Numeric.Signal:interpolate from hsignal-0.2.7.1

Percentage Accurate: 80.1% → 92.6%

Time: 5.6s

Alternatives: 23

Speedup: 0.4×

Specification

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a) {
	return x + ((y - z) * ((t - x) / (a - 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, a)
use fmin_fmax_functions
    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 - x) / (a - z)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 80.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z, double t, double a) {
	return x + ((y - z) * ((t - x) / (a - 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, a)
use fmin_fmax_functions
    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 - x) / (a - z)))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 92.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right) \cdot \frac{t - x}{a - z}\\ \mathbf{if}\;t\_1 \leq -1 \cdot 10^{-290}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_1 \leq 0:\\ \;\;\;\;\mathsf{fma}\left(-1, \frac{\left(a + \frac{t \cdot y}{x}\right) - \left(y + \frac{a \cdot t}{x}\right)}{z}, \frac{t}{x}\right) \cdot x\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+37}:\\ \;\;\;\;\mathsf{fma}\left(x, \left(1 + \frac{z}{a - z}\right) - \frac{y}{a - z}, \frac{t \cdot \left(y - z\right)}{a - z}\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y z) (/ (- t x) (- a z))))))
   (if (<= t_1 -1e-290)
     t_1
     (if (<= t_1 0.0)
       (*
        (fma -1.0 (/ (- (+ a (/ (* t y) x)) (+ y (/ (* a t) x))) z) (/ t x))
        x)
       (if (<= t_1 5e+37)
         (fma
          x
          (- (+ 1.0 (/ z (- a z))) (/ y (- a z)))
          (/ (* t (- y z)) (- a z)))
         t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -1e-290) {
		tmp = t_1;
	} else if (t_1 <= 0.0) {
		tmp = fma(-1.0, (((a + ((t * y) / x)) - (y + ((a * t) / x))) / z), (t / x)) * x;
	} else if (t_1 <= 5e+37) {
		tmp = fma(x, ((1.0 + (z / (a - z))) - (y / (a - z))), ((t * (y - z)) / (a - z)));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / Float64(a - z))))
	tmp = 0.0
	if (t_1 <= -1e-290)
		tmp = t_1;
	elseif (t_1 <= 0.0)
		tmp = Float64(fma(-1.0, Float64(Float64(Float64(a + Float64(Float64(t * y) / x)) - Float64(y + Float64(Float64(a * t) / x))) / z), Float64(t / x)) * x);
	elseif (t_1 <= 5e+37)
		tmp = fma(x, Float64(Float64(1.0 + Float64(z / Float64(a - z))) - Float64(y / Float64(a - z))), Float64(Float64(t * Float64(y - z)) / Float64(a - z)));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -1e-290], t$95$1, If[LessEqual[t$95$1, 0.0], N[(N[(-1.0 * N[(N[(N[(a + N[(N[(t * y), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision] - N[(y + N[(N[(a * t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision] + N[(t / x), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[t$95$1, 5e+37], N[(x * N[(N[(1.0 + N[(z / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t * N[(y - z), $MachinePrecision]), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -1.0000000000000001e-290 or 4.99999999999999989e37 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z))))

   1. Initial program 80.1%

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

   if -1.0000000000000001e-290 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 0.0

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in z around -inf

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

      1. lower-fma.f64 N/A

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

      2. lower-/.f64 N/A

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

      3. lower--.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. lower-/.f64 41.2

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

   7. Applied rewrites 41.2%

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

   if 0.0 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 4.99999999999999989e37

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in x around 0

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

      1. lower-fma.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 78.9

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

   7. Applied rewrites 78.9%

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

Alternative 2: 90.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t - x}{a - z}\\ \mathbf{if}\;t \leq -5.2 \cdot 10^{+37}:\\ \;\;\;\;\mathsf{fma}\left(t\_1, y - z, x\right)\\ \mathbf{elif}\;t \leq 6.1 \cdot 10^{+18}:\\ \;\;\;\;\mathsf{fma}\left(x, \left(1 + \frac{z}{a - z}\right) - \frac{y}{a - z}, \frac{t \cdot \left(y - z\right)}{a - z}\right)\\ \mathbf{else}:\\ \;\;\;\;x + \left(y - z\right) \cdot t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- t x) (- a z))))
   (if (<= t -5.2e+37)
     (fma t_1 (- y z) x)
     (if (<= t 6.1e+18)
       (fma
        x
        (- (+ 1.0 (/ z (- a z))) (/ y (- a z)))
        (/ (* t (- y z)) (- a z)))
       (+ x (* (- y z) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (t - x) / (a - z);
	double tmp;
	if (t <= -5.2e+37) {
		tmp = fma(t_1, (y - z), x);
	} else if (t <= 6.1e+18) {
		tmp = fma(x, ((1.0 + (z / (a - z))) - (y / (a - z))), ((t * (y - z)) / (a - z)));
	} else {
		tmp = x + ((y - z) * t_1);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(t - x) / Float64(a - z))
	tmp = 0.0
	if (t <= -5.2e+37)
		tmp = fma(t_1, Float64(y - z), x);
	elseif (t <= 6.1e+18)
		tmp = fma(x, Float64(Float64(1.0 + Float64(z / Float64(a - z))) - Float64(y / Float64(a - z))), Float64(Float64(t * Float64(y - z)) / Float64(a - z)));
	else
		tmp = Float64(x + Float64(Float64(y - z) * t_1));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t, -5.2e+37], N[(t$95$1 * N[(y - z), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[t, 6.1e+18], N[(x * N[(N[(1.0 + N[(z / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(t * N[(y - z), $MachinePrecision]), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(x + N[(N[(y - z), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if t < -5.1999999999999998e37

