Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTick from plot-0.2.3.4, B

Percentage Accurate: 76.6% → 88.0%

Time: 13.4s

Alternatives: 19

Speedup: 0.7×

Specification

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = (x + y) - (((z - t) * y) / (a - t))
end function

Java

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

Python

def code(x, y, z, t, a):
	return (x + y) - (((z - t) * y) / (a - t))

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 76.6% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = (x + y) - (((z - t) * y) / (a - t))
end function

Java

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

Python

def code(x, y, z, t, a):
	return (x + y) - (((z - t) * y) / (a - t))

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 88.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2 \cdot 10^{+95}:\\ \;\;\;\;y \cdot \frac{z - a}{t} + x\\ \mathbf{elif}\;t \leq 3.2 \cdot 10^{+67}:\\ \;\;\;\;\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t}\\ \mathbf{else}:\\ \;\;\;\;x - \left(a - z\right) \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -2e+95)
   (+ (* y (/ (- z a) t)) x)
   (if (<= t 3.2e+67)
     (- (+ x y) (/ (* (- z t) y) (- a t)))
     (- x (* (- a z) (/ y t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2e+95) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= 3.2e+67) {
		tmp = (x + y) - (((z - t) * y) / (a - t));
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= (-2d+95)) then
        tmp = (y * ((z - a) / t)) + x
    else if (t <= 3.2d+67) then
        tmp = (x + y) - (((z - t) * y) / (a - t))
    else
        tmp = x - ((a - z) * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2e+95) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= 3.2e+67) {
		tmp = (x + y) - (((z - t) * y) / (a - t));
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -2e+95:
		tmp = (y * ((z - a) / t)) + x
	elif t <= 3.2e+67:
		tmp = (x + y) - (((z - t) * y) / (a - t))
	else:
		tmp = x - ((a - z) * (y / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -2e+95)
		tmp = Float64(Float64(y * Float64(Float64(z - a) / t)) + x);
	elseif (t <= 3.2e+67)
		tmp = Float64(Float64(x + y) - Float64(Float64(Float64(z - t) * y) / Float64(a - t)));
	else
		tmp = Float64(x - Float64(Float64(a - z) * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -2e+95)
		tmp = (y * ((z - a) / t)) + x;
	elseif (t <= 3.2e+67)
		tmp = (x + y) - (((z - t) * y) / (a - t));
	else
		tmp = x - ((a - z) * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -2e+95], N[(N[(y * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, 3.2e+67], N[(N[(x + y), $MachinePrecision] - N[(N[(N[(z - t), $MachinePrecision] * y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a - z), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -2.00000000000000004e95

   1. Initial program 47.0%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2.00000000000000004e95 < t < 3.19999999999999983e67

   1. Initial program 93.8%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   if 3.19999999999999983e67 < t

   1. Initial program 60.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 2: 79.0% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.02 \cdot 10^{-60}:\\ \;\;\;\;y \cdot \frac{z - a}{t} + x\\ \mathbf{elif}\;t \leq -1.75 \cdot 10^{-152}:\\ \;\;\;\;\left(x + y\right) - \frac{z \cdot y}{a}\\ \mathbf{elif}\;t \leq -1.02 \cdot 10^{-240}:\\ \;\;\;\;y - \frac{\left(z - t\right) \cdot y}{a - t}\\ \mathbf{elif}\;t \leq 3.75 \cdot 10^{-41}:\\ \;\;\;\;x + \left(1 - \frac{z}{a}\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;x - \left(a - z\right) \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -1.02e-60)
   (+ (* y (/ (- z a) t)) x)
   (if (<= t -1.75e-152)
     (- (+ x y) (/ (* z y) a))
     (if (<= t -1.02e-240)
       (- y (/ (* (- z t) y) (- a t)))
       (if (<= t 3.75e-41)
         (+ x (* (- 1.0 (/ z a)) y))
         (- x (* (- a z) (/ y t))))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.02e-60) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= -1.75e-152) {
		tmp = (x + y) - ((z * y) / a);
	} else if (t <= -1.02e-240) {
		tmp = y - (((z - t) * y) / (a - t));
	} else if (t <= 3.75e-41) {
		tmp = x + ((1.0 - (z / a)) * y);
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= (-1.02d-60)) then
        tmp = (y * ((z - a) / t)) + x
    else if (t <= (-1.75d-152)) then
        tmp = (x + y) - ((z * y) / a)
    else if (t <= (-1.02d-240)) then
        tmp = y - (((z - t) * y) / (a - t))
    else if (t <= 3.75d-41) then
        tmp = x + ((1.0d0 - (z / a)) * y)
    else
        tmp = x - ((a - z) * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -1.02e-60) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= -1.75e-152) {
		tmp = (x + y) - ((z * y) / a);
	} else if (t <= -1.02e-240) {
		tmp = y - (((z - t) * y) / (a - t));
	} else if (t <= 3.75e-41) {
		tmp = x + ((1.0 - (z / a)) * y);
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -1.02e-60:
		tmp = (y * ((z - a) / t)) + x
	elif t <= -1.75e-152:
		tmp = (x + y) - ((z * y) / a)
	elif t <= -1.02e-240:
		tmp = y - (((z - t) * y) / (a - t))
	elif t <= 3.75e-41:
		tmp = x + ((1.0 - (z / a)) * y)
	else:
		tmp = x - ((a - z) * (y / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -1.02e-60)
		tmp = Float64(Float64(y * Float64(Float64(z - a) / t)) + x);
	elseif (t <= -1.75e-152)
		tmp = Float64(Float64(x + y) - Float64(Float64(z * y) / a));
	elseif (t <= -1.02e-240)
		tmp = Float64(y - Float64(Float64(Float64(z - t) * y) / Float64(a - t)));
	elseif (t <= 3.75e-41)
		tmp = Float64(x + Float64(Float64(1.0 - Float64(z / a)) * y));
	else
		tmp = Float64(x - Float64(Float64(a - z) * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -1.02e-60)
		tmp = (y * ((z - a) / t)) + x;
	elseif (t <= -1.75e-152)
		tmp = (x + y) - ((z * y) / a);
	elseif (t <= -1.02e-240)
		tmp = y - (((z - t) * y) / (a - t));
	elseif (t <= 3.75e-41)
		tmp = x + ((1.0 - (z / a)) * y);
	else
		tmp = x - ((a - z) * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -1.02e-60], N[(N[(y * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, -1.75e-152], N[(N[(x + y), $MachinePrecision] - N[(N[(z * y), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, -1.02e-240], N[(y - N[(N[(N[(z - t), $MachinePrecision] * y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 3.75e-41], N[(x + N[(N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a - z), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 5 regimes

2. if t < -1.01999999999999994e-60

   1. Initial program 59.9%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -1.01999999999999994e-60 < t < -1.7500000000000001e-152

