SynthBasics:moogVCF from YampaSynth-0.2

Percentage Accurate: 93.4% → 97.0%

Time: 5.3s

Alternatives: 11

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ x (* (* y z) (- (tanh (/ t y)) (tanh (/ x y))))))

C

double code(double x, double y, double z, double t) {
	return x + ((y * z) * (tanh((t / y)) - tanh((x / 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, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + ((y * z) * (tanh((t / y)) - tanh((x / y))))
end function

Java

public static double code(double x, double y, double z, double t) {
	return x + ((y * z) * (Math.tanh((t / y)) - Math.tanh((x / y))));
}

Python

def code(x, y, z, t):
	return x + ((y * z) * (math.tanh((t / y)) - math.tanh((x / y))))

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(y * z) * Float64(tanh(Float64(t / y)) - tanh(Float64(x / y)))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((y * z) * (tanh((t / y)) - tanh((x / y))));
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(y * z), $MachinePrecision] * N[(N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] - N[Tanh[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 93.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ x (* (* y z) (- (tanh (/ t y)) (tanh (/ x y))))))

C

double code(double x, double y, double z, double t) {
	return x + ((y * z) * (tanh((t / y)) - tanh((x / 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, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + ((y * z) * (tanh((t / y)) - tanh((x / y))))
end function

Java

public static double code(double x, double y, double z, double t) {
	return x + ((y * z) * (Math.tanh((t / y)) - Math.tanh((x / y))));
}

Python

def code(x, y, z, t):
	return x + ((y * z) * (math.tanh((t / y)) - math.tanh((x / y))))

Julia

function code(x, y, z, t)
	return Float64(x + Float64(Float64(y * z) * Float64(tanh(Float64(t / y)) - tanh(Float64(x / y)))))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = x + ((y * z) * (tanh((t / y)) - tanh((x / y))));
end

Wolfram

code[x_, y_, z_, t_] := N[(x + N[(N[(y * z), $MachinePrecision] * N[(N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] - N[Tanh[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 97.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \cdot z, y, x\right) \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (fma (* (- (tanh (/ t y)) (tanh (/ x y))) z) y x))

C

double code(double x, double y, double z, double t) {
	return fma(((tanh((t / y)) - tanh((x / y))) * z), y, x);
}

Julia

function code(x, y, z, t)
	return fma(Float64(Float64(tanh(Float64(t / y)) - tanh(Float64(x / y))) * z), y, x)
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] - N[Tanh[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision] * y + x), $MachinePrecision]

Derivation

1. Initial program 93.4%

   \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

      \[\leadsto \color{blue}{x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

   2. lift-*.f64 N/A

      \[\leadsto x + \color{blue}{\left(y \cdot z\right)} \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   3. lift-*.f64 N/A

      \[\leadsto x + \color{blue}{\left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

   4. lift--.f64 N/A

      \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

   5. lift-/.f64 N/A

      \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \color{blue}{\left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right) \]

   6. lift-tanh.f64 N/A

      \[\leadsto x + \left(y \cdot z\right) \cdot \left(\color{blue}{\tanh \left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right) \]

   7. lift-/.f64 N/A

      \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \color{blue}{\left(\frac{x}{y}\right)}\right) \]

   8. lift-tanh.f64 N/A

      \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \color{blue}{\tanh \left(\frac{x}{y}\right)}\right) \]

   9. +-commutative N/A

      \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) + x} \]

   10. associate-*l* N/A

       \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right)} + x \]

   11. lower-fma.f64 N/A

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

3. Applied rewrites 97.0%

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

   1. lift-fma.f64 N/A

      \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right) + x} \]

   2. lift-*.f64 N/A

      \[\leadsto y \cdot \color{blue}{\left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right)} + x \]

   3. lift--.f64 N/A

      \[\leadsto y \cdot \left(z \cdot \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)}\right) + x \]

   4. lift-/.f64 N/A

      \[\leadsto y \cdot \left(z \cdot \left(\tanh \color{blue}{\left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right)\right) + x \]

   5. lift-tanh.f64 N/A

      \[\leadsto y \cdot \left(z \cdot \left(\color{blue}{\tanh \left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right)\right) + x \]

   6. lift-/.f64 N/A

      \[\leadsto y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \color{blue}{\left(\frac{x}{y}\right)}\right)\right) + x \]

   7. lift-tanh.f64 N/A

      \[\leadsto y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \color{blue}{\tanh \left(\frac{x}{y}\right)}\right)\right) + x \]

