Henrywood and Agarwal, Equation (9a)

Percentage Accurate: 81.1% → 92.8%

Time: 6.3s

Alternatives: 7

Speedup: 0.6×

Specification

Math

\[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (* w0 (sqrt (- 1.0 (* (pow (/ (* M D) (* 2.0 d)) 2.0) (/ h l))))))

C

double code(double w0, double M, double D, double h, double l, double d) {
	return w0 * sqrt((1.0 - (pow(((M * D) / (2.0 * d)), 2.0) * (h / l))));
}

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(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    code = w0 * sqrt((1.0d0 - ((((m * d) / (2.0d0 * d_1)) ** 2.0d0) * (h / l))))
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	return w0 * Math.sqrt((1.0 - (Math.pow(((M * D) / (2.0 * d)), 2.0) * (h / l))));
}

Python

def code(w0, M, D, h, l, d):
	return w0 * math.sqrt((1.0 - (math.pow(((M * D) / (2.0 * d)), 2.0) * (h / l))))

Julia

function code(w0, M, D, h, l, d)
	return Float64(w0 * sqrt(Float64(1.0 - Float64((Float64(Float64(M * D) / Float64(2.0 * d)) ^ 2.0) * Float64(h / l)))))
end

MATLAB

function tmp = code(w0, M, D, h, l, d)
	tmp = w0 * sqrt((1.0 - ((((M * D) / (2.0 * d)) ^ 2.0) * (h / l))));
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := N[(w0 * N[Sqrt[N[(1.0 - N[(N[Power[N[(N[(M * D), $MachinePrecision] / N[(2.0 * d), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 81.1% accurate, 1.0× speedup

Math

\[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (* w0 (sqrt (- 1.0 (* (pow (/ (* M D) (* 2.0 d)) 2.0) (/ h l))))))

C

double code(double w0, double M, double D, double h, double l, double d) {
	return w0 * sqrt((1.0 - (pow(((M * D) / (2.0 * d)), 2.0) * (h / l))));
}

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(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    code = w0 * sqrt((1.0d0 - ((((m * d) / (2.0d0 * d_1)) ** 2.0d0) * (h / l))))
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	return w0 * Math.sqrt((1.0 - (Math.pow(((M * D) / (2.0 * d)), 2.0) * (h / l))));
}

Python

def code(w0, M, D, h, l, d):
	return w0 * math.sqrt((1.0 - (math.pow(((M * D) / (2.0 * d)), 2.0) * (h / l))))

Julia

function code(w0, M, D, h, l, d)
	return Float64(w0 * sqrt(Float64(1.0 - Float64((Float64(Float64(M * D) / Float64(2.0 * d)) ^ 2.0) * Float64(h / l)))))
end

MATLAB

function tmp = code(w0, M, D, h, l, d)
	tmp = w0 * sqrt((1.0 - ((((M * D) / (2.0 * d)) ^ 2.0) * (h / l))));
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := N[(w0 * N[Sqrt[N[(1.0 - N[(N[Power[N[(N[(M * D), $MachinePrecision] / N[(2.0 * d), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 92.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} t_0 := \mathsf{max}\left(\left|M\right|, \left|D\right|\right)\\ t_1 := \mathsf{min}\left(\left|M\right|, \left|D\right|\right)\\ t_2 := \frac{t\_0}{\left|d\right|} \cdot t\_1\\ \mathbf{if}\;{\left(\frac{t\_1 \cdot t\_0}{2 \cdot \left|d\right|}\right)}^{2} \cdot \frac{h}{\ell} \leq -1 \cdot 10^{+267}:\\ \;\;\;\;\left(\frac{w0}{\left|d\right|} \cdot \left(\left|t\_1\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right)\right) \cdot \left|t\_0\right|\\ \mathbf{else}:\\ \;\;\;\;w0 \cdot \sqrt{1 - \frac{t\_2}{\ell} \cdot \frac{t\_2 \cdot h}{4}}\\ \end{array} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (let* ((t_0 (fmax (fabs M) (fabs D)))
        (t_1 (fmin (fabs M) (fabs D)))
        (t_2 (* (/ t_0 (fabs d)) t_1)))
   (if (<= (* (pow (/ (* t_1 t_0) (* 2.0 (fabs d))) 2.0) (/ h l)) -1e+267)
     (* (* (/ w0 (fabs d)) (* (fabs t_1) (sqrt (* (/ h l) -0.25)))) (fabs t_0))
     (* w0 (sqrt (- 1.0 (* (/ t_2 l) (/ (* t_2 h) 4.0))))))))

C

double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = fmax(fabs(M), fabs(D));
	double t_1 = fmin(fabs(M), fabs(D));
	double t_2 = (t_0 / fabs(d)) * t_1;
	double tmp;
	if ((pow(((t_1 * t_0) / (2.0 * fabs(d))), 2.0) * (h / l)) <= -1e+267) {
		tmp = ((w0 / fabs(d)) * (fabs(t_1) * sqrt(((h / l) * -0.25)))) * fabs(t_0);
	} else {
		tmp = w0 * sqrt((1.0 - ((t_2 / l) * ((t_2 * h) / 4.0))));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = fmax(abs(m), abs(d))
    t_1 = fmin(abs(m), abs(d))
    t_2 = (t_0 / abs(d_1)) * t_1
    if (((((t_1 * t_0) / (2.0d0 * abs(d_1))) ** 2.0d0) * (h / l)) <= (-1d+267)) then
        tmp = ((w0 / abs(d_1)) * (abs(t_1) * sqrt(((h / l) * (-0.25d0))))) * abs(t_0)
    else
        tmp = w0 * sqrt((1.0d0 - ((t_2 / l) * ((t_2 * h) / 4.0d0))))
    end if
    code = tmp
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = fmax(Math.abs(M), Math.abs(D));
	double t_1 = fmin(Math.abs(M), Math.abs(D));
	double t_2 = (t_0 / Math.abs(d)) * t_1;
	double tmp;
	if ((Math.pow(((t_1 * t_0) / (2.0 * Math.abs(d))), 2.0) * (h / l)) <= -1e+267) {
		tmp = ((w0 / Math.abs(d)) * (Math.abs(t_1) * Math.sqrt(((h / l) * -0.25)))) * Math.abs(t_0);
	} else {
		tmp = w0 * Math.sqrt((1.0 - ((t_2 / l) * ((t_2 * h) / 4.0))));
	}
	return tmp;
}

Python

def code(w0, M, D, h, l, d):
	t_0 = fmax(math.fabs(M), math.fabs(D))
	t_1 = fmin(math.fabs(M), math.fabs(D))
	t_2 = (t_0 / math.fabs(d)) * t_1
	tmp = 0
	if (math.pow(((t_1 * t_0) / (2.0 * math.fabs(d))), 2.0) * (h / l)) <= -1e+267:
		tmp = ((w0 / math.fabs(d)) * (math.fabs(t_1) * math.sqrt(((h / l) * -0.25)))) * math.fabs(t_0)
	else:
		tmp = w0 * math.sqrt((1.0 - ((t_2 / l) * ((t_2 * h) / 4.0))))
	return tmp

