Linear.Quaternion:$ccos from linear-1.19.1.3

Percentage Accurate: 100.0% → 100.0%

Time: 4.1s

Alternatives: 16

Speedup: 1.0×

• 49.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \sin x \cdot \frac{\sinh y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (sin x) (/ (sinh y) y)))

C

double code(double x, double y) {
	return sin(x) * (sinh(y) / y);
}

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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = sin(x) * (sinh(y) / y)
end function

Java

public static double code(double x, double y) {
	return Math.sin(x) * (Math.sinh(y) / y);
}

Python

def code(x, y):
	return math.sin(x) * (math.sinh(y) / y)

Julia

function code(x, y)
	return Float64(sin(x) * Float64(sinh(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = sin(x) * (sinh(y) / y);
end

Wolfram

code[x_, y_] := N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sin x \cdot \frac{\sinh y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (sin x) (/ (sinh y) y)))

C

double code(double x, double y) {
	return sin(x) * (sinh(y) / y);
}

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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = sin(x) * (sinh(y) / y)
end function

Java

public static double code(double x, double y) {
	return Math.sin(x) * (Math.sinh(y) / y);
}

Python

def code(x, y):
	return math.sin(x) * (math.sinh(y) / y)

Julia

function code(x, y)
	return Float64(sin(x) * Float64(sinh(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = sin(x) * (sinh(y) / y);
end

Wolfram

code[x_, y_] := N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sin x \cdot \frac{\sinh y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (sin x) (/ (sinh y) y)))

C

double code(double x, double y) {
	return sin(x) * (sinh(y) / y);
}

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(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = sin(x) * (sinh(y) / y)
end function

Java

public static double code(double x, double y) {
	return Math.sin(x) * (Math.sinh(y) / y);
}

Python

def code(x, y):
	return math.sin(x) * (math.sinh(y) / y)

Julia

function code(x, y)
	return Float64(sin(x) * Float64(sinh(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = sin(x) * (sinh(y) / y);
end

Wolfram

code[x_, y_] := N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\sin x \cdot \frac{\sinh y}{y} \]
2. Add Preprocessing

Alternative 2: 74.7% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\sinh y}{y}\\ t_1 := \sin x \cdot t\_0\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot t\_0\\ \mathbf{elif}\;t\_1 \leq 1:\\ \;\;\;\;\sin x \cdot \mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (sinh y) y)) (t_1 (* (sin x) t_0)))
   (if (<= t_1 (- INFINITY))
     (* (* (* (* x x) x) -0.16666666666666666) t_0)
     (if (<= t_1 1.0)
       (* (sin x) (fma (* y y) 0.16666666666666666 1.0))
       (* t_0 x)))))

C

double code(double x, double y) {
	double t_0 = sinh(y) / y;
	double t_1 = sin(x) * t_0;
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0;
	} else if (t_1 <= 1.0) {
		tmp = sin(x) * fma((y * y), 0.16666666666666666, 1.0);
	} else {
		tmp = t_0 * x;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(sinh(y) / y)
	t_1 = Float64(sin(x) * t_0)
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(Float64(Float64(x * x) * x) * -0.16666666666666666) * t_0);
	elseif (t_1 <= 1.0)
		tmp = Float64(sin(x) * fma(Float64(y * y), 0.16666666666666666, 1.0));
	else
		tmp = Float64(t_0 * x);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]}, Block[{t$95$1 = N[(N[Sin[x], $MachinePrecision] * t$95$0), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(N[(N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * t$95$0), $MachinePrecision], If[LessEqual[t$95$1, 1.0], N[(N[Sin[x], $MachinePrecision] * N[(N[(y * y), $MachinePrecision] * 0.16666666666666666 + 1.0), $MachinePrecision]), $MachinePrecision], N[(t$95$0 * x), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < -inf.0

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   5. Taylor expanded in x around inf

      \[\leadsto \left(\frac{-1}{6} \cdot \color{blue}{{x}^{3}}\right) \cdot \frac{\sinh y}{y} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      3. unpow3 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      4. pow2 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      6. pow2 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      7. lift-*.f64 14.9

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot \frac{\sinh y}{y} \]

   7. Applied rewrites 14.9%

      \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \color{blue}{-0.16666666666666666}\right) \cdot \frac{\sinh y}{y} \]

   if -inf.0 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < 1

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in y around 0

      \[\leadsto \sin x \cdot \color{blue}{\left(1 + \frac{1}{6} \cdot {y}^{2}\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \sin x \cdot \left(\frac{1}{6} \cdot {y}^{2} + \color{blue}{1}\right) \]

      2. *-commutative N/A

         \[\leadsto \sin x \cdot \left({y}^{2} \cdot \frac{1}{6} + 1\right) \]

      3. lower-fma.f64 N/A

         \[\leadsto \sin x \cdot \mathsf{fma}\left({y}^{2}, \color{blue}{\frac{1}{6}}, 1\right) \]

      4. unpow2 N/A

         \[\leadsto \sin x \cdot \mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \]

      5. lower-*.f64 75.4

         \[\leadsto \sin x \cdot \mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \]

   4. Applied rewrites 75.4%

      \[\leadsto \sin x \cdot \color{blue}{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right)} \]

   if 1 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 74.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\sinh y}{y}\\ t_1 := \sin x \cdot t\_0\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot t\_0\\ \mathbf{elif}\;t\_1 \leq 1:\\ \;\;\;\;\sin x \cdot 1\\ \mathbf{else}:\\ \;\;\;\;t\_0 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (sinh y) y)) (t_1 (* (sin x) t_0)))
   (if (<= t_1 (- INFINITY))
     (* (* (* (* x x) x) -0.16666666666666666) t_0)
     (if (<= t_1 1.0) (* (sin x) 1.0) (* t_0 x)))))

C

double code(double x, double y) {
	double t_0 = sinh(y) / y;
	double t_1 = sin(x) * t_0;
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0;
	} else if (t_1 <= 1.0) {
		tmp = sin(x) * 1.0;
	} else {
		tmp = t_0 * x;
	}
	return tmp;
}

