Example 2 from Robby

Percentage Accurate: 99.8% → 99.8%

Time: 11.6s

Alternatives: 11

Speedup: N/A×

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\\ \left|\left(ew \cdot \cos t\right) \cdot \cos t\_1 - \left(eh \cdot \sin t\right) \cdot \sin t\_1\right| \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (atan (/ (* (- eh) (tan t)) ew))))
   (fabs (- (* (* ew (cos t)) (cos t_1)) (* (* eh (sin t)) (sin t_1))))))

C

double code(double eh, double ew, double t) {
	double t_1 = atan(((-eh * tan(t)) / ew));
	return fabs((((ew * cos(t)) * cos(t_1)) - ((eh * sin(t)) * sin(t_1))));
}

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(eh, ew, t)
use fmin_fmax_functions
    real(8), intent (in) :: eh
    real(8), intent (in) :: ew
    real(8), intent (in) :: t
    real(8) :: t_1
    t_1 = atan(((-eh * tan(t)) / ew))
    code = abs((((ew * cos(t)) * cos(t_1)) - ((eh * sin(t)) * sin(t_1))))
end function

Java

public static double code(double eh, double ew, double t) {
	double t_1 = Math.atan(((-eh * Math.tan(t)) / ew));
	return Math.abs((((ew * Math.cos(t)) * Math.cos(t_1)) - ((eh * Math.sin(t)) * Math.sin(t_1))));
}

Python

def code(eh, ew, t):
	t_1 = math.atan(((-eh * math.tan(t)) / ew))
	return math.fabs((((ew * math.cos(t)) * math.cos(t_1)) - ((eh * math.sin(t)) * math.sin(t_1))))

Julia

function code(eh, ew, t)
	t_1 = atan(Float64(Float64(Float64(-eh) * tan(t)) / ew))
	return abs(Float64(Float64(Float64(ew * cos(t)) * cos(t_1)) - Float64(Float64(eh * sin(t)) * sin(t_1))))
end

MATLAB

function tmp = code(eh, ew, t)
	t_1 = atan(((-eh * tan(t)) / ew));
	tmp = abs((((ew * cos(t)) * cos(t_1)) - ((eh * sin(t)) * sin(t_1))));
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[ArcTan[N[(N[((-eh) * N[Tan[t], $MachinePrecision]), $MachinePrecision] / ew), $MachinePrecision]], $MachinePrecision]}, N[Abs[N[(N[(N[(ew * N[Cos[t], $MachinePrecision]), $MachinePrecision] * N[Cos[t$95$1], $MachinePrecision]), $MachinePrecision] - N[(N[(eh * N[Sin[t], $MachinePrecision]), $MachinePrecision] * N[Sin[t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\\ \left|\left(ew \cdot \cos t\right) \cdot \cos t\_1 - \left(eh \cdot \sin t\right) \cdot \sin t\_1\right| \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (atan (/ (* (- eh) (tan t)) ew))))
   (fabs (- (* (* ew (cos t)) (cos t_1)) (* (* eh (sin t)) (sin t_1))))))

C

double code(double eh, double ew, double t) {
	double t_1 = atan(((-eh * tan(t)) / ew));
	return fabs((((ew * cos(t)) * cos(t_1)) - ((eh * sin(t)) * sin(t_1))));
}

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(eh, ew, t)
use fmin_fmax_functions
    real(8), intent (in) :: eh
    real(8), intent (in) :: ew
    real(8), intent (in) :: t
    real(8) :: t_1
    t_1 = atan(((-eh * tan(t)) / ew))
    code = abs((((ew * cos(t)) * cos(t_1)) - ((eh * sin(t)) * sin(t_1))))
end function

Java

public static double code(double eh, double ew, double t) {
	double t_1 = Math.atan(((-eh * Math.tan(t)) / ew));
	return Math.abs((((ew * Math.cos(t)) * Math.cos(t_1)) - ((eh * Math.sin(t)) * Math.sin(t_1))));
}

Python

def code(eh, ew, t):
	t_1 = math.atan(((-eh * math.tan(t)) / ew))
	return math.fabs((((ew * math.cos(t)) * math.cos(t_1)) - ((eh * math.sin(t)) * math.sin(t_1))))

Julia

function code(eh, ew, t)
	t_1 = atan(Float64(Float64(Float64(-eh) * tan(t)) / ew))
	return abs(Float64(Float64(Float64(ew * cos(t)) * cos(t_1)) - Float64(Float64(eh * sin(t)) * sin(t_1))))
end

MATLAB

function tmp = code(eh, ew, t)
	t_1 = atan(((-eh * tan(t)) / ew));
	tmp = abs((((ew * cos(t)) * cos(t_1)) - ((eh * sin(t)) * sin(t_1))));
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[ArcTan[N[(N[((-eh) * N[Tan[t], $MachinePrecision]), $MachinePrecision] / ew), $MachinePrecision]], $MachinePrecision]}, N[Abs[N[(N[(N[(ew * N[Cos[t], $MachinePrecision]), $MachinePrecision] * N[Cos[t$95$1], $MachinePrecision]), $MachinePrecision] - N[(N[(eh * N[Sin[t], $MachinePrecision]), $MachinePrecision] * N[Sin[t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]

Alternative 1: 99.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\\ \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh t\_1}, \left(\sin t \cdot eh\right) \cdot \tanh t\_1\right)\right| \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (asinh (* (/ (tan t) ew) (- eh)))))
   (fabs (fma (cos t) (- (/ ew (cosh t_1))) (* (* (sin t) eh) (tanh t_1))))))

C

double code(double eh, double ew, double t) {
	double t_1 = asinh(((tan(t) / ew) * -eh));
	return fabs(fma(cos(t), -(ew / cosh(t_1)), ((sin(t) * eh) * tanh(t_1))));
}