   8. *-commutative N/A

      \[\leadsto \color{blue}{\left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right) \cdot y} + x \]

   9. lower-fma.f64 N/A

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

5. Applied rewrites 97.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \cdot z, y, x\right)} \]
6. Add Preprocessing

Alternative 2: 85.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \tanh \left(\frac{t}{y}\right)\\ \mathbf{if}\;y \leq -2.25 \cdot 10^{+86}:\\ \;\;\;\;\mathsf{fma}\left(\left(t\_1 - \frac{x}{y}\right) \cdot z, y, x\right)\\ \mathbf{elif}\;y \leq 6.5 \cdot 10^{+56}:\\ \;\;\;\;\mathsf{fma}\left(t\_1 \cdot z, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t - x, z, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (tanh (/ t y))))
   (if (<= y -2.25e+86)
     (fma (* (- t_1 (/ x y)) z) y x)
     (if (<= y 6.5e+56) (fma (* t_1 z) y x) (fma (- t x) z x)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = tanh((t / y));
	double tmp;
	if (y <= -2.25e+86) {
		tmp = fma(((t_1 - (x / y)) * z), y, x);
	} else if (y <= 6.5e+56) {
		tmp = fma((t_1 * z), y, x);
	} else {
		tmp = fma((t - x), z, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = tanh(Float64(t / y))
	tmp = 0.0
	if (y <= -2.25e+86)
		tmp = fma(Float64(Float64(t_1 - Float64(x / y)) * z), y, x);
	elseif (y <= 6.5e+56)
		tmp = fma(Float64(t_1 * z), y, x);
	else
		tmp = fma(Float64(t - x), z, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[y, -2.25e+86], N[(N[(N[(t$95$1 - N[(x / y), $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision] * y + x), $MachinePrecision], If[LessEqual[y, 6.5e+56], N[(N[(t$95$1 * z), $MachinePrecision] * y + x), $MachinePrecision], N[(N[(t - x), $MachinePrecision] * z + x), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -2.24999999999999996e86

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      2. lift-*.f64 N/A

         \[\leadsto x + \color{blue}{\left(y \cdot z\right)} \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

      3. lift-*.f64 N/A

         \[\leadsto x + \color{blue}{\left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      4. lift--.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \color{blue}{\left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right) \]

      6. lift-tanh.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\color{blue}{\tanh \left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right) \]

      7. lift-/.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \color{blue}{\left(\frac{x}{y}\right)}\right) \]

      8. lift-tanh.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \color{blue}{\tanh \left(\frac{x}{y}\right)}\right) \]

      9. +-commutative N/A

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) + x} \]

      10. *-commutative N/A

          \[\leadsto \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \cdot \left(y \cdot z\right)} + x \]

      11. lower-fma.f64 N/A

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

   3. Applied rewrites 93.5%

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

   4. Taylor expanded in x around 0

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

      1. lift-/.f64 64.7

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

   6. Applied rewrites 64.7%

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

      1. lift-fma.f64 N/A

         \[\leadsto \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \frac{x}{y}\right) \cdot \left(z \cdot y\right) + x} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(\tanh \left(\frac{t}{y}\right) - \frac{x}{y}\right) \cdot \color{blue}{\left(z \cdot y\right)} + x \]

      3. associate-*r* N/A

         \[\leadsto \color{blue}{\left(\left(\tanh \left(\frac{t}{y}\right) - \frac{x}{y}\right) \cdot z\right) \cdot y} + x \]

      4. lower-fma.f64 N/A

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

      5. lower-*.f64 68.1

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

   8. Applied rewrites 68.1%

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

   if -2.24999999999999996e86 < y < 6.5000000000000001e56

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      2. lift-*.f64 N/A

         \[\leadsto x + \color{blue}{\left(y \cdot z\right)} \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

      3. lift-*.f64 N/A

         \[\leadsto x + \color{blue}{\left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      4. lift--.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \color{blue}{\left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right) \]

      6. lift-tanh.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\color{blue}{\tanh \left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right) \]

      7. lift-/.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \color{blue}{\left(\frac{x}{y}\right)}\right) \]

      8. lift-tanh.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \color{blue}{\tanh \left(\frac{x}{y}\right)}\right) \]

      9. +-commutative N/A

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) + x} \]

      10. associate-*l* N/A

          \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right)} + x \]

      11. lower-fma.f64 N/A

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

   3. Applied rewrites 97.0%

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

      1. lift-fma.f64 N/A

         \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right) + x} \]

