Toniolo and Linder, Equation (3b), real

Percentage Accurate: 94.2% → 99.7%

Time: 6.7s

Alternatives: 18

Speedup: 1.2×

Specification

Math

\[\begin{array}{l} \\ \frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (* (/ (sin ky) (sqrt (+ (pow (sin kx) 2.0) (pow (sin ky) 2.0)))) (sin th)))

C

double code(double kx, double ky, double th) {
	return (sin(ky) / sqrt((pow(sin(kx), 2.0) + pow(sin(ky), 2.0)))) * sin(th);
}

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(kx, ky, th)
use fmin_fmax_functions
    real(8), intent (in) :: kx
    real(8), intent (in) :: ky
    real(8), intent (in) :: th
    code = (sin(ky) / sqrt(((sin(kx) ** 2.0d0) + (sin(ky) ** 2.0d0)))) * sin(th)
end function

Java

public static double code(double kx, double ky, double th) {
	return (Math.sin(ky) / Math.sqrt((Math.pow(Math.sin(kx), 2.0) + Math.pow(Math.sin(ky), 2.0)))) * Math.sin(th);
}

Python

def code(kx, ky, th):
	return (math.sin(ky) / math.sqrt((math.pow(math.sin(kx), 2.0) + math.pow(math.sin(ky), 2.0)))) * math.sin(th)

Julia

function code(kx, ky, th)
	return Float64(Float64(sin(ky) / sqrt(Float64((sin(kx) ^ 2.0) + (sin(ky) ^ 2.0)))) * sin(th))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = (sin(ky) / sqrt(((sin(kx) ^ 2.0) + (sin(ky) ^ 2.0)))) * sin(th);
end

Wolfram

code[kx_, ky_, th_] := N[(N[(N[Sin[ky], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Sin[kx], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[Sin[ky], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]

Initial Program: 94.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (* (/ (sin ky) (sqrt (+ (pow (sin kx) 2.0) (pow (sin ky) 2.0)))) (sin th)))

C

double code(double kx, double ky, double th) {
	return (sin(ky) / sqrt((pow(sin(kx), 2.0) + pow(sin(ky), 2.0)))) * sin(th);
}

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(kx, ky, th)
use fmin_fmax_functions
    real(8), intent (in) :: kx
    real(8), intent (in) :: ky
    real(8), intent (in) :: th
    code = (sin(ky) / sqrt(((sin(kx) ** 2.0d0) + (sin(ky) ** 2.0d0)))) * sin(th)
end function

Java

public static double code(double kx, double ky, double th) {
	return (Math.sin(ky) / Math.sqrt((Math.pow(Math.sin(kx), 2.0) + Math.pow(Math.sin(ky), 2.0)))) * Math.sin(th);
}

Python

def code(kx, ky, th):
	return (math.sin(ky) / math.sqrt((math.pow(math.sin(kx), 2.0) + math.pow(math.sin(ky), 2.0)))) * math.sin(th)

Julia

function code(kx, ky, th)
	return Float64(Float64(sin(ky) / sqrt(Float64((sin(kx) ^ 2.0) + (sin(ky) ^ 2.0)))) * sin(th))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = (sin(ky) / sqrt(((sin(kx) ^ 2.0) + (sin(ky) ^ 2.0)))) * sin(th);
end

Wolfram

code[kx_, ky_, th_] := N[(N[(N[Sin[ky], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Sin[kx], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[Sin[ky], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \sin ky\right)}{\sin ky}} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (/ (sin th) (/ (hypot (sin kx) (sin ky)) (sin ky))))

C

double code(double kx, double ky, double th) {
	return sin(th) / (hypot(sin(kx), sin(ky)) / sin(ky));
}

Java

public static double code(double kx, double ky, double th) {
	return Math.sin(th) / (Math.hypot(Math.sin(kx), Math.sin(ky)) / Math.sin(ky));
}

Python

def code(kx, ky, th):
	return math.sin(th) / (math.hypot(math.sin(kx), math.sin(ky)) / math.sin(ky))

Julia

function code(kx, ky, th)
	return Float64(sin(th) / Float64(hypot(sin(kx), sin(ky)) / sin(ky)))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = sin(th) / (hypot(sin(kx), sin(ky)) / sin(ky));
end

Wolfram

code[kx_, ky_, th_] := N[(N[Sin[th], $MachinePrecision] / N[(N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + N[Sin[ky], $MachinePrecision] ^ 2], $MachinePrecision] / N[Sin[ky], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
2. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{\sin th \cdot \frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   3. lift-/.f64 N/A

      \[\leadsto \sin th \cdot \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   4. div-flip N/A

      \[\leadsto \sin th \cdot \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

   5. mult-flip-rev N/A

      \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

   6. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

   7. lower-/.f64 94.2

      \[\leadsto \frac{\sin th}{\color{blue}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

   8. lift-sqrt.f64 N/A

      \[\leadsto \frac{\sin th}{\frac{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

   9. lift-+.f64 N/A

      \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

   10. lift-pow.f64 N/A

       \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}}{\sin ky}} \]

   11. unpow2 N/A

       \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}}{\sin ky}} \]

   12. lift-pow.f64 N/A

       \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}}{\sin ky}} \]

   13. unpow2 N/A

       \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}}{\sin ky}} \]

   14. lower-hypot.f64 99.7

       \[\leadsto \frac{\sin th}{\frac{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}}{\sin ky}} \]

3. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \sin ky\right)}{\sin ky}}} \]
4. Add Preprocessing

Alternative 2: 99.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \frac{\sin ky}{\mathsf{hypot}\left(\sin ky, \sin kx\right)} \cdot \sin th \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (* (/ (sin ky) (hypot (sin ky) (sin kx))) (sin th)))

C

double code(double kx, double ky, double th) {
	return (sin(ky) / hypot(sin(ky), sin(kx))) * sin(th);
}

Java

public static double code(double kx, double ky, double th) {
	return (Math.sin(ky) / Math.hypot(Math.sin(ky), Math.sin(kx))) * Math.sin(th);
}

Python

def code(kx, ky, th):
	return (math.sin(ky) / math.hypot(math.sin(ky), math.sin(kx))) * math.sin(th)

Julia

function code(kx, ky, th)
	return Float64(Float64(sin(ky) / hypot(sin(ky), sin(kx))) * sin(th))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = (sin(ky) / hypot(sin(ky), sin(kx))) * sin(th);
end

Wolfram

code[kx_, ky_, th_] := N[(N[(N[Sin[ky], $MachinePrecision] / N[Sqrt[N[Sin[ky], $MachinePrecision] ^ 2 + N[Sin[kx], $MachinePrecision] ^ 2], $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
2. Step-by-step derivation

   1. lift-sqrt.f64 N/A

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

   2. lift-+.f64 N/A

      \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

   3. +-commutative N/A

      \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin ky}^{2} + {\sin kx}^{2}}}} \cdot \sin th \]

   4. lift-pow.f64 N/A

      \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin ky}^{2}} + {\sin kx}^{2}}} \cdot \sin th \]

   5. unpow2 N/A

      \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{\sin ky \cdot \sin ky} + {\sin kx}^{2}}} \cdot \sin th \]

   6. lift-pow.f64 N/A

      \[\leadsto \frac{\sin ky}{\sqrt{\sin ky \cdot \sin ky + \color{blue}{{\sin kx}^{2}}}} \cdot \sin th \]

   7. unpow2 N/A

      \[\leadsto \frac{\sin ky}{\sqrt{\sin ky \cdot \sin ky + \color{blue}{\sin kx \cdot \sin kx}}} \cdot \sin th \]

   8. lower-hypot.f64 99.7

      \[\leadsto \frac{\sin ky}{\color{blue}{\mathsf{hypot}\left(\sin ky, \sin kx\right)}} \cdot \sin th \]

3. Applied rewrites 99.7%

   \[\leadsto \frac{\sin ky}{\color{blue}{\mathsf{hypot}\left(\sin ky, \sin kx\right)}} \cdot \sin th \]
4. Add Preprocessing

Alternative 3: 99.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \sin ky \cdot \frac{\sin th}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (* (sin ky) (/ (sin th) (hypot (sin kx) (sin ky)))))

C

double code(double kx, double ky, double th) {
	return sin(ky) * (sin(th) / hypot(sin(kx), sin(ky)));
}

Java

public static double code(double kx, double ky, double th) {
	return Math.sin(ky) * (Math.sin(th) / Math.hypot(Math.sin(kx), Math.sin(ky)));
}

Python

def code(kx, ky, th):
	return math.sin(ky) * (math.sin(th) / math.hypot(math.sin(kx), math.sin(ky)))

Julia

function code(kx, ky, th)
	return Float64(sin(ky) * Float64(sin(th) / hypot(sin(kx), sin(ky))))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = sin(ky) * (sin(th) / hypot(sin(kx), sin(ky)));
end

Wolfram

code[kx_, ky_, th_] := N[(N[Sin[ky], $MachinePrecision] * N[(N[Sin[th], $MachinePrecision] / N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + N[Sin[ky], $MachinePrecision] ^ 2], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
2. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th} \]

   2. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

   3. associate-*l/ N/A

      \[\leadsto \color{blue}{\frac{\sin ky \cdot \sin th}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   4. associate-/l* N/A

      \[\leadsto \color{blue}{\sin ky \cdot \frac{\sin th}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   5. lower-*.f64 N/A

      \[\leadsto \color{blue}{\sin ky \cdot \frac{\sin th}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   6. lower-/.f64 94.2

      \[\leadsto \sin ky \cdot \color{blue}{\frac{\sin th}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   7. lift-sqrt.f64 N/A

      \[\leadsto \sin ky \cdot \frac{\sin th}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   8. lift-+.f64 N/A

      \[\leadsto \sin ky \cdot \frac{\sin th}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   9. lift-pow.f64 N/A

      \[\leadsto \sin ky \cdot \frac{\sin th}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \]

   10. unpow2 N/A

       \[\leadsto \sin ky \cdot \frac{\sin th}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \]

