Toniolo and Linder, Equation (2)

Percentage Accurate: 84.0% → 98.9%

Time: 7.7s

Alternatives: 4

Speedup: N/A×

Specification

Math

\[\begin{array}{l} \\ \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \end{array} \]

FPCore

(FPCore (t l Om Omc)
 :precision binary64
 (asin
  (sqrt (/ (- 1.0 (pow (/ Om Omc) 2.0)) (+ 1.0 (* 2.0 (pow (/ t l) 2.0)))))))

C

double code(double t, double l, double Om, double Omc) {
	return asin(sqrt(((1.0 - pow((Om / Omc), 2.0)) / (1.0 + (2.0 * pow((t / l), 2.0))))));
}

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(t, l, om, omc)
use fmin_fmax_functions
    real(8), intent (in) :: t
    real(8), intent (in) :: l
    real(8), intent (in) :: om
    real(8), intent (in) :: omc
    code = asin(sqrt(((1.0d0 - ((om / omc) ** 2.0d0)) / (1.0d0 + (2.0d0 * ((t / l) ** 2.0d0))))))
end function

Java

public static double code(double t, double l, double Om, double Omc) {
	return Math.asin(Math.sqrt(((1.0 - Math.pow((Om / Omc), 2.0)) / (1.0 + (2.0 * Math.pow((t / l), 2.0))))));
}

Python

def code(t, l, Om, Omc):
	return math.asin(math.sqrt(((1.0 - math.pow((Om / Omc), 2.0)) / (1.0 + (2.0 * math.pow((t / l), 2.0))))))

Julia

function code(t, l, Om, Omc)
	return asin(sqrt(Float64(Float64(1.0 - (Float64(Om / Omc) ^ 2.0)) / Float64(1.0 + Float64(2.0 * (Float64(t / l) ^ 2.0))))))
end

MATLAB

function tmp = code(t, l, Om, Omc)
	tmp = asin(sqrt(((1.0 - ((Om / Omc) ^ 2.0)) / (1.0 + (2.0 * ((t / l) ^ 2.0))))));
end

Wolfram

code[t_, l_, Om_, Omc_] := N[ArcSin[N[Sqrt[N[(N[(1.0 - N[Power[N[(Om / Omc), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(2.0 * N[Power[N[(t / l), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]

Initial Program: 84.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \end{array} \]

FPCore

(FPCore (t l Om Omc)
 :precision binary64
 (asin
  (sqrt (/ (- 1.0 (pow (/ Om Omc) 2.0)) (+ 1.0 (* 2.0 (pow (/ t l) 2.0)))))))

C

double code(double t, double l, double Om, double Omc) {
	return asin(sqrt(((1.0 - pow((Om / Omc), 2.0)) / (1.0 + (2.0 * pow((t / l), 2.0))))));
}

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(t, l, om, omc)
use fmin_fmax_functions
    real(8), intent (in) :: t
    real(8), intent (in) :: l
    real(8), intent (in) :: om
    real(8), intent (in) :: omc
    code = asin(sqrt(((1.0d0 - ((om / omc) ** 2.0d0)) / (1.0d0 + (2.0d0 * ((t / l) ** 2.0d0))))))
end function

Java

public static double code(double t, double l, double Om, double Omc) {
	return Math.asin(Math.sqrt(((1.0 - Math.pow((Om / Omc), 2.0)) / (1.0 + (2.0 * Math.pow((t / l), 2.0))))));
}

Python

def code(t, l, Om, Omc):
	return math.asin(math.sqrt(((1.0 - math.pow((Om / Omc), 2.0)) / (1.0 + (2.0 * math.pow((t / l), 2.0))))))

Julia

function code(t, l, Om, Omc)
	return asin(sqrt(Float64(Float64(1.0 - (Float64(Om / Omc) ^ 2.0)) / Float64(1.0 + Float64(2.0 * (Float64(t / l) ^ 2.0))))))
end

MATLAB

function tmp = code(t, l, Om, Omc)
	tmp = asin(sqrt(((1.0 - ((Om / Omc) ^ 2.0)) / (1.0 + (2.0 * ((t / l) ^ 2.0))))));
end

Wolfram

code[t_, l_, Om_, Omc_] := N[ArcSin[N[Sqrt[N[(N[(1.0 - N[Power[N[(Om / Omc), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(2.0 * N[Power[N[(t / l), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]

Alternative 1: 98.9% accurate, N/A× speedup

Math

\[\begin{array}{l} t_m = \left|t\right| \\ l_m = \left|\ell\right| \\ \begin{array}{l} t_1 := {\left(\frac{Om}{Omc}\right)}^{2}\\ t_2 := \sin^{-1} \left(\sqrt{\frac{1 - t\_1}{1 + 2 \cdot {\left(\frac{t\_m}{l\_m}\right)}^{2}}}\right)\\ \mathbf{if}\;t\_2 \leq 0:\\ \;\;\;\;\sin^{-1} \left(\frac{{0.5}^{0.5} \cdot l\_m}{t\_m} \cdot {\left(\frac{1 - {t\_1}^{2}}{1 + t\_1}\right)}^{0.5}\right)\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

t_m = (fabs.f64 t)
l_m = (fabs.f64 l)
(FPCore (t_m l_m Om Omc)
 :precision binary64
 (let* ((t_1 (pow (/ Om Omc) 2.0))
        (t_2
         (asin (sqrt (/ (- 1.0 t_1) (+ 1.0 (* 2.0 (pow (/ t_m l_m) 2.0))))))))
   (if (<= t_2 0.0)
     (asin
      (*
       (/ (* (pow 0.5 0.5) l_m) t_m)
       (pow (/ (- 1.0 (pow t_1 2.0)) (+ 1.0 t_1)) 0.5)))
     t_2)))