   1. Initial program 95.9%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -1.7500000000000001e-152 < t < -1.02e-240

   1. Initial program 95.2%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -1.02e-240 < t < 3.75000000000000024e-41

   1. Initial program 96.3%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 3.75000000000000024e-41 < t

   1. Initial program 71.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 5 regimes into one program.
4. Add Preprocessing

Alternative 3: 78.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(1 - \frac{z}{a}\right) \cdot y\\ \mathbf{if}\;a \leq -0.0003:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 1.46 \cdot 10^{-86}:\\ \;\;\;\;x - \frac{y \cdot \left(a - z\right)}{t}\\ \mathbf{elif}\;a \leq 65000000000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 5.8 \cdot 10^{+153}:\\ \;\;\;\;x - \left(a - z\right) \cdot \frac{y}{t}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- 1.0 (/ z a)) y))))
   (if (<= a -0.0003)
     t_1
     (if (<= a 1.46e-86)
       (- x (/ (* y (- a z)) t))
       (if (<= a 65000000000000.0)
         t_1
         (if (<= a 5.8e+153) (- x (* (- a z) (/ y t))) (+ y x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((1.0 - (z / a)) * y);
	double tmp;
	if (a <= -0.0003) {
		tmp = t_1;
	} else if (a <= 1.46e-86) {
		tmp = x - ((y * (a - z)) / t);
	} else if (a <= 65000000000000.0) {
		tmp = t_1;
	} else if (a <= 5.8e+153) {
		tmp = x - ((a - z) * (y / t));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + ((1.0d0 - (z / a)) * y)
    if (a <= (-0.0003d0)) then
        tmp = t_1
    else if (a <= 1.46d-86) then
        tmp = x - ((y * (a - z)) / t)
    else if (a <= 65000000000000.0d0) then
        tmp = t_1
    else if (a <= 5.8d+153) then
        tmp = x - ((a - z) * (y / t))
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((1.0 - (z / a)) * y);
	double tmp;
	if (a <= -0.0003) {
		tmp = t_1;
	} else if (a <= 1.46e-86) {
		tmp = x - ((y * (a - z)) / t);
	} else if (a <= 65000000000000.0) {
		tmp = t_1;
	} else if (a <= 5.8e+153) {
		tmp = x - ((a - z) * (y / t));
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((1.0 - (z / a)) * y)
	tmp = 0
	if a <= -0.0003:
		tmp = t_1
	elif a <= 1.46e-86:
		tmp = x - ((y * (a - z)) / t)
	elif a <= 65000000000000.0:
		tmp = t_1
	elif a <= 5.8e+153:
		tmp = x - ((a - z) * (y / t))
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(1.0 - Float64(z / a)) * y))
	tmp = 0.0
	if (a <= -0.0003)
		tmp = t_1;
	elseif (a <= 1.46e-86)
		tmp = Float64(x - Float64(Float64(y * Float64(a - z)) / t));
	elseif (a <= 65000000000000.0)
		tmp = t_1;
	elseif (a <= 5.8e+153)
		tmp = Float64(x - Float64(Float64(a - z) * Float64(y / t)));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((1.0 - (z / a)) * y);
	tmp = 0.0;
	if (a <= -0.0003)
		tmp = t_1;
	elseif (a <= 1.46e-86)
		tmp = x - ((y * (a - z)) / t);
	elseif (a <= 65000000000000.0)
		tmp = t_1;
	elseif (a <= 5.8e+153)
		tmp = x - ((a - z) * (y / t));
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -0.0003], t$95$1, If[LessEqual[a, 1.46e-86], N[(x - N[(N[(y * N[(a - z), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[a, 65000000000000.0], t$95$1, If[LessEqual[a, 5.8e+153], N[(x - N[(N[(a - z), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if a < -2.99999999999999974e-4 or 1.45999999999999993e-86 < a < 6.5e13

   1. Initial program 80.9%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2.99999999999999974e-4 < a < 1.45999999999999993e-86

   1. Initial program 76.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if 6.5e13 < a < 5.80000000000000004e153

   1. Initial program 66.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   if 5.80000000000000004e153 < a

   1. Initial program 88.6%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 4: 63.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t - a}\\ \mathbf{if}\;z \leq -7.6 \cdot 10^{+54}:\\ \;\;\;\;\frac{y}{t - a} \cdot z\\ \mathbf{elif}\;z \leq 9.9 \cdot 10^{+61}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;z \leq 1.15 \cdot 10^{+81}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 2.9 \cdot 10^{+175}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ z (- t a)))))
   (if (<= z -7.6e+54)
     (* (/ y (- t a)) z)
     (if (<= z 9.9e+61)
       (+ y x)
       (if (<= z 1.15e+81) t_1 (if (<= z 2.9e+175) (+ y x) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / (t - a));
	double tmp;
	if (z <= -7.6e+54) {
		tmp = (y / (t - a)) * z;
	} else if (z <= 9.9e+61) {
		tmp = y + x;
	} else if (z <= 1.15e+81) {
		tmp = t_1;
	} else if (z <= 2.9e+175) {
		tmp = y + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (z / (t - a))
    if (z <= (-7.6d+54)) then
        tmp = (y / (t - a)) * z
    else if (z <= 9.9d+61) then
        tmp = y + x
    else if (z <= 1.15d+81) then
        tmp = t_1
    else if (z <= 2.9d+175) then
        tmp = y + x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / (t - a));
	double tmp;
	if (z <= -7.6e+54) {
		tmp = (y / (t - a)) * z;
	} else if (z <= 9.9e+61) {
		tmp = y + x;
	} else if (z <= 1.15e+81) {
		tmp = t_1;
	} else if (z <= 2.9e+175) {
		tmp = y + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (z / (t - a))
	tmp = 0
	if z <= -7.6e+54:
		tmp = (y / (t - a)) * z
	elif z <= 9.9e+61:
		tmp = y + x
	elif z <= 1.15e+81:
		tmp = t_1
	elif z <= 2.9e+175:
		tmp = y + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(z / Float64(t - a)))
	tmp = 0.0
	if (z <= -7.6e+54)
		tmp = Float64(Float64(y / Float64(t - a)) * z);
	elseif (z <= 9.9e+61)
		tmp = Float64(y + x);
	elseif (z <= 1.15e+81)
		tmp = t_1;
	elseif (z <= 2.9e+175)
		tmp = Float64(y + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (z / (t - a));
	tmp = 0.0;
	if (z <= -7.6e+54)
		tmp = (y / (t - a)) * z;
	elseif (z <= 9.9e+61)
		tmp = y + x;
	elseif (z <= 1.15e+81)
		tmp = t_1;
	elseif (z <= 2.9e+175)
		tmp = y + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(z / N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.6e+54], N[(N[(y / N[(t - a), $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[z, 9.9e+61], N[(y + x), $MachinePrecision], If[LessEqual[z, 1.15e+81], t$95$1, If[LessEqual[z, 2.9e+175], N[(y + x), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -7.6000000000000005e54

   1. Initial program 78.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   if -7.6000000000000005e54 < z < 9.9000000000000004e61 or 1.1499999999999999e81 < z < 2.9e175