   11. lift-pow.f64 N/A

       \[\leadsto \sin ky \cdot \frac{\sin th}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \]

   12. unpow2 N/A

       \[\leadsto \sin ky \cdot \frac{\sin th}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \]

   13. lower-hypot.f64 99.6

       \[\leadsto \sin ky \cdot \frac{\sin th}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \]

3. Applied rewrites 99.6%

   \[\leadsto \color{blue}{\sin ky \cdot \frac{\sin th}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \]
4. Add Preprocessing

Alternative 4: 68.8% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;th \leq 8.5 \cdot 10^{-7}:\\ \;\;\;\;\frac{\sin ky}{\mathsf{hypot}\left(\sin ky, \sin kx\right)} \cdot th\\ \mathbf{else}:\\ \;\;\;\;\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{ky}}\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= th 8.5e-7)
   (* (/ (sin ky) (hypot (sin ky) (sin kx))) th)
   (/ (sin th) (/ (hypot (sin kx) ky) ky))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if (th <= 8.5e-7) {
		tmp = (sin(ky) / hypot(sin(ky), sin(kx))) * th;
	} else {
		tmp = sin(th) / (hypot(sin(kx), ky) / ky);
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if (th <= 8.5e-7) {
		tmp = (Math.sin(ky) / Math.hypot(Math.sin(ky), Math.sin(kx))) * th;
	} else {
		tmp = Math.sin(th) / (Math.hypot(Math.sin(kx), ky) / ky);
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if th <= 8.5e-7:
		tmp = (math.sin(ky) / math.hypot(math.sin(ky), math.sin(kx))) * th
	else:
		tmp = math.sin(th) / (math.hypot(math.sin(kx), ky) / ky)
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if (th <= 8.5e-7)
		tmp = Float64(Float64(sin(ky) / hypot(sin(ky), sin(kx))) * th);
	else
		tmp = Float64(sin(th) / Float64(hypot(sin(kx), ky) / ky));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if (th <= 8.5e-7)
		tmp = (sin(ky) / hypot(sin(ky), sin(kx))) * th;
	else
		tmp = sin(th) / (hypot(sin(kx), ky) / ky);
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[th, 8.5e-7], N[(N[(N[Sin[ky], $MachinePrecision] / N[Sqrt[N[Sin[ky], $MachinePrecision] ^ 2 + N[Sin[kx], $MachinePrecision] ^ 2], $MachinePrecision]), $MachinePrecision] * th), $MachinePrecision], N[(N[Sin[th], $MachinePrecision] / N[(N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision] / ky), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if th < 8.50000000000000014e-7

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      3. +-commutative N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin ky}^{2} + {\sin kx}^{2}}}} \cdot \sin th \]

      4. lift-pow.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin ky}^{2}} + {\sin kx}^{2}}} \cdot \sin th \]

      5. unpow2 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{\sin ky \cdot \sin ky} + {\sin kx}^{2}}} \cdot \sin th \]

      6. lift-pow.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\sin ky \cdot \sin ky + \color{blue}{{\sin kx}^{2}}}} \cdot \sin th \]

      7. unpow2 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\sin ky \cdot \sin ky + \color{blue}{\sin kx \cdot \sin kx}}} \cdot \sin th \]

      8. lower-hypot.f64 99.7

         \[\leadsto \frac{\sin ky}{\color{blue}{\mathsf{hypot}\left(\sin ky, \sin kx\right)}} \cdot \sin th \]

   3. Applied rewrites 99.7%

      \[\leadsto \frac{\sin ky}{\color{blue}{\mathsf{hypot}\left(\sin ky, \sin kx\right)}} \cdot \sin th \]

   4. Taylor expanded in th around 0

      \[\leadsto \frac{\sin ky}{\mathsf{hypot}\left(\sin ky, \sin kx\right)} \cdot \color{blue}{th} \]
   5. Step-by-step derivation

   6. Applied rewrites 51.4%

      \[\leadsto \frac{\sin ky}{\mathsf{hypot}\left(\sin ky, \sin kx\right)} \cdot \color{blue}{th} \]

   if 8.50000000000000014e-7 < th

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\sin th \cdot \frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

      3. lift-/.f64 N/A

         \[\leadsto \sin th \cdot \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

      4. div-flip N/A

         \[\leadsto \sin th \cdot \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      5. mult-flip-rev N/A

         \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      6. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      7. lower-/.f64 94.2

         \[\leadsto \frac{\sin th}{\color{blue}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sin th}{\frac{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

      10. lift-pow.f64 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}}{\sin ky}} \]

      11. unpow2 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}}{\sin ky}} \]

      12. lift-pow.f64 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}}{\sin ky}} \]

      13. unpow2 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}}{\sin ky}} \]

      14. lower-hypot.f64 99.7

          \[\leadsto \frac{\sin th}{\frac{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}}{\sin ky}} \]

   3. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \sin ky\right)}{\sin ky}}} \]

   4. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]

   7. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 64.4%

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 67.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;kx \leq 0.00185:\\ \;\;\;\;\frac{\sin ky}{\mathsf{hypot}\left(\sin ky, kx\right)} \cdot \sin th\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\frac{\left|\sin kx\right|}{\sin ky}} \cdot \sin th\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= kx 0.00185)
   (* (/ (sin ky) (hypot (sin ky) kx)) (sin th))
   (* (/ 1.0 (/ (fabs (sin kx)) (sin ky))) (sin th))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if (kx <= 0.00185) {
		tmp = (sin(ky) / hypot(sin(ky), kx)) * sin(th);
	} else {
		tmp = (1.0 / (fabs(sin(kx)) / sin(ky))) * sin(th);
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if (kx <= 0.00185) {
		tmp = (Math.sin(ky) / Math.hypot(Math.sin(ky), kx)) * Math.sin(th);
	} else {
		tmp = (1.0 / (Math.abs(Math.sin(kx)) / Math.sin(ky))) * Math.sin(th);
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if kx <= 0.00185:
		tmp = (math.sin(ky) / math.hypot(math.sin(ky), kx)) * math.sin(th)
	else:
		tmp = (1.0 / (math.fabs(math.sin(kx)) / math.sin(ky))) * math.sin(th)
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if (kx <= 0.00185)
		tmp = Float64(Float64(sin(ky) / hypot(sin(ky), kx)) * sin(th));
	else
		tmp = Float64(Float64(1.0 / Float64(abs(sin(kx)) / sin(ky))) * sin(th));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if (kx <= 0.00185)
		tmp = (sin(ky) / hypot(sin(ky), kx)) * sin(th);
	else
		tmp = (1.0 / (abs(sin(kx)) / sin(ky))) * sin(th);
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[kx, 0.00185], N[(N[(N[Sin[ky], $MachinePrecision] / N[Sqrt[N[Sin[ky], $MachinePrecision] ^ 2 + kx ^ 2], $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision], N[(N[(1.0 / N[(N[Abs[N[Sin[kx], $MachinePrecision]], $MachinePrecision] / N[Sin[ky], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if kx < 0.0018500000000000001

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. lift-+.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      3. +-commutative N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin ky}^{2} + {\sin kx}^{2}}}} \cdot \sin th \]

      4. lift-pow.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{{\sin ky}^{2}} + {\sin kx}^{2}}} \cdot \sin th \]

      5. unpow2 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\color{blue}{\sin ky \cdot \sin ky} + {\sin kx}^{2}}} \cdot \sin th \]

      6. lift-pow.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\sin ky \cdot \sin ky + \color{blue}{{\sin kx}^{2}}}} \cdot \sin th \]

      7. unpow2 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{\sin ky \cdot \sin ky + \color{blue}{\sin kx \cdot \sin kx}}} \cdot \sin th \]

      8. lower-hypot.f64 99.7

         \[\leadsto \frac{\sin ky}{\color{blue}{\mathsf{hypot}\left(\sin ky, \sin kx\right)}} \cdot \sin th \]

   3. Applied rewrites 99.7%

      \[\leadsto \frac{\sin ky}{\color{blue}{\mathsf{hypot}\left(\sin ky, \sin kx\right)}} \cdot \sin th \]

   4. Taylor expanded in kx around 0

      \[\leadsto \frac{\sin ky}{\mathsf{hypot}\left(\sin ky, \color{blue}{kx}\right)} \cdot \sin th \]
   5. Step-by-step derivation

   6. Applied rewrites 58.4%

      \[\leadsto \frac{\sin ky}{\mathsf{hypot}\left(\sin ky, \color{blue}{kx}\right)} \cdot \sin th \]

   if 0.0018500000000000001 < kx

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

   2. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   3. Step-by-step derivation

      1. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      2. lower-pow.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      3. lower-sin.f64 40.0

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. Applied rewrites 40.0%

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

      2. div-flip N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}}} \cdot \sin th \]

      3. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}}} \cdot \sin th \]

      4. lower-/.f64 39.9

         \[\leadsto \frac{1}{\color{blue}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}}} \cdot \sin th \]

      5. lift-sqrt.f64 N/A

         \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      6. pow1/2 N/A

         \[\leadsto \frac{1}{\frac{{\left({\sin kx}^{2}\right)}^{\color{blue}{\frac{1}{2}}}}{\sin ky}} \cdot \sin th \]

      7. lift-pow.f64 N/A

         \[\leadsto \frac{1}{\frac{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      8. pow2 N/A

         \[\leadsto \frac{1}{\frac{{\left(\sin kx \cdot \sin kx\right)}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      9. unpow-prod-down N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\frac{1}{2}} \cdot \color{blue}{{\sin kx}^{\frac{1}{2}}}}{\sin ky}} \cdot \sin th \]

      10. metadata-eval N/A

          \[\leadsto \frac{1}{\frac{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      11. metadata-eval N/A

          \[\leadsto \frac{1}{\frac{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\left(\frac{1}{\color{blue}{2}}\right)}}{\sin ky}} \cdot \sin th \]