C

t_m = fabs(t);
l_m = fabs(l);
double code(double t_m, double l_m, double Om, double Omc) {
	double t_1 = pow((Om / Omc), 2.0);
	double t_2 = asin(sqrt(((1.0 - t_1) / (1.0 + (2.0 * pow((t_m / l_m), 2.0))))));
	double tmp;
	if (t_2 <= 0.0) {
		tmp = asin((((pow(0.5, 0.5) * l_m) / t_m) * pow(((1.0 - pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

t_m =     private
l_m =     private
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(t_m, l_m, om, omc)
use fmin_fmax_functions
    real(8), intent (in) :: t_m
    real(8), intent (in) :: l_m
    real(8), intent (in) :: om
    real(8), intent (in) :: omc
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (om / omc) ** 2.0d0
    t_2 = asin(sqrt(((1.0d0 - t_1) / (1.0d0 + (2.0d0 * ((t_m / l_m) ** 2.0d0))))))
    if (t_2 <= 0.0d0) then
        tmp = asin(((((0.5d0 ** 0.5d0) * l_m) / t_m) * (((1.0d0 - (t_1 ** 2.0d0)) / (1.0d0 + t_1)) ** 0.5d0)))
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

t_m = Math.abs(t);
l_m = Math.abs(l);
public static double code(double t_m, double l_m, double Om, double Omc) {
	double t_1 = Math.pow((Om / Omc), 2.0);
	double t_2 = Math.asin(Math.sqrt(((1.0 - t_1) / (1.0 + (2.0 * Math.pow((t_m / l_m), 2.0))))));
	double tmp;
	if (t_2 <= 0.0) {
		tmp = Math.asin((((Math.pow(0.5, 0.5) * l_m) / t_m) * Math.pow(((1.0 - Math.pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)));
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

t_m = math.fabs(t)
l_m = math.fabs(l)
def code(t_m, l_m, Om, Omc):
	t_1 = math.pow((Om / Omc), 2.0)
	t_2 = math.asin(math.sqrt(((1.0 - t_1) / (1.0 + (2.0 * math.pow((t_m / l_m), 2.0))))))
	tmp = 0
	if t_2 <= 0.0:
		tmp = math.asin((((math.pow(0.5, 0.5) * l_m) / t_m) * math.pow(((1.0 - math.pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)))
	else:
		tmp = t_2
	return tmp

Julia

t_m = abs(t)
l_m = abs(l)
function code(t_m, l_m, Om, Omc)
	t_1 = Float64(Om / Omc) ^ 2.0
	t_2 = asin(sqrt(Float64(Float64(1.0 - t_1) / Float64(1.0 + Float64(2.0 * (Float64(t_m / l_m) ^ 2.0))))))
	tmp = 0.0
	if (t_2 <= 0.0)
		tmp = asin(Float64(Float64(Float64((0.5 ^ 0.5) * l_m) / t_m) * (Float64(Float64(1.0 - (t_1 ^ 2.0)) / Float64(1.0 + t_1)) ^ 0.5)));
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

t_m = abs(t);
l_m = abs(l);
function tmp_2 = code(t_m, l_m, Om, Omc)
	t_1 = (Om / Omc) ^ 2.0;
	t_2 = asin(sqrt(((1.0 - t_1) / (1.0 + (2.0 * ((t_m / l_m) ^ 2.0))))));
	tmp = 0.0;
	if (t_2 <= 0.0)
		tmp = asin(((((0.5 ^ 0.5) * l_m) / t_m) * (((1.0 - (t_1 ^ 2.0)) / (1.0 + t_1)) ^ 0.5)));
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

t_m = N[Abs[t], $MachinePrecision]
l_m = N[Abs[l], $MachinePrecision]
code[t$95$m_, l$95$m_, Om_, Omc_] := Block[{t$95$1 = N[Power[N[(Om / Omc), $MachinePrecision], 2.0], $MachinePrecision]}, Block[{t$95$2 = N[ArcSin[N[Sqrt[N[(N[(1.0 - t$95$1), $MachinePrecision] / N[(1.0 + N[(2.0 * N[Power[N[(t$95$m / l$95$m), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]}, If[LessEqual[t$95$2, 0.0], N[ArcSin[N[(N[(N[(N[Power[0.5, 0.5], $MachinePrecision] * l$95$m), $MachinePrecision] / t$95$m), $MachinePrecision] * N[Power[N[(N[(1.0 - N[Power[t$95$1, 2.0], $MachinePrecision]), $MachinePrecision] / N[(1.0 + t$95$1), $MachinePrecision]), $MachinePrecision], 0.5], $MachinePrecision]), $MachinePrecision]], $MachinePrecision], t$95$2]]]

Derivation

1. Split input into 2 regimes

2. if (asin.f64 (sqrt.f64 (/.f64 (-.f64 #s(literal 1 binary64) (pow.f64 (/.f64 Om Omc) #s(literal 2 binary64))) (+.f64 #s(literal 1 binary64) (*.f64 #s(literal 2 binary64) (pow.f64 (/.f64 t l) #s(literal 2 binary64))))))) < 0.0

   1. Initial program 39.6%

      \[\sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf

      \[\leadsto \sin^{-1} \color{blue}{\left(\frac{\ell \cdot \sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right)} \]
   4. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\frac{\ell \cdot \sqrt{\frac{1}{2}}}{t} \cdot \color{blue}{\sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}}\right) \]

