Henrywood and Agarwal, Equation (9a)

Percentage Accurate: 80.7% → 91.5%

Time: 6.0s

Alternatives: 3

Speedup: 0.7×

Specification

Math

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

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: 80.7% accurate, 1.0× speedup

Math

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

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: 91.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} M_m = \left|M\right| \\ D_m = \left|D\right| \\ d_m = \left|d\right| \\ [w0, M_m, D_m, h, l, d_m] = \mathsf{sort}([w0, M_m, D_m, h, l, d_m])\\ \\ \begin{array}{l} \mathbf{if}\;{\left(\frac{M\_m \cdot D\_m}{2 \cdot d\_m}\right)}^{2} \cdot \frac{h}{\ell} \leq -1:\\ \;\;\;\;w0 \cdot \left(\frac{\sqrt{-0.25 \cdot \frac{h}{\ell}} \cdot M\_m}{d\_m} \cdot D\_m\right)\\ \mathbf{else}:\\ \;\;\;\;w0 \cdot 1\\ \end{array} \end{array} \]

FPCore

M_m = (fabs.f64 M)
D_m = (fabs.f64 D)
d_m = (fabs.f64 d)
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
(FPCore (w0 M_m D_m h l d_m)
 :precision binary64
 (if (<= (* (pow (/ (* M_m D_m) (* 2.0 d_m)) 2.0) (/ h l)) -1.0)
   (* w0 (* (/ (* (sqrt (* -0.25 (/ h l))) M_m) d_m) D_m))
   (* w0 1.0)))

C

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

Fortran

M_m =     private
D_m =     private
d_m =     private
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
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_m, d_m, h, l, d_m_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m_m
    real(8), intent (in) :: d_m
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_m_1
    real(8) :: tmp
    if (((((m_m * d_m) / (2.0d0 * d_m_1)) ** 2.0d0) * (h / l)) <= (-1.0d0)) then
        tmp = w0 * (((sqrt(((-0.25d0) * (h / l))) * m_m) / d_m_1) * d_m)
    else
        tmp = w0 * 1.0d0
    end if
    code = tmp
end function

Java

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

Python

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

Julia

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

MATLAB

M_m = abs(M);
D_m = abs(D);
d_m = abs(d);
w0, M_m, D_m, h, l, d_m = num2cell(sort([w0, M_m, D_m, h, l, d_m])){:}
function tmp_2 = code(w0, M_m, D_m, h, l, d_m)
	tmp = 0.0;
	if (((((M_m * D_m) / (2.0 * d_m)) ^ 2.0) * (h / l)) <= -1.0)
		tmp = w0 * (((sqrt((-0.25 * (h / l))) * M_m) / d_m) * D_m);
	else
		tmp = w0 * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

M_m = N[Abs[M], $MachinePrecision]
D_m = N[Abs[D], $MachinePrecision]
d_m = N[Abs[d], $MachinePrecision]
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
code[w0_, M$95$m_, D$95$m_, h_, l_, d$95$m_] := If[LessEqual[N[(N[Power[N[(N[(M$95$m * D$95$m), $MachinePrecision] / N[(2.0 * d$95$m), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision], -1.0], N[(w0 * N[(N[(N[(N[Sqrt[N[(-0.25 * N[(h / l), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * M$95$m), $MachinePrecision] / d$95$m), $MachinePrecision] * D$95$m), $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)) < -1

   1. Initial program 80.7%

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

   2. Taylor expanded in M around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 16.4%

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

   5. Taylor expanded in D around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lower-sqrt.f64 N/A

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

      6. associate-*r/ N/A

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

      7. lower-/.f64 N/A

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

      8. lower-*.f64 N/A

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

      9. pow2 N/A

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

      10. lift-*.f64 N/A

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

      11. lift-*.f64 20.8

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

   7. Applied rewrites 20.8%

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

   8. Taylor expanded in d around 0

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lower-sqrt.f64 N/A

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

      5. lower-*.f64 N/A

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

      6. lift-/.f64 27.0

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

   10. Applied rewrites 27.0%

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

   if -1 < (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l))

   1. Initial program 80.7%

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

   2. Taylor expanded in M around 0

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

   4. Applied rewrites 68.2%

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

Alternative 2: 89.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} M_m = \left|M\right| \\ D_m = \left|D\right| \\ d_m = \left|d\right| \\ [w0, M_m, D_m, h, l, d_m] = \mathsf{sort}([w0, M_m, D_m, h, l, d_m])\\ \\ \begin{array}{l} \mathbf{if}\;{\left(\frac{M\_m \cdot D\_m}{2 \cdot d\_m}\right)}^{2} \cdot \frac{h}{\ell} \leq -1 \cdot 10^{+29}:\\ \;\;\;\;\frac{\left(\sqrt{-0.25 \cdot \frac{h}{\ell}} \cdot M\_m\right) \cdot D\_m}{d\_m} \cdot w0\\ \mathbf{else}:\\ \;\;\;\;w0 \cdot 1\\ \end{array} \end{array} \]