      2. lift-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right)} + x \]

      3. lift--.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)}\right) + x \]

      4. lift-/.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \left(\tanh \color{blue}{\left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right)\right) + x \]

      5. lift-tanh.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \left(\color{blue}{\tanh \left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right)\right) + x \]

      6. lift-/.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \color{blue}{\left(\frac{x}{y}\right)}\right)\right) + x \]

      7. lift-tanh.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \color{blue}{\tanh \left(\frac{x}{y}\right)}\right)\right) + x \]

      8. *-commutative N/A

         \[\leadsto \color{blue}{\left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right) \cdot y} + x \]

      9. lower-fma.f64 N/A

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

   5. Applied rewrites 97.0%

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

   6. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{e^{\frac{t}{y}}}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}} - \frac{1}{e^{\frac{t}{y}} \cdot \left(e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}\right)}\right)} \cdot z, y, x\right) \]
   7. Step-by-step derivation

      1. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\left(\frac{e^{\frac{t}{y}}}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}} - \frac{\frac{1}{e^{\frac{t}{y}}}}{\color{blue}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}}}\right) \cdot z, y, x\right) \]

      2. div-sub N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{t}{y}} - \frac{1}{e^{\frac{t}{y}}}}{\color{blue}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}}} \cdot z, y, x\right) \]

      3. rec-exp N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{t}{y}} - e^{\mathsf{neg}\left(\frac{t}{y}\right)}}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}} \cdot z, y, x\right) \]

      4. rec-exp N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{t}{y}} - e^{\mathsf{neg}\left(\frac{t}{y}\right)}}{e^{\frac{t}{y}} + e^{\mathsf{neg}\left(\frac{t}{y}\right)}} \cdot z, y, x\right) \]

      5. tanh-def-a N/A

         \[\leadsto \mathsf{fma}\left(\tanh \left(\frac{t}{y}\right) \cdot z, y, x\right) \]

      6. lift-tanh.f64 N/A

         \[\leadsto \mathsf{fma}\left(\tanh \left(\frac{t}{y}\right) \cdot z, y, x\right) \]

      7. lift-/.f64 79.5

         \[\leadsto \mathsf{fma}\left(\tanh \left(\frac{t}{y}\right) \cdot z, y, x\right) \]

   8. Applied rewrites 79.5%

      \[\leadsto \mathsf{fma}\left(\color{blue}{\tanh \left(\frac{t}{y}\right)} \cdot z, y, x\right) \]

   if 6.5000000000000001e56 < y

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 85.3% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(t - x, z, x\right)\\ \mathbf{if}\;y \leq -1.16 \cdot 10^{+114}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 6.5 \cdot 10^{+56}:\\ \;\;\;\;\mathsf{fma}\left(\tanh \left(\frac{t}{y}\right) \cdot z, y, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (fma (- t x) z x)))
   (if (<= y -1.16e+114)
     t_1
     (if (<= y 6.5e+56) (fma (* (tanh (/ t y)) z) y x) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = fma((t - x), z, x);
	double tmp;
	if (y <= -1.16e+114) {
		tmp = t_1;
	} else if (y <= 6.5e+56) {
		tmp = fma((tanh((t / y)) * z), y, x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = fma(Float64(t - x), z, x)
	tmp = 0.0
	if (y <= -1.16e+114)
		tmp = t_1;
	elseif (y <= 6.5e+56)
		tmp = fma(Float64(tanh(Float64(t / y)) * z), y, x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(t - x), $MachinePrecision] * z + x), $MachinePrecision]}, If[LessEqual[y, -1.16e+114], t$95$1, If[LessEqual[y, 6.5e+56], N[(N[(N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] * z), $MachinePrecision] * y + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.15999999999999994e114 or 6.5000000000000001e56 < y

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   if -1.15999999999999994e114 < y < 6.5000000000000001e56

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      2. lift-*.f64 N/A

         \[\leadsto x + \color{blue}{\left(y \cdot z\right)} \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

      3. lift-*.f64 N/A

         \[\leadsto x + \color{blue}{\left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      4. lift--.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)} \]

      5. lift-/.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \color{blue}{\left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right) \]

      6. lift-tanh.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\color{blue}{\tanh \left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right) \]

      7. lift-/.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \color{blue}{\left(\frac{x}{y}\right)}\right) \]

      8. lift-tanh.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \color{blue}{\tanh \left(\frac{x}{y}\right)}\right) \]

      9. +-commutative N/A

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) + x} \]