   1. Initial program 80.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lift--.f64 N/A

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

      13. lift--.f64 80.1

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

   3. Applied rewrites 80.1%

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

   if -5.1999999999999998e37 < t < 6.1e18

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in x around 0

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

      1. lower-fma.f64 N/A

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

      2. lower--.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lower-/.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lower-/.f64 N/A

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

      9. lower-*.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 78.9

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

   7. Applied rewrites 78.9%

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

   if 6.1e18 < t

   1. Initial program 80.1%

      \[x + \left(y - z\right) \cdot \frac{t - x}{a - z} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 88.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right) \cdot \frac{t - x}{a - z}\\ \mathbf{if}\;t\_1 \leq -1 \cdot 10^{-290}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{-140}:\\ \;\;\;\;\left(-\frac{\left(t - x\right) \cdot \left(y - a\right)}{z}\right) + t\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y z) (/ (- t x) (- a z))))))
   (if (<= t_1 -1e-290)
     t_1
     (if (<= t_1 5e-140) (+ (- (/ (* (- t x) (- y a)) z)) t) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -1e-290) {
		tmp = t_1;
	} else if (t_1 <= 5e-140) {
		tmp = -(((t - x) * (y - a)) / z) + t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 + ((y - z) * ((t - x) / (a - z)))
    if (t_1 <= (-1d-290)) then
        tmp = t_1
    else if (t_1 <= 5d-140) then
        tmp = -(((t - x) * (y - a)) / z) + 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 + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -1e-290) {
		tmp = t_1;
	} else if (t_1 <= 5e-140) {
		tmp = -(((t - x) * (y - a)) / z) + t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) * ((t - x) / (a - z)))
	tmp = 0
	if t_1 <= -1e-290:
		tmp = t_1
	elif t_1 <= 5e-140:
		tmp = -(((t - x) * (y - a)) / z) + t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / Float64(a - z))))
	tmp = 0.0
	if (t_1 <= -1e-290)
		tmp = t_1;
	elseif (t_1 <= 5e-140)
		tmp = Float64(Float64(-Float64(Float64(Float64(t - x) * Float64(y - a)) / z)) + t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y - z) * ((t - x) / (a - z)));
	tmp = 0.0;
	if (t_1 <= -1e-290)
		tmp = t_1;
	elseif (t_1 <= 5e-140)
		tmp = -(((t - x) * (y - a)) / z) + 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[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -1e-290], t$95$1, If[LessEqual[t$95$1, 5e-140], N[((-N[(N[(N[(t - x), $MachinePrecision] * N[(y - a), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]) + t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -1.0000000000000001e-290 or 5.00000000000000015e-140 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z))))

   1. Initial program 80.1%

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

   if -1.0000000000000001e-290 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 5.00000000000000015e-140