Julia

function code(eh, ew, t)
	t_1 = asinh(Float64(Float64(tan(t) / ew) * Float64(-eh)))
	return abs(fma(cos(t), Float64(-Float64(ew / cosh(t_1))), Float64(Float64(sin(t) * eh) * tanh(t_1))))
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[ArcSinh[N[(N[(N[Tan[t], $MachinePrecision] / ew), $MachinePrecision] * (-eh)), $MachinePrecision]], $MachinePrecision]}, N[Abs[N[(N[Cos[t], $MachinePrecision] * (-N[(ew / N[Cosh[t$95$1], $MachinePrecision]), $MachinePrecision]) + N[(N[(N[Sin[t], $MachinePrecision] * eh), $MachinePrecision] * N[Tanh[t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]

Derivation

1. Initial program 99.8%

   \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
2. Step-by-step derivation

   1. lift-fabs.f64 N/A

      \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

   2. lift--.f64 N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. lift-*.f64 N/A

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   4. fp-cancel-sub-sign-inv N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   5. fp-cancel-sign-sub-inv N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

3. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

5. Applied rewrites 99.8%

   \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)}\right| \]
6. Add Preprocessing

Alternative 2: 90.6% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right)\\ t_2 := \frac{\tan t}{ew}\\ \mathbf{if}\;eh \leq 3.8 \cdot 10^{+63}:\\ \;\;\;\;\frac{\left|\mathsf{fma}\left(\cos t, ew, \left(t\_2 \cdot eh\right) \cdot \left(\sin t \cdot eh\right)\right)\right|}{\cosh \sinh^{-1} \left(t\_2 \cdot \left(-eh\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\left|\left(ew \cdot \cos t\right) \cdot \cos t\_1 - \left(eh \cdot \sin t\right) \cdot \sin t\_1\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (atan (/ (* -1.0 (* eh t)) ew))) (t_2 (/ (tan t) ew)))
   (if (<= eh 3.8e+63)
     (/
      (fabs (fma (cos t) ew (* (* t_2 eh) (* (sin t) eh))))
      (cosh (asinh (* t_2 (- eh)))))
     (fabs (- (* (* ew (cos t)) (cos t_1)) (* (* eh (sin t)) (sin t_1)))))))

C

double code(double eh, double ew, double t) {
	double t_1 = atan(((-1.0 * (eh * t)) / ew));
	double t_2 = tan(t) / ew;
	double tmp;
	if (eh <= 3.8e+63) {
		tmp = fabs(fma(cos(t), ew, ((t_2 * eh) * (sin(t) * eh)))) / cosh(asinh((t_2 * -eh)));
	} else {
		tmp = fabs((((ew * cos(t)) * cos(t_1)) - ((eh * sin(t)) * sin(t_1))));
	}
	return tmp;
}

Julia

function code(eh, ew, t)
	t_1 = atan(Float64(Float64(-1.0 * Float64(eh * t)) / ew))
	t_2 = Float64(tan(t) / ew)
	tmp = 0.0
	if (eh <= 3.8e+63)
		tmp = Float64(abs(fma(cos(t), ew, Float64(Float64(t_2 * eh) * Float64(sin(t) * eh)))) / cosh(asinh(Float64(t_2 * Float64(-eh)))));
	else
		tmp = abs(Float64(Float64(Float64(ew * cos(t)) * cos(t_1)) - Float64(Float64(eh * sin(t)) * sin(t_1))));
	end
	return tmp
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[ArcTan[N[(N[(-1.0 * N[(eh * t), $MachinePrecision]), $MachinePrecision] / ew), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[Tan[t], $MachinePrecision] / ew), $MachinePrecision]}, If[LessEqual[eh, 3.8e+63], N[(N[Abs[N[(N[Cos[t], $MachinePrecision] * ew + N[(N[(t$95$2 * eh), $MachinePrecision] * N[(N[Sin[t], $MachinePrecision] * eh), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[Cosh[N[ArcSinh[N[(t$95$2 * (-eh)), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[Abs[N[(N[(N[(ew * N[Cos[t], $MachinePrecision]), $MachinePrecision] * N[Cos[t$95$1], $MachinePrecision]), $MachinePrecision] - N[(N[(eh * N[Sin[t], $MachinePrecision]), $MachinePrecision] * N[Sin[t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if eh < 3.8000000000000001e63

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. lift-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      5. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   5. Applied rewrites 82.1%

      \[\leadsto \color{blue}{\frac{\left|\mathsf{fma}\left(\cos t, ew, \left(\frac{\tan t}{ew} \cdot eh\right) \cdot \left(\sin t \cdot eh\right)\right)\right|}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}} \]

   if 3.8000000000000001e63 < eh

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

   2. Taylor expanded in t around 0

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\color{blue}{-1 \cdot \left(eh \cdot t\right)}}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \color{blue}{\left(eh \cdot t\right)}}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

      2. lower-*.f64 90.4

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot \color{blue}{t}\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

   4. Applied rewrites 90.4%

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\color{blue}{-1 \cdot \left(eh \cdot t\right)}}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

   5. Taylor expanded in t around 0

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\color{blue}{-1 \cdot \left(eh \cdot t\right)}}{ew}\right)\right| \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{-1 \cdot \color{blue}{\left(eh \cdot t\right)}}{ew}\right)\right| \]

      2. lower-*.f64 90.4

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot \color{blue}{t}\right)}{ew}\right)\right| \]

   7. Applied rewrites 90.4%

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\color{blue}{-1 \cdot \left(eh \cdot t\right)}}{ew}\right)\right| \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 90.4% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right)\\ \mathbf{if}\;ew \leq 1.95 \cdot 10^{+94}:\\ \;\;\;\;\left|\left(ew \cdot \cos t\right) \cdot \cos t\_1 - \left(eh \cdot \sin t\right) \cdot \sin t\_1\right|\\ \mathbf{else}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(\cos t, ew, \left(\left(eh \cdot \frac{\tan t}{ew}\right) \cdot eh\right) \cdot \sin t\right)}{1}\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (atan (/ (* -1.0 (* eh t)) ew))))
   (if (<= ew 1.95e+94)
     (fabs (- (* (* ew (cos t)) (cos t_1)) (* (* eh (sin t)) (sin t_1))))
     (fabs
      (/ (fma (cos t) ew (* (* (* eh (/ (tan t) ew)) eh) (sin t))) 1.0)))))