      2. lower-/.f64 N/A

         \[\leadsto \sin^{-1} \left(\frac{\ell \cdot \sqrt{\frac{1}{2}}}{t} \cdot \sqrt{\color{blue}{1 - \frac{{Om}^{2}}{{Omc}^{2}}}}\right) \]

      3. *-commutative N/A

         \[\leadsto \sin^{-1} \left(\frac{\sqrt{\frac{1}{2}} \cdot \ell}{t} \cdot \sqrt{\color{blue}{1} - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\frac{\sqrt{\frac{1}{2}} \cdot \ell}{t} \cdot \sqrt{\color{blue}{1} - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \]

      5. pow1/2 N/A

         \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \]

      6. lower-pow.f64 N/A

         \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \]

      7. pow1/2 N/A

         \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot {\left(1 - \frac{{Om}^{2}}{{Omc}^{2}}\right)}^{\color{blue}{\frac{1}{2}}}\right) \]

      8. lower-pow.f64 N/A

         \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot {\left(1 - \frac{{Om}^{2}}{{Omc}^{2}}\right)}^{\color{blue}{\frac{1}{2}}}\right) \]

      9. lower--.f64 N/A

         \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot {\left(1 - \frac{{Om}^{2}}{{Omc}^{2}}\right)}^{\frac{1}{2}}\right) \]

      10. lower-/.f64 N/A

          \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot {\left(1 - \frac{{Om}^{2}}{{Omc}^{2}}\right)}^{\frac{1}{2}}\right) \]

      11. unpow2 N/A

          \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot {\left(1 - \frac{Om \cdot Om}{{Omc}^{2}}\right)}^{\frac{1}{2}}\right) \]

      12. lower-*.f64 N/A

          \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot {\left(1 - \frac{Om \cdot Om}{{Omc}^{2}}\right)}^{\frac{1}{2}}\right) \]

      13. unpow2 N/A

          \[\leadsto \sin^{-1} \left(\frac{{\frac{1}{2}}^{\frac{1}{2}} \cdot \ell}{t} \cdot {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{\frac{1}{2}}\right) \]

      14. lower-*.f64 56.6

          \[\leadsto \sin^{-1} \left(\frac{{0.5}^{0.5} \cdot \ell}{t} \cdot {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{0.5}\right) \]

   5. Applied rewrites 56.6%

      \[\leadsto \sin^{-1} \color{blue}{\left(\frac{{0.5}^{0.5} \cdot \ell}{t} \cdot {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{0.5}\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 72.4%

      \[\leadsto \sin^{-1} \left(\frac{{0.5}^{0.5} \cdot \ell}{t} \cdot {\left(\frac{1 - {\left({\left(\frac{Om}{Omc}\right)}^{2}\right)}^{2}}{1 + {\left(\frac{Om}{Omc}\right)}^{2}}\right)}^{\color{blue}{0.5}}\right) \]

   if 0.0 < (asin.f64 (sqrt.f64 (/.f64 (-.f64 #s(literal 1 binary64) (pow.f64 (/.f64 Om Omc) #s(literal 2 binary64))) (+.f64 #s(literal 1 binary64) (*.f64 #s(literal 2 binary64) (pow.f64 (/.f64 t l) #s(literal 2 binary64)))))))

   1. Initial program 98.3%

      \[\sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]
   2. Add Preprocessing
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 92.7% accurate, N/A× speedup

Math

\[\begin{array}{l} t_m = \left|t\right| \\ l_m = \left|\ell\right| \\ \begin{array}{l} t_1 := {\left(\frac{Om}{Omc}\right)}^{2}\\ \mathbf{if}\;1 + 2 \cdot {\left(\frac{t\_m}{l\_m}\right)}^{2} \leq 2000:\\ \;\;\;\;\sin^{-1} \left(\sqrt{\frac{1 - t\_1}{1 + 2 \cdot \frac{t\_m \cdot t\_m}{l\_m \cdot l\_m}}}\right)\\ \mathbf{else}:\\ \;\;\;\;\sin^{-1} \left(\left(\frac{{0.5}^{0.5}}{t\_m} \cdot {\left(\frac{1 - {t\_1}^{2}}{1 + t\_1}\right)}^{0.5}\right) \cdot l\_m\right)\\ \end{array} \end{array} \]

FPCore

t_m = (fabs.f64 t)
l_m = (fabs.f64 l)
(FPCore (t_m l_m Om Omc)
 :precision binary64
 (let* ((t_1 (pow (/ Om Omc) 2.0)))
   (if (<= (+ 1.0 (* 2.0 (pow (/ t_m l_m) 2.0))) 2000.0)
     (asin (sqrt (/ (- 1.0 t_1) (+ 1.0 (* 2.0 (/ (* t_m t_m) (* l_m l_m)))))))
     (asin
      (*
       (*
        (/ (pow 0.5 0.5) t_m)
        (pow (/ (- 1.0 (pow t_1 2.0)) (+ 1.0 t_1)) 0.5))
       l_m)))))

C

t_m = fabs(t);
l_m = fabs(l);
double code(double t_m, double l_m, double Om, double Omc) {
	double t_1 = pow((Om / Omc), 2.0);
	double tmp;
	if ((1.0 + (2.0 * pow((t_m / l_m), 2.0))) <= 2000.0) {
		tmp = asin(sqrt(((1.0 - t_1) / (1.0 + (2.0 * ((t_m * t_m) / (l_m * l_m)))))));
	} else {
		tmp = asin((((pow(0.5, 0.5) / t_m) * pow(((1.0 - pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)) * l_m));
	}
	return tmp;
}