   1. Initial program 80.1%

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

   2. Taylor expanded in z around -inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. distribute-rgt-out-- N/A

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

      7. lower-*.f64 N/A

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

      8. lift--.f64 N/A

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

      9. lower--.f64 46.2

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

   4. Applied rewrites 46.2%

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

Alternative 4: 88.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t - x}{a - z}\\ t_2 := \mathsf{fma}\left(t\_1, y - z, x\right)\\ t_3 := x + \left(y - z\right) \cdot t\_1\\ \mathbf{if}\;t\_3 \leq -1 \cdot 10^{-290}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_3 \leq 5 \cdot 10^{-140}:\\ \;\;\;\;\left(-\frac{\left(t - x\right) \cdot \left(y - a\right)}{z}\right) + t\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (- t x) (- a z)))
        (t_2 (fma t_1 (- y z) x))
        (t_3 (+ x (* (- y z) t_1))))
   (if (<= t_3 -1e-290)
     t_2
     (if (<= t_3 5e-140) (+ (- (/ (* (- t x) (- y a)) z)) t) t_2))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (t - x) / (a - z);
	double t_2 = fma(t_1, (y - z), x);
	double t_3 = x + ((y - z) * t_1);
	double tmp;
	if (t_3 <= -1e-290) {
		tmp = t_2;
	} else if (t_3 <= 5e-140) {
		tmp = -(((t - x) * (y - a)) / z) + t;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(t - x) / Float64(a - z))
	t_2 = fma(t_1, Float64(y - z), x)
	t_3 = Float64(x + Float64(Float64(y - z) * t_1))
	tmp = 0.0
	if (t_3 <= -1e-290)
		tmp = t_2;
	elseif (t_3 <= 5e-140)
		tmp = Float64(Float64(-Float64(Float64(Float64(t - x) * Float64(y - a)) / z)) + t);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 * N[(y - z), $MachinePrecision] + x), $MachinePrecision]}, Block[{t$95$3 = N[(x + N[(N[(y - z), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$3, -1e-290], t$95$2, If[LessEqual[t$95$3, 5e-140], N[((-N[(N[(N[(t - x), $MachinePrecision] * N[(y - a), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]) + t), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -1.0000000000000001e-290 or 5.00000000000000015e-140 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z))))

   1. Initial program 80.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lift--.f64 N/A

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

      13. lift--.f64 80.1

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

   3. Applied rewrites 80.1%

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

   if -1.0000000000000001e-290 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 5.00000000000000015e-140

   1. Initial program 80.1%

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

   2. Taylor expanded in z around -inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. distribute-rgt-out-- N/A

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

      7. lower-*.f64 N/A

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

      8. lift--.f64 N/A

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

      9. lower--.f64 46.2

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

   4. Applied rewrites 46.2%

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

Alternative 5: 75.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{t}{a - z}\\ \mathbf{if}\;a \leq -1.45 \cdot 10^{-38}:\\ \;\;\;\;\mathsf{fma}\left(t\_1, y - z, x\right)\\ \mathbf{elif}\;a \leq 3.1 \cdot 10^{-13}:\\ \;\;\;\;\left(-\frac{\left(t - x\right) \cdot \left(y - a\right)}{z}\right) + t\\ \mathbf{else}:\\ \;\;\;\;x + \left(y - z\right) \cdot t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ t (- a z))))
   (if (<= a -1.45e-38)
     (fma t_1 (- y z) x)
     (if (<= a 3.1e-13)
       (+ (- (/ (* (- t x) (- y a)) z)) t)
       (+ x (* (- y z) t_1))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t / (a - z);
	double tmp;
	if (a <= -1.45e-38) {
		tmp = fma(t_1, (y - z), x);
	} else if (a <= 3.1e-13) {
		tmp = -(((t - x) * (y - a)) / z) + t;
	} else {
		tmp = x + ((y - z) * t_1);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t / Float64(a - z))
	tmp = 0.0
	if (a <= -1.45e-38)
		tmp = fma(t_1, Float64(y - z), x);
	elseif (a <= 3.1e-13)
		tmp = Float64(Float64(-Float64(Float64(Float64(t - x) * Float64(y - a)) / z)) + t);
	else
		tmp = Float64(x + Float64(Float64(y - z) * t_1));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -1.45e-38], N[(t$95$1 * N[(y - z), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[a, 3.1e-13], N[((-N[(N[(N[(t - x), $MachinePrecision] * N[(y - a), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]) + t), $MachinePrecision], N[(x + N[(N[(y - z), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if a < -1.44999999999999997e-38

   1. Initial program 80.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lift--.f64 N/A

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

      13. lift--.f64 80.1

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

   3. Applied rewrites 80.1%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 64.1%

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

   if -1.44999999999999997e-38 < a < 3.0999999999999999e-13

   1. Initial program 80.1%

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

   2. Taylor expanded in z around -inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. mul-1-neg N/A

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

      4. lower-neg.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. distribute-rgt-out-- N/A