FPCore

M_m = (fabs.f64 M)
D_m = (fabs.f64 D)
d_m = (fabs.f64 d)
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
(FPCore (w0 M_m D_m h l d_m)
 :precision binary64
 (if (<= (* (pow (/ (* M_m D_m) (* 2.0 d_m)) 2.0) (/ h l)) -1e+29)
   (* (/ (* (* (sqrt (* -0.25 (/ h l))) M_m) D_m) d_m) w0)
   (* w0 1.0)))

C

M_m = fabs(M);
D_m = fabs(D);
d_m = fabs(d);
assert(w0 < M_m && M_m < D_m && D_m < h && h < l && l < d_m);
double code(double w0, double M_m, double D_m, double h, double l, double d_m) {
	double tmp;
	if ((pow(((M_m * D_m) / (2.0 * d_m)), 2.0) * (h / l)) <= -1e+29) {
		tmp = (((sqrt((-0.25 * (h / l))) * M_m) * D_m) / d_m) * w0;
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Fortran

M_m =     private
D_m =     private
d_m =     private
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
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_m, d_m, h, l, d_m_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m_m
    real(8), intent (in) :: d_m
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_m_1
    real(8) :: tmp
    if (((((m_m * d_m) / (2.0d0 * d_m_1)) ** 2.0d0) * (h / l)) <= (-1d+29)) then
        tmp = (((sqrt(((-0.25d0) * (h / l))) * m_m) * d_m) / d_m_1) * w0
    else
        tmp = w0 * 1.0d0
    end if
    code = tmp
end function

Java

M_m = Math.abs(M);
D_m = Math.abs(D);
d_m = Math.abs(d);
assert w0 < M_m && M_m < D_m && D_m < h && h < l && l < d_m;
public static double code(double w0, double M_m, double D_m, double h, double l, double d_m) {
	double tmp;
	if ((Math.pow(((M_m * D_m) / (2.0 * d_m)), 2.0) * (h / l)) <= -1e+29) {
		tmp = (((Math.sqrt((-0.25 * (h / l))) * M_m) * D_m) / d_m) * w0;
	} else {
		tmp = w0 * 1.0;
	}
	return tmp;
}

Python

M_m = math.fabs(M)
D_m = math.fabs(D)
d_m = math.fabs(d)
[w0, M_m, D_m, h, l, d_m] = sort([w0, M_m, D_m, h, l, d_m])
def code(w0, M_m, D_m, h, l, d_m):
	tmp = 0
	if (math.pow(((M_m * D_m) / (2.0 * d_m)), 2.0) * (h / l)) <= -1e+29:
		tmp = (((math.sqrt((-0.25 * (h / l))) * M_m) * D_m) / d_m) * w0
	else:
		tmp = w0 * 1.0
	return tmp

Julia

M_m = abs(M)
D_m = abs(D)
d_m = abs(d)
w0, M_m, D_m, h, l, d_m = sort([w0, M_m, D_m, h, l, d_m])
function code(w0, M_m, D_m, h, l, d_m)
	tmp = 0.0
	if (Float64((Float64(Float64(M_m * D_m) / Float64(2.0 * d_m)) ^ 2.0) * Float64(h / l)) <= -1e+29)
		tmp = Float64(Float64(Float64(Float64(sqrt(Float64(-0.25 * Float64(h / l))) * M_m) * D_m) / d_m) * w0);
	else
		tmp = Float64(w0 * 1.0);
	end
	return tmp
end

MATLAB

M_m = abs(M);
D_m = abs(D);
d_m = abs(d);
w0, M_m, D_m, h, l, d_m = num2cell(sort([w0, M_m, D_m, h, l, d_m])){:}
function tmp_2 = code(w0, M_m, D_m, h, l, d_m)
	tmp = 0.0;
	if (((((M_m * D_m) / (2.0 * d_m)) ^ 2.0) * (h / l)) <= -1e+29)
		tmp = (((sqrt((-0.25 * (h / l))) * M_m) * D_m) / d_m) * w0;
	else
		tmp = w0 * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