Julia

function code(w0, M, D, h, l, d)
	t_0 = fmax(abs(M), abs(D))
	t_1 = fmin(abs(M), abs(D))
	t_2 = Float64(Float64(t_0 / abs(d)) * t_1)
	tmp = 0.0
	if (Float64((Float64(Float64(t_1 * t_0) / Float64(2.0 * abs(d))) ^ 2.0) * Float64(h / l)) <= -1e+267)
		tmp = Float64(Float64(Float64(w0 / abs(d)) * Float64(abs(t_1) * sqrt(Float64(Float64(h / l) * -0.25)))) * abs(t_0));
	else
		tmp = Float64(w0 * sqrt(Float64(1.0 - Float64(Float64(t_2 / l) * Float64(Float64(t_2 * h) / 4.0)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(w0, M, D, h, l, d)
	t_0 = max(abs(M), abs(D));
	t_1 = min(abs(M), abs(D));
	t_2 = (t_0 / abs(d)) * t_1;
	tmp = 0.0;
	if (((((t_1 * t_0) / (2.0 * abs(d))) ^ 2.0) * (h / l)) <= -1e+267)
		tmp = ((w0 / abs(d)) * (abs(t_1) * sqrt(((h / l) * -0.25)))) * abs(t_0);
	else
		tmp = w0 * sqrt((1.0 - ((t_2 / l) * ((t_2 * h) / 4.0))));
	end
	tmp_2 = tmp;
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := Block[{t$95$0 = N[Max[N[Abs[M], $MachinePrecision], N[Abs[D], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Min[N[Abs[M], $MachinePrecision], N[Abs[D], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[(t$95$0 / N[Abs[d], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision]}, If[LessEqual[N[(N[Power[N[(N[(t$95$1 * t$95$0), $MachinePrecision] / N[(2.0 * N[Abs[d], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision], -1e+267], N[(N[(N[(w0 / N[Abs[d], $MachinePrecision]), $MachinePrecision] * N[(N[Abs[t$95$1], $MachinePrecision] * N[Sqrt[N[(N[(h / l), $MachinePrecision] * -0.25), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Abs[t$95$0], $MachinePrecision]), $MachinePrecision], N[(w0 * N[Sqrt[N[(1.0 - N[(N[(t$95$2 / l), $MachinePrecision] * N[(N[(t$95$2 * h), $MachinePrecision] / 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l)) < -9.9999999999999997e266

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in d around 0

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{d}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{\color{blue}{d}} \]

   4. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{\color{blue}{d}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \]

      3. associate-/l* N/A

         \[\leadsto w0 \cdot \color{blue}{\frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \cdot \color{blue}{w0} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \cdot \color{blue}{w0} \]

   6. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot -0.25}}{d} \cdot w0} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}}}{d} \cdot \color{blue}{w0} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}}}{d} \cdot w0 \]

      3. associate-*l/ N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot w0}{\color{blue}{d}} \]

      4. associate-/l* N/A

         \[\leadsto \sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot \color{blue}{\frac{w0}{d}} \]

      5. lower-*.f64 N/A

         \[\leadsto \sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot \color{blue}{\frac{w0}{d}} \]

   8. Applied rewrites 10.9%

      \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot -0.25}\right) \cdot \color{blue}{\frac{w0}{d}} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot \color{blue}{\frac{w0}{d}} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot \frac{\color{blue}{w0}}{d} \]

      3. associate-*l* N/A

         \[\leadsto \left|D\right| \cdot \color{blue}{\left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right)} \]

      4. *-commutative N/A

         \[\leadsto \left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right) \cdot \color{blue}{\left|D\right|} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right) \cdot \color{blue}{\left|D\right|} \]

   10. Applied rewrites 13.7%

       \[\leadsto \left(\frac{w0}{d} \cdot \left(\left|M\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right)\right) \cdot \color{blue}{\left|D\right|} \]

   if -9.9999999999999997e266 < (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l))

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]
   2. Step-by-step derivation

      1. lift-pow.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{{\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2}} \cdot \frac{h}{\ell}} \]

      2. unpow2 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\left(\frac{M \cdot D}{2 \cdot d} \cdot \frac{M \cdot D}{2 \cdot d}\right)} \cdot \frac{h}{\ell}} \]

      3. lift-/.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\color{blue}{\frac{M \cdot D}{2 \cdot d}} \cdot \frac{M \cdot D}{2 \cdot d}\right) \cdot \frac{h}{\ell}} \]

      4. lift-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{M \cdot D}{\color{blue}{2 \cdot d}} \cdot \frac{M \cdot D}{2 \cdot d}\right) \cdot \frac{h}{\ell}} \]

      5. *-commutative N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{M \cdot D}{\color{blue}{d \cdot 2}} \cdot \frac{M \cdot D}{2 \cdot d}\right) \cdot \frac{h}{\ell}} \]

      6. associate-/r* N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\color{blue}{\frac{\frac{M \cdot D}{d}}{2}} \cdot \frac{M \cdot D}{2 \cdot d}\right) \cdot \frac{h}{\ell}} \]

      7. lift-/.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{\frac{M \cdot D}{d}}{2} \cdot \color{blue}{\frac{M \cdot D}{2 \cdot d}}\right) \cdot \frac{h}{\ell}} \]

      8. lift-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{\frac{M \cdot D}{d}}{2} \cdot \frac{M \cdot D}{\color{blue}{2 \cdot d}}\right) \cdot \frac{h}{\ell}} \]

      9. *-commutative N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{\frac{M \cdot D}{d}}{2} \cdot \frac{M \cdot D}{\color{blue}{d \cdot 2}}\right) \cdot \frac{h}{\ell}} \]

      10. associate-/r* N/A

          \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{\frac{M \cdot D}{d}}{2} \cdot \color{blue}{\frac{\frac{M \cdot D}{d}}{2}}\right) \cdot \frac{h}{\ell}} \]

      11. frac-times N/A

          \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{M \cdot D}{d} \cdot \frac{M \cdot D}{d}}{2 \cdot 2}} \cdot \frac{h}{\ell}} \]

      12. metadata-eval N/A

          \[\leadsto w0 \cdot \sqrt{1 - \frac{\frac{M \cdot D}{d} \cdot \frac{M \cdot D}{d}}{2 \cdot \color{blue}{\left(1 + 1\right)}} \cdot \frac{h}{\ell}} \]

      13. cosh-0-rev N/A

          \[\leadsto w0 \cdot \sqrt{1 - \frac{\frac{M \cdot D}{d} \cdot \frac{M \cdot D}{d}}{2 \cdot \left(\color{blue}{\cosh 0} + 1\right)} \cdot \frac{h}{\ell}} \]

      14. lower-/.f64 N/A

          \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{M \cdot D}{d} \cdot \frac{M \cdot D}{d}}{2 \cdot \left(\cosh 0 + 1\right)}} \cdot \frac{h}{\ell}} \]

   3. Applied rewrites 80.9%

      \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)}{4}} \cdot \frac{h}{\ell}} \]
   4. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)}{4} \cdot \frac{h}{\ell}}} \]

      2. lift-/.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)}{4}} \cdot \frac{h}{\ell}} \]

      3. lift-/.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)}{4} \cdot \color{blue}{\frac{h}{\ell}}} \]

      4. frac-times N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\left(\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)\right) \cdot h}{4 \cdot \ell}}} \]

      5. lift-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \frac{\color{blue}{\left(\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)\right)} \cdot h}{4 \cdot \ell}} \]