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

      7. lower-*.f64 N/A

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

      8. lift--.f64 N/A

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

      9. lower--.f64 46.2

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

   4. Applied rewrites 46.2%

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

   if 3.0999999999999999e-13 < a

   1. Initial program 80.1%

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

   2. Taylor expanded in x around 0

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

   4. Applied rewrites 64.1%

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

Alternative 6: 72.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \frac{y - z}{a - z}\\ \mathbf{if}\;z \leq -2.8 \cdot 10^{+52}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 5.5 \cdot 10^{-104}:\\ \;\;\;\;x + \frac{\left(t - x\right) \cdot y}{a - z}\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{+163}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{a - z}, y - z, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (/ (- y z) (- a z)))))
   (if (<= z -2.8e+52)
     t_1
     (if (<= z 5.5e-104)
       (+ x (/ (* (- t x) y) (- a z)))
       (if (<= z 1.95e+163) (fma (/ t (- a z)) (- y z) x) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * ((y - z) / (a - z));
	double tmp;
	if (z <= -2.8e+52) {
		tmp = t_1;
	} else if (z <= 5.5e-104) {
		tmp = x + (((t - x) * y) / (a - z));
	} else if (z <= 1.95e+163) {
		tmp = fma((t / (a - z)), (y - z), x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(Float64(y - z) / Float64(a - z)))
	tmp = 0.0
	if (z <= -2.8e+52)
		tmp = t_1;
	elseif (z <= 5.5e-104)
		tmp = Float64(x + Float64(Float64(Float64(t - x) * y) / Float64(a - z)));
	elseif (z <= 1.95e+163)
		tmp = fma(Float64(t / Float64(a - z)), Float64(y - z), x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.8e+52], t$95$1, If[LessEqual[z, 5.5e-104], N[(x + N[(N[(N[(t - x), $MachinePrecision] * y), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 1.95e+163], N[(N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision] * N[(y - z), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.8e52 or 1.95000000000000012e163 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in t around inf

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

      1. sub-div N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      2. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\frac{y - z}{a - z}} \]

      3. lift-/.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      4. lift--.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a} - z} \]

      5. lift--.f64 51.5

         \[\leadsto t \cdot \frac{y - z}{a - \color{blue}{z}} \]

   7. Applied rewrites 51.5%

      \[\leadsto \color{blue}{t \cdot \frac{y - z}{a - z}} \]

   if -2.8e52 < z < 5.4999999999999998e-104

   1. Initial program 80.1%

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

   2. Taylor expanded in y around inf

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 55.6

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

   4. Applied rewrites 55.6%

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

   if 5.4999999999999998e-104 < z < 1.95000000000000012e163

   1. Initial program 80.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lift--.f64 N/A

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

      13. lift--.f64 80.1

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

   3. Applied rewrites 80.1%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 64.1%

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

Alternative 7: 71.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \frac{y - z}{a - z}\\ \mathbf{if}\;z \leq -2.3 \cdot 10^{+39}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.6 \cdot 10^{-107}:\\ \;\;\;\;\mathsf{fma}\left(t - x, \frac{y - z}{a}, x\right)\\ \mathbf{elif}\;z \leq 1.95 \cdot 10^{+163}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{a - z}, y - z, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (/ (- y z) (- a z)))))
   (if (<= z -2.3e+39)
     t_1
     (if (<= z 3.6e-107)
       (fma (- t x) (/ (- y z) a) x)
       (if (<= z 1.95e+163) (fma (/ t (- a z)) (- y z) x) t_1)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * ((y - z) / (a - z));
	double tmp;
	if (z <= -2.3e+39) {
		tmp = t_1;
	} else if (z <= 3.6e-107) {
		tmp = fma((t - x), ((y - z) / a), x);
	} else if (z <= 1.95e+163) {
		tmp = fma((t / (a - z)), (y - z), x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(Float64(y - z) / Float64(a - z)))
	tmp = 0.0
	if (z <= -2.3e+39)
		tmp = t_1;
	elseif (z <= 3.6e-107)
		tmp = fma(Float64(t - x), Float64(Float64(y - z) / a), x);
	elseif (z <= 1.95e+163)
		tmp = fma(Float64(t / Float64(a - z)), Float64(y - z), x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.3e+39], t$95$1, If[LessEqual[z, 3.6e-107], N[(N[(t - x), $MachinePrecision] * N[(N[(y - z), $MachinePrecision] / a), $MachinePrecision] + x), $MachinePrecision], If[LessEqual[z, 1.95e+163], N[(N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision] * N[(y - z), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.30000000000000012e39 or 1.95000000000000012e163 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in t around inf

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

      1. sub-div N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      2. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\frac{y - z}{a - z}} \]

      3. lift-/.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      4. lift--.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a} - z} \]

      5. lift--.f64 51.5

         \[\leadsto t \cdot \frac{y - z}{a - \color{blue}{z}} \]

   7. Applied rewrites 51.5%

      \[\leadsto \color{blue}{t \cdot \frac{y - z}{a - z}} \]

   if -2.30000000000000012e39 < z < 3.59999999999999976e-107

   1. Initial program 80.1%

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

   2. Taylor expanded in a around inf

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

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

      3. lower-fma.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lift--.f64 53.8