      12. sqr-pow N/A

          \[\leadsto \frac{1}{\frac{{\sin kx}^{\color{blue}{1}}}{\sin ky}} \cdot \sin th \]

      13. unpow1 26.5

          \[\leadsto \frac{1}{\frac{\sin kx}{\sin ky}} \cdot \sin th \]

   6. Applied rewrites 26.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{\sin kx}{\sin ky}}} \cdot \sin th \]
   7. Step-by-step derivation

      1. unpow1 N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\color{blue}{1}}}{\sin ky}} \cdot \sin th \]

      2. sqr-pow N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot \color{blue}{{\sin kx}^{\left(\frac{1}{2}\right)}}}{\sin ky}} \cdot \sin th \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\frac{1}{2}} \cdot {\sin kx}^{\left(\frac{1}{2}\right)}}{\sin ky}} \cdot \sin th \]

      4. metadata-eval N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\frac{1}{2}} \cdot {\sin kx}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      5. unpow-prod-down N/A

         \[\leadsto \frac{1}{\frac{{\left(\sin kx \cdot \sin kx\right)}^{\color{blue}{\frac{1}{2}}}}{\sin ky}} \cdot \sin th \]

      6. pow2 N/A

         \[\leadsto \frac{1}{\frac{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      7. lift-pow.f64 N/A

         \[\leadsto \frac{1}{\frac{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      8. pow1/2 N/A

         \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      9. lift-pow.f64 N/A

         \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      10. lift-sin.f64 N/A

          \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      11. lift-sin.f64 N/A

          \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      12. pow2 N/A

          \[\leadsto \frac{1}{\frac{\sqrt{\sin kx \cdot \sin kx}}{\sin ky}} \cdot \sin th \]

      13. rem-sqrt-square-rev N/A

          \[\leadsto \frac{1}{\frac{\left|\sin kx\right|}{\sin ky}} \cdot \sin th \]

      14. lower-fabs.f64 42.9

          \[\leadsto \frac{1}{\frac{\left|\sin kx\right|}{\sin ky}} \cdot \sin th \]

   8. Applied rewrites 42.9%

      \[\leadsto \frac{1}{\frac{\left|\sin kx\right|}{\sin ky}} \cdot \sin th \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 64.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \leq 0.71:\\ \;\;\;\;\frac{1}{\frac{\left|\sin kx\right|}{\sin ky}} \cdot \sin th\\ \mathbf{else}:\\ \;\;\;\;\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{ky}}\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= (/ (sin ky) (sqrt (+ (pow (sin kx) 2.0) (pow (sin ky) 2.0)))) 0.71)
   (* (/ 1.0 (/ (fabs (sin kx)) (sin ky))) (sin th))
   (/ (sin th) (/ (hypot (sin kx) ky) ky))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if ((sin(ky) / sqrt((pow(sin(kx), 2.0) + pow(sin(ky), 2.0)))) <= 0.71) {
		tmp = (1.0 / (fabs(sin(kx)) / sin(ky))) * sin(th);
	} else {
		tmp = sin(th) / (hypot(sin(kx), ky) / ky);
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if ((Math.sin(ky) / Math.sqrt((Math.pow(Math.sin(kx), 2.0) + Math.pow(Math.sin(ky), 2.0)))) <= 0.71) {
		tmp = (1.0 / (Math.abs(Math.sin(kx)) / Math.sin(ky))) * Math.sin(th);
	} else {
		tmp = Math.sin(th) / (Math.hypot(Math.sin(kx), ky) / ky);
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if (math.sin(ky) / math.sqrt((math.pow(math.sin(kx), 2.0) + math.pow(math.sin(ky), 2.0)))) <= 0.71:
		tmp = (1.0 / (math.fabs(math.sin(kx)) / math.sin(ky))) * math.sin(th)
	else:
		tmp = math.sin(th) / (math.hypot(math.sin(kx), ky) / ky)
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if (Float64(sin(ky) / sqrt(Float64((sin(kx) ^ 2.0) + (sin(ky) ^ 2.0)))) <= 0.71)
		tmp = Float64(Float64(1.0 / Float64(abs(sin(kx)) / sin(ky))) * sin(th));
	else
		tmp = Float64(sin(th) / Float64(hypot(sin(kx), ky) / ky));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if ((sin(ky) / sqrt(((sin(kx) ^ 2.0) + (sin(ky) ^ 2.0)))) <= 0.71)
		tmp = (1.0 / (abs(sin(kx)) / sin(ky))) * sin(th);
	else
		tmp = sin(th) / (hypot(sin(kx), ky) / ky);
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[N[(N[Sin[ky], $MachinePrecision] / N[Sqrt[N[(N[Power[N[Sin[kx], $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[Sin[ky], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 0.71], N[(N[(1.0 / N[(N[Abs[N[Sin[kx], $MachinePrecision]], $MachinePrecision] / N[Sin[ky], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision], N[(N[Sin[th], $MachinePrecision] / N[(N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision] / ky), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (sin.f64 ky) (sqrt.f64 (+.f64 (pow.f64 (sin.f64 kx) #s(literal 2 binary64)) (pow.f64 (sin.f64 ky) #s(literal 2 binary64))))) < 0.70999999999999996

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

   2. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   3. Step-by-step derivation

      1. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      2. lower-pow.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      3. lower-sin.f64 40.0

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. Applied rewrites 40.0%

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

      2. div-flip N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}}} \cdot \sin th \]

      3. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}}} \cdot \sin th \]

      4. lower-/.f64 39.9

         \[\leadsto \frac{1}{\color{blue}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}}} \cdot \sin th \]

      5. lift-sqrt.f64 N/A

         \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      6. pow1/2 N/A

         \[\leadsto \frac{1}{\frac{{\left({\sin kx}^{2}\right)}^{\color{blue}{\frac{1}{2}}}}{\sin ky}} \cdot \sin th \]

      7. lift-pow.f64 N/A

         \[\leadsto \frac{1}{\frac{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      8. pow2 N/A

         \[\leadsto \frac{1}{\frac{{\left(\sin kx \cdot \sin kx\right)}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      9. unpow-prod-down N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\frac{1}{2}} \cdot \color{blue}{{\sin kx}^{\frac{1}{2}}}}{\sin ky}} \cdot \sin th \]

      10. metadata-eval N/A

          \[\leadsto \frac{1}{\frac{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      11. metadata-eval N/A

          \[\leadsto \frac{1}{\frac{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\left(\frac{1}{\color{blue}{2}}\right)}}{\sin ky}} \cdot \sin th \]

      12. sqr-pow N/A

          \[\leadsto \frac{1}{\frac{{\sin kx}^{\color{blue}{1}}}{\sin ky}} \cdot \sin th \]

      13. unpow1 26.5

          \[\leadsto \frac{1}{\frac{\sin kx}{\sin ky}} \cdot \sin th \]

   6. Applied rewrites 26.5%

      \[\leadsto \color{blue}{\frac{1}{\frac{\sin kx}{\sin ky}}} \cdot \sin th \]
   7. Step-by-step derivation

      1. unpow1 N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\color{blue}{1}}}{\sin ky}} \cdot \sin th \]

      2. sqr-pow N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot \color{blue}{{\sin kx}^{\left(\frac{1}{2}\right)}}}{\sin ky}} \cdot \sin th \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\frac{1}{2}} \cdot {\sin kx}^{\left(\frac{1}{2}\right)}}{\sin ky}} \cdot \sin th \]

      4. metadata-eval N/A

         \[\leadsto \frac{1}{\frac{{\sin kx}^{\frac{1}{2}} \cdot {\sin kx}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      5. unpow-prod-down N/A

         \[\leadsto \frac{1}{\frac{{\left(\sin kx \cdot \sin kx\right)}^{\color{blue}{\frac{1}{2}}}}{\sin ky}} \cdot \sin th \]

      6. pow2 N/A

         \[\leadsto \frac{1}{\frac{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      7. lift-pow.f64 N/A

         \[\leadsto \frac{1}{\frac{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}}{\sin ky}} \cdot \sin th \]

      8. pow1/2 N/A

         \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      9. lift-pow.f64 N/A

         \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      10. lift-sin.f64 N/A

          \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      11. lift-sin.f64 N/A

          \[\leadsto \frac{1}{\frac{\sqrt{{\sin kx}^{2}}}{\sin ky}} \cdot \sin th \]

      12. pow2 N/A

          \[\leadsto \frac{1}{\frac{\sqrt{\sin kx \cdot \sin kx}}{\sin ky}} \cdot \sin th \]

      13. rem-sqrt-square-rev N/A

          \[\leadsto \frac{1}{\frac{\left|\sin kx\right|}{\sin ky}} \cdot \sin th \]

      14. lower-fabs.f64 42.9

          \[\leadsto \frac{1}{\frac{\left|\sin kx\right|}{\sin ky}} \cdot \sin th \]

   8. Applied rewrites 42.9%

      \[\leadsto \frac{1}{\frac{\left|\sin kx\right|}{\sin ky}} \cdot \sin th \]

   if 0.70999999999999996 < (/.f64 (sin.f64 ky) (sqrt.f64 (+.f64 (pow.f64 (sin.f64 kx) #s(literal 2 binary64)) (pow.f64 (sin.f64 ky) #s(literal 2 binary64)))))

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\sin th \cdot \frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

      3. lift-/.f64 N/A

         \[\leadsto \sin th \cdot \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

      4. div-flip N/A

         \[\leadsto \sin th \cdot \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      5. mult-flip-rev N/A

         \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      6. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      7. lower-/.f64 94.2

         \[\leadsto \frac{\sin th}{\color{blue}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sin th}{\frac{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

      10. lift-pow.f64 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}}{\sin ky}} \]

      11. unpow2 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}}{\sin ky}} \]

      12. lift-pow.f64 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}}{\sin ky}} \]

      13. unpow2 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}}{\sin ky}} \]

      14. lower-hypot.f64 99.7

          \[\leadsto \frac{\sin th}{\frac{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}}{\sin ky}} \]