Java

public static double code(double x, double y) {
	double t_0 = Math.sinh(y) / y;
	double t_1 = Math.sin(x) * t_0;
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0;
	} else if (t_1 <= 1.0) {
		tmp = Math.sin(x) * 1.0;
	} else {
		tmp = t_0 * x;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = math.sinh(y) / y
	t_1 = math.sin(x) * t_0
	tmp = 0
	if t_1 <= -math.inf:
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0
	elif t_1 <= 1.0:
		tmp = math.sin(x) * 1.0
	else:
		tmp = t_0 * x
	return tmp

Julia

function code(x, y)
	t_0 = Float64(sinh(y) / y)
	t_1 = Float64(sin(x) * t_0)
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(Float64(Float64(x * x) * x) * -0.16666666666666666) * t_0);
	elseif (t_1 <= 1.0)
		tmp = Float64(sin(x) * 1.0);
	else
		tmp = Float64(t_0 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = sinh(y) / y;
	t_1 = sin(x) * t_0;
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0;
	elseif (t_1 <= 1.0)
		tmp = sin(x) * 1.0;
	else
		tmp = t_0 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]}, Block[{t$95$1 = N[(N[Sin[x], $MachinePrecision] * t$95$0), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(N[(N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * t$95$0), $MachinePrecision], If[LessEqual[t$95$1, 1.0], N[(N[Sin[x], $MachinePrecision] * 1.0), $MachinePrecision], N[(t$95$0 * x), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < -inf.0

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   5. Taylor expanded in x around inf

      \[\leadsto \left(\frac{-1}{6} \cdot \color{blue}{{x}^{3}}\right) \cdot \frac{\sinh y}{y} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      3. unpow3 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      4. pow2 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      6. pow2 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      7. lift-*.f64 14.9

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot \frac{\sinh y}{y} \]

   7. Applied rewrites 14.9%

      \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \color{blue}{-0.16666666666666666}\right) \cdot \frac{\sinh y}{y} \]

   if -inf.0 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < 1

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 51.3%

      \[\leadsto \sin x \cdot \color{blue}{1} \]

   if 1 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 62.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\sinh y}{y}\\ \mathbf{if}\;\sin x \cdot t\_0 \leq 0.05:\\ \;\;\;\;\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot t\_0\\ \mathbf{else}:\\ \;\;\;\;\frac{\sinh y \cdot x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (sinh y) y)))
   (if (<= (* (sin x) t_0) 0.05)
     (* (* (fma -0.16666666666666666 (* x x) 1.0) x) t_0)
     (/ (* (sinh y) x) y))))

C

double code(double x, double y) {
	double t_0 = sinh(y) / y;
	double tmp;
	if ((sin(x) * t_0) <= 0.05) {
		tmp = (fma(-0.16666666666666666, (x * x), 1.0) * x) * t_0;
	} else {
		tmp = (sinh(y) * x) / y;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(sinh(y) / y)
	tmp = 0.0
	if (Float64(sin(x) * t_0) <= 0.05)
		tmp = Float64(Float64(fma(-0.16666666666666666, Float64(x * x), 1.0) * x) * t_0);
	else
		tmp = Float64(Float64(sinh(y) * x) / y);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[N[(N[Sin[x], $MachinePrecision] * t$95$0), $MachinePrecision], 0.05], N[(N[(N[(-0.16666666666666666 * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * x), $MachinePrecision] * t$95$0), $MachinePrecision], N[(N[(N[Sinh[y], $MachinePrecision] * x), $MachinePrecision] / y), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < 0.050000000000000003

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   if 0.050000000000000003 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot \color{blue}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot x \]

      3. lift-sinh.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot x \]

      4. associate-*l/ N/A

         \[\leadsto \frac{\sinh y \cdot x}{\color{blue}{y}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\sinh y \cdot x}{\color{blue}{y}} \]

      6. lower-*.f64 N/A

         \[\leadsto \frac{\sinh y \cdot x}{y} \]

      7. lift-sinh.f64 50.4

         \[\leadsto \frac{\sinh y \cdot x}{y} \]

   6. Applied rewrites 50.4%

      \[\leadsto \frac{\sinh y \cdot x}{\color{blue}{y}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 50.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\sinh y}{y}\\ \mathbf{if}\;\sin x \cdot t\_0 \leq -0.05:\\ \;\;\;\;\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot t\_0\\ \mathbf{else}:\\ \;\;\;\;t\_0 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (sinh y) y)))
   (if (<= (* (sin x) t_0) -0.05)
     (* (* (* (* x x) x) -0.16666666666666666) t_0)
     (* t_0 x))))

C

double code(double x, double y) {
	double t_0 = sinh(y) / y;
	double tmp;
	if ((sin(x) * t_0) <= -0.05) {
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0;
	} else {
		tmp = t_0 * x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = sinh(y) / y
    if ((sin(x) * t_0) <= (-0.05d0)) then
        tmp = (((x * x) * x) * (-0.16666666666666666d0)) * t_0
    else
        tmp = t_0 * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = Math.sinh(y) / y;
	double tmp;
	if ((Math.sin(x) * t_0) <= -0.05) {
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0;
	} else {
		tmp = t_0 * x;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = math.sinh(y) / y
	tmp = 0
	if (math.sin(x) * t_0) <= -0.05:
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0
	else:
		tmp = t_0 * x
	return tmp

Julia

function code(x, y)
	t_0 = Float64(sinh(y) / y)
	tmp = 0.0
	if (Float64(sin(x) * t_0) <= -0.05)
		tmp = Float64(Float64(Float64(Float64(x * x) * x) * -0.16666666666666666) * t_0);
	else
		tmp = Float64(t_0 * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = sinh(y) / y;
	tmp = 0.0;
	if ((sin(x) * t_0) <= -0.05)
		tmp = (((x * x) * x) * -0.16666666666666666) * t_0;
	else
		tmp = t_0 * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[N[(N[Sin[x], $MachinePrecision] * t$95$0), $MachinePrecision], -0.05], N[(N[(N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * t$95$0), $MachinePrecision], N[(t$95$0 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < -0.050000000000000003