   1. Initial program 77.4%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 9.9000000000000004e61 < z < 1.1499999999999999e81 or 2.9e175 < z

   1. Initial program 86.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 63.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t - a}\\ \mathbf{if}\;z \leq -8 \cdot 10^{+54}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 9 \cdot 10^{+62}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{+80}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 1.22 \cdot 10^{+176}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ z (- t a)))))
   (if (<= z -8e+54)
     t_1
     (if (<= z 9e+62)
       (+ y x)
       (if (<= z 6.5e+80) t_1 (if (<= z 1.22e+176) (+ y x) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / (t - a));
	double tmp;
	if (z <= -8e+54) {
		tmp = t_1;
	} else if (z <= 9e+62) {
		tmp = y + x;
	} else if (z <= 6.5e+80) {
		tmp = t_1;
	} else if (z <= 1.22e+176) {
		tmp = y + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (z / (t - a))
    if (z <= (-8d+54)) then
        tmp = t_1
    else if (z <= 9d+62) then
        tmp = y + x
    else if (z <= 6.5d+80) then
        tmp = t_1
    else if (z <= 1.22d+176) then
        tmp = y + x
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / (t - a));
	double tmp;
	if (z <= -8e+54) {
		tmp = t_1;
	} else if (z <= 9e+62) {
		tmp = y + x;
	} else if (z <= 6.5e+80) {
		tmp = t_1;
	} else if (z <= 1.22e+176) {
		tmp = y + x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (z / (t - a))
	tmp = 0
	if z <= -8e+54:
		tmp = t_1
	elif z <= 9e+62:
		tmp = y + x
	elif z <= 6.5e+80:
		tmp = t_1
	elif z <= 1.22e+176:
		tmp = y + x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(z / Float64(t - a)))
	tmp = 0.0
	if (z <= -8e+54)
		tmp = t_1;
	elseif (z <= 9e+62)
		tmp = Float64(y + x);
	elseif (z <= 6.5e+80)
		tmp = t_1;
	elseif (z <= 1.22e+176)
		tmp = Float64(y + x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (z / (t - a));
	tmp = 0.0;
	if (z <= -8e+54)
		tmp = t_1;
	elseif (z <= 9e+62)
		tmp = y + x;
	elseif (z <= 6.5e+80)
		tmp = t_1;
	elseif (z <= 1.22e+176)
		tmp = y + x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(z / N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -8e+54], t$95$1, If[LessEqual[z, 9e+62], N[(y + x), $MachinePrecision], If[LessEqual[z, 6.5e+80], t$95$1, If[LessEqual[z, 1.22e+176], N[(y + x), $MachinePrecision], t$95$1]]]]]

Derivation

1. Split input into 2 regimes

2. if z < -8.0000000000000006e54 or 8.99999999999999997e62 < z < 6.4999999999999998e80 or 1.2199999999999999e176 < z

   1. Initial program 81.3%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   if -8.0000000000000006e54 < z < 8.99999999999999997e62 or 6.4999999999999998e80 < z < 1.2199999999999999e176

   1. Initial program 77.4%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 56.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t}\\ \mathbf{if}\;z \leq -1.8 \cdot 10^{+57}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 1.25 \cdot 10^{+67}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{+80}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 7.5 \cdot 10^{+162}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;\frac{z \cdot y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ z t))))
   (if (<= z -1.8e+57)
     t_1
     (if (<= z 1.25e+67)
       (+ y x)
       (if (<= z 6.5e+80) t_1 (if (<= z 7.5e+162) (+ y x) (/ (* z y) t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / t);
	double tmp;
	if (z <= -1.8e+57) {
		tmp = t_1;
	} else if (z <= 1.25e+67) {
		tmp = y + x;
	} else if (z <= 6.5e+80) {
		tmp = t_1;
	} else if (z <= 7.5e+162) {
		tmp = y + x;
	} else {
		tmp = (z * y) / t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (z / t)
    if (z <= (-1.8d+57)) then
        tmp = t_1
    else if (z <= 1.25d+67) then
        tmp = y + x
    else if (z <= 6.5d+80) then
        tmp = t_1
    else if (z <= 7.5d+162) then
        tmp = y + x
    else
        tmp = (z * y) / t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / t);
	double tmp;
	if (z <= -1.8e+57) {
		tmp = t_1;
	} else if (z <= 1.25e+67) {
		tmp = y + x;
	} else if (z <= 6.5e+80) {
		tmp = t_1;
	} else if (z <= 7.5e+162) {
		tmp = y + x;
	} else {
		tmp = (z * y) / t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (z / t)
	tmp = 0
	if z <= -1.8e+57:
		tmp = t_1
	elif z <= 1.25e+67:
		tmp = y + x
	elif z <= 6.5e+80:
		tmp = t_1
	elif z <= 7.5e+162:
		tmp = y + x
	else:
		tmp = (z * y) / t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(z / t))
	tmp = 0.0
	if (z <= -1.8e+57)
		tmp = t_1;
	elseif (z <= 1.25e+67)
		tmp = Float64(y + x);
	elseif (z <= 6.5e+80)
		tmp = t_1;
	elseif (z <= 7.5e+162)
		tmp = Float64(y + x);
	else
		tmp = Float64(Float64(z * y) / t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (z / t);
	tmp = 0.0;
	if (z <= -1.8e+57)
		tmp = t_1;
	elseif (z <= 1.25e+67)
		tmp = y + x;
	elseif (z <= 6.5e+80)
		tmp = t_1;
	elseif (z <= 7.5e+162)
		tmp = y + x;
	else
		tmp = (z * y) / t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.8e+57], t$95$1, If[LessEqual[z, 1.25e+67], N[(y + x), $MachinePrecision], If[LessEqual[z, 6.5e+80], t$95$1, If[LessEqual[z, 7.5e+162], N[(y + x), $MachinePrecision], N[(N[(z * y), $MachinePrecision] / t), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.8000000000000001e57 or 1.24999999999999994e67 < z < 6.4999999999999998e80

   1. Initial program 74.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   10. Taylor expanded in t around inf 0

       \[\leadsto expr\]

   12. Simplified 0

       \[\leadsto expr\]

   if -1.8000000000000001e57 < z < 1.24999999999999994e67 or 6.4999999999999998e80 < z < 7.50000000000000033e162

   1. Initial program 77.9%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 7.50000000000000033e162 < z

   1. Initial program 93.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   8. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   10. Simplified 0