      6. associate-*l* N/A

         \[\leadsto w0 \cdot \sqrt{1 - \frac{\color{blue}{\left(\frac{D}{d} \cdot M\right) \cdot \left(\left(\frac{D}{d} \cdot M\right) \cdot h\right)}}{4 \cdot \ell}} \]

      7. *-commutative N/A

         \[\leadsto w0 \cdot \sqrt{1 - \frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\left(\frac{D}{d} \cdot M\right) \cdot h\right)}{\color{blue}{\ell \cdot 4}}} \]

      8. times-frac N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{D}{d} \cdot M}{\ell} \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{4}}} \]

      9. lower-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{D}{d} \cdot M}{\ell} \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{4}}} \]

      10. lower-/.f64 N/A

          \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{D}{d} \cdot M}{\ell}} \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{4}} \]

      11. lower-/.f64 N/A

          \[\leadsto w0 \cdot \sqrt{1 - \frac{\frac{D}{d} \cdot M}{\ell} \cdot \color{blue}{\frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{4}}} \]

      12. lower-*.f64 88.4%

          \[\leadsto w0 \cdot \sqrt{1 - \frac{\frac{D}{d} \cdot M}{\ell} \cdot \frac{\color{blue}{\left(\frac{D}{d} \cdot M\right) \cdot h}}{4}} \]

   5. Applied rewrites 88.4%

      \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{D}{d} \cdot M}{\ell} \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{4}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 92.7% accurate, 0.4× speedup

Math

\[\begin{array}{l} t_0 := \mathsf{max}\left(\left|M\right|, \left|D\right|\right)\\ t_1 := \mathsf{min}\left(\left|M\right|, \left|D\right|\right)\\ t_2 := \frac{t\_0}{\left|d\right|} \cdot t\_1\\ \mathbf{if}\;{\left(\frac{t\_1 \cdot t\_0}{2 \cdot \left|d\right|}\right)}^{2} \cdot \frac{h}{\ell} \leq -2 \cdot 10^{+269}:\\ \;\;\;\;\left(\frac{w0}{\left|d\right|} \cdot \left(\left|t\_1\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right)\right) \cdot \left|t\_0\right|\\ \mathbf{else}:\\ \;\;\;\;w0 \cdot \sqrt{1 - \left(t\_2 \cdot 0.25\right) \cdot \frac{t\_2 \cdot h}{\ell}}\\ \end{array} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (let* ((t_0 (fmax (fabs M) (fabs D)))
        (t_1 (fmin (fabs M) (fabs D)))
        (t_2 (* (/ t_0 (fabs d)) t_1)))
   (if (<= (* (pow (/ (* t_1 t_0) (* 2.0 (fabs d))) 2.0) (/ h l)) -2e+269)
     (* (* (/ w0 (fabs d)) (* (fabs t_1) (sqrt (* (/ h l) -0.25)))) (fabs t_0))
     (* w0 (sqrt (- 1.0 (* (* t_2 0.25) (/ (* t_2 h) l))))))))

C

double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = fmax(fabs(M), fabs(D));
	double t_1 = fmin(fabs(M), fabs(D));
	double t_2 = (t_0 / fabs(d)) * t_1;
	double tmp;
	if ((pow(((t_1 * t_0) / (2.0 * fabs(d))), 2.0) * (h / l)) <= -2e+269) {
		tmp = ((w0 / fabs(d)) * (fabs(t_1) * sqrt(((h / l) * -0.25)))) * fabs(t_0);
	} else {
		tmp = w0 * sqrt((1.0 - ((t_2 * 0.25) * ((t_2 * h) / l))));
	}
	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(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = fmax(abs(m), abs(d))
    t_1 = fmin(abs(m), abs(d))
    t_2 = (t_0 / abs(d_1)) * t_1
    if (((((t_1 * t_0) / (2.0d0 * abs(d_1))) ** 2.0d0) * (h / l)) <= (-2d+269)) then
        tmp = ((w0 / abs(d_1)) * (abs(t_1) * sqrt(((h / l) * (-0.25d0))))) * abs(t_0)
    else
        tmp = w0 * sqrt((1.0d0 - ((t_2 * 0.25d0) * ((t_2 * h) / l))))
    end if
    code = tmp
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = fmax(Math.abs(M), Math.abs(D));
	double t_1 = fmin(Math.abs(M), Math.abs(D));
	double t_2 = (t_0 / Math.abs(d)) * t_1;
	double tmp;
	if ((Math.pow(((t_1 * t_0) / (2.0 * Math.abs(d))), 2.0) * (h / l)) <= -2e+269) {
		tmp = ((w0 / Math.abs(d)) * (Math.abs(t_1) * Math.sqrt(((h / l) * -0.25)))) * Math.abs(t_0);
	} else {
		tmp = w0 * Math.sqrt((1.0 - ((t_2 * 0.25) * ((t_2 * h) / l))));
	}
	return tmp;
}

Python

def code(w0, M, D, h, l, d):
	t_0 = fmax(math.fabs(M), math.fabs(D))
	t_1 = fmin(math.fabs(M), math.fabs(D))
	t_2 = (t_0 / math.fabs(d)) * t_1
	tmp = 0
	if (math.pow(((t_1 * t_0) / (2.0 * math.fabs(d))), 2.0) * (h / l)) <= -2e+269:
		tmp = ((w0 / math.fabs(d)) * (math.fabs(t_1) * math.sqrt(((h / l) * -0.25)))) * math.fabs(t_0)
	else:
		tmp = w0 * math.sqrt((1.0 - ((t_2 * 0.25) * ((t_2 * h) / l))))
	return tmp

Julia

function code(w0, M, D, h, l, d)
	t_0 = fmax(abs(M), abs(D))
	t_1 = fmin(abs(M), abs(D))
	t_2 = Float64(Float64(t_0 / abs(d)) * t_1)
	tmp = 0.0
	if (Float64((Float64(Float64(t_1 * t_0) / Float64(2.0 * abs(d))) ^ 2.0) * Float64(h / l)) <= -2e+269)
		tmp = Float64(Float64(Float64(w0 / abs(d)) * Float64(abs(t_1) * sqrt(Float64(Float64(h / l) * -0.25)))) * abs(t_0));
	else
		tmp = Float64(w0 * sqrt(Float64(1.0 - Float64(Float64(t_2 * 0.25) * Float64(Float64(t_2 * h) / l)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(w0, M, D, h, l, d)
	t_0 = max(abs(M), abs(D));
	t_1 = min(abs(M), abs(D));
	t_2 = (t_0 / abs(d)) * t_1;
	tmp = 0.0;
	if (((((t_1 * t_0) / (2.0 * abs(d))) ^ 2.0) * (h / l)) <= -2e+269)
		tmp = ((w0 / abs(d)) * (abs(t_1) * sqrt(((h / l) * -0.25)))) * abs(t_0);
	else
		tmp = w0 * sqrt((1.0 - ((t_2 * 0.25) * ((t_2 * h) / l))));
	end
	tmp_2 = tmp;
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := Block[{t$95$0 = N[Max[N[Abs[M], $MachinePrecision], N[Abs[D], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Min[N[Abs[M], $MachinePrecision], N[Abs[D], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[(t$95$0 / N[Abs[d], $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision]}, If[LessEqual[N[(N[Power[N[(N[(t$95$1 * t$95$0), $MachinePrecision] / N[(2.0 * N[Abs[d], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision], -2e+269], N[(N[(N[(w0 / N[Abs[d], $MachinePrecision]), $MachinePrecision] * N[(N[Abs[t$95$1], $MachinePrecision] * N[Sqrt[N[(N[(h / l), $MachinePrecision] * -0.25), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Abs[t$95$0], $MachinePrecision]), $MachinePrecision], N[(w0 * N[Sqrt[N[(1.0 - N[(N[(t$95$2 * 0.25), $MachinePrecision] * N[(N[(t$95$2 * h), $MachinePrecision] / l), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l)) < -2.0000000000000001e269