Fortran

t_m =     private
l_m =     private
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(t_m, l_m, om, omc)
use fmin_fmax_functions
    real(8), intent (in) :: t_m
    real(8), intent (in) :: l_m
    real(8), intent (in) :: om
    real(8), intent (in) :: omc
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (om / omc) ** 2.0d0
    if ((1.0d0 + (2.0d0 * ((t_m / l_m) ** 2.0d0))) <= 2000.0d0) then
        tmp = asin(sqrt(((1.0d0 - t_1) / (1.0d0 + (2.0d0 * ((t_m * t_m) / (l_m * l_m)))))))
    else
        tmp = asin(((((0.5d0 ** 0.5d0) / t_m) * (((1.0d0 - (t_1 ** 2.0d0)) / (1.0d0 + t_1)) ** 0.5d0)) * l_m))
    end if
    code = tmp
end function

Java

t_m = Math.abs(t);
l_m = Math.abs(l);
public static double code(double t_m, double l_m, double Om, double Omc) {
	double t_1 = Math.pow((Om / Omc), 2.0);
	double tmp;
	if ((1.0 + (2.0 * Math.pow((t_m / l_m), 2.0))) <= 2000.0) {
		tmp = Math.asin(Math.sqrt(((1.0 - t_1) / (1.0 + (2.0 * ((t_m * t_m) / (l_m * l_m)))))));
	} else {
		tmp = Math.asin((((Math.pow(0.5, 0.5) / t_m) * Math.pow(((1.0 - Math.pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)) * l_m));
	}
	return tmp;
}

Python

t_m = math.fabs(t)
l_m = math.fabs(l)
def code(t_m, l_m, Om, Omc):
	t_1 = math.pow((Om / Omc), 2.0)
	tmp = 0
	if (1.0 + (2.0 * math.pow((t_m / l_m), 2.0))) <= 2000.0:
		tmp = math.asin(math.sqrt(((1.0 - t_1) / (1.0 + (2.0 * ((t_m * t_m) / (l_m * l_m)))))))
	else:
		tmp = math.asin((((math.pow(0.5, 0.5) / t_m) * math.pow(((1.0 - math.pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)) * l_m))
	return tmp

Julia

t_m = abs(t)
l_m = abs(l)
function code(t_m, l_m, Om, Omc)
	t_1 = Float64(Om / Omc) ^ 2.0
	tmp = 0.0
	if (Float64(1.0 + Float64(2.0 * (Float64(t_m / l_m) ^ 2.0))) <= 2000.0)
		tmp = asin(sqrt(Float64(Float64(1.0 - t_1) / Float64(1.0 + Float64(2.0 * Float64(Float64(t_m * t_m) / Float64(l_m * l_m)))))));
	else
		tmp = asin(Float64(Float64(Float64((0.5 ^ 0.5) / t_m) * (Float64(Float64(1.0 - (t_1 ^ 2.0)) / Float64(1.0 + t_1)) ^ 0.5)) * l_m));
	end
	return tmp
end

MATLAB

t_m = abs(t);
l_m = abs(l);
function tmp_2 = code(t_m, l_m, Om, Omc)
	t_1 = (Om / Omc) ^ 2.0;
	tmp = 0.0;
	if ((1.0 + (2.0 * ((t_m / l_m) ^ 2.0))) <= 2000.0)
		tmp = asin(sqrt(((1.0 - t_1) / (1.0 + (2.0 * ((t_m * t_m) / (l_m * l_m)))))));
	else
		tmp = asin(((((0.5 ^ 0.5) / t_m) * (((1.0 - (t_1 ^ 2.0)) / (1.0 + t_1)) ^ 0.5)) * l_m));
	end
	tmp_2 = tmp;
end

Wolfram

t_m = N[Abs[t], $MachinePrecision]
l_m = N[Abs[l], $MachinePrecision]
code[t$95$m_, l$95$m_, Om_, Omc_] := Block[{t$95$1 = N[Power[N[(Om / Omc), $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[N[(1.0 + N[(2.0 * N[Power[N[(t$95$m / l$95$m), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 2000.0], N[ArcSin[N[Sqrt[N[(N[(1.0 - t$95$1), $MachinePrecision] / N[(1.0 + N[(2.0 * N[(N[(t$95$m * t$95$m), $MachinePrecision] / N[(l$95$m * l$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision], N[ArcSin[N[(N[(N[(N[Power[0.5, 0.5], $MachinePrecision] / t$95$m), $MachinePrecision] * N[Power[N[(N[(1.0 - N[Power[t$95$1, 2.0], $MachinePrecision]), $MachinePrecision] / N[(1.0 + t$95$1), $MachinePrecision]), $MachinePrecision], 0.5], $MachinePrecision]), $MachinePrecision] * l$95$m), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 #s(literal 1 binary64) (*.f64 #s(literal 2 binary64) (pow.f64 (/.f64 t l) #s(literal 2 binary64)))) < 2e3

   1. Initial program 98.0%

      \[\sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0

      \[\leadsto \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot \color{blue}{\frac{{t}^{2}}{{\ell}^{2}}}}}\right) \]
   4. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot \frac{{t}^{2}}{\color{blue}{{\ell}^{2}}}}}\right) \]

      2. unpow2 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot \frac{t \cdot t}{{\color{blue}{\ell}}^{2}}}}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot \frac{t \cdot t}{{\color{blue}{\ell}}^{2}}}}\right) \]

      4. unpow2 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot \frac{t \cdot t}{\ell \cdot \color{blue}{\ell}}}}\right) \]