       \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 57.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t}\\ \mathbf{if}\;z \leq -8.5 \cdot 10^{+55}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.8 \cdot 10^{+66}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;z \leq 6.5 \cdot 10^{+80}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 2.2 \cdot 10^{+162}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;\frac{z}{\frac{t}{y}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ z t))))
   (if (<= z -8.5e+55)
     t_1
     (if (<= z 3.8e+66)
       (+ y x)
       (if (<= z 6.5e+80) t_1 (if (<= z 2.2e+162) (+ y x) (/ z (/ t y))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / t);
	double tmp;
	if (z <= -8.5e+55) {
		tmp = t_1;
	} else if (z <= 3.8e+66) {
		tmp = y + x;
	} else if (z <= 6.5e+80) {
		tmp = t_1;
	} else if (z <= 2.2e+162) {
		tmp = y + x;
	} else {
		tmp = z / (t / y);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (z / t)
    if (z <= (-8.5d+55)) then
        tmp = t_1
    else if (z <= 3.8d+66) then
        tmp = y + x
    else if (z <= 6.5d+80) then
        tmp = t_1
    else if (z <= 2.2d+162) then
        tmp = y + x
    else
        tmp = z / (t / y)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / t);
	double tmp;
	if (z <= -8.5e+55) {
		tmp = t_1;
	} else if (z <= 3.8e+66) {
		tmp = y + x;
	} else if (z <= 6.5e+80) {
		tmp = t_1;
	} else if (z <= 2.2e+162) {
		tmp = y + x;
	} else {
		tmp = z / (t / y);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (z / t)
	tmp = 0
	if z <= -8.5e+55:
		tmp = t_1
	elif z <= 3.8e+66:
		tmp = y + x
	elif z <= 6.5e+80:
		tmp = t_1
	elif z <= 2.2e+162:
		tmp = y + x
	else:
		tmp = z / (t / y)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(z / t))
	tmp = 0.0
	if (z <= -8.5e+55)
		tmp = t_1;
	elseif (z <= 3.8e+66)
		tmp = Float64(y + x);
	elseif (z <= 6.5e+80)
		tmp = t_1;
	elseif (z <= 2.2e+162)
		tmp = Float64(y + x);
	else
		tmp = Float64(z / Float64(t / y));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (z / t);
	tmp = 0.0;
	if (z <= -8.5e+55)
		tmp = t_1;
	elseif (z <= 3.8e+66)
		tmp = y + x;
	elseif (z <= 6.5e+80)
		tmp = t_1;
	elseif (z <= 2.2e+162)
		tmp = y + x;
	else
		tmp = z / (t / y);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -8.5e+55], t$95$1, If[LessEqual[z, 3.8e+66], N[(y + x), $MachinePrecision], If[LessEqual[z, 6.5e+80], t$95$1, If[LessEqual[z, 2.2e+162], N[(y + x), $MachinePrecision], N[(z / N[(t / y), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -8.50000000000000002e55 or 3.8000000000000002e66 < z < 6.4999999999999998e80

   1. Initial program 74.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   10. Taylor expanded in t around inf 0

       \[\leadsto expr\]

   12. Simplified 0

       \[\leadsto expr\]

   if -8.50000000000000002e55 < z < 3.8000000000000002e66 or 6.4999999999999998e80 < z < 2.2000000000000002e162

   1. Initial program 77.9%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 2.2000000000000002e162 < z

   1. Initial program 93.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   10. Taylor expanded in t around inf 0

       \[\leadsto expr\]

   12. Simplified 0

       \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 57.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t}\\ \mathbf{if}\;z \leq -1.1 \cdot 10^{+56}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{+66}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;z \leq 7.5 \cdot 10^{+80}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 1.38 \cdot 10^{+162}:\\ \;\;\;\;y + x\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{t} \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ z t))))
   (if (<= z -1.1e+56)
     t_1
     (if (<= z 9.5e+66)
       (+ y x)
       (if (<= z 7.5e+80) t_1 (if (<= z 1.38e+162) (+ y x) (* (/ y t) z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / t);
	double tmp;
	if (z <= -1.1e+56) {
		tmp = t_1;
	} else if (z <= 9.5e+66) {
		tmp = y + x;
	} else if (z <= 7.5e+80) {
		tmp = t_1;
	} else if (z <= 1.38e+162) {
		tmp = y + x;
	} else {
		tmp = (y / t) * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (z / t)
    if (z <= (-1.1d+56)) then
        tmp = t_1
    else if (z <= 9.5d+66) then
        tmp = y + x
    else if (z <= 7.5d+80) then
        tmp = t_1
    else if (z <= 1.38d+162) then
        tmp = y + x
    else
        tmp = (y / t) * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / t);
	double tmp;
	if (z <= -1.1e+56) {
		tmp = t_1;
	} else if (z <= 9.5e+66) {
		tmp = y + x;
	} else if (z <= 7.5e+80) {
		tmp = t_1;
	} else if (z <= 1.38e+162) {
		tmp = y + x;
	} else {
		tmp = (y / t) * z;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (z / t)
	tmp = 0
	if z <= -1.1e+56:
		tmp = t_1
	elif z <= 9.5e+66:
		tmp = y + x
	elif z <= 7.5e+80:
		tmp = t_1
	elif z <= 1.38e+162:
		tmp = y + x
	else:
		tmp = (y / t) * z
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(z / t))
	tmp = 0.0
	if (z <= -1.1e+56)
		tmp = t_1;
	elseif (z <= 9.5e+66)
		tmp = Float64(y + x);
	elseif (z <= 7.5e+80)
		tmp = t_1;
	elseif (z <= 1.38e+162)
		tmp = Float64(y + x);
	else
		tmp = Float64(Float64(y / t) * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (z / t);
	tmp = 0.0;
	if (z <= -1.1e+56)
		tmp = t_1;
	elseif (z <= 9.5e+66)
		tmp = y + x;
	elseif (z <= 7.5e+80)
		tmp = t_1;
	elseif (z <= 1.38e+162)
		tmp = y + x;
	else
		tmp = (y / t) * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.1e+56], t$95$1, If[LessEqual[z, 9.5e+66], N[(y + x), $MachinePrecision], If[LessEqual[z, 7.5e+80], t$95$1, If[LessEqual[z, 1.38e+162], N[(y + x), $MachinePrecision], N[(N[(y / t), $MachinePrecision] * z), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.10000000000000008e56 or 9.50000000000000051e66 < z < 7.49999999999999994e80

   1. Initial program 74.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   10. Taylor expanded in t around inf 0

       \[\leadsto expr\]

   12. Simplified 0

       \[\leadsto expr\]

   if -1.10000000000000008e56 < z < 9.50000000000000051e66 or 7.49999999999999994e80 < z < 1.38e162

   1. Initial program 77.9%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 1.38e162 < z

   1. Initial program 93.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   10. Taylor expanded in t around inf 0

       \[\leadsto expr\]

   12. Simplified 0

       \[\leadsto expr\]