      10. associate-*l* N/A

          \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right)} + x \]

      11. lower-fma.f64 N/A

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

   3. Applied rewrites 97.0%

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

      1. lift-fma.f64 N/A

         \[\leadsto \color{blue}{y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right) + x} \]

      2. lift-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right)} + x \]

      3. lift--.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \color{blue}{\left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)}\right) + x \]

      4. lift-/.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \left(\tanh \color{blue}{\left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right)\right) + x \]

      5. lift-tanh.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \left(\color{blue}{\tanh \left(\frac{t}{y}\right)} - \tanh \left(\frac{x}{y}\right)\right)\right) + x \]

      6. lift-/.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \color{blue}{\left(\frac{x}{y}\right)}\right)\right) + x \]

      7. lift-tanh.f64 N/A

         \[\leadsto y \cdot \left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \color{blue}{\tanh \left(\frac{x}{y}\right)}\right)\right) + x \]

      8. *-commutative N/A

         \[\leadsto \color{blue}{\left(z \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\right) \cdot y} + x \]

      9. lower-fma.f64 N/A

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

   5. Applied rewrites 97.0%

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

   6. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{e^{\frac{t}{y}}}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}} - \frac{1}{e^{\frac{t}{y}} \cdot \left(e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}\right)}\right)} \cdot z, y, x\right) \]
   7. Step-by-step derivation

      1. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\left(\frac{e^{\frac{t}{y}}}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}} - \frac{\frac{1}{e^{\frac{t}{y}}}}{\color{blue}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}}}\right) \cdot z, y, x\right) \]

      2. div-sub N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{t}{y}} - \frac{1}{e^{\frac{t}{y}}}}{\color{blue}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}}} \cdot z, y, x\right) \]

      3. rec-exp N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{t}{y}} - e^{\mathsf{neg}\left(\frac{t}{y}\right)}}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}} \cdot z, y, x\right) \]

      4. rec-exp N/A

         \[\leadsto \mathsf{fma}\left(\frac{e^{\frac{t}{y}} - e^{\mathsf{neg}\left(\frac{t}{y}\right)}}{e^{\frac{t}{y}} + e^{\mathsf{neg}\left(\frac{t}{y}\right)}} \cdot z, y, x\right) \]

      5. tanh-def-a N/A

         \[\leadsto \mathsf{fma}\left(\tanh \left(\frac{t}{y}\right) \cdot z, y, x\right) \]

      6. lift-tanh.f64 N/A

         \[\leadsto \mathsf{fma}\left(\tanh \left(\frac{t}{y}\right) \cdot z, y, x\right) \]

      7. lift-/.f64 79.5

         \[\leadsto \mathsf{fma}\left(\tanh \left(\frac{t}{y}\right) \cdot z, y, x\right) \]

   8. Applied rewrites 79.5%

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

Alternative 4: 85.1% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.6 \cdot 10^{+86}:\\ \;\;\;\;\mathsf{fma}\left(1 - z, x, z \cdot t\right)\\ \mathbf{elif}\;y \leq 6.5 \cdot 10^{+56}:\\ \;\;\;\;\mathsf{fma}\left(z \cdot y, \tanh \left(\frac{t}{y}\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t - x, z, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -3.6e+86)
   (fma (- 1.0 z) x (* z t))
   (if (<= y 6.5e+56) (fma (* z y) (tanh (/ t y)) x) (fma (- t x) z x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -3.6e+86) {
		tmp = fma((1.0 - z), x, (z * t));
	} else if (y <= 6.5e+56) {
		tmp = fma((z * y), tanh((t / y)), x);
	} else {
		tmp = fma((t - x), z, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -3.6e+86)
		tmp = fma(Float64(1.0 - z), x, Float64(z * t));
	elseif (y <= 6.5e+56)
		tmp = fma(Float64(z * y), tanh(Float64(t / y)), x);
	else
		tmp = fma(Float64(t - x), z, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -3.6e+86], N[(N[(1.0 - z), $MachinePrecision] * x + N[(z * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 6.5e+56], N[(N[(z * y), $MachinePrecision] * N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] + x), $MachinePrecision], N[(N[(t - x), $MachinePrecision] * z + x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -3.60000000000000005e86

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in x around 0

      \[\leadsto t \cdot z + x \cdot \color{blue}{\left(1 - z\right)} \]
   9. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(1 - z\right) + t \cdot z \]

      2. *-commutative N/A

         \[\leadsto \left(1 - z\right) \cdot x + t \cdot z \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(1 - z, x, t \cdot z\right) \]