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in d around 0

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{d}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{\color{blue}{d}} \]

   4. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{\color{blue}{d}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \]

      3. associate-/l* N/A

         \[\leadsto w0 \cdot \color{blue}{\frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \cdot \color{blue}{w0} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \cdot \color{blue}{w0} \]

   6. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot -0.25}}{d} \cdot w0} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}}}{d} \cdot \color{blue}{w0} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}}}{d} \cdot w0 \]

      3. associate-*l/ N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot w0}{\color{blue}{d}} \]

      4. associate-/l* N/A

         \[\leadsto \sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot \color{blue}{\frac{w0}{d}} \]

      5. lower-*.f64 N/A

         \[\leadsto \sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot \color{blue}{\frac{w0}{d}} \]

   8. Applied rewrites 10.9%

      \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot -0.25}\right) \cdot \color{blue}{\frac{w0}{d}} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot \color{blue}{\frac{w0}{d}} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot \frac{\color{blue}{w0}}{d} \]

      3. associate-*l* N/A

         \[\leadsto \left|D\right| \cdot \color{blue}{\left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right)} \]

      4. *-commutative N/A

         \[\leadsto \left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right) \cdot \color{blue}{\left|D\right|} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right) \cdot \color{blue}{\left|D\right|} \]

   10. Applied rewrites 13.7%

       \[\leadsto \left(\frac{w0}{d} \cdot \left(\left|M\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right)\right) \cdot \color{blue}{\left|D\right|} \]

   if -2.0000000000000001e269 < (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l))

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]
   2. Step-by-step derivation

      1. lift-pow.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{{\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2}} \cdot \frac{h}{\ell}} \]

      2. unpow2 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\left(\frac{M \cdot D}{2 \cdot d} \cdot \frac{M \cdot D}{2 \cdot d}\right)} \cdot \frac{h}{\ell}} \]

      3. lift-/.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\color{blue}{\frac{M \cdot D}{2 \cdot d}} \cdot \frac{M \cdot D}{2 \cdot d}\right) \cdot \frac{h}{\ell}} \]

      4. lift-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{M \cdot D}{\color{blue}{2 \cdot d}} \cdot \frac{M \cdot D}{2 \cdot d}\right) \cdot \frac{h}{\ell}} \]

      5. *-commutative N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{M \cdot D}{\color{blue}{d \cdot 2}} \cdot \frac{M \cdot D}{2 \cdot d}\right) \cdot \frac{h}{\ell}} \]

      6. associate-/r* N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\color{blue}{\frac{\frac{M \cdot D}{d}}{2}} \cdot \frac{M \cdot D}{2 \cdot d}\right) \cdot \frac{h}{\ell}} \]

      7. lift-/.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{\frac{M \cdot D}{d}}{2} \cdot \color{blue}{\frac{M \cdot D}{2 \cdot d}}\right) \cdot \frac{h}{\ell}} \]

      8. lift-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{\frac{M \cdot D}{d}}{2} \cdot \frac{M \cdot D}{\color{blue}{2 \cdot d}}\right) \cdot \frac{h}{\ell}} \]

      9. *-commutative N/A

         \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{\frac{M \cdot D}{d}}{2} \cdot \frac{M \cdot D}{\color{blue}{d \cdot 2}}\right) \cdot \frac{h}{\ell}} \]

      10. associate-/r* N/A

          \[\leadsto w0 \cdot \sqrt{1 - \left(\frac{\frac{M \cdot D}{d}}{2} \cdot \color{blue}{\frac{\frac{M \cdot D}{d}}{2}}\right) \cdot \frac{h}{\ell}} \]

      11. frac-times N/A

          \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{M \cdot D}{d} \cdot \frac{M \cdot D}{d}}{2 \cdot 2}} \cdot \frac{h}{\ell}} \]

      12. metadata-eval N/A

          \[\leadsto w0 \cdot \sqrt{1 - \frac{\frac{M \cdot D}{d} \cdot \frac{M \cdot D}{d}}{2 \cdot \color{blue}{\left(1 + 1\right)}} \cdot \frac{h}{\ell}} \]

      13. cosh-0-rev N/A

          \[\leadsto w0 \cdot \sqrt{1 - \frac{\frac{M \cdot D}{d} \cdot \frac{M \cdot D}{d}}{2 \cdot \left(\color{blue}{\cosh 0} + 1\right)} \cdot \frac{h}{\ell}} \]

      14. lower-/.f64 N/A

          \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{M \cdot D}{d} \cdot \frac{M \cdot D}{d}}{2 \cdot \left(\cosh 0 + 1\right)}} \cdot \frac{h}{\ell}} \]

   3. Applied rewrites 80.9%

      \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)}{4}} \cdot \frac{h}{\ell}} \]
   4. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)}{4} \cdot \frac{h}{\ell}}} \]

      2. lift-/.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)}{4}} \cdot \frac{h}{\ell}} \]

      3. lift-/.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \frac{\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)}{4} \cdot \color{blue}{\frac{h}{\ell}}} \]

      4. frac-times N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\left(\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)\right) \cdot h}{4 \cdot \ell}}} \]

      5. lift-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \frac{\color{blue}{\left(\left(\frac{D}{d} \cdot M\right) \cdot \left(\frac{D}{d} \cdot M\right)\right)} \cdot h}{4 \cdot \ell}} \]

      6. associate-*l* N/A

         \[\leadsto w0 \cdot \sqrt{1 - \frac{\color{blue}{\left(\frac{D}{d} \cdot M\right) \cdot \left(\left(\frac{D}{d} \cdot M\right) \cdot h\right)}}{4 \cdot \ell}} \]

      7. times-frac N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{D}{d} \cdot M}{4} \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{\ell}}} \]

      8. lower-*.f64 N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{\frac{D}{d} \cdot M}{4} \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{\ell}}} \]

      9. mult-flip N/A

         \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\left(\left(\frac{D}{d} \cdot M\right) \cdot \frac{1}{4}\right)} \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{\ell}} \]

      10. metadata-eval N/A

          \[\leadsto w0 \cdot \sqrt{1 - \left(\left(\frac{D}{d} \cdot M\right) \cdot \color{blue}{\frac{1}{4}}\right) \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{\ell}} \]

      11. lower-*.f64 N/A

          \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\left(\left(\frac{D}{d} \cdot M\right) \cdot \frac{1}{4}\right)} \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{\ell}} \]

      12. lower-/.f64 N/A

          \[\leadsto w0 \cdot \sqrt{1 - \left(\left(\frac{D}{d} \cdot M\right) \cdot \frac{1}{4}\right) \cdot \color{blue}{\frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{\ell}}} \]