C

double code(double eh, double ew, double t) {
	double t_1 = atan(((-1.0 * (eh * t)) / ew));
	double tmp;
	if (ew <= 1.95e+94) {
		tmp = fabs((((ew * cos(t)) * cos(t_1)) - ((eh * sin(t)) * sin(t_1))));
	} else {
		tmp = fabs((fma(cos(t), ew, (((eh * (tan(t) / ew)) * eh) * sin(t))) / 1.0));
	}
	return tmp;
}

Julia

function code(eh, ew, t)
	t_1 = atan(Float64(Float64(-1.0 * Float64(eh * t)) / ew))
	tmp = 0.0
	if (ew <= 1.95e+94)
		tmp = abs(Float64(Float64(Float64(ew * cos(t)) * cos(t_1)) - Float64(Float64(eh * sin(t)) * sin(t_1))));
	else
		tmp = abs(Float64(fma(cos(t), ew, Float64(Float64(Float64(eh * Float64(tan(t) / ew)) * eh) * sin(t))) / 1.0));
	end
	return tmp
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[ArcTan[N[(N[(-1.0 * N[(eh * t), $MachinePrecision]), $MachinePrecision] / ew), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[ew, 1.95e+94], N[Abs[N[(N[(N[(ew * N[Cos[t], $MachinePrecision]), $MachinePrecision] * N[Cos[t$95$1], $MachinePrecision]), $MachinePrecision] - N[(N[(eh * N[Sin[t], $MachinePrecision]), $MachinePrecision] * N[Sin[t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[Abs[N[(N[(N[Cos[t], $MachinePrecision] * ew + N[(N[(N[(eh * N[(N[Tan[t], $MachinePrecision] / ew), $MachinePrecision]), $MachinePrecision] * eh), $MachinePrecision] * N[Sin[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 1.0), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if ew < 1.94999999999999993e94

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

   2. Taylor expanded in t around 0

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\color{blue}{-1 \cdot \left(eh \cdot t\right)}}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   3. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \color{blue}{\left(eh \cdot t\right)}}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

      2. lower-*.f64 90.4

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot \color{blue}{t}\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

   4. Applied rewrites 90.4%

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\color{blue}{-1 \cdot \left(eh \cdot t\right)}}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

   5. Taylor expanded in t around 0

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\color{blue}{-1 \cdot \left(eh \cdot t\right)}}{ew}\right)\right| \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{-1 \cdot \color{blue}{\left(eh \cdot t\right)}}{ew}\right)\right| \]

      2. lower-*.f64 90.4

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot \color{blue}{t}\right)}{ew}\right)\right| \]

   7. Applied rewrites 90.4%

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{-1 \cdot \left(eh \cdot t\right)}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\color{blue}{-1 \cdot \left(eh \cdot t\right)}}{ew}\right)\right| \]

   if 1.94999999999999993e94 < ew

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. fabs-sub N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      4. lower-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

   3. Applied rewrites 82.1%

      \[\leadsto \color{blue}{\left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   4. Taylor expanded in eh around 0

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\color{blue}{1}}\right| \]
   5. Step-by-step derivation

   6. Applied rewrites 62.2%

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\color{blue}{1}}\right| \]

   8. Applied rewrites 62.1%

      \[\leadsto \color{blue}{\left|\frac{\mathsf{fma}\left(\cos t, ew, \left(\left(eh \cdot \frac{\tan t}{ew}\right) \cdot eh\right) \cdot \sin t\right)}{1}\right|} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 89.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \sinh^{-1} \left(\frac{t}{ew} \cdot \left(-eh\right)\right)\\ \mathbf{if}\;ew \leq 1.95 \cdot 10^{+94}:\\ \;\;\;\;\left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh t\_1}, \left(\sin t \cdot eh\right) \cdot \tanh t\_1\right)\right|\\ \mathbf{else}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(\cos t, ew, \left(\left(eh \cdot \frac{\tan t}{ew}\right) \cdot eh\right) \cdot \sin t\right)}{1}\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (asinh (* (/ t ew) (- eh)))))
   (if (<= ew 1.95e+94)
     (fabs (fma (cos t) (- (/ ew (cosh t_1))) (* (* (sin t) eh) (tanh t_1))))
     (fabs
      (/ (fma (cos t) ew (* (* (* eh (/ (tan t) ew)) eh) (sin t))) 1.0)))))

C

double code(double eh, double ew, double t) {
	double t_1 = asinh(((t / ew) * -eh));
	double tmp;
	if (ew <= 1.95e+94) {
		tmp = fabs(fma(cos(t), -(ew / cosh(t_1)), ((sin(t) * eh) * tanh(t_1))));
	} else {
		tmp = fabs((fma(cos(t), ew, (((eh * (tan(t) / ew)) * eh) * sin(t))) / 1.0));
	}
	return tmp;
}

Julia

function code(eh, ew, t)
	t_1 = asinh(Float64(Float64(t / ew) * Float64(-eh)))
	tmp = 0.0
	if (ew <= 1.95e+94)
		tmp = abs(fma(cos(t), Float64(-Float64(ew / cosh(t_1))), Float64(Float64(sin(t) * eh) * tanh(t_1))));
	else
		tmp = abs(Float64(fma(cos(t), ew, Float64(Float64(Float64(eh * Float64(tan(t) / ew)) * eh) * sin(t))) / 1.0));
	end
	return tmp
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[ArcSinh[N[(N[(t / ew), $MachinePrecision] * (-eh)), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[ew, 1.95e+94], N[Abs[N[(N[Cos[t], $MachinePrecision] * (-N[(ew / N[Cosh[t$95$1], $MachinePrecision]), $MachinePrecision]) + N[(N[(N[Sin[t], $MachinePrecision] * eh), $MachinePrecision] * N[Tanh[t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[Abs[N[(N[(N[Cos[t], $MachinePrecision] * ew + N[(N[(N[(eh * N[(N[Tan[t], $MachinePrecision] / ew), $MachinePrecision]), $MachinePrecision] * eh), $MachinePrecision] * N[Sin[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 1.0), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if ew < 1.94999999999999993e94