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   5. Taylor expanded in x around inf

      \[\leadsto \left(\frac{-1}{6} \cdot \color{blue}{{x}^{3}}\right) \cdot \frac{\sinh y}{y} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      3. unpow3 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      4. pow2 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      6. pow2 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{\sinh y}{y} \]

      7. lift-*.f64 14.9

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot \frac{\sinh y}{y} \]

   7. Applied rewrites 14.9%

      \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \color{blue}{-0.16666666666666666}\right) \cdot \frac{\sinh y}{y} \]

   if -0.050000000000000003 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 49.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\sinh y}{y}\\ \mathbf{if}\;\sin x \cdot t\_0 \leq -0.05:\\ \;\;\;\;\frac{\left(\mathsf{fma}\left(x \cdot x, -0.16666666666666666, 1\right) \cdot x\right) \cdot y}{y}\\ \mathbf{else}:\\ \;\;\;\;t\_0 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (/ (sinh y) y)))
   (if (<= (* (sin x) t_0) -0.05)
     (/ (* (* (fma (* x x) -0.16666666666666666 1.0) x) y) y)
     (* t_0 x))))

C

double code(double x, double y) {
	double t_0 = sinh(y) / y;
	double tmp;
	if ((sin(x) * t_0) <= -0.05) {
		tmp = ((fma((x * x), -0.16666666666666666, 1.0) * x) * y) / y;
	} else {
		tmp = t_0 * x;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(sinh(y) / y)
	tmp = 0.0
	if (Float64(sin(x) * t_0) <= -0.05)
		tmp = Float64(Float64(Float64(fma(Float64(x * x), -0.16666666666666666, 1.0) * x) * y) / y);
	else
		tmp = Float64(t_0 * x);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[N[(N[Sin[x], $MachinePrecision] * t$95$0), $MachinePrecision], -0.05], N[(N[(N[(N[(N[(x * x), $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * x), $MachinePrecision] * y), $MachinePrecision] / y), $MachinePrecision], N[(t$95$0 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < -0.050000000000000003

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]
   6. Step-by-step derivation

   7. Applied rewrites 34.7%

      \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{y}{y}} \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \color{blue}{\frac{y}{y}} \]

      3. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{\left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot y}{y}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot y}{y}} \]

   9. Applied rewrites 25.4%

      \[\leadsto \color{blue}{\frac{\left(\mathsf{fma}\left(x \cdot x, -0.16666666666666666, 1\right) \cdot x\right) \cdot y}{y}} \]

   if -0.050000000000000003 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 49.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin x \cdot \frac{\sinh y}{y} \leq 0.05:\\ \;\;\;\;\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{y}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y\right) \cdot x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (sin x) (/ (sinh y) y)) 0.05)
   (* (* (fma -0.16666666666666666 (* x x) 1.0) x) (/ y y))
   (/ (* (* (fma (* y y) 0.16666666666666666 1.0) y) x) y)))

C

double code(double x, double y) {
	double tmp;
	if ((sin(x) * (sinh(y) / y)) <= 0.05) {
		tmp = (fma(-0.16666666666666666, (x * x), 1.0) * x) * (y / y);
	} else {
		tmp = ((fma((y * y), 0.16666666666666666, 1.0) * y) * x) / y;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(sin(x) * Float64(sinh(y) / y)) <= 0.05)
		tmp = Float64(Float64(fma(-0.16666666666666666, Float64(x * x), 1.0) * x) * Float64(y / y));
	else
		tmp = Float64(Float64(Float64(fma(Float64(y * y), 0.16666666666666666, 1.0) * y) * x) / y);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], 0.05], N[(N[(N[(-0.16666666666666666 * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * x), $MachinePrecision] * N[(y / y), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(N[(y * y), $MachinePrecision] * 0.16666666666666666 + 1.0), $MachinePrecision] * y), $MachinePrecision] * x), $MachinePrecision] / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < 0.050000000000000003

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]
   6. Step-by-step derivation

   7. Applied rewrites 34.7%

      \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]

   if 0.050000000000000003 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto \frac{y \cdot \left(1 + \frac{1}{6} \cdot {y}^{2}\right)}{y} \cdot x \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      3. *-commutative N/A

         \[\leadsto \frac{\left(1 + {y}^{2} \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left(1 + \left(y \cdot y\right) \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      5. +-commutative N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot \frac{1}{6} + 1\right) \cdot y}{y} \cdot x \]

      6. lift-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      7. lift-*.f64 52.1

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   7. Applied rewrites 52.1%

      \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot \color{blue}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      3. associate-*l/ N/A

         \[\leadsto \frac{\left(\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y\right) \cdot x}{\color{blue}{y}} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\left(\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y\right) \cdot x}{\color{blue}{y}} \]

      5. lower-*.f64 40.9

         \[\leadsto \frac{\left(\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y\right) \cdot x}{y} \]

   9. Applied rewrites 40.9%

      \[\leadsto \frac{\left(\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y\right) \cdot x}{\color{blue}{y}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 49.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sin x \cdot \frac{\sinh y}{y}\\ \mathbf{if}\;t\_0 \leq -0.05:\\ \;\;\;\;\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot \frac{y}{y}\\ \mathbf{elif}\;t\_0 \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.16666666666666666, x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666\right) \cdot x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* (sin x) (/ (sinh y) y))))
   (if (<= t_0 -0.05)
     (* (* (* (* x x) x) -0.16666666666666666) (/ y y))
     (if (<= t_0 1.0)
       (fma (* (* y y) x) 0.16666666666666666 x)
       (/ (* (* (* (* y y) y) 0.16666666666666666) x) y)))))