   14. Applied egg-rr 0

       \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 60.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z}{t}\\ \mathbf{if}\;a \leq -2.55 \cdot 10^{-144}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 4.4 \cdot 10^{-229}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 6.2 \cdot 10^{-161}:\\ \;\;\;\;x\\ \mathbf{elif}\;a \leq 2.05 \cdot 10^{-128}:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* y (/ z t))))
   (if (<= a -2.55e-144)
     (+ y x)
     (if (<= a 4.4e-229)
       t_1
       (if (<= a 6.2e-161) x (if (<= a 2.05e-128) t_1 (+ y x)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / t);
	double tmp;
	if (a <= -2.55e-144) {
		tmp = y + x;
	} else if (a <= 4.4e-229) {
		tmp = t_1;
	} else if (a <= 6.2e-161) {
		tmp = x;
	} else if (a <= 2.05e-128) {
		tmp = t_1;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = y * (z / t)
    if (a <= (-2.55d-144)) then
        tmp = y + x
    else if (a <= 4.4d-229) then
        tmp = t_1
    else if (a <= 6.2d-161) then
        tmp = x
    else if (a <= 2.05d-128) then
        tmp = t_1
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = y * (z / t);
	double tmp;
	if (a <= -2.55e-144) {
		tmp = y + x;
	} else if (a <= 4.4e-229) {
		tmp = t_1;
	} else if (a <= 6.2e-161) {
		tmp = x;
	} else if (a <= 2.05e-128) {
		tmp = t_1;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = y * (z / t)
	tmp = 0
	if a <= -2.55e-144:
		tmp = y + x
	elif a <= 4.4e-229:
		tmp = t_1
	elif a <= 6.2e-161:
		tmp = x
	elif a <= 2.05e-128:
		tmp = t_1
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(y * Float64(z / t))
	tmp = 0.0
	if (a <= -2.55e-144)
		tmp = Float64(y + x);
	elseif (a <= 4.4e-229)
		tmp = t_1;
	elseif (a <= 6.2e-161)
		tmp = x;
	elseif (a <= 2.05e-128)
		tmp = t_1;
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = y * (z / t);
	tmp = 0.0;
	if (a <= -2.55e-144)
		tmp = y + x;
	elseif (a <= 4.4e-229)
		tmp = t_1;
	elseif (a <= 6.2e-161)
		tmp = x;
	elseif (a <= 2.05e-128)
		tmp = t_1;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(y * N[(z / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -2.55e-144], N[(y + x), $MachinePrecision], If[LessEqual[a, 4.4e-229], t$95$1, If[LessEqual[a, 6.2e-161], x, If[LessEqual[a, 2.05e-128], t$95$1, N[(y + x), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if a < -2.55e-144 or 2.05e-128 < a

   1. Initial program 80.6%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2.55e-144 < a < 4.3999999999999998e-229 or 6.1999999999999997e-161 < a < 2.05e-128

   1. Initial program 73.9%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in z around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]

   10. Taylor expanded in t around inf 0

       \[\leadsto expr\]

   12. Simplified 0

       \[\leadsto expr\]

   if 4.3999999999999998e-229 < a < 6.1999999999999997e-161

   1. Initial program 82.2%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 81.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -4.8 \cdot 10^{+100}:\\ \;\;\;\;y \cdot \frac{z - a}{t} + x\\ \mathbf{elif}\;t \leq -1.75 \cdot 10^{-81}:\\ \;\;\;\;x + \frac{z \cdot y}{t}\\ \mathbf{elif}\;t \leq 1.45 \cdot 10^{-43}:\\ \;\;\;\;x + \left(1 - \frac{z}{a}\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;x - \left(a - z\right) \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -4.8e+100)
   (+ (* y (/ (- z a) t)) x)
   (if (<= t -1.75e-81)
     (+ x (/ (* z y) t))
     (if (<= t 1.45e-43)
       (+ x (* (- 1.0 (/ z a)) y))
       (- x (* (- a z) (/ y t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -4.8e+100) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= -1.75e-81) {
		tmp = x + ((z * y) / t);
	} else if (t <= 1.45e-43) {
		tmp = x + ((1.0 - (z / a)) * y);
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= (-4.8d+100)) then
        tmp = (y * ((z - a) / t)) + x
    else if (t <= (-1.75d-81)) then
        tmp = x + ((z * y) / t)
    else if (t <= 1.45d-43) then
        tmp = x + ((1.0d0 - (z / a)) * y)
    else
        tmp = x - ((a - z) * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -4.8e+100) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= -1.75e-81) {
		tmp = x + ((z * y) / t);
	} else if (t <= 1.45e-43) {
		tmp = x + ((1.0 - (z / a)) * y);
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -4.8e+100:
		tmp = (y * ((z - a) / t)) + x
	elif t <= -1.75e-81:
		tmp = x + ((z * y) / t)
	elif t <= 1.45e-43:
		tmp = x + ((1.0 - (z / a)) * y)
	else:
		tmp = x - ((a - z) * (y / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -4.8e+100)
		tmp = Float64(Float64(y * Float64(Float64(z - a) / t)) + x);
	elseif (t <= -1.75e-81)
		tmp = Float64(x + Float64(Float64(z * y) / t));
	elseif (t <= 1.45e-43)
		tmp = Float64(x + Float64(Float64(1.0 - Float64(z / a)) * y));
	else
		tmp = Float64(x - Float64(Float64(a - z) * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -4.8e+100)
		tmp = (y * ((z - a) / t)) + x;
	elseif (t <= -1.75e-81)
		tmp = x + ((z * y) / t);
	elseif (t <= 1.45e-43)
		tmp = x + ((1.0 - (z / a)) * y);
	else
		tmp = x - ((a - z) * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -4.8e+100], N[(N[(y * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, -1.75e-81], N[(x + N[(N[(z * y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 1.45e-43], N[(x + N[(N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a - z), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if t < -4.80000000000000023e100

   1. Initial program 47.8%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -4.80000000000000023e100 < t < -1.74999999999999993e-81

   1. Initial program 83.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -1.74999999999999993e-81 < t < 1.4500000000000001e-43