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

   4. Applied rewrites 53.8%

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

   if 3.59999999999999976e-107 < z < 1.95000000000000012e163

   1. Initial program 80.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lift--.f64 N/A

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

      13. lift--.f64 80.1

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

   3. Applied rewrites 80.1%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 64.1%

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

Alternative 8: 68.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \frac{y - z}{a - z}\\ \mathbf{if}\;z \leq -2.3 \cdot 10^{+39}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 1.5 \cdot 10^{-69}:\\ \;\;\;\;\mathsf{fma}\left(t - x, \frac{y - z}{a}, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (/ (- y z) (- a z)))))
   (if (<= z -2.3e+39)
     t_1
     (if (<= z 1.5e-69) (fma (- t x) (/ (- y z) a) x) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * ((y - z) / (a - z));
	double tmp;
	if (z <= -2.3e+39) {
		tmp = t_1;
	} else if (z <= 1.5e-69) {
		tmp = fma((t - x), ((y - z) / a), x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(Float64(y - z) / Float64(a - z)))
	tmp = 0.0
	if (z <= -2.3e+39)
		tmp = t_1;
	elseif (z <= 1.5e-69)
		tmp = fma(Float64(t - x), Float64(Float64(y - z) / a), x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -2.3e+39], t$95$1, If[LessEqual[z, 1.5e-69], N[(N[(t - x), $MachinePrecision] * N[(N[(y - z), $MachinePrecision] / a), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -2.30000000000000012e39 or 1.49999999999999995e-69 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in t around inf

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

      1. sub-div N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      2. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\frac{y - z}{a - z}} \]

      3. lift-/.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      4. lift--.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a} - z} \]

      5. lift--.f64 51.5

         \[\leadsto t \cdot \frac{y - z}{a - \color{blue}{z}} \]

   7. Applied rewrites 51.5%

      \[\leadsto \color{blue}{t \cdot \frac{y - z}{a - z}} \]

   if -2.30000000000000012e39 < z < 1.49999999999999995e-69

   1. Initial program 80.1%

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

   2. Taylor expanded in a around inf

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

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

      3. lower-fma.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lower-/.f64 N/A

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

      6. lift--.f64 53.8

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

   4. Applied rewrites 53.8%

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

Alternative 9: 66.8% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \frac{y - z}{a - z}\\ \mathbf{if}\;z \leq -1.85 \cdot 10^{+39}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{-102}:\\ \;\;\;\;x + \left(t - x\right) \cdot \frac{y}{a}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (/ (- y z) (- a z)))))
   (if (<= z -1.85e+39)
     t_1
     (if (<= z 1.35e-102) (+ x (* (- t x) (/ y a))) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * ((y - z) / (a - z));
	double tmp;
	if (z <= -1.85e+39) {
		tmp = t_1;
	} else if (z <= 1.35e-102) {
		tmp = x + ((t - x) * (y / a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 = t * ((y - z) / (a - z))
    if (z <= (-1.85d+39)) then
        tmp = t_1
    else if (z <= 1.35d-102) then
        tmp = x + ((t - x) * (y / a))
    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 = t * ((y - z) / (a - z));
	double tmp;
	if (z <= -1.85e+39) {
		tmp = t_1;
	} else if (z <= 1.35e-102) {
		tmp = x + ((t - x) * (y / a));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t * ((y - z) / (a - z))
	tmp = 0
	if z <= -1.85e+39:
		tmp = t_1
	elif z <= 1.35e-102:
		tmp = x + ((t - x) * (y / a))
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(Float64(y - z) / Float64(a - z)))
	tmp = 0.0
	if (z <= -1.85e+39)
		tmp = t_1;
	elseif (z <= 1.35e-102)
		tmp = Float64(x + Float64(Float64(t - x) * Float64(y / a)));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t * ((y - z) / (a - z));
	tmp = 0.0;
	if (z <= -1.85e+39)
		tmp = t_1;
	elseif (z <= 1.35e-102)
		tmp = x + ((t - x) * (y / a));
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.85e+39], t$95$1, If[LessEqual[z, 1.35e-102], N[(x + N[(N[(t - x), $MachinePrecision] * N[(y / a), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.85000000000000006e39 or 1.35e-102 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in t around inf

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

      1. sub-div N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      2. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\frac{y - z}{a - z}} \]

      3. lift-/.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      4. lift--.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a} - z} \]

      5. lift--.f64 51.5

         \[\leadsto t \cdot \frac{y - z}{a - \color{blue}{z}} \]