C

double code(double x, double y) {
	double t_0 = sin(x) * (sinh(y) / y);
	double tmp;
	if (t_0 <= -0.05) {
		tmp = (((x * x) * x) * -0.16666666666666666) * (y / y);
	} else if (t_0 <= 1.0) {
		tmp = fma(((y * y) * x), 0.16666666666666666, x);
	} else {
		tmp = ((((y * y) * y) * 0.16666666666666666) * x) / y;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(sin(x) * Float64(sinh(y) / y))
	tmp = 0.0
	if (t_0 <= -0.05)
		tmp = Float64(Float64(Float64(Float64(x * x) * x) * -0.16666666666666666) * Float64(y / y));
	elseif (t_0 <= 1.0)
		tmp = fma(Float64(Float64(y * y) * x), 0.16666666666666666, x);
	else
		tmp = Float64(Float64(Float64(Float64(Float64(y * y) * y) * 0.16666666666666666) * x) / y);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -0.05], N[(N[(N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * N[(y / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 1.0], N[(N[(N[(y * y), $MachinePrecision] * x), $MachinePrecision] * 0.16666666666666666 + x), $MachinePrecision], N[(N[(N[(N[(N[(y * y), $MachinePrecision] * y), $MachinePrecision] * 0.16666666666666666), $MachinePrecision] * x), $MachinePrecision] / y), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < -0.050000000000000003

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]
   6. Step-by-step derivation

   7. Applied rewrites 34.7%

      \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]

   8. Taylor expanded in x around inf

      \[\leadsto \left(\frac{-1}{6} \cdot \color{blue}{{x}^{3}}\right) \cdot \frac{y}{y} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      3. unpow3 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      4. pow2 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      6. pow2 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      7. lift-*.f64 11.1

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot \frac{y}{y} \]

   10. Applied rewrites 11.1%

       \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \color{blue}{-0.16666666666666666}\right) \cdot \frac{y}{y} \]

   if -0.050000000000000003 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < 1

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto x + \color{blue}{\frac{1}{6} \cdot \left(x \cdot {y}^{2}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{6} \cdot \left(x \cdot {y}^{2}\right) + x \]

      2. *-commutative N/A

         \[\leadsto \left(x \cdot {y}^{2}\right) \cdot \frac{1}{6} + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot {y}^{2}, \frac{1}{6}, x\right) \]

      4. *-commutative N/A

         \[\leadsto \mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{6}, x\right) \]

      5. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{6}, x\right) \]

      6. pow2 N/A

         \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{6}, x\right) \]

      7. lift-*.f64 47.4

         \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.16666666666666666, x\right) \]

   7. Applied rewrites 47.4%

      \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \color{blue}{0.16666666666666666}, x\right) \]

   if 1 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto \frac{y \cdot \left(1 + \frac{1}{6} \cdot {y}^{2}\right)}{y} \cdot x \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      3. *-commutative N/A

         \[\leadsto \frac{\left(1 + {y}^{2} \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left(1 + \left(y \cdot y\right) \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      5. +-commutative N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot \frac{1}{6} + 1\right) \cdot y}{y} \cdot x \]

      6. lift-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      7. lift-*.f64 52.1

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   7. Applied rewrites 52.1%

      \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   8. Taylor expanded in y around inf

      \[\leadsto \frac{\frac{1}{6} \cdot {y}^{3}}{y} \cdot x \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{y}^{3} \cdot \frac{1}{6}}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{y}^{3} \cdot \frac{1}{6}}{y} \cdot x \]

      3. unpow3 N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left({y}^{2} \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left({y}^{2} \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      6. pow2 N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      7. lift-*.f64 28.9

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666}{y} \cdot x \]

   10. Applied rewrites 28.9%

       \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666}{y} \cdot x \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot \color{blue}{x} \]

       2. lift-/.f64 N/A

          \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

       3. associate-*l/ N/A

          \[\leadsto \frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}\right) \cdot x}{\color{blue}{y}} \]

       4. lower-/.f64 N/A

          \[\leadsto \frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}\right) \cdot x}{\color{blue}{y}} \]

       5. lower-*.f64 29.3

          \[\leadsto \frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666\right) \cdot x}{y} \]

   12. Applied rewrites 29.3%

       \[\leadsto \frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666\right) \cdot x}{\color{blue}{y}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 47.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin x \cdot \frac{\sinh y}{y} \leq -0.05:\\ \;\;\;\;\frac{\left(\mathsf{fma}\left(x \cdot x, -0.16666666666666666, 1\right) \cdot x\right) \cdot y}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, y \cdot 0.16666666666666666, 1\right) \cdot y}{y} \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (sin x) (/ (sinh y) y)) -0.05)
   (/ (* (* (fma (* x x) -0.16666666666666666 1.0) x) y) y)
   (* (/ (* (fma y (* y 0.16666666666666666) 1.0) y) y) x)))

C

double code(double x, double y) {
	double tmp;
	if ((sin(x) * (sinh(y) / y)) <= -0.05) {
		tmp = ((fma((x * x), -0.16666666666666666, 1.0) * x) * y) / y;
	} else {
		tmp = ((fma(y, (y * 0.16666666666666666), 1.0) * y) / y) * x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(sin(x) * Float64(sinh(y) / y)) <= -0.05)
		tmp = Float64(Float64(Float64(fma(Float64(x * x), -0.16666666666666666, 1.0) * x) * y) / y);
	else
		tmp = Float64(Float64(Float64(fma(y, Float64(y * 0.16666666666666666), 1.0) * y) / y) * x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], -0.05], N[(N[(N[(N[(N[(x * x), $MachinePrecision] * -0.16666666666666666 + 1.0), $MachinePrecision] * x), $MachinePrecision] * y), $MachinePrecision] / y), $MachinePrecision], N[(N[(N[(N[(y * N[(y * 0.16666666666666666), $MachinePrecision] + 1.0), $MachinePrecision] * y), $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < -0.050000000000000003

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]
   6. Step-by-step derivation

   7. Applied rewrites 34.7%

      \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{y}{y}} \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \color{blue}{\frac{y}{y}} \]

      3. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{\left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot y}{y}} \]

      4. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot y}{y}} \]

   9. Applied rewrites 25.4%

      \[\leadsto \color{blue}{\frac{\left(\mathsf{fma}\left(x \cdot x, -0.16666666666666666, 1\right) \cdot x\right) \cdot y}{y}} \]

   if -0.050000000000000003 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto \frac{y \cdot \left(1 + \frac{1}{6} \cdot {y}^{2}\right)}{y} \cdot x \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      3. *-commutative N/A

         \[\leadsto \frac{\left(1 + {y}^{2} \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left(1 + \left(y \cdot y\right) \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      5. +-commutative N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot \frac{1}{6} + 1\right) \cdot y}{y} \cdot x \]