   1. Initial program 96.7%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 1.4500000000000001e-43 < t

   1. Initial program 71.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 11: 81.3% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := y \cdot \frac{z - a}{t} + x\\ \mathbf{if}\;t \leq -4.8 \cdot 10^{+100}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq -1 \cdot 10^{-81}:\\ \;\;\;\;x + \frac{z \cdot y}{t}\\ \mathbf{elif}\;t \leq 8 \cdot 10^{-35}:\\ \;\;\;\;x + \left(1 - \frac{z}{a}\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ (* y (/ (- z a) t)) x)))
   (if (<= t -4.8e+100)
     t_1
     (if (<= t -1e-81)
       (+ x (/ (* z y) t))
       (if (<= t 8e-35) (+ x (* (- 1.0 (/ z a)) y)) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y * ((z - a) / t)) + x;
	double tmp;
	if (t <= -4.8e+100) {
		tmp = t_1;
	} else if (t <= -1e-81) {
		tmp = x + ((z * y) / t);
	} else if (t <= 8e-35) {
		tmp = x + ((1.0 - (z / a)) * y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (y * ((z - a) / t)) + x
    if (t <= (-4.8d+100)) then
        tmp = t_1
    else if (t <= (-1d-81)) then
        tmp = x + ((z * y) / t)
    else if (t <= 8d-35) then
        tmp = x + ((1.0d0 - (z / a)) * y)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (y * ((z - a) / t)) + x;
	double tmp;
	if (t <= -4.8e+100) {
		tmp = t_1;
	} else if (t <= -1e-81) {
		tmp = x + ((z * y) / t);
	} else if (t <= 8e-35) {
		tmp = x + ((1.0 - (z / a)) * y);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y * ((z - a) / t)) + x
	tmp = 0
	if t <= -4.8e+100:
		tmp = t_1
	elif t <= -1e-81:
		tmp = x + ((z * y) / t)
	elif t <= 8e-35:
		tmp = x + ((1.0 - (z / a)) * y)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y * Float64(Float64(z - a) / t)) + x)
	tmp = 0.0
	if (t <= -4.8e+100)
		tmp = t_1;
	elseif (t <= -1e-81)
		tmp = Float64(x + Float64(Float64(z * y) / t));
	elseif (t <= 8e-35)
		tmp = Float64(x + Float64(Float64(1.0 - Float64(z / a)) * y));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y * ((z - a) / t)) + x;
	tmp = 0.0;
	if (t <= -4.8e+100)
		tmp = t_1;
	elseif (t <= -1e-81)
		tmp = x + ((z * y) / t);
	elseif (t <= 8e-35)
		tmp = x + ((1.0 - (z / a)) * y);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]}, If[LessEqual[t, -4.8e+100], t$95$1, If[LessEqual[t, -1e-81], N[(x + N[(N[(z * y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[t, 8e-35], N[(x + N[(N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if t < -4.80000000000000023e100 or 8.00000000000000006e-35 < t

   1. Initial program 60.1%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -4.80000000000000023e100 < t < -9.9999999999999996e-82

   1. Initial program 83.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   if -9.9999999999999996e-82 < t < 8.00000000000000006e-35

   1. Initial program 96.7%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 12: 89.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.1 \cdot 10^{+96}:\\ \;\;\;\;y \cdot \frac{z - a}{t} + x\\ \mathbf{elif}\;t \leq 4.6 \cdot 10^{+54}:\\ \;\;\;\;\left(x + y\right) - \frac{y}{a - t} \cdot \left(z - t\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(a - z\right) \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -2.1e+96)
   (+ (* y (/ (- z a) t)) x)
   (if (<= t 4.6e+54)
     (- (+ x y) (* (/ y (- a t)) (- z t)))
     (- x (* (- a z) (/ y t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.1e+96) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= 4.6e+54) {
		tmp = (x + y) - ((y / (a - t)) * (z - t));
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= (-2.1d+96)) then
        tmp = (y * ((z - a) / t)) + x
    else if (t <= 4.6d+54) then
        tmp = (x + y) - ((y / (a - t)) * (z - t))
    else
        tmp = x - ((a - z) * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.1e+96) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= 4.6e+54) {
		tmp = (x + y) - ((y / (a - t)) * (z - t));
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -2.1e+96:
		tmp = (y * ((z - a) / t)) + x
	elif t <= 4.6e+54:
		tmp = (x + y) - ((y / (a - t)) * (z - t))
	else:
		tmp = x - ((a - z) * (y / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -2.1e+96)
		tmp = Float64(Float64(y * Float64(Float64(z - a) / t)) + x);
	elseif (t <= 4.6e+54)
		tmp = Float64(Float64(x + y) - Float64(Float64(y / Float64(a - t)) * Float64(z - t)));
	else
		tmp = Float64(x - Float64(Float64(a - z) * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -2.1e+96)
		tmp = (y * ((z - a) / t)) + x;
	elseif (t <= 4.6e+54)
		tmp = (x + y) - ((y / (a - t)) * (z - t));
	else
		tmp = x - ((a - z) * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -2.1e+96], N[(N[(y * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, 4.6e+54], N[(N[(x + y), $MachinePrecision] - N[(N[(y / N[(a - t), $MachinePrecision]), $MachinePrecision] * N[(z - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a - z), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -2.1000000000000001e96

   1. Initial program 47.0%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2.1000000000000001e96 < t < 4.59999999999999988e54

   1. Initial program 93.7%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   4. Applied egg-rr 0

      \[\leadsto expr\]

   if 4.59999999999999988e54 < t

   1. Initial program 64.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 13: 91.0% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.9 \cdot 10^{+98}:\\ \;\;\;\;y \cdot \frac{z - a}{t} + x\\ \mathbf{elif}\;t \leq 1.65 \cdot 10^{+69}:\\ \;\;\;\;x - y \cdot \left(-1 - \frac{z - t}{t - a}\right)\\ \mathbf{else}:\\ \;\;\;\;x - \left(a - z\right) \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -2.9e+98)
   (+ (* y (/ (- z a) t)) x)
   (if (<= t 1.65e+69)
     (- x (* y (- -1.0 (/ (- z t) (- t a)))))
     (- x (* (- a z) (/ y t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.9e+98) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= 1.65e+69) {
		tmp = x - (y * (-1.0 - ((z - t) / (t - a))));
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= (-2.9d+98)) then
        tmp = (y * ((z - a) / t)) + x
    else if (t <= 1.65d+69) then
        tmp = x - (y * ((-1.0d0) - ((z - t) / (t - a))))
    else
        tmp = x - ((a - z) * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.9e+98) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= 1.65e+69) {
		tmp = x - (y * (-1.0 - ((z - t) / (t - a))));
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -2.9e+98:
		tmp = (y * ((z - a) / t)) + x
	elif t <= 1.65e+69:
		tmp = x - (y * (-1.0 - ((z - t) / (t - a))))
	else:
		tmp = x - ((a - z) * (y / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -2.9e+98)
		tmp = Float64(Float64(y * Float64(Float64(z - a) / t)) + x);
	elseif (t <= 1.65e+69)
		tmp = Float64(x - Float64(y * Float64(-1.0 - Float64(Float64(z - t) / Float64(t - a)))));
	else
		tmp = Float64(x - Float64(Float64(a - z) * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -2.9e+98)
		tmp = (y * ((z - a) / t)) + x;
	elseif (t <= 1.65e+69)
		tmp = x - (y * (-1.0 - ((z - t) / (t - a))));
	else
		tmp = x - ((a - z) * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -2.9e+98], N[(N[(y * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, 1.65e+69], N[(x - N[(y * N[(-1.0 - N[(N[(z - t), $MachinePrecision] / N[(t - a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a - z), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -2.9000000000000001e98

   1. Initial program 47.0%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2.9000000000000001e98 < t < 1.6499999999999999e69

