) * M))
end

MATLAB

function tmp = code(c0, w, h, D, d, M)
	tmp = (c0 / (2.0 * w)) * (sqrt(-1.0) * M);
end

Wolfram

code[c0_, w_, h_, D_, d_, M_] := N[(N[(c0 / N[(2.0 * w), $MachinePrecision]), $MachinePrecision] * N[(N[Sqrt[-1.0], $MachinePrecision] * M), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 24.3%

   \[\frac{c0}{2 \cdot w} \cdot \left(\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} + \sqrt{\frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} \cdot \frac{c0 \cdot \left(d \cdot d\right)}{\left(w \cdot h\right) \cdot \left(D \cdot D\right)} - M \cdot M}\right) \]

2. Taylor expanded in M around inf

   \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\left(M \cdot \sqrt{-1}\right)} \]
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(\sqrt{-1} \cdot \color{blue}{M}\right) \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(\sqrt{-1} \cdot \color{blue}{M}\right) \]

   3. lower-sqrt.f64 0.0

      \[\leadsto \frac{c0}{2 \cdot w} \cdot \left(\sqrt{-1} \cdot M\right) \]

4. Applied rewrites 0.0%

   \[\leadsto \frac{c0}{2 \cdot w} \cdot \color{blue}{\left(\sqrt{-1} \cdot M\right)} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025142 
(FPCore (c0 w h D d M)
  :name "Henrywood and Agarwal, Equation (13)"
  :precision binary64
  (* (/ c0 (* 2.0 w)) (+ (/ (* c0 (* d d)) (* (* w h) (* D D))) (sqrt (- (* (/ (* c0 (* d d)) (* (* w h) (* D D))) (/ (* c0 (* d d)) (* (* w h) (* D D)))) (* M M))))))