      6. lift-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      7. lift-*.f64 52.1

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   7. Applied rewrites 52.1%

      \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      2. lift-fma.f64 N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot \frac{1}{6} + 1\right) \cdot y}{y} \cdot x \]

      3. associate-*l* N/A

         \[\leadsto \frac{\left(y \cdot \left(y \cdot \frac{1}{6}\right) + 1\right) \cdot y}{y} \cdot x \]

      4. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y, y \cdot \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      5. lower-*.f64 52.1

         \[\leadsto \frac{\mathsf{fma}\left(y, y \cdot 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   9. Applied rewrites 52.1%

      \[\leadsto \frac{\mathsf{fma}\left(y, y \cdot 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 44.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin x \cdot \frac{\sinh y}{y} \leq -0.05:\\ \;\;\;\;\left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot \frac{y}{y}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, y \cdot 0.16666666666666666, 1\right) \cdot y}{y} \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (sin x) (/ (sinh y) y)) -0.05)
   (* (* (* (* x x) x) -0.16666666666666666) (/ y y))
   (* (/ (* (fma y (* y 0.16666666666666666) 1.0) y) y) x)))

C

double code(double x, double y) {
	double tmp;
	if ((sin(x) * (sinh(y) / y)) <= -0.05) {
		tmp = (((x * x) * x) * -0.16666666666666666) * (y / y);
	} else {
		tmp = ((fma(y, (y * 0.16666666666666666), 1.0) * y) / y) * x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(sin(x) * Float64(sinh(y) / y)) <= -0.05)
		tmp = Float64(Float64(Float64(Float64(x * x) * x) * -0.16666666666666666) * Float64(y / y));
	else
		tmp = Float64(Float64(Float64(fma(y, Float64(y * 0.16666666666666666), 1.0) * y) / y) * x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], -0.05], N[(N[(N[(N[(x * x), $MachinePrecision] * x), $MachinePrecision] * -0.16666666666666666), $MachinePrecision] * N[(y / y), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(y * N[(y * 0.16666666666666666), $MachinePrecision] + 1.0), $MachinePrecision] * y), $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < -0.050000000000000003

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\left(x \cdot \left(1 + \frac{-1}{6} \cdot {x}^{2}\right)\right)} \cdot \frac{\sinh y}{y} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\left(1 + \frac{-1}{6} \cdot {x}^{2}\right) \cdot \color{blue}{x}\right) \cdot \frac{\sinh y}{y} \]

      3. +-commutative N/A

         \[\leadsto \left(\left(\frac{-1}{6} \cdot {x}^{2} + 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      4. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, {x}^{2}, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      5. unpow2 N/A

         \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

      6. lower-*.f64 62.6

         \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\sinh y}{y} \]

   4. Applied rewrites 62.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right)} \cdot \frac{\sinh y}{y} \]

   5. Taylor expanded in y around 0

      \[\leadsto \left(\mathsf{fma}\left(\frac{-1}{6}, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]
   6. Step-by-step derivation

   7. Applied rewrites 34.7%

      \[\leadsto \left(\mathsf{fma}\left(-0.16666666666666666, x \cdot x, 1\right) \cdot x\right) \cdot \frac{\color{blue}{y}}{y} \]

   8. Taylor expanded in x around inf

      \[\leadsto \left(\frac{-1}{6} \cdot \color{blue}{{x}^{3}}\right) \cdot \frac{y}{y} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      2. lower-*.f64 N/A

         \[\leadsto \left({x}^{3} \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      3. unpow3 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      4. pow2 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      5. lower-*.f64 N/A

         \[\leadsto \left(\left({x}^{2} \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      6. pow2 N/A

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \frac{-1}{6}\right) \cdot \frac{y}{y} \]

      7. lift-*.f64 11.1

         \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot -0.16666666666666666\right) \cdot \frac{y}{y} \]

   10. Applied rewrites 11.1%

       \[\leadsto \left(\left(\left(x \cdot x\right) \cdot x\right) \cdot \color{blue}{-0.16666666666666666}\right) \cdot \frac{y}{y} \]

   if -0.050000000000000003 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto \frac{y \cdot \left(1 + \frac{1}{6} \cdot {y}^{2}\right)}{y} \cdot x \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      3. *-commutative N/A

         \[\leadsto \frac{\left(1 + {y}^{2} \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left(1 + \left(y \cdot y\right) \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      5. +-commutative N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot \frac{1}{6} + 1\right) \cdot y}{y} \cdot x \]

      6. lift-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      7. lift-*.f64 52.1

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   7. Applied rewrites 52.1%

      \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      2. lift-fma.f64 N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot \frac{1}{6} + 1\right) \cdot y}{y} \cdot x \]

      3. associate-*l* N/A

         \[\leadsto \frac{\left(y \cdot \left(y \cdot \frac{1}{6}\right) + 1\right) \cdot y}{y} \cdot x \]

      4. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y, y \cdot \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      5. lower-*.f64 52.1

         \[\leadsto \frac{\mathsf{fma}\left(y, y \cdot 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   9. Applied rewrites 52.1%

      \[\leadsto \frac{\mathsf{fma}\left(y, y \cdot 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 43.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin x \cdot \frac{\sinh y}{y} \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.16666666666666666, x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666\right) \cdot x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (sin x) (/ (sinh y) y)) 1.0)
   (fma (* (* y y) x) 0.16666666666666666 x)
   (/ (* (* (* (* y y) y) 0.16666666666666666) x) y)))