      5. lower-*.f64 87.9

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot \frac{t \cdot t}{\ell \cdot \color{blue}{\ell}}}}\right) \]

   5. Applied rewrites 87.9%

      \[\leadsto \sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot \color{blue}{\frac{t \cdot t}{\ell \cdot \ell}}}}\right) \]

   if 2e3 < (+.f64 #s(literal 1 binary64) (*.f64 #s(literal 2 binary64) (pow.f64 (/.f64 t l) #s(literal 2 binary64))))

   1. Initial program 63.6%

      \[\sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in l around 0

      \[\leadsto \sin^{-1} \color{blue}{\left(\ell \cdot \left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \sin^{-1} \left(\left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \color{blue}{\ell}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \color{blue}{\ell}\right) \]

   5. Applied rewrites 44.3%

      \[\leadsto \sin^{-1} \color{blue}{\left(\mathsf{fma}\left(-0.125 \cdot \frac{\ell \cdot \ell}{\left(\left(t \cdot t\right) \cdot t\right) \cdot {0.5}^{0.5}}, {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{0.5}, \frac{{0.5}^{0.5}}{t} \cdot {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{0.5}\right) \cdot \ell\right)} \]

   6. Taylor expanded in t around inf

      \[\leadsto \sin^{-1} \left(\left(\frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \ell\right) \]

   8. Applied rewrites 62.4%

      \[\leadsto \sin^{-1} \left(\left(\frac{{0.5}^{0.5}}{t} \cdot {\left(\frac{1 - {\left({\left(\frac{Om}{Omc}\right)}^{2}\right)}^{2}}{1 + {\left(\frac{Om}{Omc}\right)}^{2}}\right)}^{0.5}\right) \cdot \ell\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 73.5% accurate, N/A× speedup

Math

\[\begin{array}{l} t_m = \left|t\right| \\ l_m = \left|\ell\right| \\ \begin{array}{l} t_1 := 1 + 2 \cdot {\left(\frac{t\_m}{l\_m}\right)}^{2}\\ t_2 := {\left(\frac{Om}{Omc}\right)}^{2}\\ \mathbf{if}\;t\_1 \leq 200000000000:\\ \;\;\;\;\sin^{-1} \left(\sqrt{\left(Om \cdot Om\right) \cdot \left({\left(\left(Om \cdot Om\right) \cdot t\_1\right)}^{-1} - {\left(\left(Omc \cdot Omc\right) \cdot t\_1\right)}^{-1}\right)}\right)\\ \mathbf{else}:\\ \;\;\;\;\sin^{-1} \left(\left(\frac{{0.5}^{0.5}}{t\_m} \cdot {\left(\frac{1 - {t\_2}^{2}}{1 + t\_2}\right)}^{0.5}\right) \cdot l\_m\right)\\ \end{array} \end{array} \]

FPCore

t_m = (fabs.f64 t)
l_m = (fabs.f64 l)
(FPCore (t_m l_m Om Omc)
 :precision binary64
 (let* ((t_1 (+ 1.0 (* 2.0 (pow (/ t_m l_m) 2.0)))) (t_2 (pow (/ Om Omc) 2.0)))
   (if (<= t_1 200000000000.0)
     (asin
      (sqrt
       (*
        (* Om Om)
        (- (pow (* (* Om Om) t_1) -1.0) (pow (* (* Omc Omc) t_1) -1.0)))))
     (asin
      (*
       (*
        (/ (pow 0.5 0.5) t_m)
        (pow (/ (- 1.0 (pow t_2 2.0)) (+ 1.0 t_2)) 0.5))
       l_m)))))

C

t_m = fabs(t);
l_m = fabs(l);
double code(double t_m, double l_m, double Om, double Omc) {
	double t_1 = 1.0 + (2.0 * pow((t_m / l_m), 2.0));
	double t_2 = pow((Om / Omc), 2.0);
	double tmp;
	if (t_1 <= 200000000000.0) {
		tmp = asin(sqrt(((Om * Om) * (pow(((Om * Om) * t_1), -1.0) - pow(((Omc * Omc) * t_1), -1.0)))));
	} else {
		tmp = asin((((pow(0.5, 0.5) / t_m) * pow(((1.0 - pow(t_2, 2.0)) / (1.0 + t_2)), 0.5)) * l_m));
	}
	return tmp;
}

Fortran

t_m =     private
l_m =     private
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(t_m, l_m, om, omc)
use fmin_fmax_functions
    real(8), intent (in) :: t_m
    real(8), intent (in) :: l_m
    real(8), intent (in) :: om
    real(8), intent (in) :: omc
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = 1.0d0 + (2.0d0 * ((t_m / l_m) ** 2.0d0))
    t_2 = (om / omc) ** 2.0d0
    if (t_1 <= 200000000000.0d0) then
        tmp = asin(sqrt(((om * om) * ((((om * om) * t_1) ** (-1.0d0)) - (((omc * omc) * t_1) ** (-1.0d0))))))
    else
        tmp = asin(((((0.5d0 ** 0.5d0) / t_m) * (((1.0d0 - (t_2 ** 2.0d0)) / (1.0d0 + t_2)) ** 0.5d0)) * l_m))
    end if
    code = tmp
end function