   3. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \sin ky\right)}{\sin ky}}} \]

   4. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]

   7. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 64.4%

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 64.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin ky \leq -0.05:\\ \;\;\;\;\sin ky \cdot \frac{\sin th}{\sin kx}\\ \mathbf{else}:\\ \;\;\;\;\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{ky}}\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= (sin ky) -0.05)
   (* (sin ky) (/ (sin th) (sin kx)))
   (/ (sin th) (/ (hypot (sin kx) ky) ky))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if (sin(ky) <= -0.05) {
		tmp = sin(ky) * (sin(th) / sin(kx));
	} else {
		tmp = sin(th) / (hypot(sin(kx), ky) / ky);
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if (Math.sin(ky) <= -0.05) {
		tmp = Math.sin(ky) * (Math.sin(th) / Math.sin(kx));
	} else {
		tmp = Math.sin(th) / (Math.hypot(Math.sin(kx), ky) / ky);
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if math.sin(ky) <= -0.05:
		tmp = math.sin(ky) * (math.sin(th) / math.sin(kx))
	else:
		tmp = math.sin(th) / (math.hypot(math.sin(kx), ky) / ky)
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if (sin(ky) <= -0.05)
		tmp = Float64(sin(ky) * Float64(sin(th) / sin(kx)));
	else
		tmp = Float64(sin(th) / Float64(hypot(sin(kx), ky) / ky));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if (sin(ky) <= -0.05)
		tmp = sin(ky) * (sin(th) / sin(kx));
	else
		tmp = sin(th) / (hypot(sin(kx), ky) / ky);
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[N[Sin[ky], $MachinePrecision], -0.05], N[(N[Sin[ky], $MachinePrecision] * N[(N[Sin[th], $MachinePrecision] / N[Sin[kx], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Sin[th], $MachinePrecision] / N[(N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision] / ky), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (sin.f64 ky) < -0.050000000000000003

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

   2. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   3. Step-by-step derivation

      1. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      2. lower-pow.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      3. lower-sin.f64 40.0

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. Applied rewrites 40.0%

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th} \]

      2. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

      3. associate-*l/ N/A

         \[\leadsto \color{blue}{\frac{\sin ky \cdot \sin th}{\sqrt{{\sin kx}^{2}}}} \]

      4. associate-/l* N/A

         \[\leadsto \color{blue}{\sin ky \cdot \frac{\sin th}{\sqrt{{\sin kx}^{2}}}} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{\sin ky \cdot \frac{\sin th}{\sqrt{{\sin kx}^{2}}}} \]

      6. lower-/.f64 40.0

         \[\leadsto \sin ky \cdot \color{blue}{\frac{\sin th}{\sqrt{{\sin kx}^{2}}}} \]

      7. lift-sqrt.f64 N/A

         \[\leadsto \sin ky \cdot \frac{\sin th}{\sqrt{{\sin kx}^{2}}} \]

      8. pow1/2 N/A

         \[\leadsto \sin ky \cdot \frac{\sin th}{{\left({\sin kx}^{2}\right)}^{\color{blue}{\frac{1}{2}}}} \]

      9. lift-pow.f64 N/A

         \[\leadsto \sin ky \cdot \frac{\sin th}{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}} \]

      10. pow2 N/A

          \[\leadsto \sin ky \cdot \frac{\sin th}{{\left(\sin kx \cdot \sin kx\right)}^{\frac{1}{2}}} \]

      11. unpow-prod-down N/A

          \[\leadsto \sin ky \cdot \frac{\sin th}{{\sin kx}^{\frac{1}{2}} \cdot \color{blue}{{\sin kx}^{\frac{1}{2}}}} \]

      12. metadata-eval N/A

          \[\leadsto \sin ky \cdot \frac{\sin th}{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\frac{1}{2}}} \]

      13. metadata-eval N/A

          \[\leadsto \sin ky \cdot \frac{\sin th}{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\left(\frac{1}{\color{blue}{2}}\right)}} \]

   6. Applied rewrites 26.5%

      \[\leadsto \color{blue}{\sin ky \cdot \frac{\sin th}{\sin kx}} \]

   if -0.050000000000000003 < (sin.f64 ky)

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\sin th \cdot \frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

      3. lift-/.f64 N/A

         \[\leadsto \sin th \cdot \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

      4. div-flip N/A

         \[\leadsto \sin th \cdot \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      5. mult-flip-rev N/A

         \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      6. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      7. lower-/.f64 94.2

         \[\leadsto \frac{\sin th}{\color{blue}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sin th}{\frac{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

      10. lift-pow.f64 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}}{\sin ky}} \]

      11. unpow2 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}}{\sin ky}} \]

      12. lift-pow.f64 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}}{\sin ky}} \]

      13. unpow2 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}}{\sin ky}} \]

      14. lower-hypot.f64 99.7

          \[\leadsto \frac{\sin th}{\frac{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}}{\sin ky}} \]

   3. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \sin ky\right)}{\sin ky}}} \]

   4. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]

   7. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 64.4%

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 62.0% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin ky \leq -0.05:\\ \;\;\;\;\frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot th\\ \mathbf{else}:\\ \;\;\;\;\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{ky}}\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= (sin ky) -0.05)
   (* (/ (sin ky) (sqrt (pow (sin kx) 2.0))) th)
   (/ (sin th) (/ (hypot (sin kx) ky) ky))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if (sin(ky) <= -0.05) {
		tmp = (sin(ky) / sqrt(pow(sin(kx), 2.0))) * th;
	} else {
		tmp = sin(th) / (hypot(sin(kx), ky) / ky);
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if (Math.sin(ky) <= -0.05) {
		tmp = (Math.sin(ky) / Math.sqrt(Math.pow(Math.sin(kx), 2.0))) * th;
	} else {
		tmp = Math.sin(th) / (Math.hypot(Math.sin(kx), ky) / ky);
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if math.sin(ky) <= -0.05:
		tmp = (math.sin(ky) / math.sqrt(math.pow(math.sin(kx), 2.0))) * th
	else:
		tmp = math.sin(th) / (math.hypot(math.sin(kx), ky) / ky)
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if (sin(ky) <= -0.05)
		tmp = Float64(Float64(sin(ky) / sqrt((sin(kx) ^ 2.0))) * th);
	else
		tmp = Float64(sin(th) / Float64(hypot(sin(kx), ky) / ky));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if (sin(ky) <= -0.05)
		tmp = (sin(ky) / sqrt((sin(kx) ^ 2.0))) * th;
	else
		tmp = sin(th) / (hypot(sin(kx), ky) / ky);
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[N[Sin[ky], $MachinePrecision], -0.05], N[(N[(N[Sin[ky], $MachinePrecision] / N[Sqrt[N[Power[N[Sin[kx], $MachinePrecision], 2.0], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * th), $MachinePrecision], N[(N[Sin[th], $MachinePrecision] / N[(N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision] / ky), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (sin.f64 ky) < -0.050000000000000003

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

   2. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   3. Step-by-step derivation

      1. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      2. lower-pow.f64 N/A

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      3. lower-sin.f64 40.0

         \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. Applied rewrites 40.0%

      \[\leadsto \frac{\sin ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

   5. Taylor expanded in th around 0

      \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \color{blue}{th} \]
   6. Step-by-step derivation

   7. Applied rewrites 21.1%

      \[\leadsto \frac{\sin ky}{\sqrt{{\sin kx}^{2}}} \cdot \color{blue}{th} \]

   if -0.050000000000000003 < (sin.f64 ky)

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\sin th \cdot \frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

      3. lift-/.f64 N/A

         \[\leadsto \sin th \cdot \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

      4. div-flip N/A

         \[\leadsto \sin th \cdot \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      5. mult-flip-rev N/A

         \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      6. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      7. lower-/.f64 94.2

         \[\leadsto \frac{\sin th}{\color{blue}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \frac{\sin th}{\frac{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

      9. lift-+.f64 N/A

         \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

      10. lift-pow.f64 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}}{\sin ky}} \]

      11. unpow2 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}}{\sin ky}} \]

      12. lift-pow.f64 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}}{\sin ky}} \]

      13. unpow2 N/A

          \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}}{\sin ky}} \]

      14. lower-hypot.f64 99.7

          \[\leadsto \frac{\sin th}{\frac{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}}{\sin ky}} \]

   3. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \sin ky\right)}{\sin ky}}} \]

   4. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]

   7. Taylor expanded in ky around 0

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
   8. Step-by-step derivation

   9. Applied rewrites 64.4%

      \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 58.2% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{ky}} \end{array} \]

FPCore

(FPCore (kx ky th) :precision binary64 (/ (sin th) (/ (hypot (sin kx) ky) ky)))

C

double code(double kx, double ky, double th) {
	return sin(th) / (hypot(sin(kx), ky) / ky);
}

Java

public static double code(double kx, double ky, double th) {
	return Math.sin(th) / (Math.hypot(Math.sin(kx), ky) / ky);
}

Python

def code(kx, ky, th):
	return math.sin(th) / (math.hypot(math.sin(kx), ky) / ky)

Julia

function code(kx, ky, th)
	return Float64(sin(th) / Float64(hypot(sin(kx), ky) / ky))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = sin(th) / (hypot(sin(kx), ky) / ky);
end

Wolfram

code[kx_, ky_, th_] := N[(N[Sin[th], $MachinePrecision] / N[(N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision] / ky), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
2. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{\sin th \cdot \frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   3. lift-/.f64 N/A

      \[\leadsto \sin th \cdot \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \]

   4. div-flip N/A

      \[\leadsto \sin th \cdot \color{blue}{\frac{1}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

   5. mult-flip-rev N/A

      \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

   6. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin th}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

   7. lower-/.f64 94.2

      \[\leadsto \frac{\sin th}{\color{blue}{\frac{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}{\sin ky}}} \]

   8. lift-sqrt.f64 N/A

      \[\leadsto \frac{\sin th}{\frac{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

   9. lift-+.f64 N/A

      \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}}{\sin ky}} \]