      4. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(1 - z, x, t \cdot z\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(1 - z, x, z \cdot t\right) \]

      6. lower-*.f64 60.7

         \[\leadsto \mathsf{fma}\left(1 - z, x, z \cdot t\right) \]

   10. Applied rewrites 60.7%

       \[\leadsto \mathsf{fma}\left(1 - z, x, z \cdot t\right) \]

   if -3.60000000000000005e86 < y < 6.5000000000000001e56

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in x around 0

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

      1. associate-/r* N/A

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

      2. div-sub N/A

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

      3. rec-exp N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \frac{e^{\frac{t}{y}} - e^{\mathsf{neg}\left(\frac{t}{y}\right)}}{e^{\frac{t}{y}} + \frac{1}{e^{\frac{t}{y}}}} \]

      4. rec-exp N/A

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

      5. tanh-def-a N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \tanh \left(\frac{t}{y}\right) \]

      6. lift-tanh.f64 N/A

         \[\leadsto x + \left(y \cdot z\right) \cdot \tanh \left(\frac{t}{y}\right) \]

      7. lift-/.f64 79.0

         \[\leadsto x + \left(y \cdot z\right) \cdot \tanh \left(\frac{t}{y}\right) \]

   4. Applied rewrites 79.0%

      \[\leadsto x + \left(y \cdot z\right) \cdot \color{blue}{\tanh \left(\frac{t}{y}\right)} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

         \[\leadsto \color{blue}{x + \left(y \cdot z\right) \cdot \tanh \left(\frac{t}{y}\right)} \]

      2. +-commutative N/A

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot \tanh \left(\frac{t}{y}\right) + x} \]

      3. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot \tanh \left(\frac{t}{y}\right)} + x \]

      4. lower-fma.f64 79.0

         \[\leadsto \color{blue}{\mathsf{fma}\left(y \cdot z, \tanh \left(\frac{t}{y}\right), x\right)} \]

      5. lift-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot z}, \tanh \left(\frac{t}{y}\right), x\right) \]

      6. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{z \cdot y}, \tanh \left(\frac{t}{y}\right), x\right) \]

      7. lift-*.f64 79.0

         \[\leadsto \mathsf{fma}\left(\color{blue}{z \cdot y}, \tanh \left(\frac{t}{y}\right), x\right) \]

   6. Applied rewrites 79.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z \cdot y, \tanh \left(\frac{t}{y}\right), x\right)} \]

   if 6.5000000000000001e56 < y

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 77.7% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{+83}:\\ \;\;\;\;\mathsf{fma}\left(1 - z, x, z \cdot t\right)\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-29}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t - x, z, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.05e+83)
   (fma (- 1.0 z) x (* z t))
   (if (<= y 1.3e-29) (* 1.0 x) (fma (- t x) z x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.05e+83) {
		tmp = fma((1.0 - z), x, (z * t));
	} else if (y <= 1.3e-29) {
		tmp = 1.0 * x;
	} else {
		tmp = fma((t - x), z, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.05e+83)
		tmp = fma(Float64(1.0 - z), x, Float64(z * t));
	elseif (y <= 1.3e-29)
		tmp = Float64(1.0 * x);
	else
		tmp = fma(Float64(t - x), z, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.05e+83], N[(N[(1.0 - z), $MachinePrecision] * x + N[(z * t), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.3e-29], N[(1.0 * x), $MachinePrecision], N[(N[(t - x), $MachinePrecision] * z + x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.05000000000000001e83

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in x around 0

      \[\leadsto t \cdot z + x \cdot \color{blue}{\left(1 - z\right)} \]
   9. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \left(1 - z\right) + t \cdot z \]

      2. *-commutative N/A

         \[\leadsto \left(1 - z\right) \cdot x + t \cdot z \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(1 - z, x, t \cdot z\right) \]

      4. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(1 - z, x, t \cdot z\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(1 - z, x, z \cdot t\right) \]

      6. lower-*.f64 60.7

         \[\leadsto \mathsf{fma}\left(1 - z, x, z \cdot t\right) \]