Java

t_m = Math.abs(t);
l_m = Math.abs(l);
public static double code(double t_m, double l_m, double Om, double Omc) {
	double t_1 = 1.0 + (2.0 * Math.pow((t_m / l_m), 2.0));
	double t_2 = Math.pow((Om / Omc), 2.0);
	double tmp;
	if (t_1 <= 200000000000.0) {
		tmp = Math.asin(Math.sqrt(((Om * Om) * (Math.pow(((Om * Om) * t_1), -1.0) - Math.pow(((Omc * Omc) * t_1), -1.0)))));
	} else {
		tmp = Math.asin((((Math.pow(0.5, 0.5) / t_m) * Math.pow(((1.0 - Math.pow(t_2, 2.0)) / (1.0 + t_2)), 0.5)) * l_m));
	}
	return tmp;
}

Python

t_m = math.fabs(t)
l_m = math.fabs(l)
def code(t_m, l_m, Om, Omc):
	t_1 = 1.0 + (2.0 * math.pow((t_m / l_m), 2.0))
	t_2 = math.pow((Om / Omc), 2.0)
	tmp = 0
	if t_1 <= 200000000000.0:
		tmp = math.asin(math.sqrt(((Om * Om) * (math.pow(((Om * Om) * t_1), -1.0) - math.pow(((Omc * Omc) * t_1), -1.0)))))
	else:
		tmp = math.asin((((math.pow(0.5, 0.5) / t_m) * math.pow(((1.0 - math.pow(t_2, 2.0)) / (1.0 + t_2)), 0.5)) * l_m))
	return tmp

Julia

t_m = abs(t)
l_m = abs(l)
function code(t_m, l_m, Om, Omc)
	t_1 = Float64(1.0 + Float64(2.0 * (Float64(t_m / l_m) ^ 2.0)))
	t_2 = Float64(Om / Omc) ^ 2.0
	tmp = 0.0
	if (t_1 <= 200000000000.0)
		tmp = asin(sqrt(Float64(Float64(Om * Om) * Float64((Float64(Float64(Om * Om) * t_1) ^ -1.0) - (Float64(Float64(Omc * Omc) * t_1) ^ -1.0)))));
	else
		tmp = asin(Float64(Float64(Float64((0.5 ^ 0.5) / t_m) * (Float64(Float64(1.0 - (t_2 ^ 2.0)) / Float64(1.0 + t_2)) ^ 0.5)) * l_m));
	end
	return tmp
end

MATLAB

t_m = abs(t);
l_m = abs(l);
function tmp_2 = code(t_m, l_m, Om, Omc)
	t_1 = 1.0 + (2.0 * ((t_m / l_m) ^ 2.0));
	t_2 = (Om / Omc) ^ 2.0;
	tmp = 0.0;
	if (t_1 <= 200000000000.0)
		tmp = asin(sqrt(((Om * Om) * ((((Om * Om) * t_1) ^ -1.0) - (((Omc * Omc) * t_1) ^ -1.0)))));
	else
		tmp = asin(((((0.5 ^ 0.5) / t_m) * (((1.0 - (t_2 ^ 2.0)) / (1.0 + t_2)) ^ 0.5)) * l_m));
	end
	tmp_2 = tmp;
end

Wolfram

t_m = N[Abs[t], $MachinePrecision]
l_m = N[Abs[l], $MachinePrecision]
code[t$95$m_, l$95$m_, Om_, Omc_] := Block[{t$95$1 = N[(1.0 + N[(2.0 * N[Power[N[(t$95$m / l$95$m), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[Power[N[(Om / Omc), $MachinePrecision], 2.0], $MachinePrecision]}, If[LessEqual[t$95$1, 200000000000.0], N[ArcSin[N[Sqrt[N[(N[(Om * Om), $MachinePrecision] * N[(N[Power[N[(N[(Om * Om), $MachinePrecision] * t$95$1), $MachinePrecision], -1.0], $MachinePrecision] - N[Power[N[(N[(Omc * Omc), $MachinePrecision] * t$95$1), $MachinePrecision], -1.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision], N[ArcSin[N[(N[(N[(N[Power[0.5, 0.5], $MachinePrecision] / t$95$m), $MachinePrecision] * N[Power[N[(N[(1.0 - N[Power[t$95$2, 2.0], $MachinePrecision]), $MachinePrecision] / N[(1.0 + t$95$2), $MachinePrecision]), $MachinePrecision], 0.5], $MachinePrecision]), $MachinePrecision] * l$95$m), $MachinePrecision]], $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 #s(literal 1 binary64) (*.f64 #s(literal 2 binary64) (pow.f64 (/.f64 t l) #s(literal 2 binary64)))) < 2e11

   1. Initial program 98.0%

      \[\sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in Om around inf

      \[\leadsto \sin^{-1} \left(\sqrt{\frac{\color{blue}{{Om}^{2} \cdot \left(\frac{1}{{Om}^{2}} - \frac{1}{{Omc}^{2}}\right)}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left(\frac{1}{{Om}^{2}} - \frac{1}{{Omc}^{2}}\right) \cdot \color{blue}{{Om}^{2}}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left(\frac{1}{{Om}^{2}} - \frac{1}{{Omc}^{2}}\right) \cdot \color{blue}{{Om}^{2}}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      3. lower--.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left(\frac{1}{{Om}^{2}} - \frac{1}{{Omc}^{2}}\right) \cdot {\color{blue}{Om}}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      4. inv-pow N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left({Om}^{2}\right)}^{-1} - \frac{1}{{Omc}^{2}}\right) \cdot {Om}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      5. lower-pow.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left({Om}^{2}\right)}^{-1} - \frac{1}{{Omc}^{2}}\right) \cdot {Om}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      6. unpow2 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left(Om \cdot Om\right)}^{-1} - \frac{1}{{Omc}^{2}}\right) \cdot {Om}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      7. lower-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left(Om \cdot Om\right)}^{-1} - \frac{1}{{Omc}^{2}}\right) \cdot {Om}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      8. inv-pow N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left(Om \cdot Om\right)}^{-1} - {\left({Omc}^{2}\right)}^{-1}\right) \cdot {Om}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      9. lower-pow.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left(Om \cdot Om\right)}^{-1} - {\left({Omc}^{2}\right)}^{-1}\right) \cdot {Om}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      10. unpow2 N/A