   10. lift-pow.f64 N/A

       \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}}{\sin ky}} \]

   11. unpow2 N/A

       \[\leadsto \frac{\sin th}{\frac{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}}{\sin ky}} \]

   12. lift-pow.f64 N/A

       \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}}{\sin ky}} \]

   13. unpow2 N/A

       \[\leadsto \frac{\sin th}{\frac{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}}{\sin ky}} \]

   14. lower-hypot.f64 99.7

       \[\leadsto \frac{\sin th}{\frac{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}}{\sin ky}} \]

3. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \sin ky\right)}{\sin ky}}} \]

4. Taylor expanded in ky around 0

   \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]
5. Step-by-step derivation

6. Applied rewrites 51.1%

   \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)}{\sin ky}} \]

7. Taylor expanded in ky around 0

   \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
8. Step-by-step derivation

9. Applied rewrites 64.4%

   \[\leadsto \frac{\sin th}{\frac{\mathsf{hypot}\left(\sin kx, ky\right)}{\color{blue}{ky}}} \]
10. Add Preprocessing

Alternative 10: 58.2% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \frac{ky}{\mathsf{hypot}\left(ky, \sin kx\right)} \cdot \sin th \end{array} \]

FPCore

(FPCore (kx ky th) :precision binary64 (* (/ ky (hypot ky (sin kx))) (sin th)))

C

double code(double kx, double ky, double th) {
	return (ky / hypot(ky, sin(kx))) * sin(th);
}

Java

public static double code(double kx, double ky, double th) {
	return (ky / Math.hypot(ky, Math.sin(kx))) * Math.sin(th);
}

Python

def code(kx, ky, th):
	return (ky / math.hypot(ky, math.sin(kx))) * math.sin(th)

Julia

function code(kx, ky, th)
	return Float64(Float64(ky / hypot(ky, sin(kx))) * sin(th))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = (ky / hypot(ky, sin(kx))) * sin(th);
end

Wolfram

code[kx_, ky_, th_] := N[(N[(ky / N[Sqrt[ky ^ 2 + N[Sin[kx], $MachinePrecision] ^ 2], $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

   2. mult-flip N/A

      \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

   3. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

   4. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

   5. metadata-eval N/A

      \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

   6. lower-/.f64 N/A

      \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

   7. metadata-eval 94.1

      \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

   8. lift-sqrt.f64 N/A

      \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

   9. lift-+.f64 N/A

      \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

   10. lift-pow.f64 N/A

       \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

   11. unpow2 N/A

       \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

   12. lift-pow.f64 N/A

       \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

   13. unpow2 N/A

       \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

   14. lower-hypot.f64 99.5

       \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

3. Applied rewrites 99.5%

   \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

4. Taylor expanded in ky around 0

   \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
5. Step-by-step derivation

6. Applied rewrites 51.1%

   \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

7. Taylor expanded in ky around 0

   \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
8. Step-by-step derivation

9. Applied rewrites 64.3%

   \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
10. Step-by-step derivation

    1. lift-*.f64 N/A

       \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right)} \cdot \sin th \]

    2. *-commutative N/A

       \[\leadsto \color{blue}{\left(ky \cdot \frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)}\right)} \cdot \sin th \]

    3. lift-/.f64 N/A

       \[\leadsto \left(ky \cdot \color{blue}{\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)}}\right) \cdot \sin th \]

    4. mult-flip-rev N/A

       \[\leadsto \color{blue}{\frac{ky}{\mathsf{hypot}\left(\sin kx, ky\right)}} \cdot \sin th \]

    5. lower-/.f64 64.4

       \[\leadsto \color{blue}{\frac{ky}{\mathsf{hypot}\left(\sin kx, ky\right)}} \cdot \sin th \]

    6. lift-hypot.f64 N/A

       \[\leadsto \frac{ky}{\color{blue}{\sqrt{\sin kx \cdot \sin kx + ky \cdot ky}}} \cdot \sin th \]

    7. pow2 N/A

       \[\leadsto \frac{ky}{\sqrt{\color{blue}{{\sin kx}^{2}} + ky \cdot ky}} \cdot \sin th \]

    8. lift-pow.f64 N/A

       \[\leadsto \frac{ky}{\sqrt{\color{blue}{{\sin kx}^{2}} + ky \cdot ky}} \cdot \sin th \]

    9. +-commutative N/A

       \[\leadsto \frac{ky}{\sqrt{\color{blue}{ky \cdot ky + {\sin kx}^{2}}}} \cdot \sin th \]

    10. lift-pow.f64 N/A

        \[\leadsto \frac{ky}{\sqrt{ky \cdot ky + \color{blue}{{\sin kx}^{2}}}} \cdot \sin th \]

    11. pow2 N/A

        \[\leadsto \frac{ky}{\sqrt{ky \cdot ky + \color{blue}{\sin kx \cdot \sin kx}}} \cdot \sin th \]

    12. lower-hypot.f64 64.4

        \[\leadsto \frac{ky}{\color{blue}{\mathsf{hypot}\left(ky, \sin kx\right)}} \cdot \sin th \]

11. Applied rewrites 64.4%

    \[\leadsto \color{blue}{\frac{ky}{\mathsf{hypot}\left(ky, \sin kx\right)}} \cdot \sin th \]
12. Add Preprocessing

Alternative 11: 56.6% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;{\sin kx}^{2} \leq 5 \cdot 10^{-6}:\\ \;\;\;\;\left(\frac{1}{\mathsf{hypot}\left(kx \cdot \left(1 + -0.16666666666666666 \cdot {kx}^{2}\right), ky\right)} \cdot ky\right) \cdot \sin th\\ \mathbf{else}:\\ \;\;\;\;\frac{ky}{\sqrt{0.5 - 0.5 \cdot \cos \left(kx + kx\right)}} \cdot \sin th\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= (pow (sin kx) 2.0) 5e-6)
   (*
    (*
     (/ 1.0 (hypot (* kx (+ 1.0 (* -0.16666666666666666 (pow kx 2.0)))) ky))
     ky)
    (sin th))
   (* (/ ky (sqrt (- 0.5 (* 0.5 (cos (+ kx kx)))))) (sin th))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if (pow(sin(kx), 2.0) <= 5e-6) {
		tmp = ((1.0 / hypot((kx * (1.0 + (-0.16666666666666666 * pow(kx, 2.0)))), ky)) * ky) * sin(th);
	} else {
		tmp = (ky / sqrt((0.5 - (0.5 * cos((kx + kx)))))) * sin(th);
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if (Math.pow(Math.sin(kx), 2.0) <= 5e-6) {
		tmp = ((1.0 / Math.hypot((kx * (1.0 + (-0.16666666666666666 * Math.pow(kx, 2.0)))), ky)) * ky) * Math.sin(th);
	} else {
		tmp = (ky / Math.sqrt((0.5 - (0.5 * Math.cos((kx + kx)))))) * Math.sin(th);
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if math.pow(math.sin(kx), 2.0) <= 5e-6:
		tmp = ((1.0 / math.hypot((kx * (1.0 + (-0.16666666666666666 * math.pow(kx, 2.0)))), ky)) * ky) * math.sin(th)
	else:
		tmp = (ky / math.sqrt((0.5 - (0.5 * math.cos((kx + kx)))))) * math.sin(th)
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if ((sin(kx) ^ 2.0) <= 5e-6)
		tmp = Float64(Float64(Float64(1.0 / hypot(Float64(kx * Float64(1.0 + Float64(-0.16666666666666666 * (kx ^ 2.0)))), ky)) * ky) * sin(th));
	else
		tmp = Float64(Float64(ky / sqrt(Float64(0.5 - Float64(0.5 * cos(Float64(kx + kx)))))) * sin(th));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if ((sin(kx) ^ 2.0) <= 5e-6)
		tmp = ((1.0 / hypot((kx * (1.0 + (-0.16666666666666666 * (kx ^ 2.0)))), ky)) * ky) * sin(th);
	else
		tmp = (ky / sqrt((0.5 - (0.5 * cos((kx + kx)))))) * sin(th);
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[N[Power[N[Sin[kx], $MachinePrecision], 2.0], $MachinePrecision], 5e-6], N[(N[(N[(1.0 / N[Sqrt[N[(kx * N[(1.0 + N[(-0.16666666666666666 * N[Power[kx, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision], N[(N[(ky / N[Sqrt[N[(0.5 - N[(0.5 * N[Cos[N[(kx + kx), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (pow.f64 (sin.f64 kx) #s(literal 2 binary64)) < 5.00000000000000041e-6

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      5. metadata-eval N/A

         \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      6. lower-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      7. metadata-eval 94.1

         \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      9. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      10. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      11. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      12. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      13. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

      14. lower-hypot.f64 99.5

          \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

   3. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

   4. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

   7. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
   8. Step-by-step derivation

   9. Applied rewrites 64.3%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]

   10. Taylor expanded in kx around 0

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx \cdot \left(1 + \frac{-1}{6} \cdot {kx}^{2}\right)}, ky\right)} \cdot ky\right) \cdot \sin th \]
   11. Step-by-step derivation

       1. lower-*.f64 N/A

          \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(kx \cdot \color{blue}{\left(1 + \frac{-1}{6} \cdot {kx}^{2}\right)}, ky\right)} \cdot ky\right) \cdot \sin th \]

       2. lower-+.f64 N/A

          \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(kx \cdot \left(1 + \color{blue}{\frac{-1}{6} \cdot {kx}^{2}}\right), ky\right)} \cdot ky\right) \cdot \sin th \]

       3. lower-*.f64 N/A

          \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(kx \cdot \left(1 + \frac{-1}{6} \cdot \color{blue}{{kx}^{2}}\right), ky\right)} \cdot ky\right) \cdot \sin th \]

       4. lower-pow.f64 45.5

          \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(kx \cdot \left(1 + -0.16666666666666666 \cdot {kx}^{\color{blue}{2}}\right), ky\right)} \cdot ky\right) \cdot \sin th \]