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. lift-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      5. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   5. Applied rewrites 99.8%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)}\right| \]

   6. Taylor expanded in t around 0

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\color{blue}{\frac{t}{ew}} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]
   7. Step-by-step derivation

      1. lower-/.f64 90.1

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{t}{\color{blue}{ew}} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

   8. Applied rewrites 90.1%

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\color{blue}{\frac{t}{ew}} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

   9. Taylor expanded in t around 0

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{t}{ew} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\color{blue}{\frac{t}{ew}} \cdot \left(-eh\right)\right)\right)\right| \]
   10. Step-by-step derivation

       1. lower-/.f64 90.2

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{t}{ew} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{t}{\color{blue}{ew}} \cdot \left(-eh\right)\right)\right)\right| \]

   11. Applied rewrites 90.2%

       \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{t}{ew} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\color{blue}{\frac{t}{ew}} \cdot \left(-eh\right)\right)\right)\right| \]

   if 1.94999999999999993e94 < ew

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. fabs-sub N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      4. lower-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

   3. Applied rewrites 82.1%

      \[\leadsto \color{blue}{\left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   4. Taylor expanded in eh around 0

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\color{blue}{1}}\right| \]
   5. Step-by-step derivation

   6. Applied rewrites 62.2%

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\color{blue}{1}}\right| \]

   8. Applied rewrites 62.1%

      \[\leadsto \color{blue}{\left|\frac{\mathsf{fma}\left(\cos t, ew, \left(\left(eh \cdot \frac{\tan t}{ew}\right) \cdot eh\right) \cdot \sin t\right)}{1}\right|} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 89.9% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -1 \cdot \frac{eh \cdot t}{ew}\\ \mathbf{if}\;ew \leq 2.8 \cdot 10^{+79}:\\ \;\;\;\;\left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh t\_1}, \left(\sin t \cdot eh\right) \cdot \tanh t\_1\right)\right|\\ \mathbf{else}:\\ \;\;\;\;\left|\frac{\mathsf{fma}\left(\cos t, ew, \left(\left(eh \cdot \frac{\tan t}{ew}\right) \cdot eh\right) \cdot \sin t\right)}{1}\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (* -1.0 (/ (* eh t) ew))))
   (if (<= ew 2.8e+79)
     (fabs (fma (cos t) (- (/ ew (cosh t_1))) (* (* (sin t) eh) (tanh t_1))))
     (fabs
      (/ (fma (cos t) ew (* (* (* eh (/ (tan t) ew)) eh) (sin t))) 1.0)))))

C

double code(double eh, double ew, double t) {
	double t_1 = -1.0 * ((eh * t) / ew);
	double tmp;
	if (ew <= 2.8e+79) {
		tmp = fabs(fma(cos(t), -(ew / cosh(t_1)), ((sin(t) * eh) * tanh(t_1))));
	} else {
		tmp = fabs((fma(cos(t), ew, (((eh * (tan(t) / ew)) * eh) * sin(t))) / 1.0));
	}
	return tmp;
}

Julia

function code(eh, ew, t)
	t_1 = Float64(-1.0 * Float64(Float64(eh * t) / ew))
	tmp = 0.0
	if (ew <= 2.8e+79)
		tmp = abs(fma(cos(t), Float64(-Float64(ew / cosh(t_1))), Float64(Float64(sin(t) * eh) * tanh(t_1))));
	else
		tmp = abs(Float64(fma(cos(t), ew, Float64(Float64(Float64(eh * Float64(tan(t) / ew)) * eh) * sin(t))) / 1.0));
	end
	return tmp
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[(-1.0 * N[(N[(eh * t), $MachinePrecision] / ew), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[ew, 2.8e+79], N[Abs[N[(N[Cos[t], $MachinePrecision] * (-N[(ew / N[Cosh[t$95$1], $MachinePrecision]), $MachinePrecision]) + N[(N[(N[Sin[t], $MachinePrecision] * eh), $MachinePrecision] * N[Tanh[t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[Abs[N[(N[(N[Cos[t], $MachinePrecision] * ew + N[(N[(N[(eh * N[(N[Tan[t], $MachinePrecision] / ew), $MachinePrecision]), $MachinePrecision] * eh), $MachinePrecision] * N[Sin[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 1.0), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if ew < 2.8000000000000001e79

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. lift-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      5. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   5. Applied rewrites 99.8%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)}\right| \]

   6. Taylor expanded in t around 0

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \color{blue}{\frac{eh \cdot t}{ew}}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

      2. lower-/.f64 N/A

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{\color{blue}{ew}}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

      3. lower-*.f64 89.6

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

   8. Applied rewrites 89.6%

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

   9. Taylor expanded in t around 0

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}\right)\right| \]
   10. Step-by-step derivation

       1. lower-*.f64 N/A

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \color{blue}{\frac{eh \cdot t}{ew}}\right)\right)\right| \]

       2. lower-/.f64 N/A

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \frac{eh \cdot t}{\color{blue}{ew}}\right)\right)\right| \]

       3. lower-*.f64 89.8

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)\right)\right| \]

   11. Applied rewrites 89.8%

       \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}\right)\right| \]

   if 2.8000000000000001e79 < ew

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. fabs-sub N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      4. lower-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

   3. Applied rewrites 82.1%

      \[\leadsto \color{blue}{\left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   4. Taylor expanded in eh around 0

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\color{blue}{1}}\right| \]
   5. Step-by-step derivation

   6. Applied rewrites 62.2%

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\color{blue}{1}}\right| \]