          \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left(Om \cdot Om\right)}^{-1} - {\left(Omc \cdot Omc\right)}^{-1}\right) \cdot {Om}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      11. lower-*.f64 N/A

          \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left(Om \cdot Om\right)}^{-1} - {\left(Omc \cdot Omc\right)}^{-1}\right) \cdot {Om}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      12. unpow2 N/A

          \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left(Om \cdot Om\right)}^{-1} - {\left(Omc \cdot Omc\right)}^{-1}\right) \cdot \left(Om \cdot \color{blue}{Om}\right)}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

      13. lower-*.f64 41.6

          \[\leadsto \sin^{-1} \left(\sqrt{\frac{\left({\left(Om \cdot Om\right)}^{-1} - {\left(Omc \cdot Omc\right)}^{-1}\right) \cdot \left(Om \cdot \color{blue}{Om}\right)}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

   5. Applied rewrites 41.6%

      \[\leadsto \sin^{-1} \left(\sqrt{\frac{\color{blue}{\left({\left(Om \cdot Om\right)}^{-1} - {\left(Omc \cdot Omc\right)}^{-1}\right) \cdot \left(Om \cdot Om\right)}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]

   6. Taylor expanded in Om around inf

      \[\leadsto \sin^{-1} \left(\sqrt{\color{blue}{{Om}^{2} \cdot \left(\frac{1}{{Om}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)} - \frac{1}{{Omc}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)}\right)}}\right) \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{{Om}^{2} \cdot \color{blue}{\left(\frac{1}{{Om}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)} - \frac{1}{{Omc}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)}\right)}}\right) \]

      2. pow2 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\left(Om \cdot Om\right) \cdot \left(\color{blue}{\frac{1}{{Om}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)}} - \frac{1}{{Omc}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)}\right)}\right) \]

      3. lift-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\left(Om \cdot Om\right) \cdot \left(\color{blue}{\frac{1}{{Om}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)}} - \frac{1}{{Omc}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)}\right)}\right) \]

      4. lower--.f64 N/A

         \[\leadsto \sin^{-1} \left(\sqrt{\left(Om \cdot Om\right) \cdot \left(\frac{1}{{Om}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)} - \color{blue}{\frac{1}{{Omc}^{2} \cdot \left(1 + 2 \cdot \frac{{t}^{2}}{{\ell}^{2}}\right)}}\right)}\right) \]

   8. Applied rewrites 41.6%

      \[\leadsto \sin^{-1} \left(\sqrt{\color{blue}{\left(Om \cdot Om\right) \cdot \left({\left(\left(Om \cdot Om\right) \cdot \left(1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}\right)\right)}^{-1} - {\left(\left(Omc \cdot Omc\right) \cdot \left(1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}\right)\right)}^{-1}\right)}}\right) \]

   if 2e11 < (+.f64 #s(literal 1 binary64) (*.f64 #s(literal 2 binary64) (pow.f64 (/.f64 t l) #s(literal 2 binary64))))

   1. Initial program 62.7%

      \[\sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in l around 0

      \[\leadsto \sin^{-1} \color{blue}{\left(\ell \cdot \left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \sin^{-1} \left(\left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \color{blue}{\ell}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \sin^{-1} \left(\left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \color{blue}{\ell}\right) \]

   5. Applied rewrites 45.5%

      \[\leadsto \sin^{-1} \color{blue}{\left(\mathsf{fma}\left(-0.125 \cdot \frac{\ell \cdot \ell}{\left(\left(t \cdot t\right) \cdot t\right) \cdot {0.5}^{0.5}}, {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{0.5}, \frac{{0.5}^{0.5}}{t} \cdot {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{0.5}\right) \cdot \ell\right)} \]

   6. Taylor expanded in t around inf

      \[\leadsto \sin^{-1} \left(\left(\frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \ell\right) \]

   8. Applied rewrites 62.3%

      \[\leadsto \sin^{-1} \left(\left(\frac{{0.5}^{0.5}}{t} \cdot {\left(\frac{1 - {\left({\left(\frac{Om}{Omc}\right)}^{2}\right)}^{2}}{1 + {\left(\frac{Om}{Omc}\right)}^{2}}\right)}^{0.5}\right) \cdot \ell\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 49.5% accurate, N/A× speedup

Math

\[\begin{array}{l} t_m = \left|t\right| \\ l_m = \left|\ell\right| \\ \begin{array}{l} t_1 := {\left(\frac{Om}{Omc}\right)}^{2}\\ \sin^{-1} \left(\left(\frac{{0.5}^{0.5}}{t\_m} \cdot {\left(\frac{1 - {t\_1}^{2}}{1 + t\_1}\right)}^{0.5}\right) \cdot l\_m\right) \end{array} \end{array} \]

FPCore

t_m = (fabs.f64 t)
l_m = (fabs.f64 l)
(FPCore (t_m l_m Om Omc)
 :precision binary64
 (let* ((t_1 (pow (/ Om Omc) 2.0)))
   (asin
    (*
     (* (/ (pow 0.5 0.5) t_m) (pow (/ (- 1.0 (pow t_1 2.0)) (+ 1.0 t_1)) 0.5))
     l_m))))