   10. Applied rewrites 60.7%

       \[\leadsto \mathsf{fma}\left(1 - z, x, z \cdot t\right) \]

   if -1.05000000000000001e83 < y < 1.3000000000000001e-29

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in z around 0

      \[\leadsto 1 \cdot x \]
   9. Step-by-step derivation

   10. Applied rewrites 60.4%

       \[\leadsto 1 \cdot x \]

   if 1.3000000000000001e-29 < y

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 77.5% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \mathsf{fma}\left(t - x, z, x\right)\\ \mathbf{if}\;y \leq -1.05 \cdot 10^{+83}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-29}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (fma (- t x) z x)))
   (if (<= y -1.05e+83) t_1 (if (<= y 1.3e-29) (* 1.0 x) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = fma((t - x), z, x);
	double tmp;
	if (y <= -1.05e+83) {
		tmp = t_1;
	} else if (y <= 1.3e-29) {
		tmp = 1.0 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = fma(Float64(t - x), z, x)
	tmp = 0.0
	if (y <= -1.05e+83)
		tmp = t_1;
	elseif (y <= 1.3e-29)
		tmp = Float64(1.0 * x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(t - x), $MachinePrecision] * z + x), $MachinePrecision]}, If[LessEqual[y, -1.05e+83], t$95$1, If[LessEqual[y, 1.3e-29], N[(1.0 * x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.05000000000000001e83 or 1.3000000000000001e-29 < y

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   if -1.05000000000000001e83 < y < 1.3000000000000001e-29

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in z around 0

      \[\leadsto 1 \cdot x \]
   9. Step-by-step derivation

   10. Applied rewrites 60.4%

       \[\leadsto 1 \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 72.4% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.25 \cdot 10^{+89}:\\ \;\;\;\;\left(1 - z\right) \cdot x\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-29}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t, z, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -2.25e+89)
   (* (- 1.0 z) x)
   (if (<= y 1.3e-29) (* 1.0 x) (fma t z x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -2.25e+89) {
		tmp = (1.0 - z) * x;
	} else if (y <= 1.3e-29) {
		tmp = 1.0 * x;
	} else {
		tmp = fma(t, z, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -2.25e+89)
		tmp = Float64(Float64(1.0 - z) * x);
	elseif (y <= 1.3e-29)
		tmp = Float64(1.0 * x);
	else
		tmp = fma(t, z, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -2.25e+89], N[(N[(1.0 - z), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[y, 1.3e-29], N[(1.0 * x), $MachinePrecision], N[(t * z + x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -2.25e89

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in x around inf

      \[\leadsto \left(1 - z\right) \cdot x \]
   9. Step-by-step derivation

      1. lower--.f64 54.1

         \[\leadsto \left(1 - z\right) \cdot x \]

   10. Applied rewrites 54.1%

       \[\leadsto \left(1 - z\right) \cdot x \]

   if -2.25e89 < y < 1.3000000000000001e-29

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in z around 0

      \[\leadsto 1 \cdot x \]
   9. Step-by-step derivation

   10. Applied rewrites 60.4%

       \[\leadsto 1 \cdot x \]

   if 1.3000000000000001e-29 < y

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(t, z, x\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 58.4%

      \[\leadsto \mathsf{fma}\left(t, z, x\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 70.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := z \cdot \left(t - x\right)\\ t_2 := x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\\ \mathbf{if}\;t\_2 \leq -\infty:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 2 \cdot 10^{+304}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* z (- t x)))
        (t_2 (+ x (* (* y z) (- (tanh (/ t y)) (tanh (/ x y)))))))
   (if (<= t_2 (- INFINITY)) t_1 (if (<= t_2 2e+304) (* 1.0 x) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = z * (t - x);
	double t_2 = x + ((y * z) * (tanh((t / y)) - tanh((x / y))));
	double tmp;
	if (t_2 <= -((double) INFINITY)) {
		tmp = t_1;
	} else if (t_2 <= 2e+304) {
		tmp = 1.0 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = z * (t - x);
	double t_2 = x + ((y * z) * (Math.tanh((t / y)) - Math.tanh((x / y))));
	double tmp;
	if (t_2 <= -Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else if (t_2 <= 2e+304) {
		tmp = 1.0 * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = z * (t - x)
	t_2 = x + ((y * z) * (math.tanh((t / y)) - math.tanh((x / y))))
	tmp = 0
	if t_2 <= -math.inf:
		tmp = t_1
	elif t_2 <= 2e+304:
		tmp = 1.0 * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(z * Float64(t - x))
	t_2 = Float64(x + Float64(Float64(y * z) * Float64(tanh(Float64(t / y)) - tanh(Float64(x / y)))))
	tmp = 0.0
	if (t_2 <= Float64(-Inf))
		tmp = t_1;
	elseif (t_2 <= 2e+304)
		tmp = Float64(1.0 * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = z * (t - x);
	t_2 = x + ((y * z) * (tanh((t / y)) - tanh((x / y))));
	tmp = 0.0;
	if (t_2 <= -Inf)
		tmp = t_1;
	elseif (t_2 <= 2e+304)
		tmp = 1.0 * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(z * N[(t - x), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(x + N[(N[(y * z), $MachinePrecision] * N[(N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] - N[Tanh[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, (-Infinity)], t$95$1, If[LessEqual[t$95$2, 2e+304], N[(1.0 * x), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x (*.f64 (*.f64 y z) (-.f64 (tanh.f64 (/.f64 t y)) (tanh.f64 (/.f64 x y))))) < -inf.0 or 1.9999999999999999e304 < (+.f64 x (*.f64 (*.f64 y z) (-.f64 (tanh.f64 (/.f64 t y)) (tanh.f64 (/.f64 x y)))))