   7. Applied rewrites 51.5%

      \[\leadsto \color{blue}{t \cdot \frac{y - z}{a - z}} \]

   if -1.85000000000000006e39 < z < 1.35e-102

   1. Initial program 80.1%

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

   2. Taylor expanded in z around 0

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lift--.f64 45.1

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

   4. Applied rewrites 45.1%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. associate-/l* N/A

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

      5. lower-*.f64 N/A

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

      6. lift--.f64 N/A

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

      7. lower-/.f64 49.5

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

   6. Applied rewrites 49.5%

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

Alternative 10: 65.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \frac{y - z}{a - z}\\ \mathbf{if}\;z \leq -1.85 \cdot 10^{+39}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{-102}:\\ \;\;\;\;\mathsf{fma}\left(y, \frac{t - x}{a}, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (/ (- y z) (- a z)))))
   (if (<= z -1.85e+39)
     t_1
     (if (<= z 1.35e-102) (fma y (/ (- t x) a) x) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * ((y - z) / (a - z));
	double tmp;
	if (z <= -1.85e+39) {
		tmp = t_1;
	} else if (z <= 1.35e-102) {
		tmp = fma(y, ((t - x) / a), x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(Float64(y - z) / Float64(a - z)))
	tmp = 0.0
	if (z <= -1.85e+39)
		tmp = t_1;
	elseif (z <= 1.35e-102)
		tmp = fma(y, Float64(Float64(t - x) / a), x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(N[(y - z), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.85e+39], t$95$1, If[LessEqual[z, 1.35e-102], N[(y * N[(N[(t - x), $MachinePrecision] / a), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.85000000000000006e39 or 1.35e-102 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in t around inf

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

      1. sub-div N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      2. lower-*.f64 N/A

         \[\leadsto t \cdot \color{blue}{\frac{y - z}{a - z}} \]

      3. lift-/.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a - z}} \]

      4. lift--.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{\color{blue}{a} - z} \]

      5. lift--.f64 51.5

         \[\leadsto t \cdot \frac{y - z}{a - \color{blue}{z}} \]

   7. Applied rewrites 51.5%

      \[\leadsto \color{blue}{t \cdot \frac{y - z}{a - z}} \]

   if -1.85000000000000006e39 < z < 1.35e-102

   1. Initial program 80.1%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

         \[\leadsto y \cdot \frac{t - x}{a} + x \]

      3. lower-fma.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lift--.f64 48.1

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

   4. Applied rewrites 48.1%

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

Alternative 11: 59.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(x \cdot \frac{1}{x}\right)\\ \mathbf{if}\;z \leq -8 \cdot 10^{+45}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 4.8 \cdot 10^{+43}:\\ \;\;\;\;\mathsf{fma}\left(y, \frac{t - x}{a}, x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (* x (/ 1.0 x)))))
   (if (<= z -8e+45) t_1 (if (<= z 4.8e+43) (fma y (/ (- t x) a) x) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (x * (1.0 / x));
	double tmp;
	if (z <= -8e+45) {
		tmp = t_1;
	} else if (z <= 4.8e+43) {
		tmp = fma(y, ((t - x) / a), x);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(x * Float64(1.0 / x)))
	tmp = 0.0
	if (z <= -8e+45)
		tmp = t_1;
	elseif (z <= 4.8e+43)
		tmp = fma(y, Float64(Float64(t - x) / a), x);
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(x * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -8e+45], t$95$1, If[LessEqual[z, 4.8e+43], N[(y * N[(N[(t - x), $MachinePrecision] / a), $MachinePrecision] + x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -7.9999999999999994e45 or 4.80000000000000046e43 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in t around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. sub-div N/A

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

      4. lower-/.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lift--.f64 42.4

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

   7. Applied rewrites 42.4%

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

   8. Taylor expanded in z around inf

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

      1. lower-/.f64 25.1

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

   10. Applied rewrites 25.1%

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

   if -7.9999999999999994e45 < z < 4.80000000000000046e43

   1. Initial program 80.1%

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

   2. Taylor expanded in z around 0

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

      1. +-commutative N/A

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

      2. associate-/l* N/A

         \[\leadsto y \cdot \frac{t - x}{a} + x \]