   12. Applied rewrites 45.5%

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx \cdot \left(1 + -0.16666666666666666 \cdot {kx}^{2}\right)}, ky\right)} \cdot ky\right) \cdot \sin th \]

   if 5.00000000000000041e-6 < (pow.f64 (sin.f64 kx) #s(literal 2 binary64))

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

   2. Taylor expanded in ky around 0

      \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      3. lower-pow.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      4. lower-sin.f64 34.8

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. Applied rewrites 34.8%

      \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   5. Step-by-step derivation

      1. lift-pow.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      2. pow2 N/A

         \[\leadsto \frac{ky}{\sqrt{\sin kx \cdot \sin kx}} \cdot \sin th \]

      3. lift-sin.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{\sin kx \cdot \sin kx}} \cdot \sin th \]

      4. lift-sin.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{\sin kx \cdot \sin kx}} \cdot \sin th \]

      5. sqr-sin-a N/A

         \[\leadsto \frac{ky}{\sqrt{\frac{1}{2} - \frac{1}{2} \cdot \cos \left(2 \cdot kx\right)}} \cdot \sin th \]

      6. lower--.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{\frac{1}{2} - \frac{1}{2} \cdot \cos \left(2 \cdot kx\right)}} \cdot \sin th \]

      7. count-2-rev N/A

         \[\leadsto \frac{ky}{\sqrt{\frac{1}{2} - \frac{1}{2} \cdot \cos \left(kx + kx\right)}} \cdot \sin th \]

      8. lower-*.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{\frac{1}{2} - \frac{1}{2} \cdot \cos \left(kx + kx\right)}} \cdot \sin th \]

      9. lower-cos.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{\frac{1}{2} - \frac{1}{2} \cdot \cos \left(kx + kx\right)}} \cdot \sin th \]

      10. lower-+.f64 26.0

          \[\leadsto \frac{ky}{\sqrt{0.5 - 0.5 \cdot \cos \left(kx + kx\right)}} \cdot \sin th \]

   6. Applied rewrites 26.0%

      \[\leadsto \frac{ky}{\sqrt{0.5 - 0.5 \cdot \cos \left(kx + kx\right)}} \cdot \sin th \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 56.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin kx \leq -0.25:\\ \;\;\;\;\left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot th\\ \mathbf{elif}\;\sin kx \leq 4 \cdot 10^{-6}:\\ \;\;\;\;\left(\frac{1}{\mathsf{hypot}\left(kx, ky\right)} \cdot ky\right) \cdot \sin th\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{1}{\sin kx} \cdot ky\right) \cdot \sin th\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= (sin kx) -0.25)
   (* (* (/ 1.0 (hypot (sin kx) ky)) ky) th)
   (if (<= (sin kx) 4e-6)
     (* (* (/ 1.0 (hypot kx ky)) ky) (sin th))
     (* (* (/ 1.0 (sin kx)) ky) (sin th)))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if (sin(kx) <= -0.25) {
		tmp = ((1.0 / hypot(sin(kx), ky)) * ky) * th;
	} else if (sin(kx) <= 4e-6) {
		tmp = ((1.0 / hypot(kx, ky)) * ky) * sin(th);
	} else {
		tmp = ((1.0 / sin(kx)) * ky) * sin(th);
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if (Math.sin(kx) <= -0.25) {
		tmp = ((1.0 / Math.hypot(Math.sin(kx), ky)) * ky) * th;
	} else if (Math.sin(kx) <= 4e-6) {
		tmp = ((1.0 / Math.hypot(kx, ky)) * ky) * Math.sin(th);
	} else {
		tmp = ((1.0 / Math.sin(kx)) * ky) * Math.sin(th);
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if math.sin(kx) <= -0.25:
		tmp = ((1.0 / math.hypot(math.sin(kx), ky)) * ky) * th
	elif math.sin(kx) <= 4e-6:
		tmp = ((1.0 / math.hypot(kx, ky)) * ky) * math.sin(th)
	else:
		tmp = ((1.0 / math.sin(kx)) * ky) * math.sin(th)
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if (sin(kx) <= -0.25)
		tmp = Float64(Float64(Float64(1.0 / hypot(sin(kx), ky)) * ky) * th);
	elseif (sin(kx) <= 4e-6)
		tmp = Float64(Float64(Float64(1.0 / hypot(kx, ky)) * ky) * sin(th));
	else
		tmp = Float64(Float64(Float64(1.0 / sin(kx)) * ky) * sin(th));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if (sin(kx) <= -0.25)
		tmp = ((1.0 / hypot(sin(kx), ky)) * ky) * th;
	elseif (sin(kx) <= 4e-6)
		tmp = ((1.0 / hypot(kx, ky)) * ky) * sin(th);
	else
		tmp = ((1.0 / sin(kx)) * ky) * sin(th);
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[N[Sin[kx], $MachinePrecision], -0.25], N[(N[(N[(1.0 / N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * th), $MachinePrecision], If[LessEqual[N[Sin[kx], $MachinePrecision], 4e-6], N[(N[(N[(1.0 / N[Sqrt[kx ^ 2 + ky ^ 2], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision], N[(N[(N[(1.0 / N[Sin[kx], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (sin.f64 kx) < -0.25

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      5. metadata-eval N/A

         \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      6. lower-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      7. metadata-eval 94.1

         \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      9. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      10. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      11. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      12. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      13. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

      14. lower-hypot.f64 99.5

          \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

   3. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

   4. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

   7. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
   8. Step-by-step derivation

   9. Applied rewrites 64.3%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]

   10. Taylor expanded in th around 0

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot \color{blue}{th} \]
   11. Step-by-step derivation

   12. Applied rewrites 33.6%

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot \color{blue}{th} \]

   if -0.25 < (sin.f64 kx) < 3.99999999999999982e-6

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      5. metadata-eval N/A

         \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      6. lower-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      7. metadata-eval 94.1

         \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      9. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      10. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      11. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      12. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      13. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

      14. lower-hypot.f64 99.5

          \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

   3. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

   4. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

   7. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
   8. Step-by-step derivation

   9. Applied rewrites 64.3%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]

   10. Taylor expanded in kx around 0

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx}, ky\right)} \cdot ky\right) \cdot \sin th \]
   11. Step-by-step derivation

   12. Applied rewrites 46.2%

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx}, ky\right)} \cdot ky\right) \cdot \sin th \]

   if 3.99999999999999982e-6 < (sin.f64 kx)

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

   2. Taylor expanded in ky around 0

      \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      3. lower-pow.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      4. lower-sin.f64 34.8

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. Applied rewrites 34.8%

      \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

      2. mult-flip N/A

         \[\leadsto \left(ky \cdot \color{blue}{\frac{1}{\sqrt{{\sin kx}^{2}}}}\right) \cdot \sin th \]

      3. *-commutative N/A

         \[\leadsto \left(\frac{1}{\sqrt{{\sin kx}^{2}}} \cdot \color{blue}{ky}\right) \cdot \sin th \]

      4. lift-sqrt.f64 N/A

         \[\leadsto \left(\frac{1}{\sqrt{{\sin kx}^{2}}} \cdot ky\right) \cdot \sin th \]

      5. pow1/2 N/A

         \[\leadsto \left(\frac{1}{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}} \cdot ky\right) \cdot \sin th \]

      6. lift-pow.f64 N/A

         \[\leadsto \left(\frac{1}{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}} \cdot ky\right) \cdot \sin th \]

      7. pow2 N/A

         \[\leadsto \left(\frac{1}{{\left(\sin kx \cdot \sin kx\right)}^{\frac{1}{2}}} \cdot ky\right) \cdot \sin th \]

      8. unpow-prod-down N/A

         \[\leadsto \left(\frac{1}{{\sin kx}^{\frac{1}{2}} \cdot {\sin kx}^{\frac{1}{2}}} \cdot ky\right) \cdot \sin th \]

      9. metadata-eval N/A

         \[\leadsto \left(\frac{1}{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\frac{1}{2}}} \cdot ky\right) \cdot \sin th \]

      10. metadata-eval N/A

          \[\leadsto \left(\frac{1}{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\left(\frac{1}{2}\right)}} \cdot ky\right) \cdot \sin th \]

      11. sqr-pow N/A

          \[\leadsto \left(\frac{1}{{\sin kx}^{1}} \cdot ky\right) \cdot \sin th \]

      12. unpow1 N/A

          \[\leadsto \left(\frac{1}{\sin kx} \cdot ky\right) \cdot \sin th \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\frac{1}{\sin kx} \cdot \color{blue}{ky}\right) \cdot \sin th \]

      14. lower-/.f64 24.1

          \[\leadsto \left(\frac{1}{\sin kx} \cdot ky\right) \cdot \sin th \]