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in z around 0

      \[\leadsto 1 \cdot x \]
   9. Step-by-step derivation

   10. Applied rewrites 60.4%

       \[\leadsto 1 \cdot x \]

   11. Taylor expanded in z around inf

       \[\leadsto z \cdot \color{blue}{\left(t - x\right)} \]
   12. Step-by-step derivation

       1. lower-*.f64 N/A

          \[\leadsto z \cdot \left(t - \color{blue}{x}\right) \]

       2. lift--.f64 27.2

          \[\leadsto z \cdot \left(t - x\right) \]

   13. Applied rewrites 27.2%

       \[\leadsto z \cdot \color{blue}{\left(t - x\right)} \]

   if -inf.0 < (+.f64 x (*.f64 (*.f64 y z) (-.f64 (tanh.f64 (/.f64 t y)) (tanh.f64 (/.f64 x y))))) < 1.9999999999999999e304

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in z around 0

      \[\leadsto 1 \cdot x \]
   9. Step-by-step derivation

   10. Applied rewrites 60.4%

       \[\leadsto 1 \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 68.9% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.05 \cdot 10^{+78}:\\ \;\;\;\;\mathsf{fma}\left(t, z, x\right)\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-29}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t, z, x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= y -1.05e+78) (fma t z x) (if (<= y 1.3e-29) (* 1.0 x) (fma t z x))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (y <= -1.05e+78) {
		tmp = fma(t, z, x);
	} else if (y <= 1.3e-29) {
		tmp = 1.0 * x;
	} else {
		tmp = fma(t, z, x);
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (y <= -1.05e+78)
		tmp = fma(t, z, x);
	elseif (y <= 1.3e-29)
		tmp = Float64(1.0 * x);
	else
		tmp = fma(t, z, x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[y, -1.05e+78], N[(t * z + x), $MachinePrecision], If[LessEqual[y, 1.3e-29], N[(1.0 * x), $MachinePrecision], N[(t * z + x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.05e78 or 1.3000000000000001e-29 < y

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(t, z, x\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 58.4%

      \[\leadsto \mathsf{fma}\left(t, z, x\right) \]

   if -1.05e78 < y < 1.3000000000000001e-29

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in z around 0

      \[\leadsto 1 \cdot x \]
   9. Step-by-step derivation

   10. Applied rewrites 60.4%

       \[\leadsto 1 \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 66.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right)\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;t \cdot z\\ \mathbf{elif}\;t\_1 \leq 2 \cdot 10^{+304}:\\ \;\;\;\;1 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ x (* (* y z) (- (tanh (/ t y)) (tanh (/ x y)))))))
   (if (<= t_1 (- INFINITY)) (* t z) (if (<= t_1 2e+304) (* 1.0 x) (* t z)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = x + ((y * z) * (tanh((t / y)) - tanh((x / y))));
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = t * z;
	} else if (t_1 <= 2e+304) {
		tmp = 1.0 * x;
	} else {
		tmp = t * z;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = x + ((y * z) * (Math.tanh((t / y)) - Math.tanh((x / y))));
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = t * z;
	} else if (t_1 <= 2e+304) {
		tmp = 1.0 * x;
	} else {
		tmp = t * z;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = x + ((y * z) * (math.tanh((t / y)) - math.tanh((x / y))))
	tmp = 0
	if t_1 <= -math.inf:
		tmp = t * z
	elif t_1 <= 2e+304:
		tmp = 1.0 * x
	else:
		tmp = t * z
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(x + Float64(Float64(y * z) * Float64(tanh(Float64(t / y)) - tanh(Float64(x / y)))))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(t * z);
	elseif (t_1 <= 2e+304)
		tmp = Float64(1.0 * x);
	else
		tmp = Float64(t * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = x + ((y * z) * (tanh((t / y)) - tanh((x / y))));
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = t * z;
	elseif (t_1 <= 2e+304)
		tmp = 1.0 * x;
	else
		tmp = t * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(x + N[(N[(y * z), $MachinePrecision] * N[(N[Tanh[N[(t / y), $MachinePrecision]], $MachinePrecision] - N[Tanh[N[(x / y), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(t * z), $MachinePrecision], If[LessEqual[t$95$1, 2e+304], N[(1.0 * x), $MachinePrecision], N[(t * z), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x (*.f64 (*.f64 y z) (-.f64 (tanh.f64 (/.f64 t y)) (tanh.f64 (/.f64 x y))))) < -inf.0 or 1.9999999999999999e304 < (+.f64 x (*.f64 (*.f64 y z) (-.f64 (tanh.f64 (/.f64 t y)) (tanh.f64 (/.f64 x y)))))