      3. lower-fma.f64 N/A

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

      4. lower-/.f64 N/A

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

      5. lift--.f64 48.1

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

   4. Applied rewrites 48.1%

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

Alternative 12: 51.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := t \cdot \left(x \cdot \frac{1}{x}\right)\\ \mathbf{if}\;z \leq -7.8 \cdot 10^{+45}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.2 \cdot 10^{+43}:\\ \;\;\;\;x + \frac{t \cdot y}{a}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* t (* x (/ 1.0 x)))))
   (if (<= z -7.8e+45) t_1 (if (<= z 3.2e+43) (+ x (/ (* t y) a)) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = t * (x * (1.0 / x));
	double tmp;
	if (z <= -7.8e+45) {
		tmp = t_1;
	} else if (z <= 3.2e+43) {
		tmp = x + ((t * y) / a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 = t * (x * (1.0d0 / x))
    if (z <= (-7.8d+45)) then
        tmp = t_1
    else if (z <= 3.2d+43) then
        tmp = x + ((t * y) / a)
    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 = t * (x * (1.0 / x));
	double tmp;
	if (z <= -7.8e+45) {
		tmp = t_1;
	} else if (z <= 3.2e+43) {
		tmp = x + ((t * y) / a);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = t * (x * (1.0 / x))
	tmp = 0
	if z <= -7.8e+45:
		tmp = t_1
	elif z <= 3.2e+43:
		tmp = x + ((t * y) / a)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(t * Float64(x * Float64(1.0 / x)))
	tmp = 0.0
	if (z <= -7.8e+45)
		tmp = t_1;
	elseif (z <= 3.2e+43)
		tmp = Float64(x + Float64(Float64(t * y) / a));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = t * (x * (1.0 / x));
	tmp = 0.0;
	if (z <= -7.8e+45)
		tmp = t_1;
	elseif (z <= 3.2e+43)
		tmp = x + ((t * y) / a);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(t * N[(x * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -7.8e+45], t$95$1, If[LessEqual[z, 3.2e+43], N[(x + N[(N[(t * y), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -7.7999999999999999e45 or 3.20000000000000014e43 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in t around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. sub-div N/A

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

      4. lower-/.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lift--.f64 42.4

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

   7. Applied rewrites 42.4%

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

   8. Taylor expanded in z around inf

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

      1. lower-/.f64 25.1

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

   10. Applied rewrites 25.1%

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

   if -7.7999999999999999e45 < z < 3.20000000000000014e43

   1. Initial program 80.1%

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

   2. Taylor expanded in z around 0

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lift--.f64 45.1

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

   4. Applied rewrites 45.1%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + \frac{t \cdot y}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 38.5%

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

Alternative 13: 48.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -5.5 \cdot 10^{+77}:\\ \;\;\;\;\frac{t}{x} \cdot x\\ \mathbf{elif}\;z \leq 2.1 \cdot 10^{+57}:\\ \;\;\;\;x + \frac{t \cdot y}{a}\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -5.5e+77)
   (* (/ t x) x)
   (if (<= z 2.1e+57) (+ x (/ (* t y) a)) (+ x t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -5.5e+77) {
		tmp = (t / x) * x;
	} else if (z <= 2.1e+57) {
		tmp = x + ((t * y) / a);
	} else {
		tmp = x + t;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 (z <= (-5.5d+77)) then
        tmp = (t / x) * x
    else if (z <= 2.1d+57) then
        tmp = x + ((t * y) / a)
    else
        tmp = x + 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 (z <= -5.5e+77) {
		tmp = (t / x) * x;
	} else if (z <= 2.1e+57) {
		tmp = x + ((t * y) / a);
	} else {
		tmp = x + t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -5.5e+77:
		tmp = (t / x) * x
	elif z <= 2.1e+57:
		tmp = x + ((t * y) / a)
	else:
		tmp = x + t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -5.5e+77)
		tmp = Float64(Float64(t / x) * x);
	elseif (z <= 2.1e+57)
		tmp = Float64(x + Float64(Float64(t * y) / a));
	else
		tmp = Float64(x + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -5.5e+77)
		tmp = (t / x) * x;
	elseif (z <= 2.1e+57)
		tmp = x + ((t * y) / a);
	else
		tmp = x + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -5.5e+77], N[(N[(t / x), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[z, 2.1e+57], N[(x + N[(N[(t * y), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], N[(x + t), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -5.50000000000000036e77

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in z around inf

      \[\leadsto \frac{t}{x} \cdot x \]
   6. Step-by-step derivation

      1. lower-/.f64 22.0

         \[\leadsto \frac{t}{x} \cdot x \]

   7. Applied rewrites 22.0%

      \[\leadsto \frac{t}{x} \cdot x \]

   if -5.50000000000000036e77 < z < 2.09999999999999991e57

   1. Initial program 80.1%

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

   2. Taylor expanded in z around 0

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lift--.f64 45.1

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

   4. Applied rewrites 45.1%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + \frac{t \cdot y}{a} \]
   6. Step-by-step derivation