   6. Applied rewrites 24.1%

      \[\leadsto \left(\frac{1}{\sin kx} \cdot \color{blue}{ky}\right) \cdot \sin th \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 13: 54.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin kx \leq -0.25:\\ \;\;\;\;\left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot th\\ \mathbf{elif}\;\sin kx \leq 4 \cdot 10^{-6}:\\ \;\;\;\;\left(\frac{1}{\mathsf{hypot}\left(kx, ky\right)} \cdot ky\right) \cdot \sin th\\ \mathbf{else}:\\ \;\;\;\;\sin th \cdot \frac{ky}{\sin kx}\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= (sin kx) -0.25)
   (* (* (/ 1.0 (hypot (sin kx) ky)) ky) th)
   (if (<= (sin kx) 4e-6)
     (* (* (/ 1.0 (hypot kx ky)) ky) (sin th))
     (* (sin th) (/ ky (sin kx))))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if (sin(kx) <= -0.25) {
		tmp = ((1.0 / hypot(sin(kx), ky)) * ky) * th;
	} else if (sin(kx) <= 4e-6) {
		tmp = ((1.0 / hypot(kx, ky)) * ky) * sin(th);
	} else {
		tmp = sin(th) * (ky / sin(kx));
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if (Math.sin(kx) <= -0.25) {
		tmp = ((1.0 / Math.hypot(Math.sin(kx), ky)) * ky) * th;
	} else if (Math.sin(kx) <= 4e-6) {
		tmp = ((1.0 / Math.hypot(kx, ky)) * ky) * Math.sin(th);
	} else {
		tmp = Math.sin(th) * (ky / Math.sin(kx));
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if math.sin(kx) <= -0.25:
		tmp = ((1.0 / math.hypot(math.sin(kx), ky)) * ky) * th
	elif math.sin(kx) <= 4e-6:
		tmp = ((1.0 / math.hypot(kx, ky)) * ky) * math.sin(th)
	else:
		tmp = math.sin(th) * (ky / math.sin(kx))
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if (sin(kx) <= -0.25)
		tmp = Float64(Float64(Float64(1.0 / hypot(sin(kx), ky)) * ky) * th);
	elseif (sin(kx) <= 4e-6)
		tmp = Float64(Float64(Float64(1.0 / hypot(kx, ky)) * ky) * sin(th));
	else
		tmp = Float64(sin(th) * Float64(ky / sin(kx)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if (sin(kx) <= -0.25)
		tmp = ((1.0 / hypot(sin(kx), ky)) * ky) * th;
	elseif (sin(kx) <= 4e-6)
		tmp = ((1.0 / hypot(kx, ky)) * ky) * sin(th);
	else
		tmp = sin(th) * (ky / sin(kx));
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[N[Sin[kx], $MachinePrecision], -0.25], N[(N[(N[(1.0 / N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * th), $MachinePrecision], If[LessEqual[N[Sin[kx], $MachinePrecision], 4e-6], N[(N[(N[(1.0 / N[Sqrt[kx ^ 2 + ky ^ 2], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision], N[(N[Sin[th], $MachinePrecision] * N[(ky / N[Sin[kx], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (sin.f64 kx) < -0.25

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      5. metadata-eval N/A

         \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      6. lower-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      7. metadata-eval 94.1

         \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      9. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      10. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      11. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      12. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      13. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

      14. lower-hypot.f64 99.5

          \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

   3. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

   4. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

   7. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
   8. Step-by-step derivation

   9. Applied rewrites 64.3%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]

   10. Taylor expanded in th around 0

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot \color{blue}{th} \]
   11. Step-by-step derivation

   12. Applied rewrites 33.6%

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot \color{blue}{th} \]

   if -0.25 < (sin.f64 kx) < 3.99999999999999982e-6

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      5. metadata-eval N/A

         \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      6. lower-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      7. metadata-eval 94.1

         \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      9. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      10. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      11. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      12. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      13. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

      14. lower-hypot.f64 99.5

          \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

   3. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

   4. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

   7. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
   8. Step-by-step derivation

   9. Applied rewrites 64.3%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]

   10. Taylor expanded in kx around 0

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx}, ky\right)} \cdot ky\right) \cdot \sin th \]
   11. Step-by-step derivation

   12. Applied rewrites 46.2%

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx}, ky\right)} \cdot ky\right) \cdot \sin th \]

   if 3.99999999999999982e-6 < (sin.f64 kx)

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

   2. Taylor expanded in ky around 0

      \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      3. lower-pow.f64 N/A

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

      4. lower-sin.f64 34.8

         \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. Applied rewrites 34.8%

      \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\sin th \cdot \frac{ky}{\sqrt{{\sin kx}^{2}}}} \]

      3. lower-*.f64 34.8

         \[\leadsto \color{blue}{\sin th \cdot \frac{ky}{\sqrt{{\sin kx}^{2}}}} \]

      4. lift-sqrt.f64 N/A

         \[\leadsto \sin th \cdot \frac{ky}{\sqrt{{\sin kx}^{2}}} \]

      5. pow1/2 N/A

         \[\leadsto \sin th \cdot \frac{ky}{{\left({\sin kx}^{2}\right)}^{\color{blue}{\frac{1}{2}}}} \]

      6. lift-pow.f64 N/A

         \[\leadsto \sin th \cdot \frac{ky}{{\left({\sin kx}^{2}\right)}^{\frac{1}{2}}} \]

      7. pow2 N/A

         \[\leadsto \sin th \cdot \frac{ky}{{\left(\sin kx \cdot \sin kx\right)}^{\frac{1}{2}}} \]

      8. unpow-prod-down N/A

         \[\leadsto \sin th \cdot \frac{ky}{{\sin kx}^{\frac{1}{2}} \cdot \color{blue}{{\sin kx}^{\frac{1}{2}}}} \]

      9. metadata-eval N/A

         \[\leadsto \sin th \cdot \frac{ky}{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\frac{1}{2}}} \]

      10. metadata-eval N/A

          \[\leadsto \sin th \cdot \frac{ky}{{\sin kx}^{\left(\frac{1}{2}\right)} \cdot {\sin kx}^{\left(\frac{1}{\color{blue}{2}}\right)}} \]

      11. sqr-pow N/A

          \[\leadsto \sin th \cdot \frac{ky}{{\sin kx}^{\color{blue}{1}}} \]

      12. unpow1 24.1

          \[\leadsto \sin th \cdot \frac{ky}{\sin kx} \]

   6. Applied rewrites 24.1%

      \[\leadsto \color{blue}{\sin th \cdot \frac{ky}{\sin kx}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 14: 46.2% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;th \leq 0.00062:\\ \;\;\;\;\left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot th\\ \mathbf{else}:\\ \;\;\;\;\left(\frac{1}{\mathsf{hypot}\left(kx, ky\right)} \cdot ky\right) \cdot \sin th\\ \end{array} \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (if (<= th 0.00062)
   (* (* (/ 1.0 (hypot (sin kx) ky)) ky) th)
   (* (* (/ 1.0 (hypot kx ky)) ky) (sin th))))

C

double code(double kx, double ky, double th) {
	double tmp;
	if (th <= 0.00062) {
		tmp = ((1.0 / hypot(sin(kx), ky)) * ky) * th;
	} else {
		tmp = ((1.0 / hypot(kx, ky)) * ky) * sin(th);
	}
	return tmp;
}

Java

public static double code(double kx, double ky, double th) {
	double tmp;
	if (th <= 0.00062) {
		tmp = ((1.0 / Math.hypot(Math.sin(kx), ky)) * ky) * th;
	} else {
		tmp = ((1.0 / Math.hypot(kx, ky)) * ky) * Math.sin(th);
	}
	return tmp;
}

Python

def code(kx, ky, th):
	tmp = 0
	if th <= 0.00062:
		tmp = ((1.0 / math.hypot(math.sin(kx), ky)) * ky) * th
	else:
		tmp = ((1.0 / math.hypot(kx, ky)) * ky) * math.sin(th)
	return tmp

Julia

function code(kx, ky, th)
	tmp = 0.0
	if (th <= 0.00062)
		tmp = Float64(Float64(Float64(1.0 / hypot(sin(kx), ky)) * ky) * th);
	else
		tmp = Float64(Float64(Float64(1.0 / hypot(kx, ky)) * ky) * sin(th));
	end
	return tmp
end

MATLAB

function tmp_2 = code(kx, ky, th)
	tmp = 0.0;
	if (th <= 0.00062)
		tmp = ((1.0 / hypot(sin(kx), ky)) * ky) * th;
	else
		tmp = ((1.0 / hypot(kx, ky)) * ky) * sin(th);
	end
	tmp_2 = tmp;
end

Wolfram

code[kx_, ky_, th_] := If[LessEqual[th, 0.00062], N[(N[(N[(1.0 / N[Sqrt[N[Sin[kx], $MachinePrecision] ^ 2 + ky ^ 2], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * th), $MachinePrecision], N[(N[(N[(1.0 / N[Sqrt[kx ^ 2 + ky ^ 2], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if th < 6.2e-4

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      5. metadata-eval N/A

         \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      6. lower-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      7. metadata-eval 94.1

         \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      9. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      10. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      11. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      12. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      13. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

      14. lower-hypot.f64 99.5

          \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

   3. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

   4. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

   7. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
   8. Step-by-step derivation

   9. Applied rewrites 64.3%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]

   10. Taylor expanded in th around 0

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot \color{blue}{th} \]
   11. Step-by-step derivation

   12. Applied rewrites 33.6%

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot ky\right) \cdot \color{blue}{th} \]

   if 6.2e-4 < th

   1. Initial program 94.2%

      \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

      2. mult-flip N/A

         \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

      5. metadata-eval N/A

         \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      6. lower-/.f64 N/A

         \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      7. metadata-eval 94.1

         \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      8. lift-sqrt.f64 N/A

         \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      9. lift-+.f64 N/A

         \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      10. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      11. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

      12. lift-pow.f64 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

      13. unpow2 N/A

          \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

      14. lower-hypot.f64 99.5

          \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

   3. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

   4. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
   5. Step-by-step derivation

   6. Applied rewrites 51.1%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

   7. Taylor expanded in ky around 0

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
   8. Step-by-step derivation

   9. Applied rewrites 64.3%

      \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]

   10. Taylor expanded in kx around 0

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx}, ky\right)} \cdot ky\right) \cdot \sin th \]
   11. Step-by-step derivation

   12. Applied rewrites 46.2%

       \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx}, ky\right)} \cdot ky\right) \cdot \sin th \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 43.6% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ \left(\frac{1}{\mathsf{hypot}\left(kx, ky\right)} \cdot ky\right) \cdot \sin th \end{array} \]

FPCore

(FPCore (kx ky th)
 :precision binary64
 (* (* (/ 1.0 (hypot kx ky)) ky) (sin th)))

C

double code(double kx, double ky, double th) {
	return ((1.0 / hypot(kx, ky)) * ky) * sin(th);
}

Java

public static double code(double kx, double ky, double th) {
	return ((1.0 / Math.hypot(kx, ky)) * ky) * Math.sin(th);
}

Python

def code(kx, ky, th):
	return ((1.0 / math.hypot(kx, ky)) * ky) * math.sin(th)