C

double code(double x, double y) {
	double tmp;
	if ((sin(x) * (sinh(y) / y)) <= 1.0) {
		tmp = fma(((y * y) * x), 0.16666666666666666, x);
	} else {
		tmp = ((((y * y) * y) * 0.16666666666666666) * x) / y;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(sin(x) * Float64(sinh(y) / y)) <= 1.0)
		tmp = fma(Float64(Float64(y * y) * x), 0.16666666666666666, x);
	else
		tmp = Float64(Float64(Float64(Float64(Float64(y * y) * y) * 0.16666666666666666) * x) / y);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], 1.0], N[(N[(N[(y * y), $MachinePrecision] * x), $MachinePrecision] * 0.16666666666666666 + x), $MachinePrecision], N[(N[(N[(N[(N[(y * y), $MachinePrecision] * y), $MachinePrecision] * 0.16666666666666666), $MachinePrecision] * x), $MachinePrecision] / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < 1

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto x + \color{blue}{\frac{1}{6} \cdot \left(x \cdot {y}^{2}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{6} \cdot \left(x \cdot {y}^{2}\right) + x \]

      2. *-commutative N/A

         \[\leadsto \left(x \cdot {y}^{2}\right) \cdot \frac{1}{6} + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot {y}^{2}, \frac{1}{6}, x\right) \]

      4. *-commutative N/A

         \[\leadsto \mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{6}, x\right) \]

      5. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{6}, x\right) \]

      6. pow2 N/A

         \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{6}, x\right) \]

      7. lift-*.f64 47.4

         \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.16666666666666666, x\right) \]

   7. Applied rewrites 47.4%

      \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \color{blue}{0.16666666666666666}, x\right) \]

   if 1 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto \frac{y \cdot \left(1 + \frac{1}{6} \cdot {y}^{2}\right)}{y} \cdot x \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      3. *-commutative N/A

         \[\leadsto \frac{\left(1 + {y}^{2} \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left(1 + \left(y \cdot y\right) \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      5. +-commutative N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot \frac{1}{6} + 1\right) \cdot y}{y} \cdot x \]

      6. lift-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      7. lift-*.f64 52.1

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   7. Applied rewrites 52.1%

      \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   8. Taylor expanded in y around inf

      \[\leadsto \frac{\frac{1}{6} \cdot {y}^{3}}{y} \cdot x \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{y}^{3} \cdot \frac{1}{6}}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{y}^{3} \cdot \frac{1}{6}}{y} \cdot x \]

      3. unpow3 N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left({y}^{2} \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left({y}^{2} \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      6. pow2 N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      7. lift-*.f64 28.9

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666}{y} \cdot x \]

   10. Applied rewrites 28.9%

       \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666}{y} \cdot x \]
   11. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot \color{blue}{x} \]

       2. lift-/.f64 N/A

          \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

       3. associate-*l/ N/A

          \[\leadsto \frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}\right) \cdot x}{\color{blue}{y}} \]

       4. lower-/.f64 N/A

          \[\leadsto \frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}\right) \cdot x}{\color{blue}{y}} \]

       5. lower-*.f64 29.3

          \[\leadsto \frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666\right) \cdot x}{y} \]

   12. Applied rewrites 29.3%

       \[\leadsto \frac{\left(\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666\right) \cdot x}{\color{blue}{y}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 43.3% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 0.0036:\\ \;\;\;\;\mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.16666666666666666, x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666}{y} \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y 0.0036)
   (fma (* (* y y) x) 0.16666666666666666 x)
   (* (/ (* (* (* y y) y) 0.16666666666666666) y) x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= 0.0036) {
		tmp = fma(((y * y) * x), 0.16666666666666666, x);
	} else {
		tmp = ((((y * y) * y) * 0.16666666666666666) / y) * x;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (y <= 0.0036)
		tmp = fma(Float64(Float64(y * y) * x), 0.16666666666666666, x);
	else
		tmp = Float64(Float64(Float64(Float64(Float64(y * y) * y) * 0.16666666666666666) / y) * x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[y, 0.0036], N[(N[(N[(y * y), $MachinePrecision] * x), $MachinePrecision] * 0.16666666666666666 + x), $MachinePrecision], N[(N[(N[(N[(N[(y * y), $MachinePrecision] * y), $MachinePrecision] * 0.16666666666666666), $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 0.0035999999999999999

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto x + \color{blue}{\frac{1}{6} \cdot \left(x \cdot {y}^{2}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{6} \cdot \left(x \cdot {y}^{2}\right) + x \]

      2. *-commutative N/A

         \[\leadsto \left(x \cdot {y}^{2}\right) \cdot \frac{1}{6} + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(x \cdot {y}^{2}, \frac{1}{6}, x\right) \]

      4. *-commutative N/A

         \[\leadsto \mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{6}, x\right) \]

      5. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{6}, x\right) \]

      6. pow2 N/A

         \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{6}, x\right) \]

      7. lift-*.f64 47.4

         \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.16666666666666666, x\right) \]

   7. Applied rewrites 47.4%

      \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \color{blue}{0.16666666666666666}, x\right) \]

   if 0.0035999999999999999 < y

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

   5. Taylor expanded in y around 0

      \[\leadsto \frac{y \cdot \left(1 + \frac{1}{6} \cdot {y}^{2}\right)}{y} \cdot x \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(1 + \frac{1}{6} \cdot {y}^{2}\right) \cdot y}{y} \cdot x \]

      3. *-commutative N/A

         \[\leadsto \frac{\left(1 + {y}^{2} \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left(1 + \left(y \cdot y\right) \cdot \frac{1}{6}\right) \cdot y}{y} \cdot x \]

      5. +-commutative N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot \frac{1}{6} + 1\right) \cdot y}{y} \cdot x \]

      6. lift-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, \frac{1}{6}, 1\right) \cdot y}{y} \cdot x \]

      7. lift-*.f64 52.1

         \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   7. Applied rewrites 52.1%

      \[\leadsto \frac{\mathsf{fma}\left(y \cdot y, 0.16666666666666666, 1\right) \cdot y}{y} \cdot x \]

   8. Taylor expanded in y around inf

      \[\leadsto \frac{\frac{1}{6} \cdot {y}^{3}}{y} \cdot x \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{y}^{3} \cdot \frac{1}{6}}{y} \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{y}^{3} \cdot \frac{1}{6}}{y} \cdot x \]