C

t_m = fabs(t);
l_m = fabs(l);
double code(double t_m, double l_m, double Om, double Omc) {
	double t_1 = pow((Om / Omc), 2.0);
	return asin((((pow(0.5, 0.5) / t_m) * pow(((1.0 - pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)) * l_m));
}

Fortran

t_m =     private
l_m =     private
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(t_m, l_m, om, omc)
use fmin_fmax_functions
    real(8), intent (in) :: t_m
    real(8), intent (in) :: l_m
    real(8), intent (in) :: om
    real(8), intent (in) :: omc
    real(8) :: t_1
    t_1 = (om / omc) ** 2.0d0
    code = asin(((((0.5d0 ** 0.5d0) / t_m) * (((1.0d0 - (t_1 ** 2.0d0)) / (1.0d0 + t_1)) ** 0.5d0)) * l_m))
end function

Java

t_m = Math.abs(t);
l_m = Math.abs(l);
public static double code(double t_m, double l_m, double Om, double Omc) {
	double t_1 = Math.pow((Om / Omc), 2.0);
	return Math.asin((((Math.pow(0.5, 0.5) / t_m) * Math.pow(((1.0 - Math.pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)) * l_m));
}

Python

t_m = math.fabs(t)
l_m = math.fabs(l)
def code(t_m, l_m, Om, Omc):
	t_1 = math.pow((Om / Omc), 2.0)
	return math.asin((((math.pow(0.5, 0.5) / t_m) * math.pow(((1.0 - math.pow(t_1, 2.0)) / (1.0 + t_1)), 0.5)) * l_m))

Julia

t_m = abs(t)
l_m = abs(l)
function code(t_m, l_m, Om, Omc)
	t_1 = Float64(Om / Omc) ^ 2.0
	return asin(Float64(Float64(Float64((0.5 ^ 0.5) / t_m) * (Float64(Float64(1.0 - (t_1 ^ 2.0)) / Float64(1.0 + t_1)) ^ 0.5)) * l_m))
end

MATLAB

t_m = abs(t);
l_m = abs(l);
function tmp = code(t_m, l_m, Om, Omc)
	t_1 = (Om / Omc) ^ 2.0;
	tmp = asin(((((0.5 ^ 0.5) / t_m) * (((1.0 - (t_1 ^ 2.0)) / (1.0 + t_1)) ^ 0.5)) * l_m));
end

Wolfram

t_m = N[Abs[t], $MachinePrecision]
l_m = N[Abs[l], $MachinePrecision]
code[t$95$m_, l$95$m_, Om_, Omc_] := Block[{t$95$1 = N[Power[N[(Om / Omc), $MachinePrecision], 2.0], $MachinePrecision]}, N[ArcSin[N[(N[(N[(N[Power[0.5, 0.5], $MachinePrecision] / t$95$m), $MachinePrecision] * N[Power[N[(N[(1.0 - N[Power[t$95$1, 2.0], $MachinePrecision]), $MachinePrecision] / N[(1.0 + t$95$1), $MachinePrecision]), $MachinePrecision], 0.5], $MachinePrecision]), $MachinePrecision] * l$95$m), $MachinePrecision]], $MachinePrecision]]

Derivation

1. Initial program 82.3%

   \[\sin^{-1} \left(\sqrt{\frac{1 - {\left(\frac{Om}{Omc}\right)}^{2}}{1 + 2 \cdot {\left(\frac{t}{\ell}\right)}^{2}}}\right) \]
2. Add Preprocessing

3. Taylor expanded in l around 0

   \[\leadsto \sin^{-1} \color{blue}{\left(\ell \cdot \left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right)\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \sin^{-1} \left(\left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \color{blue}{\ell}\right) \]

   2. lower-*.f64 N/A

      \[\leadsto \sin^{-1} \left(\left(\frac{-1}{8} \cdot \left(\frac{{\ell}^{2}}{{t}^{3} \cdot \sqrt{\frac{1}{2}}} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) + \frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \color{blue}{\ell}\right) \]

5. Applied rewrites 20.5%

   \[\leadsto \sin^{-1} \color{blue}{\left(\mathsf{fma}\left(-0.125 \cdot \frac{\ell \cdot \ell}{\left(\left(t \cdot t\right) \cdot t\right) \cdot {0.5}^{0.5}}, {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{0.5}, \frac{{0.5}^{0.5}}{t} \cdot {\left(1 - \frac{Om \cdot Om}{Omc \cdot Omc}\right)}^{0.5}\right) \cdot \ell\right)} \]

6. Taylor expanded in t around inf

   \[\leadsto \sin^{-1} \left(\left(\frac{\sqrt{\frac{1}{2}}}{t} \cdot \sqrt{1 - \frac{{Om}^{2}}{{Omc}^{2}}}\right) \cdot \ell\right) \]

8. Applied rewrites 28.6%

   \[\leadsto \sin^{-1} \left(\left(\frac{{0.5}^{0.5}}{t} \cdot {\left(\frac{1 - {\left({\left(\frac{Om}{Omc}\right)}^{2}\right)}^{2}}{1 + {\left(\frac{Om}{Omc}\right)}^{2}}\right)}^{0.5}\right) \cdot \ell\right) \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2025066 
(FPCore (t l Om Omc)
  :name "Toniolo and Linder, Equation (2)"
  :precision binary64
  (asin (sqrt (/ (- 1.0 (pow (/ Om Omc) 2.0)) (+ 1.0 (* 2.0 (pow (/ t l) 2.0)))))))