M_m = N[Abs[M], $MachinePrecision]
D_m = N[Abs[D], $MachinePrecision]
d_m = N[Abs[d], $MachinePrecision]
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
code[w0_, M$95$m_, D$95$m_, h_, l_, d$95$m_] := If[LessEqual[N[(N[Power[N[(N[(M$95$m * D$95$m), $MachinePrecision] / N[(2.0 * d$95$m), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision], -1e+29], N[(N[(N[(N[(N[Sqrt[N[(-0.25 * N[(h / l), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * M$95$m), $MachinePrecision] * D$95$m), $MachinePrecision] / d$95$m), $MachinePrecision] * w0), $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)) < -9.99999999999999914e28

   1. Initial program 80.7%

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

   2. Taylor expanded in M around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 16.4%

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

   5. Taylor expanded in D around 0

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lower-*.f64 N/A

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

      5. lower-sqrt.f64 N/A

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

      6. associate-*r/ N/A

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

      7. lower-/.f64 N/A

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

      8. lower-*.f64 N/A

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

      9. pow2 N/A

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

      10. lift-*.f64 N/A

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

      11. lift-*.f64 20.8

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

   7. Applied rewrites 20.8%

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

   8. Taylor expanded in d around 0

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-sqrt.f64 N/A

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

      7. lower-*.f64 N/A

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

      8. lift-/.f64 25.7

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

   10. Applied rewrites 25.7%

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

       1. lift-*.f64 N/A

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

       2. *-commutative N/A

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

       3. lower-*.f64 25.7

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

   12. Applied rewrites 25.7%

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

   if -9.99999999999999914e28 < (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 #s(literal 2 binary64) d)) #s(literal 2 binary64)) (/.f64 h l))

   1. Initial program 80.7%

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

   2. Taylor expanded in M around 0

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

   4. Applied rewrites 68.2%

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

Alternative 3: 68.2% accurate, 10.1× speedup

Math

\[\begin{array}{l} M_m = \left|M\right| \\ D_m = \left|D\right| \\ d_m = \left|d\right| \\ [w0, M_m, D_m, h, l, d_m] = \mathsf{sort}([w0, M_m, D_m, h, l, d_m])\\ \\ w0 \cdot 1 \end{array} \]

FPCore

M_m = (fabs.f64 M)
D_m = (fabs.f64 D)
d_m = (fabs.f64 d)
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
(FPCore (w0 M_m D_m h l d_m) :precision binary64 (* w0 1.0))

C

M_m = fabs(M);
D_m = fabs(D);
d_m = fabs(d);
assert(w0 < M_m && M_m < D_m && D_m < h && h < l && l < d_m);
double code(double w0, double M_m, double D_m, double h, double l, double d_m) {
	return w0 * 1.0;
}

Fortran

M_m =     private
D_m =     private
d_m =     private
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
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_m, d_m, h, l, d_m_1)
use fmin_fmax_functions
    real(8), intent (in) :: w0
    real(8), intent (in) :: m_m
    real(8), intent (in) :: d_m
    real(8), intent (in) :: h
    real(8), intent (in) :: l
    real(8), intent (in) :: d_m_1
    code = w0 * 1.0d0
end function

Java

M_m = Math.abs(M);
D_m = Math.abs(D);
d_m = Math.abs(d);
assert w0 < M_m && M_m < D_m && D_m < h && h < l && l < d_m;
public static double code(double w0, double M_m, double D_m, double h, double l, double d_m) {
	return w0 * 1.0;
}

Python

M_m = math.fabs(M)
D_m = math.fabs(D)
d_m = math.fabs(d)
[w0, M_m, D_m, h, l, d_m] = sort([w0, M_m, D_m, h, l, d_m])
def code(w0, M_m, D_m, h, l, d_m):
	return w0 * 1.0

Julia

M_m = abs(M)
D_m = abs(D)
d_m = abs(d)
w0, M_m, D_m, h, l, d_m = sort([w0, M_m, D_m, h, l, d_m])
function code(w0, M_m, D_m, h, l, d_m)
	return Float64(w0 * 1.0)
end

MATLAB

M_m = abs(M);
D_m = abs(D);
d_m = abs(d);
w0, M_m, D_m, h, l, d_m = num2cell(sort([w0, M_m, D_m, h, l, d_m])){:}
function tmp = code(w0, M_m, D_m, h, l, d_m)
	tmp = w0 * 1.0;
end

Wolfram

M_m = N[Abs[M], $MachinePrecision]
D_m = N[Abs[D], $MachinePrecision]
d_m = N[Abs[d], $MachinePrecision]
NOTE: w0, M_m, D_m, h, l, and d_m should be sorted in increasing order before calling this function.
code[w0_, M$95$m_, D$95$m_, h_, l_, d$95$m_] := N[(w0 * 1.0), $MachinePrecision]

Derivation

1. Initial program 80.7%

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

2. Taylor expanded in M around 0

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

4. Applied rewrites 68.2%

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

Reproduce

herbie shell --seed 2025140 
(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))))))