      3. unpow3 N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      4. pow2 N/A

         \[\leadsto \frac{\left({y}^{2} \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left({y}^{2} \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      6. pow2 N/A

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot \frac{1}{6}}{y} \cdot x \]

      7. lift-*.f64 28.9

         \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666}{y} \cdot x \]

   10. Applied rewrites 28.9%

       \[\leadsto \frac{\left(\left(y \cdot y\right) \cdot y\right) \cdot 0.16666666666666666}{y} \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 43.1% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.16666666666666666, x\right) \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (fma (* (* y y) x) 0.16666666666666666 x))

C

double code(double x, double y) {
	return fma(((y * y) * x), 0.16666666666666666, x);
}

Julia

function code(x, y)
	return fma(Float64(Float64(y * y) * x), 0.16666666666666666, x)
end

Wolfram

code[x_, y_] := N[(N[(N[(y * y), $MachinePrecision] * x), $MachinePrecision] * 0.16666666666666666 + x), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\sin x \cdot \frac{\sinh y}{y} \]

2. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
3. Step-by-step derivation

   1. associate-/l* N/A

      \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

   2. *-commutative N/A

      \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

   3. associate-*l* N/A

      \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

   4. lower-*.f64 N/A

      \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

   5. *-commutative N/A

      \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

   6. associate-*l/ N/A

      \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

   7. metadata-eval N/A

      \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

   8. mult-flip N/A

      \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

   9. rec-exp N/A

      \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

   10. sinh-def N/A

       \[\leadsto \frac{\sinh y}{y} \cdot x \]

   11. lift-sinh.f64 N/A

       \[\leadsto \frac{\sinh y}{y} \cdot x \]

   12. lift-/.f64 61.9

       \[\leadsto \frac{\sinh y}{y} \cdot x \]

4. Applied rewrites 61.9%

   \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]

5. Taylor expanded in y around 0

   \[\leadsto x + \color{blue}{\frac{1}{6} \cdot \left(x \cdot {y}^{2}\right)} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{1}{6} \cdot \left(x \cdot {y}^{2}\right) + x \]

   2. *-commutative N/A

      \[\leadsto \left(x \cdot {y}^{2}\right) \cdot \frac{1}{6} + x \]

   3. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(x \cdot {y}^{2}, \frac{1}{6}, x\right) \]

   4. *-commutative N/A

      \[\leadsto \mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{6}, x\right) \]

   5. lower-*.f64 N/A

      \[\leadsto \mathsf{fma}\left({y}^{2} \cdot x, \frac{1}{6}, x\right) \]

   6. pow2 N/A

      \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \frac{1}{6}, x\right) \]

   7. lift-*.f64 47.4

      \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, 0.16666666666666666, x\right) \]

7. Applied rewrites 47.4%

   \[\leadsto \mathsf{fma}\left(\left(y \cdot y\right) \cdot x, \color{blue}{0.16666666666666666}, x\right) \]
8. Add Preprocessing

Alternative 14: 29.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin x \cdot \frac{\sinh y}{y} \leq 2 \cdot 10^{-47}:\\ \;\;\;\;x \cdot 1\\ \mathbf{else}:\\ \;\;\;\;\left(y \cdot x\right) \cdot \frac{1}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (sin x) (/ (sinh y) y)) 2e-47) (* x 1.0) (* (* y x) (/ 1.0 y))))

C

double code(double x, double y) {
	double tmp;
	if ((sin(x) * (sinh(y) / y)) <= 2e-47) {
		tmp = x * 1.0;
	} else {
		tmp = (y * x) * (1.0 / y);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((sin(x) * (sinh(y) / y)) <= 2d-47) then
        tmp = x * 1.0d0
    else
        tmp = (y * x) * (1.0d0 / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((Math.sin(x) * (Math.sinh(y) / y)) <= 2e-47) {
		tmp = x * 1.0;
	} else {
		tmp = (y * x) * (1.0 / y);
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (math.sin(x) * (math.sinh(y) / y)) <= 2e-47:
		tmp = x * 1.0
	else:
		tmp = (y * x) * (1.0 / y)
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(sin(x) * Float64(sinh(y) / y)) <= 2e-47)
		tmp = Float64(x * 1.0);
	else
		tmp = Float64(Float64(y * x) * Float64(1.0 / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((sin(x) * (sinh(y) / y)) <= 2e-47)
		tmp = x * 1.0;
	else
		tmp = (y * x) * (1.0 / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], 2e-47], N[(x * 1.0), $MachinePrecision], N[(N[(y * x), $MachinePrecision] * N[(1.0 / y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < 1.9999999999999999e-47

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 51.3%

      \[\leadsto \sin x \cdot \color{blue}{1} \]

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 26.6%

      \[\leadsto \color{blue}{x} \cdot 1 \]

   if 1.9999999999999999e-47 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot \color{blue}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot x \]

      3. lift-sinh.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot x \]

      4. associate-*l/ N/A

         \[\leadsto \frac{\sinh y \cdot x}{\color{blue}{y}} \]

      5. mult-flip N/A

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \color{blue}{\frac{1}{y}} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \color{blue}{\frac{1}{y}} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \frac{\color{blue}{1}}{y} \]

      8. lift-sinh.f64 N/A

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \frac{1}{y} \]

      9. lift-/.f64 50.4

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \frac{1}{\color{blue}{y}} \]

   6. Applied rewrites 50.4%

      \[\leadsto \left(\sinh y \cdot x\right) \cdot \color{blue}{\frac{1}{y}} \]

   7. Taylor expanded in y around 0

      \[\leadsto \left(y \cdot x\right) \cdot \frac{1}{y} \]
   8. Step-by-step derivation

   9. Applied rewrites 21.1%

      \[\leadsto \left(y \cdot x\right) \cdot \frac{1}{y} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 15: 29.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\sin x \cdot \frac{\sinh y}{y} \leq 0.8:\\ \;\;\;\;x \cdot 1\\ \mathbf{else}:\\ \;\;\;\;\frac{y \cdot x}{y}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* (sin x) (/ (sinh y) y)) 0.8) (* x 1.0) (/ (* y x) y)))