      13. lower-*.f64 88.4%

          \[\leadsto w0 \cdot \sqrt{1 - \left(\left(\frac{D}{d} \cdot M\right) \cdot 0.25\right) \cdot \frac{\color{blue}{\left(\frac{D}{d} \cdot M\right) \cdot h}}{\ell}} \]

   5. Applied rewrites 88.4%

      \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\left(\left(\frac{D}{d} \cdot M\right) \cdot 0.25\right) \cdot \frac{\left(\frac{D}{d} \cdot M\right) \cdot h}{\ell}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 90.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} t_0 := \mathsf{max}\left(\left|M\right|, \left|D\right|\right)\\ t_1 := \mathsf{min}\left(\left|M\right|, \left|D\right|\right)\\ \mathbf{if}\;{\left(\frac{t\_1 \cdot t\_0}{2 \cdot \left|d\right|}\right)}^{2} \cdot \frac{h}{\ell} \leq -2 \cdot 10^{+97}:\\ \;\;\;\;\left(\frac{w0}{\left|d\right|} \cdot \left(\left|t\_1\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right)\right) \cdot \left|t\_0\right|\\ \mathbf{else}:\\ \;\;\;\;w0 \cdot 1\\ \end{array} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (let* ((t_0 (fmax (fabs M) (fabs D))) (t_1 (fmin (fabs M) (fabs D))))
   (if (<= (* (pow (/ (* t_1 t_0) (* 2.0 (fabs d))) 2.0) (/ h l)) -2e+97)
     (* (* (/ w0 (fabs d)) (* (fabs t_1) (sqrt (* (/ h l) -0.25)))) (fabs t_0))
     (* w0 1.0))))

C

double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = fmax(fabs(M), fabs(D));
	double t_1 = fmin(fabs(M), fabs(D));
	double tmp;
	if ((pow(((t_1 * t_0) / (2.0 * fabs(d))), 2.0) * (h / l)) <= -2e+97) {
		tmp = ((w0 / fabs(d)) * (fabs(t_1) * sqrt(((h / l) * -0.25)))) * fabs(t_0);
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = fmax(abs(m), abs(d))
    t_1 = fmin(abs(m), abs(d))
    if (((((t_1 * t_0) / (2.0d0 * abs(d_1))) ** 2.0d0) * (h / l)) <= (-2d+97)) then
        tmp = ((w0 / abs(d_1)) * (abs(t_1) * sqrt(((h / l) * (-0.25d0))))) * abs(t_0)
    else
        tmp = w0 * 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = fmax(Math.abs(M), Math.abs(D));
	double t_1 = fmin(Math.abs(M), Math.abs(D));
	double tmp;
	if ((Math.pow(((t_1 * t_0) / (2.0 * Math.abs(d))), 2.0) * (h / l)) <= -2e+97) {
		tmp = ((w0 / Math.abs(d)) * (Math.abs(t_1) * Math.sqrt(((h / l) * -0.25)))) * Math.abs(t_0);
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Python

def code(w0, M, D, h, l, d):
	t_0 = fmax(math.fabs(M), math.fabs(D))
	t_1 = fmin(math.fabs(M), math.fabs(D))
	tmp = 0
	if (math.pow(((t_1 * t_0) / (2.0 * math.fabs(d))), 2.0) * (h / l)) <= -2e+97:
		tmp = ((w0 / math.fabs(d)) * (math.fabs(t_1) * math.sqrt(((h / l) * -0.25)))) * math.fabs(t_0)
	else:
		tmp = w0 * 1.0
	return tmp

Julia

function code(w0, M, D, h, l, d)
	t_0 = fmax(abs(M), abs(D))
	t_1 = fmin(abs(M), abs(D))
	tmp = 0.0
	if (Float64((Float64(Float64(t_1 * t_0) / Float64(2.0 * abs(d))) ^ 2.0) * Float64(h / l)) <= -2e+97)
		tmp = Float64(Float64(Float64(w0 / abs(d)) * Float64(abs(t_1) * sqrt(Float64(Float64(h / l) * -0.25)))) * abs(t_0));
	else
		tmp = Float64(w0 * 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(w0, M, D, h, l, d)
	t_0 = max(abs(M), abs(D));
	t_1 = min(abs(M), abs(D));
	tmp = 0.0;
	if (((((t_1 * t_0) / (2.0 * abs(d))) ^ 2.0) * (h / l)) <= -2e+97)
		tmp = ((w0 / abs(d)) * (abs(t_1) * sqrt(((h / l) * -0.25)))) * abs(t_0);
	else
		tmp = w0 * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := Block[{t$95$0 = N[Max[N[Abs[M], $MachinePrecision], N[Abs[D], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Min[N[Abs[M], $MachinePrecision], N[Abs[D], $MachinePrecision]], $MachinePrecision]}, If[LessEqual[N[(N[Power[N[(N[(t$95$1 * t$95$0), $MachinePrecision] / N[(2.0 * N[Abs[d], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision], -2e+97], N[(N[(N[(w0 / N[Abs[d], $MachinePrecision]), $MachinePrecision] * N[(N[Abs[t$95$1], $MachinePrecision] * N[Sqrt[N[(N[(h / l), $MachinePrecision] * -0.25), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Abs[t$95$0], $MachinePrecision]), $MachinePrecision], N[(w0 * 1.0), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l)) < -2.0000000000000001e97

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in d around 0

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{d}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{\color{blue}{d}} \]

   4. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{\color{blue}{d}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \]

      3. associate-/l* N/A

         \[\leadsto w0 \cdot \color{blue}{\frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \cdot \color{blue}{w0} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \cdot \color{blue}{w0} \]

   6. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot -0.25}}{d} \cdot w0} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}}}{d} \cdot \color{blue}{w0} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}}}{d} \cdot w0 \]

      3. associate-*l/ N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot w0}{\color{blue}{d}} \]

      4. associate-/l* N/A

         \[\leadsto \sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot \color{blue}{\frac{w0}{d}} \]

      5. lower-*.f64 N/A

         \[\leadsto \sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot \color{blue}{\frac{w0}{d}} \]

   8. Applied rewrites 10.9%

      \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot -0.25}\right) \cdot \color{blue}{\frac{w0}{d}} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot \color{blue}{\frac{w0}{d}} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot \frac{\color{blue}{w0}}{d} \]

      3. associate-*l* N/A

         \[\leadsto \left|D\right| \cdot \color{blue}{\left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right)} \]

      4. *-commutative N/A

         \[\leadsto \left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right) \cdot \color{blue}{\left|D\right|} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}} \cdot \frac{w0}{d}\right) \cdot \color{blue}{\left|D\right|} \]

   10. Applied rewrites 13.7%

       \[\leadsto \left(\frac{w0}{d} \cdot \left(\left|M\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right)\right) \cdot \color{blue}{\left|D\right|} \]

   if -2.0000000000000001e97 < (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l))

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in M around 0

      \[\leadsto w0 \cdot \color{blue}{1} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.0%

      \[\leadsto w0 \cdot \color{blue}{1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 90.7% accurate, 0.5× speedup