Julia

function code(kx, ky, th)
	return Float64(Float64(Float64(1.0 / hypot(kx, ky)) * ky) * sin(th))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = ((1.0 / hypot(kx, ky)) * ky) * sin(th);
end

Wolfram

code[kx_, ky_, th_] := N[(N[(N[(1.0 / N[Sqrt[kx ^ 2 + ky ^ 2], $MachinePrecision]), $MachinePrecision] * ky), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin th \]

   2. mult-flip N/A

      \[\leadsto \color{blue}{\left(\sin ky \cdot \frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}\right)} \cdot \sin th \]

   3. *-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

   4. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\frac{1}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right)} \cdot \sin th \]

   5. metadata-eval N/A

      \[\leadsto \left(\frac{\color{blue}{\frac{2}{2}}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

   6. lower-/.f64 N/A

      \[\leadsto \left(\color{blue}{\frac{\frac{2}{2}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

   7. metadata-eval 94.1

      \[\leadsto \left(\frac{\color{blue}{1}}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

   8. lift-sqrt.f64 N/A

      \[\leadsto \left(\frac{1}{\color{blue}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

   9. lift-+.f64 N/A

      \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2} + {\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

   10. lift-pow.f64 N/A

       \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{{\sin kx}^{2}} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

   11. unpow2 N/A

       \[\leadsto \left(\frac{1}{\sqrt{\color{blue}{\sin kx \cdot \sin kx} + {\sin ky}^{2}}} \cdot \sin ky\right) \cdot \sin th \]

   12. lift-pow.f64 N/A

       \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{{\sin ky}^{2}}}} \cdot \sin ky\right) \cdot \sin th \]

   13. unpow2 N/A

       \[\leadsto \left(\frac{1}{\sqrt{\sin kx \cdot \sin kx + \color{blue}{\sin ky \cdot \sin ky}}} \cdot \sin ky\right) \cdot \sin th \]

   14. lower-hypot.f64 99.5

       \[\leadsto \left(\frac{1}{\color{blue}{\mathsf{hypot}\left(\sin kx, \sin ky\right)}} \cdot \sin ky\right) \cdot \sin th \]

3. Applied rewrites 99.5%

   \[\leadsto \color{blue}{\left(\frac{1}{\mathsf{hypot}\left(\sin kx, \sin ky\right)} \cdot \sin ky\right)} \cdot \sin th \]

4. Taylor expanded in ky around 0

   \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]
5. Step-by-step derivation

6. Applied rewrites 51.1%

   \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, \color{blue}{ky}\right)} \cdot \sin ky\right) \cdot \sin th \]

7. Taylor expanded in ky around 0

   \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]
8. Step-by-step derivation

9. Applied rewrites 64.3%

   \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\sin kx, ky\right)} \cdot \color{blue}{ky}\right) \cdot \sin th \]

10. Taylor expanded in kx around 0

    \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx}, ky\right)} \cdot ky\right) \cdot \sin th \]
11. Step-by-step derivation

12. Applied rewrites 46.2%

    \[\leadsto \left(\frac{1}{\mathsf{hypot}\left(\color{blue}{kx}, ky\right)} \cdot ky\right) \cdot \sin th \]
13. Add Preprocessing

Alternative 16: 15.8% accurate, 4.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\frac{kx}{ky}} \cdot \sin th \end{array} \]

FPCore

(FPCore (kx ky th) :precision binary64 (* (/ 1.0 (/ kx ky)) (sin th)))

C

double code(double kx, double ky, double th) {
	return (1.0 / (kx / ky)) * sin(th);
}

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(kx, ky, th)
use fmin_fmax_functions
    real(8), intent (in) :: kx
    real(8), intent (in) :: ky
    real(8), intent (in) :: th
    code = (1.0d0 / (kx / ky)) * sin(th)
end function

Java

public static double code(double kx, double ky, double th) {
	return (1.0 / (kx / ky)) * Math.sin(th);
}

Python

def code(kx, ky, th):
	return (1.0 / (kx / ky)) * math.sin(th)

Julia

function code(kx, ky, th)
	return Float64(Float64(1.0 / Float64(kx / ky)) * sin(th))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = (1.0 / (kx / ky)) * sin(th);
end

Wolfram

code[kx_, ky_, th_] := N[(N[(1.0 / N[(kx / ky), $MachinePrecision]), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

2. Taylor expanded in ky around 0

   \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

   2. lower-sqrt.f64 N/A

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   3. lower-pow.f64 N/A

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. lower-sin.f64 34.8

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

4. Applied rewrites 34.8%

   \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

5. Taylor expanded in kx around 0

   \[\leadsto \frac{ky}{\color{blue}{kx}} \cdot \sin th \]
6. Step-by-step derivation

   1. lower-/.f64 15.8

      \[\leadsto \frac{ky}{kx} \cdot \sin th \]

7. Applied rewrites 15.8%

   \[\leadsto \frac{ky}{\color{blue}{kx}} \cdot \sin th \]
8. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \frac{ky}{kx} \cdot \sin th \]

   2. div-flip N/A

      \[\leadsto \frac{1}{\frac{kx}{\color{blue}{ky}}} \cdot \sin th \]

   3. lower-/.f64 N/A

      \[\leadsto \frac{1}{\frac{kx}{\color{blue}{ky}}} \cdot \sin th \]

   4. lower-/.f64 15.8

      \[\leadsto \frac{1}{\frac{kx}{ky}} \cdot \sin th \]

9. Applied rewrites 15.8%

   \[\leadsto \frac{1}{\frac{kx}{\color{blue}{ky}}} \cdot \sin th \]
10. Add Preprocessing

Alternative 17: 15.8% accurate, 4.4× speedup

Math

\[\begin{array}{l} \\ \frac{ky}{kx} \cdot \sin th \end{array} \]

FPCore

(FPCore (kx ky th) :precision binary64 (* (/ ky kx) (sin th)))

C

double code(double kx, double ky, double th) {
	return (ky / kx) * sin(th);
}

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(kx, ky, th)
use fmin_fmax_functions
    real(8), intent (in) :: kx
    real(8), intent (in) :: ky
    real(8), intent (in) :: th
    code = (ky / kx) * sin(th)
end function

Java

public static double code(double kx, double ky, double th) {
	return (ky / kx) * Math.sin(th);
}

Python

def code(kx, ky, th):
	return (ky / kx) * math.sin(th)

Julia

function code(kx, ky, th)
	return Float64(Float64(ky / kx) * sin(th))
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = (ky / kx) * sin(th);
end

Wolfram

code[kx_, ky_, th_] := N[(N[(ky / kx), $MachinePrecision] * N[Sin[th], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

2. Taylor expanded in ky around 0

   \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

   2. lower-sqrt.f64 N/A

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   3. lower-pow.f64 N/A

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. lower-sin.f64 34.8

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

4. Applied rewrites 34.8%

   \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

5. Taylor expanded in kx around 0

   \[\leadsto \frac{ky}{\color{blue}{kx}} \cdot \sin th \]
6. Step-by-step derivation

   1. lower-/.f64 15.8

      \[\leadsto \frac{ky}{kx} \cdot \sin th \]

7. Applied rewrites 15.8%

   \[\leadsto \frac{ky}{\color{blue}{kx}} \cdot \sin th \]
8. Add Preprocessing

Alternative 18: 12.6% accurate, 23.3× speedup

Math

\[\begin{array}{l} \\ \frac{ky}{kx} \cdot th \end{array} \]

FPCore

(FPCore (kx ky th) :precision binary64 (* (/ ky kx) th))

C

double code(double kx, double ky, double th) {
	return (ky / kx) * th;
}

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(kx, ky, th)
use fmin_fmax_functions
    real(8), intent (in) :: kx
    real(8), intent (in) :: ky
    real(8), intent (in) :: th
    code = (ky / kx) * th
end function

Java

public static double code(double kx, double ky, double th) {
	return (ky / kx) * th;
}

Python

def code(kx, ky, th):
	return (ky / kx) * th

Julia

function code(kx, ky, th)
	return Float64(Float64(ky / kx) * th)
end

MATLAB

function tmp = code(kx, ky, th)
	tmp = (ky / kx) * th;
end

Wolfram

code[kx_, ky_, th_] := N[(N[(ky / kx), $MachinePrecision] * th), $MachinePrecision]

Derivation

1. Initial program 94.2%

   \[\frac{\sin ky}{\sqrt{{\sin kx}^{2} + {\sin ky}^{2}}} \cdot \sin th \]

2. Taylor expanded in ky around 0

   \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]
3. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{ky}{\color{blue}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

   2. lower-sqrt.f64 N/A

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   3. lower-pow.f64 N/A

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

   4. lower-sin.f64 34.8

      \[\leadsto \frac{ky}{\sqrt{{\sin kx}^{2}}} \cdot \sin th \]

4. Applied rewrites 34.8%

   \[\leadsto \color{blue}{\frac{ky}{\sqrt{{\sin kx}^{2}}}} \cdot \sin th \]

5. Taylor expanded in kx around 0

   \[\leadsto \frac{ky}{\color{blue}{kx}} \cdot \sin th \]
6. Step-by-step derivation

   1. lower-/.f64 15.8

      \[\leadsto \frac{ky}{kx} \cdot \sin th \]

7. Applied rewrites 15.8%

   \[\leadsto \frac{ky}{\color{blue}{kx}} \cdot \sin th \]

8. Taylor expanded in th around 0

   \[\leadsto \frac{ky}{kx} \cdot \color{blue}{th} \]
9. Step-by-step derivation

10. Applied rewrites 12.6%

    \[\leadsto \frac{ky}{kx} \cdot \color{blue}{th} \]
11. Add Preprocessing

Reproduce

herbie shell --seed 2025151 
(FPCore (kx ky th)
  :name "Toniolo and Linder, Equation (3b), real"
  :precision binary64
  (* (/ (sin ky) (sqrt (+ (pow (sin kx) 2.0) (pow (sin ky) 2.0)))) (sin th)))