   1. Initial program 93.8%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   if 1.6499999999999999e69 < t

   1. Initial program 60.5%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 14: 80.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -2.2 \cdot 10^{-61}:\\ \;\;\;\;y \cdot \frac{z - a}{t} + x\\ \mathbf{elif}\;t \leq 8.6 \cdot 10^{-41}:\\ \;\;\;\;\left(x + y\right) - \frac{z \cdot y}{a}\\ \mathbf{else}:\\ \;\;\;\;x - \left(a - z\right) \cdot \frac{y}{t}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= t -2.2e-61)
   (+ (* y (/ (- z a) t)) x)
   (if (<= t 8.6e-41) (- (+ x y) (/ (* z y) a)) (- x (* (- a z) (/ y t))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.2e-61) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= 8.6e-41) {
		tmp = (x + y) - ((z * y) / a);
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (t <= (-2.2d-61)) then
        tmp = (y * ((z - a) / t)) + x
    else if (t <= 8.6d-41) then
        tmp = (x + y) - ((z * y) / a)
    else
        tmp = x - ((a - z) * (y / t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= -2.2e-61) {
		tmp = (y * ((z - a) / t)) + x;
	} else if (t <= 8.6e-41) {
		tmp = (x + y) - ((z * y) / a);
	} else {
		tmp = x - ((a - z) * (y / t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= -2.2e-61:
		tmp = (y * ((z - a) / t)) + x
	elif t <= 8.6e-41:
		tmp = (x + y) - ((z * y) / a)
	else:
		tmp = x - ((a - z) * (y / t))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= -2.2e-61)
		tmp = Float64(Float64(y * Float64(Float64(z - a) / t)) + x);
	elseif (t <= 8.6e-41)
		tmp = Float64(Float64(x + y) - Float64(Float64(z * y) / a));
	else
		tmp = Float64(x - Float64(Float64(a - z) * Float64(y / t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= -2.2e-61)
		tmp = (y * ((z - a) / t)) + x;
	elseif (t <= 8.6e-41)
		tmp = (x + y) - ((z * y) / a);
	else
		tmp = x - ((a - z) * (y / t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, -2.2e-61], N[(N[(y * N[(N[(z - a), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, 8.6e-41], N[(N[(x + y), $MachinePrecision] - N[(N[(z * y), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], N[(x - N[(N[(a - z), $MachinePrecision] * N[(y / t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if t < -2.20000000000000009e-61

   1. Initial program 59.9%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -2.20000000000000009e-61 < t < 8.5999999999999997e-41

   1. Initial program 96.1%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if 8.5999999999999997e-41 < t

   1. Initial program 71.4%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in t around inf 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]

   9. Applied egg-rr 0

      \[\leadsto expr\]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 15: 81.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(1 - \frac{z}{a}\right) \cdot y\\ \mathbf{if}\;a \leq -1.6 \cdot 10^{-22}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 3.25 \cdot 10^{-87}:\\ \;\;\;\;x + \frac{z \cdot y}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- 1.0 (/ z a)) y))))
   (if (<= a -1.6e-22) t_1 (if (<= a 3.25e-87) (+ x (/ (* z y) t)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((1.0 - (z / a)) * y);
	double tmp;
	if (a <= -1.6e-22) {
		tmp = t_1;
	} else if (a <= 3.25e-87) {
		tmp = x + ((z * y) / t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: tmp
    t_1 = x + ((1.0d0 - (z / a)) * y)
    if (a <= (-1.6d-22)) then
        tmp = t_1
    else if (a <= 3.25d-87) then
        tmp = x + ((z * y) / t)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((1.0 - (z / a)) * y);
	double tmp;
	if (a <= -1.6e-22) {
		tmp = t_1;
	} else if (a <= 3.25e-87) {
		tmp = x + ((z * y) / t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((1.0 - (z / a)) * y)
	tmp = 0
	if a <= -1.6e-22:
		tmp = t_1
	elif a <= 3.25e-87:
		tmp = x + ((z * y) / t)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(1.0 - Float64(z / a)) * y))
	tmp = 0.0
	if (a <= -1.6e-22)
		tmp = t_1;
	elseif (a <= 3.25e-87)
		tmp = Float64(x + Float64(Float64(z * y) / t));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((1.0 - (z / a)) * y);
	tmp = 0.0;
	if (a <= -1.6e-22)
		tmp = t_1;
	elseif (a <= 3.25e-87)
		tmp = x + ((z * y) / t);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(1.0 - N[(z / a), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.6e-22], t$95$1, If[LessEqual[a, 3.25e-87], N[(x + N[(N[(z * y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -1.59999999999999994e-22 or 3.2500000000000001e-87 < a

   1. Initial program 80.1%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in t around 0 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -1.59999999999999994e-22 < a < 3.2500000000000001e-87

   1. Initial program 77.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 16: 73.5% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -9.6 \cdot 10^{+129}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 7.5 \cdot 10^{+47}:\\ \;\;\;\;x + \frac{z \cdot y}{t}\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -9.6e+129) (+ y x) (if (<= a 7.5e+47) (+ x (/ (* z y) t)) (+ y x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -9.6e+129) {
		tmp = y + x;
	} else if (a <= 7.5e+47) {
		tmp = x + ((z * y) / t);
	} else {
		tmp = y + x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-9.6d+129)) then
        tmp = y + x
    else if (a <= 7.5d+47) then
        tmp = x + ((z * y) / t)
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -9.6e+129) {
		tmp = y + x;
	} else if (a <= 7.5e+47) {
		tmp = x + ((z * y) / t);
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -9.6e+129:
		tmp = y + x
	elif a <= 7.5e+47:
		tmp = x + ((z * y) / t)
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -9.6e+129)
		tmp = Float64(y + x);
	elseif (a <= 7.5e+47)
		tmp = Float64(x + Float64(Float64(z * y) / t));
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -9.6e+129)
		tmp = y + x;
	elseif (a <= 7.5e+47)
		tmp = x + ((z * y) / t);
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -9.6e+129], N[(y + x), $MachinePrecision], If[LessEqual[a, 7.5e+47], N[(x + N[(N[(z * y), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], N[(y + x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if a < -9.5999999999999995e129 or 7.4999999999999999e47 < a

   1. Initial program 84.1%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -9.5999999999999995e129 < a < 7.4999999999999999e47

   1. Initial program 76.2%

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

   3. Simplified 0

      \[\leadsto expr\]
   4. Add Preprocessing

   5. Taylor expanded in a around 0 0

      \[\leadsto expr\]

   7. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 17: 62.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -1.25 \cdot 10^{-221}:\\ \;\;\;\;y + x\\ \mathbf{elif}\;a \leq 2.7 \cdot 10^{-23}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y + x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= a -1.25e-221) (+ y x) (if (<= a 2.7e-23) x (+ y x))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.25e-221) {
		tmp = y + x;
	} else if (a <= 2.7e-23) {
		tmp = x;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a <= (-1.25d-221)) then
        tmp = y + x
    else if (a <= 2.7d-23) then
        tmp = x
    else
        tmp = y + x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a <= -1.25e-221) {
		tmp = y + x;
	} else if (a <= 2.7e-23) {
		tmp = x;
	} else {
		tmp = y + x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a <= -1.25e-221:
		tmp = y + x
	elif a <= 2.7e-23:
		tmp = x
	else:
		tmp = y + x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a <= -1.25e-221)
		tmp = Float64(y + x);
	elseif (a <= 2.7e-23)
		tmp = x;
	else
		tmp = Float64(y + x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a <= -1.25e-221)
		tmp = y + x;
	elseif (a <= 2.7e-23)
		tmp = x;
	else
		tmp = y + x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[a, -1.25e-221], N[(y + x), $MachinePrecision], If[LessEqual[a, 2.7e-23], x, N[(y + x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if a < -1.24999999999999999e-221 or 2.69999999999999985e-23 < a