Math

\[\begin{array}{l} t_0 := \mathsf{max}\left(\left|M\right|, D\right)\\ t_1 := \mathsf{min}\left(\left|M\right|, D\right)\\ \mathbf{if}\;{\left(\frac{t\_1 \cdot t\_0}{2 \cdot \left|d\right|}\right)}^{2} \cdot \frac{h}{\ell} \leq -5000000000:\\ \;\;\;\;\frac{\left(\left(\left|t\_1\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right) \cdot \left|t\_0\right|\right) \cdot w0}{\left|d\right|}\\ \mathbf{else}:\\ \;\;\;\;w0 \cdot 1\\ \end{array} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (let* ((t_0 (fmax (fabs M) D)) (t_1 (fmin (fabs M) D)))
   (if (<=
        (* (pow (/ (* t_1 t_0) (* 2.0 (fabs d))) 2.0) (/ h l))
        -5000000000.0)
     (/ (* (* (* (fabs t_1) (sqrt (* (/ h l) -0.25))) (fabs t_0)) w0) (fabs d))
     (* w0 1.0))))

C

double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = fmax(fabs(M), D);
	double t_1 = fmin(fabs(M), D);
	double tmp;
	if ((pow(((t_1 * t_0) / (2.0 * fabs(d))), 2.0) * (h / l)) <= -5000000000.0) {
		tmp = (((fabs(t_1) * sqrt(((h / l) * -0.25))) * fabs(t_0)) * w0) / fabs(d);
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = fmax(abs(m), d)
    t_1 = fmin(abs(m), d)
    if (((((t_1 * t_0) / (2.0d0 * abs(d_1))) ** 2.0d0) * (h / l)) <= (-5000000000.0d0)) then
        tmp = (((abs(t_1) * sqrt(((h / l) * (-0.25d0)))) * abs(t_0)) * w0) / abs(d_1)
    else
        tmp = w0 * 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = fmax(Math.abs(M), D);
	double t_1 = fmin(Math.abs(M), D);
	double tmp;
	if ((Math.pow(((t_1 * t_0) / (2.0 * Math.abs(d))), 2.0) * (h / l)) <= -5000000000.0) {
		tmp = (((Math.abs(t_1) * Math.sqrt(((h / l) * -0.25))) * Math.abs(t_0)) * w0) / Math.abs(d);
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Python

def code(w0, M, D, h, l, d):
	t_0 = fmax(math.fabs(M), D)
	t_1 = fmin(math.fabs(M), D)
	tmp = 0
	if (math.pow(((t_1 * t_0) / (2.0 * math.fabs(d))), 2.0) * (h / l)) <= -5000000000.0:
		tmp = (((math.fabs(t_1) * math.sqrt(((h / l) * -0.25))) * math.fabs(t_0)) * w0) / math.fabs(d)
	else:
		tmp = w0 * 1.0
	return tmp

Julia

function code(w0, M, D, h, l, d)
	t_0 = fmax(abs(M), D)
	t_1 = fmin(abs(M), D)
	tmp = 0.0
	if (Float64((Float64(Float64(t_1 * t_0) / Float64(2.0 * abs(d))) ^ 2.0) * Float64(h / l)) <= -5000000000.0)
		tmp = Float64(Float64(Float64(Float64(abs(t_1) * sqrt(Float64(Float64(h / l) * -0.25))) * abs(t_0)) * w0) / abs(d));
	else
		tmp = Float64(w0 * 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(w0, M, D, h, l, d)
	t_0 = max(abs(M), D);
	t_1 = min(abs(M), D);
	tmp = 0.0;
	if (((((t_1 * t_0) / (2.0 * abs(d))) ^ 2.0) * (h / l)) <= -5000000000.0)
		tmp = (((abs(t_1) * sqrt(((h / l) * -0.25))) * abs(t_0)) * w0) / abs(d);
	else
		tmp = w0 * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := Block[{t$95$0 = N[Max[N[Abs[M], $MachinePrecision], D], $MachinePrecision]}, Block[{t$95$1 = N[Min[N[Abs[M], $MachinePrecision], D], $MachinePrecision]}, If[LessEqual[N[(N[Power[N[(N[(t$95$1 * t$95$0), $MachinePrecision] / N[(2.0 * N[Abs[d], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision], -5000000000.0], N[(N[(N[(N[(N[Abs[t$95$1], $MachinePrecision] * N[Sqrt[N[(N[(h / l), $MachinePrecision] * -0.25), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[Abs[t$95$0], $MachinePrecision]), $MachinePrecision] * w0), $MachinePrecision] / N[Abs[d], $MachinePrecision]), $MachinePrecision], N[(w0 * 1.0), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l)) < -5e9

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in d around 0

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{d}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{\color{blue}{d}} \]

   4. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{\color{blue}{d}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \]

      3. associate-/l* N/A

         \[\leadsto w0 \cdot \color{blue}{\frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]

      4. *-commutative N/A

         \[\leadsto \frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \cdot \color{blue}{w0} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d} \cdot \color{blue}{w0} \]

   6. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot -0.25}}{d} \cdot w0} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}}}{d} \cdot \color{blue}{w0} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}}}{d} \cdot w0 \]

      3. associate-*l/ N/A

         \[\leadsto \frac{\sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot w0}{\color{blue}{d}} \]

      4. associate-/l* N/A

         \[\leadsto \sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot \color{blue}{\frac{w0}{d}} \]

      5. lower-*.f64 N/A

         \[\leadsto \sqrt{\left(\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \left(D \cdot D\right)\right) \cdot \frac{-1}{4}} \cdot \color{blue}{\frac{w0}{d}} \]

   8. Applied rewrites 10.9%

      \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot -0.25}\right) \cdot \color{blue}{\frac{w0}{d}} \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot \color{blue}{\frac{w0}{d}} \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot \frac{w0}{\color{blue}{d}} \]

      3. associate-*r/ N/A

         \[\leadsto \frac{\left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot w0}{\color{blue}{d}} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\left(\left|D\right| \cdot \sqrt{\frac{\left(M \cdot M\right) \cdot h}{\ell} \cdot \frac{-1}{4}}\right) \cdot w0}{\color{blue}{d}} \]

   10. Applied rewrites 13.8%

       \[\leadsto \frac{\left(\left(\left|M\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right) \cdot \left|D\right|\right) \cdot w0}{\color{blue}{d}} \]

   if -5e9 < (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l))

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in M around 0

      \[\leadsto w0 \cdot \color{blue}{1} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.0%

      \[\leadsto w0 \cdot \color{blue}{1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 90.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \mathbf{if}\;{\left(\frac{\left|M\right| \cdot \left|D\right|}{2 \cdot \left|d\right|}\right)}^{2} \cdot \frac{h}{\ell} \leq -5000000000:\\ \;\;\;\;\frac{\left(\left(\left|D\right| \cdot \left|M\right|\right) \cdot w0\right) \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}}{\left|d\right|}\\ \mathbf{else}:\\ \;\;\;\;w0 \cdot 1\\ \end{array} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (if (<=
      (* (pow (/ (* (fabs M) (fabs D)) (* 2.0 (fabs d))) 2.0) (/ h l))
      -5000000000.0)
   (/ (* (* (* (fabs D) (fabs M)) w0) (sqrt (* (/ h l) -0.25))) (fabs d))
   (* w0 1.0)))