C

double code(double x, double y) {
	double tmp;
	if ((sin(x) * (sinh(y) / y)) <= 0.8) {
		tmp = x * 1.0;
	} else {
		tmp = (y * x) / y;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((sin(x) * (sinh(y) / y)) <= 0.8d0) then
        tmp = x * 1.0d0
    else
        tmp = (y * x) / y
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((Math.sin(x) * (Math.sinh(y) / y)) <= 0.8) {
		tmp = x * 1.0;
	} else {
		tmp = (y * x) / y;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (math.sin(x) * (math.sinh(y) / y)) <= 0.8:
		tmp = x * 1.0
	else:
		tmp = (y * x) / y
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(sin(x) * Float64(sinh(y) / y)) <= 0.8)
		tmp = Float64(x * 1.0);
	else
		tmp = Float64(Float64(y * x) / y);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((sin(x) * (sinh(y) / y)) <= 0.8)
		tmp = x * 1.0;
	else
		tmp = (y * x) / y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[N[(N[Sin[x], $MachinePrecision] * N[(N[Sinh[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], 0.8], N[(x * 1.0), $MachinePrecision], N[(N[(y * x), $MachinePrecision] / y), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y)) < 0.80000000000000004

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in y around 0

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

   4. Applied rewrites 51.3%

      \[\leadsto \sin x \cdot \color{blue}{1} \]

   5. Taylor expanded in x around 0

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

   7. Applied rewrites 26.6%

      \[\leadsto \color{blue}{x} \cdot 1 \]

   if 0.80000000000000004 < (*.f64 (sin.f64 x) (/.f64 (sinh.f64 y) y))

   1. Initial program 100.0%

      \[\sin x \cdot \frac{\sinh y}{y} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{x \cdot \left(e^{y} - \frac{1}{e^{y}}\right)}{y}} \]
   3. Step-by-step derivation

      1. associate-/l* N/A

         \[\leadsto \frac{1}{2} \cdot \left(x \cdot \color{blue}{\frac{e^{y} - \frac{1}{e^{y}}}{y}}\right) \]

      2. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \color{blue}{x}\right) \]

      3. associate-*l* N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{e^{y} - \frac{1}{e^{y}}}{y}\right) \cdot \color{blue}{x} \]

      5. *-commutative N/A

         \[\leadsto \left(\frac{e^{y} - \frac{1}{e^{y}}}{y} \cdot \frac{1}{2}\right) \cdot x \]

      6. associate-*l/ N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      7. metadata-eval N/A

         \[\leadsto \frac{\left(e^{y} - \frac{1}{e^{y}}\right) \cdot \frac{1}{2}}{y} \cdot x \]

      8. mult-flip N/A

         \[\leadsto \frac{\frac{e^{y} - \frac{1}{e^{y}}}{2}}{y} \cdot x \]

      9. rec-exp N/A

         \[\leadsto \frac{\frac{e^{y} - e^{\mathsf{neg}\left(y\right)}}{2}}{y} \cdot x \]

      10. sinh-def N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      11. lift-sinh.f64 N/A

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

      12. lift-/.f64 61.9

          \[\leadsto \frac{\sinh y}{y} \cdot x \]

   4. Applied rewrites 61.9%

      \[\leadsto \color{blue}{\frac{\sinh y}{y} \cdot x} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot \color{blue}{x} \]

      2. lift-/.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot x \]

      3. lift-sinh.f64 N/A

         \[\leadsto \frac{\sinh y}{y} \cdot x \]

      4. associate-*l/ N/A

         \[\leadsto \frac{\sinh y \cdot x}{\color{blue}{y}} \]

      5. mult-flip N/A

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \color{blue}{\frac{1}{y}} \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \color{blue}{\frac{1}{y}} \]

      7. lower-*.f64 N/A

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \frac{\color{blue}{1}}{y} \]

      8. lift-sinh.f64 N/A

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \frac{1}{y} \]

      9. lift-/.f64 50.4

         \[\leadsto \left(\sinh y \cdot x\right) \cdot \frac{1}{\color{blue}{y}} \]

   6. Applied rewrites 50.4%

      \[\leadsto \left(\sinh y \cdot x\right) \cdot \color{blue}{\frac{1}{y}} \]

   7. Taylor expanded in y around 0

      \[\leadsto \left(y \cdot x\right) \cdot \frac{1}{y} \]
   8. Step-by-step derivation

   9. Applied rewrites 21.1%

      \[\leadsto \left(y \cdot x\right) \cdot \frac{1}{y} \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \left(y \cdot x\right) \cdot \color{blue}{\frac{1}{y}} \]

       2. lift-/.f64 N/A

          \[\leadsto \left(y \cdot x\right) \cdot \frac{1}{\color{blue}{y}} \]

       3. mult-flip-rev N/A

          \[\leadsto \frac{y \cdot x}{\color{blue}{y}} \]

       4. lower-/.f64 21.1

          \[\leadsto \frac{y \cdot x}{\color{blue}{y}} \]

   11. Applied rewrites 21.1%

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

Alternative 16: 26.6% accurate, 13.0× speedup

Math

\[\begin{array}{l} \\ x \cdot 1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x 1.0))

C

double code(double x, double y) {
	return x * 1.0;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * 1.0d0
end function

Java

public static double code(double x, double y) {
	return x * 1.0;
}

Python

def code(x, y):
	return x * 1.0

Julia

function code(x, y)
	return Float64(x * 1.0)
end

MATLAB

function tmp = code(x, y)
	tmp = x * 1.0;
end

Wolfram

code[x_, y_] := N[(x * 1.0), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\sin x \cdot \frac{\sinh y}{y} \]

2. Taylor expanded in y around 0

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

4. Applied rewrites 51.3%

   \[\leadsto \sin x \cdot \color{blue}{1} \]

5. Taylor expanded in x around 0

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

7. Applied rewrites 26.6%

   \[\leadsto \color{blue}{x} \cdot 1 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025134 
(FPCore (x y)
  :name "Linear.Quaternion:$ccos from linear-1.19.1.3"
  :precision binary64
  (* (sin x) (/ (sinh y) y)))