   1. Initial program 82.6%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -1.24999999999999999e-221 < a < 2.69999999999999985e-23

   1. Initial program 71.3%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 18: 52.9% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.5 \cdot 10^{-81}:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{-167}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= x -1.5e-81) x (if (<= x 2.6e-167) y x)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -1.5e-81) {
		tmp = x;
	} else if (x <= 2.6e-167) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (x <= (-1.5d-81)) then
        tmp = x
    else if (x <= 2.6d-167) then
        tmp = y
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (x <= -1.5e-81) {
		tmp = x;
	} else if (x <= 2.6e-167) {
		tmp = y;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if x <= -1.5e-81:
		tmp = x
	elif x <= 2.6e-167:
		tmp = y
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (x <= -1.5e-81)
		tmp = x;
	elseif (x <= 2.6e-167)
		tmp = y;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (x <= -1.5e-81)
		tmp = x;
	elseif (x <= 2.6e-167)
		tmp = y;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[x, -1.5e-81], x, If[LessEqual[x, 2.6e-167], y, x]]

Derivation

1. Split input into 2 regimes

2. if x < -1.4999999999999999e-81 or 2.5999999999999999e-167 < x

   1. Initial program 80.0%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   if -1.4999999999999999e-81 < x < 2.5999999999999999e-167

   1. Initial program 76.1%

      \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
   2. Add Preprocessing

   3. Taylor expanded in a around inf 0

      \[\leadsto expr\]

   5. Simplified 0

      \[\leadsto expr\]

   6. Taylor expanded in y around inf 0

      \[\leadsto expr\]

   8. Simplified 0

      \[\leadsto expr\]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 19: 49.9% accurate, 13.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

function code(x, y, z, t, a)
	return x
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := x

Derivation

1. Initial program 78.8%

   \[\left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t} \]
2. Add Preprocessing

3. Taylor expanded in x around inf 0

   \[\leadsto expr\]

5. Simplified 0

   \[\leadsto expr\]
6. Add Preprocessing

Developer target: 88.3% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(y + x\right) - \left(\left(z - t\right) \cdot \frac{1}{a - t}\right) \cdot y\\ t_2 := \left(x + y\right) - \frac{\left(z - t\right) \cdot y}{a - t}\\ \mathbf{if}\;t\_2 < -1.3664970889390727 \cdot 10^{-7}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 < 1.4754293444577233 \cdot 10^{-239}:\\ \;\;\;\;\frac{y \cdot \left(a - z\right) - x \cdot t}{a - t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (- (+ y x) (* (* (- z t) (/ 1.0 (- a t))) y)))
        (t_2 (- (+ x y) (/ (* (- z t) y) (- a t)))))
   (if (< t_2 -1.3664970889390727e-7)
     t_1
     (if (< t_2 1.4754293444577233e-239)
       (/ (- (* y (- a z)) (* x t)) (- a t))
       t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (y + x) - (((z - t) * (1.0 / (a - t))) * y);
	double t_2 = (x + y) - (((z - t) * y) / (a - t));
	double tmp;
	if (t_2 < -1.3664970889390727e-7) {
		tmp = t_1;
	} else if (t_2 < 1.4754293444577233e-239) {
		tmp = ((y * (a - z)) - (x * t)) / (a - t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t, a)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (y + x) - (((z - t) * (1.0d0 / (a - t))) * y)
    t_2 = (x + y) - (((z - t) * y) / (a - t))
    if (t_2 < (-1.3664970889390727d-7)) then
        tmp = t_1
    else if (t_2 < 1.4754293444577233d-239) then
        tmp = ((y * (a - z)) - (x * t)) / (a - t)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = (y + x) - (((z - t) * (1.0 / (a - t))) * y);
	double t_2 = (x + y) - (((z - t) * y) / (a - t));
	double tmp;
	if (t_2 < -1.3664970889390727e-7) {
		tmp = t_1;
	} else if (t_2 < 1.4754293444577233e-239) {
		tmp = ((y * (a - z)) - (x * t)) / (a - t);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (y + x) - (((z - t) * (1.0 / (a - t))) * y)
	t_2 = (x + y) - (((z - t) * y) / (a - t))
	tmp = 0
	if t_2 < -1.3664970889390727e-7:
		tmp = t_1
	elif t_2 < 1.4754293444577233e-239:
		tmp = ((y * (a - z)) - (x * t)) / (a - t)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(y + x) - Float64(Float64(Float64(z - t) * Float64(1.0 / Float64(a - t))) * y))
	t_2 = Float64(Float64(x + y) - Float64(Float64(Float64(z - t) * y) / Float64(a - t)))
	tmp = 0.0
	if (t_2 < -1.3664970889390727e-7)
		tmp = t_1;
	elseif (t_2 < 1.4754293444577233e-239)
		tmp = Float64(Float64(Float64(y * Float64(a - z)) - Float64(x * t)) / Float64(a - t));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (y + x) - (((z - t) * (1.0 / (a - t))) * y);
	t_2 = (x + y) - (((z - t) * y) / (a - t));
	tmp = 0.0;
	if (t_2 < -1.3664970889390727e-7)
		tmp = t_1;
	elseif (t_2 < 1.4754293444577233e-239)
		tmp = ((y * (a - z)) - (x * t)) / (a - t);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(y + x), $MachinePrecision] - N[(N[(N[(z - t), $MachinePrecision] * N[(1.0 / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x + y), $MachinePrecision] - N[(N[(N[(z - t), $MachinePrecision] * y), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[Less[t$95$2, -1.3664970889390727e-7], t$95$1, If[Less[t$95$2, 1.4754293444577233e-239], N[(N[(N[(y * N[(a - z), $MachinePrecision]), $MachinePrecision] - N[(x * t), $MachinePrecision]), $MachinePrecision] / N[(a - t), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Reproduce

herbie shell --seed 2024110 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTick from plot-0.2.3.4, B"
  :precision binary64

  :alt
  (if (< (- (+ x y) (/ (* (- z t) y) (- a t))) -1.3664970889390727e-7) (- (+ y x) (* (* (- z t) (/ 1.0 (- a t))) y)) (if (< (- (+ x y) (/ (* (- z t) y) (- a t))) 1.4754293444577233e-239) (/ (- (* y (- a z)) (* x t)) (- a t)) (- (+ y x) (* (* (- z t) (/ 1.0 (- a t))) y))))

  (- (+ x y) (/ (* (- z t) y) (- a t))))