   8. Applied rewrites 62.1%

      \[\leadsto \color{blue}{\left|\frac{\mathsf{fma}\left(\cos t, ew, \left(\left(eh \cdot \frac{\tan t}{ew}\right) \cdot eh\right) \cdot \sin t\right)}{1}\right|} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 89.9% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \sin t \cdot eh\\ t_2 := -1 \cdot \frac{eh \cdot t}{ew}\\ \mathbf{if}\;ew \leq 2.8 \cdot 10^{+79}:\\ \;\;\;\;\left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh t\_2}, t\_1 \cdot \tanh t\_2\right)\right|\\ \mathbf{else}:\\ \;\;\;\;\left|\frac{t\_1 \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{1}\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (* (sin t) eh)) (t_2 (* -1.0 (/ (* eh t) ew))))
   (if (<= ew 2.8e+79)
     (fabs (fma (cos t) (- (/ ew (cosh t_2))) (* t_1 (tanh t_2))))
     (fabs (/ (- (* t_1 (* (/ (tan t) ew) (- eh))) (* (cos t) ew)) 1.0)))))

C

double code(double eh, double ew, double t) {
	double t_1 = sin(t) * eh;
	double t_2 = -1.0 * ((eh * t) / ew);
	double tmp;
	if (ew <= 2.8e+79) {
		tmp = fabs(fma(cos(t), -(ew / cosh(t_2)), (t_1 * tanh(t_2))));
	} else {
		tmp = fabs((((t_1 * ((tan(t) / ew) * -eh)) - (cos(t) * ew)) / 1.0));
	}
	return tmp;
}

Julia

function code(eh, ew, t)
	t_1 = Float64(sin(t) * eh)
	t_2 = Float64(-1.0 * Float64(Float64(eh * t) / ew))
	tmp = 0.0
	if (ew <= 2.8e+79)
		tmp = abs(fma(cos(t), Float64(-Float64(ew / cosh(t_2))), Float64(t_1 * tanh(t_2))));
	else
		tmp = abs(Float64(Float64(Float64(t_1 * Float64(Float64(tan(t) / ew) * Float64(-eh))) - Float64(cos(t) * ew)) / 1.0));
	end
	return tmp
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[(N[Sin[t], $MachinePrecision] * eh), $MachinePrecision]}, Block[{t$95$2 = N[(-1.0 * N[(N[(eh * t), $MachinePrecision] / ew), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[ew, 2.8e+79], N[Abs[N[(N[Cos[t], $MachinePrecision] * (-N[(ew / N[Cosh[t$95$2], $MachinePrecision]), $MachinePrecision]) + N[(t$95$1 * N[Tanh[t$95$2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[Abs[N[(N[(N[(t$95$1 * N[(N[(N[Tan[t], $MachinePrecision] / ew), $MachinePrecision] * (-eh)), $MachinePrecision]), $MachinePrecision] - N[(N[Cos[t], $MachinePrecision] * ew), $MachinePrecision]), $MachinePrecision] / 1.0), $MachinePrecision]], $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if ew < 2.8000000000000001e79

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. lift-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      5. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   5. Applied rewrites 99.8%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)}\right| \]

   6. Taylor expanded in t around 0

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \color{blue}{\frac{eh \cdot t}{ew}}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

      2. lower-/.f64 N/A

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{\color{blue}{ew}}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

      3. lower-*.f64 89.6

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

   8. Applied rewrites 89.6%

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

   9. Taylor expanded in t around 0

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}\right)\right| \]
   10. Step-by-step derivation

       1. lower-*.f64 N/A

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \color{blue}{\frac{eh \cdot t}{ew}}\right)\right)\right| \]

       2. lower-/.f64 N/A

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \frac{eh \cdot t}{\color{blue}{ew}}\right)\right)\right| \]

       3. lower-*.f64 89.8

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)\right)\right| \]

   11. Applied rewrites 89.8%

       \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}\right)\right| \]

   if 2.8000000000000001e79 < ew

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. fabs-sub N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      4. lower-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

   3. Applied rewrites 82.1%

      \[\leadsto \color{blue}{\left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   4. Taylor expanded in eh around 0

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\color{blue}{1}}\right| \]
   5. Step-by-step derivation

   6. Applied rewrites 62.2%

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\color{blue}{1}}\right| \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 89.4% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := -1 \cdot \frac{eh \cdot t}{ew}\\ \mathbf{if}\;ew \leq 3.2 \cdot 10^{+80}:\\ \;\;\;\;\left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh t\_1}, \left(\sin t \cdot eh\right) \cdot \tanh t\_1\right)\right|\\ \mathbf{else}:\\ \;\;\;\;\left|-1 \cdot \left(ew \cdot \cos t\right)\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (* -1.0 (/ (* eh t) ew))))
   (if (<= ew 3.2e+80)
     (fabs (fma (cos t) (- (/ ew (cosh t_1))) (* (* (sin t) eh) (tanh t_1))))
     (fabs (* -1.0 (* ew (cos t)))))))

C

double code(double eh, double ew, double t) {
	double t_1 = -1.0 * ((eh * t) / ew);
	double tmp;
	if (ew <= 3.2e+80) {
		tmp = fabs(fma(cos(t), -(ew / cosh(t_1)), ((sin(t) * eh) * tanh(t_1))));
	} else {
		tmp = fabs((-1.0 * (ew * cos(t))));
	}
	return tmp;
}

Julia

function code(eh, ew, t)
	t_1 = Float64(-1.0 * Float64(Float64(eh * t) / ew))
	tmp = 0.0
	if (ew <= 3.2e+80)
		tmp = abs(fma(cos(t), Float64(-Float64(ew / cosh(t_1))), Float64(Float64(sin(t) * eh) * tanh(t_1))));
	else
		tmp = abs(Float64(-1.0 * Float64(ew * cos(t))));
	end
	return tmp
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[(-1.0 * N[(N[(eh * t), $MachinePrecision] / ew), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[ew, 3.2e+80], N[Abs[N[(N[Cos[t], $MachinePrecision] * (-N[(ew / N[Cosh[t$95$1], $MachinePrecision]), $MachinePrecision]) + N[(N[(N[Sin[t], $MachinePrecision] * eh), $MachinePrecision] * N[Tanh[t$95$1], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[Abs[N[(-1.0 * N[(ew * N[Cos[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if ew < 3.1999999999999999e80

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. lift-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      5. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   5. Applied rewrites 99.8%

      \[\leadsto \left|\color{blue}{\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)}\right| \]

   6. Taylor expanded in t around 0

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \color{blue}{\frac{eh \cdot t}{ew}}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