C

double code(double w0, double M, double D, double h, double l, double d) {
	double tmp;
	if ((pow(((fabs(M) * fabs(D)) / (2.0 * fabs(d))), 2.0) * (h / l)) <= -5000000000.0) {
		tmp = (((fabs(D) * fabs(M)) * w0) * sqrt(((h / l) * -0.25))) / fabs(d);
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    real(8) :: tmp
    if (((((abs(m) * abs(d)) / (2.0d0 * abs(d_1))) ** 2.0d0) * (h / l)) <= (-5000000000.0d0)) then
        tmp = (((abs(d) * abs(m)) * w0) * sqrt(((h / l) * (-0.25d0)))) / abs(d_1)
    else
        tmp = w0 * 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	double tmp;
	if ((Math.pow(((Math.abs(M) * Math.abs(D)) / (2.0 * Math.abs(d))), 2.0) * (h / l)) <= -5000000000.0) {
		tmp = (((Math.abs(D) * Math.abs(M)) * w0) * Math.sqrt(((h / l) * -0.25))) / Math.abs(d);
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Python

def code(w0, M, D, h, l, d):
	tmp = 0
	if (math.pow(((math.fabs(M) * math.fabs(D)) / (2.0 * math.fabs(d))), 2.0) * (h / l)) <= -5000000000.0:
		tmp = (((math.fabs(D) * math.fabs(M)) * w0) * math.sqrt(((h / l) * -0.25))) / math.fabs(d)
	else:
		tmp = w0 * 1.0
	return tmp

Julia

function code(w0, M, D, h, l, d)
	tmp = 0.0
	if (Float64((Float64(Float64(abs(M) * abs(D)) / Float64(2.0 * abs(d))) ^ 2.0) * Float64(h / l)) <= -5000000000.0)
		tmp = Float64(Float64(Float64(Float64(abs(D) * abs(M)) * w0) * sqrt(Float64(Float64(h / l) * -0.25))) / abs(d));
	else
		tmp = Float64(w0 * 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(w0, M, D, h, l, d)
	tmp = 0.0;
	if (((((abs(M) * abs(D)) / (2.0 * abs(d))) ^ 2.0) * (h / l)) <= -5000000000.0)
		tmp = (((abs(D) * abs(M)) * w0) * sqrt(((h / l) * -0.25))) / abs(d);
	else
		tmp = w0 * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := If[LessEqual[N[(N[Power[N[(N[(N[Abs[M], $MachinePrecision] * N[Abs[D], $MachinePrecision]), $MachinePrecision] / N[(2.0 * N[Abs[d], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision], -5000000000.0], N[(N[(N[(N[(N[Abs[D], $MachinePrecision] * N[Abs[M], $MachinePrecision]), $MachinePrecision] * w0), $MachinePrecision] * N[Sqrt[N[(N[(h / l), $MachinePrecision] * -0.25), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / N[Abs[d], $MachinePrecision]), $MachinePrecision], N[(w0 * 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l)) < -5e9

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in d around 0

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{d}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{\color{blue}{d}} \]

   4. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]

   5. Taylor expanded in M around 0

      \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}\right)}\right)}{d} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}\right)}\right)}{d} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}\right)}\right)}{d} \]

      3. lower-sqrt.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}\right)}\right)}{d} \]

      4. lower-neg.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

      6. lower-/.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

      8. lower-pow.f64 11.2%

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

   7. Applied rewrites 11.2%

      \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

   8. Taylor expanded in D around 0

      \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]
   9. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]

      4. lower-sqrt.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]

      5. lower-neg.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)\right)}{d} \]

      6. lower-*.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)\right)}{d} \]

      7. lower-/.f64 14.2%

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-0.25 \cdot \frac{h}{\ell}}\right)\right)}{d} \]

   10. Applied rewrites 14.2%

       \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-0.25 \cdot \frac{h}{\ell}}\right)\right)}{d} \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)\right)}{d} \]

       2. lift-*.f64 N/A

          \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)\right)}{d} \]

       3. associate-*r* N/A

          \[\leadsto \frac{\left(D \cdot M\right) \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)}{d} \]

       4. *-commutative N/A

          \[\leadsto \frac{\left(M \cdot D\right) \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)}{d} \]

       5. lift-*.f64 N/A

          \[\leadsto \frac{\left(M \cdot D\right) \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)}{d} \]

       6. associate-*r* N/A

          \[\leadsto \frac{\left(\left(M \cdot D\right) \cdot w0\right) \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}}{d} \]

       7. lower-*.f64 N/A

          \[\leadsto \frac{\left(\left(M \cdot D\right) \cdot w0\right) \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}}{d} \]

       8. lower-*.f64 N/A

          \[\leadsto \frac{\left(\left(M \cdot D\right) \cdot w0\right) \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}}{d} \]

       9. *-commutative N/A

          \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}}{d} \]

       10. lower-*.f64 14.0%

           \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{-0.25 \cdot \frac{h}{\ell}}}{d} \]

       11. lift-neg.f64 N/A

           \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}}{d} \]

       12. lift-*.f64 N/A

           \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}}{d} \]

       13. *-commutative N/A

           \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{\mathsf{neg}\left(\frac{h}{\ell} \cdot \frac{1}{4}\right)}}{d} \]

       14. distribute-rgt-neg-in N/A

           \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{\frac{h}{\ell} \cdot \left(\mathsf{neg}\left(\frac{1}{4}\right)\right)}}{d} \]

       15. metadata-eval N/A

           \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{\frac{h}{\ell} \cdot \frac{-1}{4}}}{d} \]

       16. lower-*.f64 14.0%

           \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}}{d} \]

   12. Applied rewrites 14.0%

       \[\leadsto \frac{\left(\left(D \cdot M\right) \cdot w0\right) \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}}{d} \]

   if -5e9 < (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l))

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in M around 0

      \[\leadsto w0 \cdot \color{blue}{1} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.0%

      \[\leadsto w0 \cdot \color{blue}{1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 90.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \mathbf{if}\;{\left(\frac{\left|M\right| \cdot \left|D\right|}{2 \cdot \left|d\right|}\right)}^{2} \cdot \frac{h}{\ell} \leq -5000000000:\\ \;\;\;\;\frac{\left|D\right| \cdot \left(\left(\left|M\right| \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right) \cdot w0\right)}{\left|d\right|}\\ \mathbf{else}:\\ \;\;\;\;w0 \cdot 1\\ \end{array} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (if (<=
      (* (pow (/ (* (fabs M) (fabs D)) (* 2.0 (fabs d))) 2.0) (/ h l))
      -5000000000.0)
   (/ (* (fabs D) (* (* (fabs M) (sqrt (* (/ h l) -0.25))) w0)) (fabs d))
   (* w0 1.0)))