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around 0

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

      1. lower-*.f64 16.9

         \[\leadsto t \cdot z \]

   7. Applied rewrites 16.9%

      \[\leadsto t \cdot \color{blue}{z} \]

   if -inf.0 < (+.f64 x (*.f64 (*.f64 y z) (-.f64 (tanh.f64 (/.f64 t y)) (tanh.f64 (/.f64 x y))))) < 1.9999999999999999e304

   1. Initial program 93.4%

      \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

   2. Taylor expanded in y around inf

      \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

      2. *-commutative N/A

         \[\leadsto \left(t - x\right) \cdot z + x \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

      4. lower--.f64 61.0

         \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

   4. Applied rewrites 61.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

   5. Taylor expanded in x around inf

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

      1. *-commutative N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 + \left(-1 \cdot z + \frac{t \cdot z}{x}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      4. lower-+.f64 N/A

         \[\leadsto \left(\left(-1 \cdot z + \frac{t \cdot z}{x}\right) + 1\right) \cdot x \]

      5. +-commutative N/A

         \[\leadsto \left(\left(\frac{t \cdot z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      6. associate-/l* N/A

         \[\leadsto \left(\left(t \cdot \frac{z}{x} + -1 \cdot z\right) + 1\right) \cdot x \]

      7. lower-fma.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      8. lower-/.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -1 \cdot z\right) + 1\right) \cdot x \]

      9. mul-1-neg N/A

         \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, \mathsf{neg}\left(z\right)\right) + 1\right) \cdot x \]

      10. lower-neg.f64 55.5

          \[\leadsto \left(\mathsf{fma}\left(t, \frac{z}{x}, -z\right) + 1\right) \cdot x \]

   7. Applied rewrites 55.5%

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

   8. Taylor expanded in z around 0

      \[\leadsto 1 \cdot x \]
   9. Step-by-step derivation

   10. Applied rewrites 60.4%

       \[\leadsto 1 \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 16.9% accurate, 8.8× speedup

Math

\[\begin{array}{l} \\ t \cdot z \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (* t z))

C

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

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

Java

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

Python

def code(x, y, z, t):
	return t * z

Julia

function code(x, y, z, t)
	return Float64(t * z)
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := N[(t * z), $MachinePrecision]

Derivation

1. Initial program 93.4%

   \[x + \left(y \cdot z\right) \cdot \left(\tanh \left(\frac{t}{y}\right) - \tanh \left(\frac{x}{y}\right)\right) \]

2. Taylor expanded in y around inf

   \[\leadsto \color{blue}{x + z \cdot \left(t - x\right)} \]
3. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto z \cdot \left(t - x\right) + \color{blue}{x} \]

   2. *-commutative N/A

      \[\leadsto \left(t - x\right) \cdot z + x \]

   3. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(t - x, \color{blue}{z}, x\right) \]

   4. lower--.f64 61.0

      \[\leadsto \mathsf{fma}\left(t - x, z, x\right) \]

4. Applied rewrites 61.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(t - x, z, x\right)} \]

5. Taylor expanded in x around 0

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

   1. lower-*.f64 16.9

      \[\leadsto t \cdot z \]

7. Applied rewrites 16.9%

   \[\leadsto t \cdot \color{blue}{z} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025142 
(FPCore (x y z t)
  :name "SynthBasics:moogVCF from YampaSynth-0.2"
  :precision binary64
  (+ x (* (* y z) (- (tanh (/ t y)) (tanh (/ x y))))))