      2. lower-/.f64 N/A

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{\color{blue}{ew}}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

      3. lower-*.f64 89.6

         \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

   8. Applied rewrites 89.6%

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}}, \left(\sin t \cdot eh\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)\right)\right| \]

   9. Taylor expanded in t around 0

      \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}\right)\right| \]
   10. Step-by-step derivation

       1. lower-*.f64 N/A

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \color{blue}{\frac{eh \cdot t}{ew}}\right)\right)\right| \]

       2. lower-/.f64 N/A

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \frac{eh \cdot t}{\color{blue}{ew}}\right)\right)\right| \]

       3. lower-*.f64 89.8

          \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)\right)\right| \]

   11. Applied rewrites 89.8%

       \[\leadsto \left|\mathsf{fma}\left(\cos t, -\frac{ew}{\cosh \left(-1 \cdot \frac{eh \cdot t}{ew}\right)}, \left(\sin t \cdot eh\right) \cdot \tanh \color{blue}{\left(-1 \cdot \frac{eh \cdot t}{ew}\right)}\right)\right| \]

   if 3.1999999999999999e80 < ew

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. lift-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      5. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   4. Taylor expanded in eh around 0

      \[\leadsto \left|\color{blue}{-1 \cdot \left(ew \cdot \cos t\right)}\right| \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|-1 \cdot \color{blue}{\left(ew \cdot \cos t\right)}\right| \]

      2. lower-*.f64 N/A

         \[\leadsto \left|-1 \cdot \left(ew \cdot \color{blue}{\cos t}\right)\right| \]

      3. lower-cos.f64 61.7

         \[\leadsto \left|-1 \cdot \left(ew \cdot \cos t\right)\right| \]

   6. Applied rewrites 61.7%

      \[\leadsto \left|\color{blue}{-1 \cdot \left(ew \cdot \cos t\right)}\right| \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 67.7% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{ew} \cdot \left(-eh\right)\\ \mathbf{if}\;t \leq 3 \cdot 10^{+72}:\\ \;\;\;\;\left|\frac{\left(\sin t \cdot eh\right) \cdot t\_1 - \cos t \cdot ew}{\cosh \sinh^{-1} t\_1}\right|\\ \mathbf{else}:\\ \;\;\;\;\left|-1 \cdot \left(ew \cdot \cos t\right)\right|\\ \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (* (/ t ew) (- eh))))
   (if (<= t 3e+72)
     (fabs (/ (- (* (* (sin t) eh) t_1) (* (cos t) ew)) (cosh (asinh t_1))))
     (fabs (* -1.0 (* ew (cos t)))))))

C

double code(double eh, double ew, double t) {
	double t_1 = (t / ew) * -eh;
	double tmp;
	if (t <= 3e+72) {
		tmp = fabs(((((sin(t) * eh) * t_1) - (cos(t) * ew)) / cosh(asinh(t_1))));
	} else {
		tmp = fabs((-1.0 * (ew * cos(t))));
	}
	return tmp;
}

Python

def code(eh, ew, t):
	t_1 = (t / ew) * -eh
	tmp = 0
	if t <= 3e+72:
		tmp = math.fabs(((((math.sin(t) * eh) * t_1) - (math.cos(t) * ew)) / math.cosh(math.asinh(t_1))))
	else:
		tmp = math.fabs((-1.0 * (ew * math.cos(t))))
	return tmp

Julia

function code(eh, ew, t)
	t_1 = Float64(Float64(t / ew) * Float64(-eh))
	tmp = 0.0
	if (t <= 3e+72)
		tmp = abs(Float64(Float64(Float64(Float64(sin(t) * eh) * t_1) - Float64(cos(t) * ew)) / cosh(asinh(t_1))));
	else
		tmp = abs(Float64(-1.0 * Float64(ew * cos(t))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(eh, ew, t)
	t_1 = (t / ew) * -eh;
	tmp = 0.0;
	if (t <= 3e+72)
		tmp = abs(((((sin(t) * eh) * t_1) - (cos(t) * ew)) / cosh(asinh(t_1))));
	else
		tmp = abs((-1.0 * (ew * cos(t))));
	end
	tmp_2 = tmp;
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[(N[(t / ew), $MachinePrecision] * (-eh)), $MachinePrecision]}, If[LessEqual[t, 3e+72], N[Abs[N[(N[(N[(N[(N[Sin[t], $MachinePrecision] * eh), $MachinePrecision] * t$95$1), $MachinePrecision] - N[(N[Cos[t], $MachinePrecision] * ew), $MachinePrecision]), $MachinePrecision] / N[Cosh[N[ArcSinh[t$95$1], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[Abs[N[(-1.0 * N[(ew * N[Cos[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if t < 3.00000000000000003e72

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. fabs-sub N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      4. lower-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

   3. Applied rewrites 82.1%

      \[\leadsto \color{blue}{\left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   4. Taylor expanded in t around 0

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\color{blue}{\frac{t}{ew}} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right| \]
   5. Step-by-step derivation

      1. lower-/.f64 64.8

         \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{t}{\color{blue}{ew}} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right| \]

   6. Applied rewrites 64.8%

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\color{blue}{\frac{t}{ew}} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right| \]

   7. Taylor expanded in t around 0

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\color{blue}{\frac{t}{ew}} \cdot \left(-eh\right)\right)}\right| \]
   8. Step-by-step derivation

      1. lower-/.f64 64.0

         \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{t}{\color{blue}{ew}} \cdot \left(-eh\right)\right)}\right| \]

   9. Applied rewrites 64.0%

      \[\leadsto \left|\frac{\left(\sin t \cdot eh\right) \cdot \left(\frac{t}{ew} \cdot \left(-eh\right)\right) - \cos t \cdot ew}{\cosh \sinh^{-1} \left(\color{blue}{\frac{t}{ew}} \cdot \left(-eh\right)\right)}\right| \]

   if 3.00000000000000003e72 < t

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. lift-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      5. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   4. Taylor expanded in eh around 0

      \[\leadsto \left|\color{blue}{-1 \cdot \left(ew \cdot \cos t\right)}\right| \]
   5. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left|-1 \cdot \color{blue}{\left(ew \cdot \cos t\right)}\right| \]