C

double code(double w0, double M, double D, double h, double l, double d) {
	double tmp;
	if ((pow(((fabs(M) * fabs(D)) / (2.0 * fabs(d))), 2.0) * (h / l)) <= -5000000000.0) {
		tmp = (fabs(D) * ((fabs(M) * sqrt(((h / l) * -0.25))) * w0)) / fabs(d);
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    real(8) :: tmp
    if (((((abs(m) * abs(d)) / (2.0d0 * abs(d_1))) ** 2.0d0) * (h / l)) <= (-5000000000.0d0)) then
        tmp = (abs(d) * ((abs(m) * sqrt(((h / l) * (-0.25d0)))) * w0)) / abs(d_1)
    else
        tmp = w0 * 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	double tmp;
	if ((Math.pow(((Math.abs(M) * Math.abs(D)) / (2.0 * Math.abs(d))), 2.0) * (h / l)) <= -5000000000.0) {
		tmp = (Math.abs(D) * ((Math.abs(M) * Math.sqrt(((h / l) * -0.25))) * w0)) / Math.abs(d);
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Python

def code(w0, M, D, h, l, d):
	tmp = 0
	if (math.pow(((math.fabs(M) * math.fabs(D)) / (2.0 * math.fabs(d))), 2.0) * (h / l)) <= -5000000000.0:
		tmp = (math.fabs(D) * ((math.fabs(M) * math.sqrt(((h / l) * -0.25))) * w0)) / math.fabs(d)
	else:
		tmp = w0 * 1.0
	return tmp

Julia

function code(w0, M, D, h, l, d)
	tmp = 0.0
	if (Float64((Float64(Float64(abs(M) * abs(D)) / Float64(2.0 * abs(d))) ^ 2.0) * Float64(h / l)) <= -5000000000.0)
		tmp = Float64(Float64(abs(D) * Float64(Float64(abs(M) * sqrt(Float64(Float64(h / l) * -0.25))) * w0)) / abs(d));
	else
		tmp = Float64(w0 * 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(w0, M, D, h, l, d)
	tmp = 0.0;
	if (((((abs(M) * abs(D)) / (2.0 * abs(d))) ^ 2.0) * (h / l)) <= -5000000000.0)
		tmp = (abs(D) * ((abs(M) * sqrt(((h / l) * -0.25))) * w0)) / abs(d);
	else
		tmp = w0 * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := If[LessEqual[N[(N[Power[N[(N[(N[Abs[M], $MachinePrecision] * N[Abs[D], $MachinePrecision]), $MachinePrecision] / N[(2.0 * N[Abs[d], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision], -5000000000.0], N[(N[(N[Abs[D], $MachinePrecision] * N[(N[(N[Abs[M], $MachinePrecision] * N[Sqrt[N[(N[(h / l), $MachinePrecision] * -0.25), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * w0), $MachinePrecision]), $MachinePrecision] / N[Abs[d], $MachinePrecision]), $MachinePrecision], N[(w0 * 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l)) < -5e9

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in d around 0

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{d}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}\right)}}{\color{blue}{d}} \]

   4. Applied rewrites 9.4%

      \[\leadsto \color{blue}{\frac{w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot \left({M}^{2} \cdot h\right)}{\ell}}}{d}} \]

   5. Taylor expanded in M around 0

      \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}\right)}\right)}{d} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}\right)}\right)}{d} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}\right)}\right)}{d} \]

      3. lower-sqrt.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}\right)}\right)}{d} \]

      4. lower-neg.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

      6. lower-/.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

      8. lower-pow.f64 11.2%

         \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

   7. Applied rewrites 11.2%

      \[\leadsto \frac{M \cdot \left(w0 \cdot \sqrt{-0.25 \cdot \frac{{D}^{2} \cdot h}{\ell}}\right)}{d} \]

   8. Taylor expanded in D around 0

      \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]
   9. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]

      4. lower-sqrt.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right)\right)}{d} \]

      5. lower-neg.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)\right)}{d} \]

      6. lower-*.f64 N/A

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)\right)}{d} \]

      7. lower-/.f64 14.2%

         \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-0.25 \cdot \frac{h}{\ell}}\right)\right)}{d} \]

   10. Applied rewrites 14.2%

       \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-0.25 \cdot \frac{h}{\ell}}\right)\right)}{d} \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)\right)}{d} \]

       2. lift-*.f64 N/A

          \[\leadsto \frac{D \cdot \left(M \cdot \left(w0 \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right)\right)}{d} \]

       3. *-commutative N/A

          \[\leadsto \frac{D \cdot \left(M \cdot \left(\sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}} \cdot w0\right)\right)}{d} \]

       4. associate-*r* N/A

          \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right) \cdot w0\right)}{d} \]

       5. lower-*.f64 N/A

          \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{-\frac{1}{4} \cdot \frac{h}{\ell}}\right) \cdot w0\right)}{d} \]

       6. lower-*.f64 14.1%

          \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{-0.25 \cdot \frac{h}{\ell}}\right) \cdot w0\right)}{d} \]

       7. lift-neg.f64 N/A

          \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right) \cdot w0\right)}{d} \]

       8. lift-*.f64 N/A

          \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{\mathsf{neg}\left(\frac{1}{4} \cdot \frac{h}{\ell}\right)}\right) \cdot w0\right)}{d} \]

       9. *-commutative N/A

          \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{\mathsf{neg}\left(\frac{h}{\ell} \cdot \frac{1}{4}\right)}\right) \cdot w0\right)}{d} \]

       10. distribute-rgt-neg-in N/A

           \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{\frac{h}{\ell} \cdot \left(\mathsf{neg}\left(\frac{1}{4}\right)\right)}\right) \cdot w0\right)}{d} \]

       11. metadata-eval N/A

           \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{\frac{h}{\ell} \cdot \frac{-1}{4}}\right) \cdot w0\right)}{d} \]

       12. lower-*.f64 14.1%

           \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right) \cdot w0\right)}{d} \]

   12. Applied rewrites 14.1%

       \[\leadsto \frac{D \cdot \left(\left(M \cdot \sqrt{\frac{h}{\ell} \cdot -0.25}\right) \cdot w0\right)}{d} \]

   if -5e9 < (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l))

   1. Initial program 81.1%

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Taylor expanded in M around 0

      \[\leadsto w0 \cdot \color{blue}{1} \]
   3. Step-by-step derivation

   4. Applied rewrites 68.0%

      \[\leadsto w0 \cdot \color{blue}{1} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 68.0% accurate, 10.1× speedup

Math

\[w0 \cdot 1 \]

FPCore

(FPCore (w0 M D h l d) :precision binary64 (* w0 1.0))

C

double code(double w0, double M, double D, double h, double l, double d) {
	return w0 * 1.0;
}

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(w0, m, d, h, l, d_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m
    real(8), intent (in) :: d
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_1
    code = w0 * 1.0d0
end function

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	return w0 * 1.0;
}

Python

def code(w0, M, D, h, l, d):
	return w0 * 1.0

Julia

function code(w0, M, D, h, l, d)
	return Float64(w0 * 1.0)
end

MATLAB

function tmp = code(w0, M, D, h, l, d)
	tmp = w0 * 1.0;
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := N[(w0 * 1.0), $MachinePrecision]

Derivation

1. Initial program 81.1%

   \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

2. Taylor expanded in M around 0

   \[\leadsto w0 \cdot \color{blue}{1} \]
3. Step-by-step derivation

4. Applied rewrites 68.0%

   \[\leadsto w0 \cdot \color{blue}{1} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025193 
(FPCore (w0 M D h l d)
  :name "Henrywood and Agarwal, Equation (9a)"
  :precision binary64
  (* w0 (sqrt (- 1.0 (* (pow (/ (* M D) (* 2.0 d)) 2.0) (/ h l))))))