      2. lower-*.f64 N/A

         \[\leadsto \left|-1 \cdot \left(ew \cdot \color{blue}{\cos t}\right)\right| \]

      3. lower-cos.f64 61.7

         \[\leadsto \left|-1 \cdot \left(ew \cdot \cos t\right)\right| \]

   6. Applied rewrites 61.7%

      \[\leadsto \left|\color{blue}{-1 \cdot \left(ew \cdot \cos t\right)}\right| \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 61.7% accurate, 6.2× speedup

Math

\[\begin{array}{l} \\ \left|-1 \cdot \left(ew \cdot \cos t\right)\right| \end{array} \]

FPCore

(FPCore (eh ew t) :precision binary64 (fabs (* -1.0 (* ew (cos t)))))

C

double code(double eh, double ew, double t) {
	return fabs((-1.0 * (ew * cos(t))));
}

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(eh, ew, t)
use fmin_fmax_functions
    real(8), intent (in) :: eh
    real(8), intent (in) :: ew
    real(8), intent (in) :: t
    code = abs(((-1.0d0) * (ew * cos(t))))
end function

Java

public static double code(double eh, double ew, double t) {
	return Math.abs((-1.0 * (ew * Math.cos(t))));
}

Python

def code(eh, ew, t):
	return math.fabs((-1.0 * (ew * math.cos(t))))

Julia

function code(eh, ew, t)
	return abs(Float64(-1.0 * Float64(ew * cos(t))))
end

MATLAB

function tmp = code(eh, ew, t)
	tmp = abs((-1.0 * (ew * cos(t))));
end

Wolfram

code[eh_, ew_, t_] := N[Abs[N[(-1.0 * N[(ew * N[Cos[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
2. Step-by-step derivation

   1. lift-fabs.f64 N/A

      \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

   2. lift--.f64 N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. lift-*.f64 N/A

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   4. fp-cancel-sub-sign-inv N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   5. fp-cancel-sign-sub-inv N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

3. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

4. Taylor expanded in eh around 0

   \[\leadsto \left|\color{blue}{-1 \cdot \left(ew \cdot \cos t\right)}\right| \]
5. Step-by-step derivation

   1. lower-*.f64 N/A

      \[\leadsto \left|-1 \cdot \color{blue}{\left(ew \cdot \cos t\right)}\right| \]

   2. lower-*.f64 N/A

      \[\leadsto \left|-1 \cdot \left(ew \cdot \color{blue}{\cos t}\right)\right| \]

   3. lower-cos.f64 61.7

      \[\leadsto \left|-1 \cdot \left(ew \cdot \cos t\right)\right| \]

6. Applied rewrites 61.7%

   \[\leadsto \left|\color{blue}{-1 \cdot \left(ew \cdot \cos t\right)}\right| \]
7. Add Preprocessing

Alternative 10: 51.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := ew \cdot \cos t\\ t_2 := \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\\ \mathbf{if}\;t\_1 \cdot \cos t\_2 - \left(eh \cdot \sin t\right) \cdot \sin t\_2 \leq 10^{-152}:\\ \;\;\;\;\left|-ew\right|\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (eh ew t)
 :precision binary64
 (let* ((t_1 (* ew (cos t))) (t_2 (atan (/ (* (- eh) (tan t)) ew))))
   (if (<= (- (* t_1 (cos t_2)) (* (* eh (sin t)) (sin t_2))) 1e-152)
     (fabs (- ew))
     t_1)))

C

double code(double eh, double ew, double t) {
	double t_1 = ew * cos(t);
	double t_2 = atan(((-eh * tan(t)) / ew));
	double tmp;
	if (((t_1 * cos(t_2)) - ((eh * sin(t)) * sin(t_2))) <= 1e-152) {
		tmp = fabs(-ew);
	} else {
		tmp = t_1;
	}
	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(eh, ew, t)
use fmin_fmax_functions
    real(8), intent (in) :: eh
    real(8), intent (in) :: ew
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = ew * cos(t)
    t_2 = atan(((-eh * tan(t)) / ew))
    if (((t_1 * cos(t_2)) - ((eh * sin(t)) * sin(t_2))) <= 1d-152) then
        tmp = abs(-ew)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double eh, double ew, double t) {
	double t_1 = ew * Math.cos(t);
	double t_2 = Math.atan(((-eh * Math.tan(t)) / ew));
	double tmp;
	if (((t_1 * Math.cos(t_2)) - ((eh * Math.sin(t)) * Math.sin(t_2))) <= 1e-152) {
		tmp = Math.abs(-ew);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(eh, ew, t):
	t_1 = ew * math.cos(t)
	t_2 = math.atan(((-eh * math.tan(t)) / ew))
	tmp = 0
	if ((t_1 * math.cos(t_2)) - ((eh * math.sin(t)) * math.sin(t_2))) <= 1e-152:
		tmp = math.fabs(-ew)
	else:
		tmp = t_1
	return tmp

Julia

function code(eh, ew, t)
	t_1 = Float64(ew * cos(t))
	t_2 = atan(Float64(Float64(Float64(-eh) * tan(t)) / ew))
	tmp = 0.0
	if (Float64(Float64(t_1 * cos(t_2)) - Float64(Float64(eh * sin(t)) * sin(t_2))) <= 1e-152)
		tmp = abs(Float64(-ew));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(eh, ew, t)
	t_1 = ew * cos(t);
	t_2 = atan(((-eh * tan(t)) / ew));
	tmp = 0.0;
	if (((t_1 * cos(t_2)) - ((eh * sin(t)) * sin(t_2))) <= 1e-152)
		tmp = abs(-ew);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[eh_, ew_, t_] := Block[{t$95$1 = N[(ew * N[Cos[t], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[ArcTan[N[(N[((-eh) * N[Tan[t], $MachinePrecision]), $MachinePrecision] / ew), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[N[(N[(t$95$1 * N[Cos[t$95$2], $MachinePrecision]), $MachinePrecision] - N[(N[(eh * N[Sin[t], $MachinePrecision]), $MachinePrecision] * N[Sin[t$95$2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1e-152], N[Abs[(-ew)], $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (*.f64 (*.f64 ew (cos.f64 t)) (cos.f64 (atan.f64 (/.f64 (*.f64 (neg.f64 eh) (tan.f64 t)) ew)))) (*.f64 (*.f64 eh (sin.f64 t)) (sin.f64 (atan.f64 (/.f64 (*.f64 (neg.f64 eh) (tan.f64 t)) ew))))) < 1.00000000000000007e-152

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
   2. Step-by-step derivation

      1. lift-fabs.f64 N/A

         \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

      2. lift--.f64 N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      3. lift-*.f64 N/A

         \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

      5. fp-cancel-sign-sub-inv N/A

         \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

   4. Taylor expanded in t around 0

      \[\leadsto \left|\color{blue}{-1 \cdot ew}\right| \]
   5. Step-by-step derivation

      1. lower-*.f64 41.8

         \[\leadsto \left|-1 \cdot \color{blue}{ew}\right| \]

   6. Applied rewrites 41.8%

      \[\leadsto \left|\color{blue}{-1 \cdot ew}\right| \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left|-1 \cdot \color{blue}{ew}\right| \]

      2. mul-1-neg N/A

         \[\leadsto \left|\mathsf{neg}\left(ew\right)\right| \]

      3. lower-neg.f64 41.8

         \[\leadsto \left|-ew\right| \]

   8. Applied rewrites 41.8%

      \[\leadsto \left|\color{blue}{-ew}\right| \]

   if 1.00000000000000007e-152 < (-.f64 (*.f64 (*.f64 ew (cos.f64 t)) (cos.f64 (atan.f64 (/.f64 (*.f64 (neg.f64 eh) (tan.f64 t)) ew)))) (*.f64 (*.f64 eh (sin.f64 t)) (sin.f64 (atan.f64 (/.f64 (*.f64 (neg.f64 eh) (tan.f64 t)) ew)))))

   1. Initial program 99.8%

      \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]

   3. Applied rewrites 47.6%

      \[\leadsto \color{blue}{\left(\frac{1}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)} - \frac{\tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}{ew} \cdot \left(\tan t \cdot eh\right)\right) \cdot \left(\cos t \cdot ew\right)} \]

   5. Applied rewrites 42.3%

      \[\leadsto \color{blue}{\frac{-1}{\frac{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}{-\mathsf{fma}\left(\cos t, ew, \left(\frac{\tan t}{ew} \cdot eh\right) \cdot \left(\sin t \cdot eh\right)\right)}}} \]

   6. Taylor expanded in eh around 0

      \[\leadsto \color{blue}{ew \cdot \cos t} \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto ew \cdot \color{blue}{\cos t} \]

      2. lower-cos.f64 32.4

         \[\leadsto ew \cdot \cos t \]

   8. Applied rewrites 32.4%

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

Alternative 11: 41.8% accurate, 78.2× speedup

Math

\[\begin{array}{l} \\ \left|-ew\right| \end{array} \]

FPCore

(FPCore (eh ew t) :precision binary64 (fabs (- ew)))

C

double code(double eh, double ew, double t) {
	return fabs(-ew);
}

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(eh, ew, t)
use fmin_fmax_functions
    real(8), intent (in) :: eh
    real(8), intent (in) :: ew
    real(8), intent (in) :: t
    code = abs(-ew)
end function

Java

public static double code(double eh, double ew, double t) {
	return Math.abs(-ew);
}

Python

def code(eh, ew, t):
	return math.fabs(-ew)

Julia

function code(eh, ew, t)
	return abs(Float64(-ew))
end

MATLAB

function tmp = code(eh, ew, t)
	tmp = abs(-ew);
end

Wolfram

code[eh_, ew_, t_] := N[Abs[(-ew)], $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right| \]
2. Step-by-step derivation

   1. lift-fabs.f64 N/A

      \[\leadsto \color{blue}{\left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)\right|} \]

   2. lift--.f64 N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   3. lift-*.f64 N/A

      \[\leadsto \left|\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \color{blue}{\left(eh \cdot \sin t\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   4. fp-cancel-sub-sign-inv N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) + \left(\mathsf{neg}\left(eh \cdot \sin t\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

   5. fp-cancel-sign-sub-inv N/A

      \[\leadsto \left|\color{blue}{\left(ew \cdot \cos t\right) \cdot \cos \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right) - \left(\mathsf{neg}\left(\left(\mathsf{neg}\left(eh \cdot \sin t\right)\right)\right)\right) \cdot \sin \tan^{-1} \left(\frac{\left(-eh\right) \cdot \tan t}{ew}\right)}\right| \]

3. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\left|\left(-\sin t \cdot \left(-eh\right)\right) \cdot \tanh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right) - \frac{\cos t \cdot ew}{\cosh \sinh^{-1} \left(\frac{\tan t}{ew} \cdot \left(-eh\right)\right)}\right|} \]

4. Taylor expanded in t around 0

   \[\leadsto \left|\color{blue}{-1 \cdot ew}\right| \]
5. Step-by-step derivation

   1. lower-*.f64 41.8

      \[\leadsto \left|-1 \cdot \color{blue}{ew}\right| \]

6. Applied rewrites 41.8%

   \[\leadsto \left|\color{blue}{-1 \cdot ew}\right| \]
7. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \left|-1 \cdot \color{blue}{ew}\right| \]

   2. mul-1-neg N/A

      \[\leadsto \left|\mathsf{neg}\left(ew\right)\right| \]

   3. lower-neg.f64 41.8

      \[\leadsto \left|-ew\right| \]

8. Applied rewrites 41.8%

   \[\leadsto \left|\color{blue}{-ew}\right| \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2025156 
(FPCore (eh ew t)
  :name "Example 2 from Robby"
  :precision binary64
  (fabs (- (* (* ew (cos t)) (cos (atan (/ (* (- eh) (tan t)) ew)))) (* (* eh (sin t)) (sin (atan (/ (* (- eh) (tan t)) ew)))))))