   7. Applied rewrites 38.5%

      \[\leadsto x + \frac{t \cdot y}{a} \]

   if 2.09999999999999991e57 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

      \[\leadsto x + t \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 14: 46.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.5 \cdot 10^{+37}:\\ \;\;\;\;\frac{t}{x} \cdot x\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{-69}:\\ \;\;\;\;\left(1 - \frac{y}{a}\right) \cdot x\\ \mathbf{elif}\;z \leq 3 \cdot 10^{+31}:\\ \;\;\;\;\frac{\left(y - z\right) \cdot t}{a}\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.5e+37)
   (* (/ t x) x)
   (if (<= z 1.35e-69)
     (* (- 1.0 (/ y a)) x)
     (if (<= z 3e+31) (/ (* (- y z) t) a) (+ x t)))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.5e+37) {
		tmp = (t / x) * x;
	} else if (z <= 1.35e-69) {
		tmp = (1.0 - (y / a)) * x;
	} else if (z <= 3e+31) {
		tmp = ((y - z) * t) / a;
	} else {
		tmp = x + t;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 (z <= (-2.5d+37)) then
        tmp = (t / x) * x
    else if (z <= 1.35d-69) then
        tmp = (1.0d0 - (y / a)) * x
    else if (z <= 3d+31) then
        tmp = ((y - z) * t) / a
    else
        tmp = x + 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 (z <= -2.5e+37) {
		tmp = (t / x) * x;
	} else if (z <= 1.35e-69) {
		tmp = (1.0 - (y / a)) * x;
	} else if (z <= 3e+31) {
		tmp = ((y - z) * t) / a;
	} else {
		tmp = x + t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.5e+37:
		tmp = (t / x) * x
	elif z <= 1.35e-69:
		tmp = (1.0 - (y / a)) * x
	elif z <= 3e+31:
		tmp = ((y - z) * t) / a
	else:
		tmp = x + t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.5e+37)
		tmp = Float64(Float64(t / x) * x);
	elseif (z <= 1.35e-69)
		tmp = Float64(Float64(1.0 - Float64(y / a)) * x);
	elseif (z <= 3e+31)
		tmp = Float64(Float64(Float64(y - z) * t) / a);
	else
		tmp = Float64(x + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.5e+37)
		tmp = (t / x) * x;
	elseif (z <= 1.35e-69)
		tmp = (1.0 - (y / a)) * x;
	elseif (z <= 3e+31)
		tmp = ((y - z) * t) / a;
	else
		tmp = x + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.5e+37], N[(N[(t / x), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[z, 1.35e-69], N[(N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[z, 3e+31], N[(N[(N[(y - z), $MachinePrecision] * t), $MachinePrecision] / a), $MachinePrecision], N[(x + t), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if z < -2.49999999999999994e37

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in z around inf

      \[\leadsto \frac{t}{x} \cdot x \]
   6. Step-by-step derivation

      1. lower-/.f64 22.0

         \[\leadsto \frac{t}{x} \cdot x \]

   7. Applied rewrites 22.0%

      \[\leadsto \frac{t}{x} \cdot x \]

   if -2.49999999999999994e37 < z < 1.3499999999999999e-69

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 36.9

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

   7. Applied rewrites 36.9%

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

   if 1.3499999999999999e-69 < z < 2.99999999999999989e31

   1. Initial program 80.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lift--.f64 N/A

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

      13. lift--.f64 80.1

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

   3. Applied rewrites 80.1%

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

   4. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 39.3

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

   6. Applied rewrites 39.3%

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

   7. Taylor expanded in z around 0

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

   9. Applied rewrites 19.8%

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

   if 2.99999999999999989e31 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

      \[\leadsto x + t \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 15: 45.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.5 \cdot 10^{+37}:\\ \;\;\;\;\frac{t}{x} \cdot x\\ \mathbf{elif}\;z \leq 4.3 \cdot 10^{-41}:\\ \;\;\;\;\left(1 - \frac{y}{a}\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;x + t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= z -2.5e+37)
   (* (/ t x) x)
   (if (<= z 4.3e-41) (* (- 1.0 (/ y a)) x) (+ x t))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (z <= -2.5e+37) {
		tmp = (t / x) * x;
	} else if (z <= 4.3e-41) {
		tmp = (1.0 - (y / a)) * x;
	} else {
		tmp = x + t;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 (z <= (-2.5d+37)) then
        tmp = (t / x) * x
    else if (z <= 4.3d-41) then
        tmp = (1.0d0 - (y / a)) * x
    else
        tmp = x + 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 (z <= -2.5e+37) {
		tmp = (t / x) * x;
	} else if (z <= 4.3e-41) {
		tmp = (1.0 - (y / a)) * x;
	} else {
		tmp = x + t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if z <= -2.5e+37:
		tmp = (t / x) * x
	elif z <= 4.3e-41:
		tmp = (1.0 - (y / a)) * x
	else:
		tmp = x + t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (z <= -2.5e+37)
		tmp = Float64(Float64(t / x) * x);
	elseif (z <= 4.3e-41)
		tmp = Float64(Float64(1.0 - Float64(y / a)) * x);
	else
		tmp = Float64(x + t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (z <= -2.5e+37)
		tmp = (t / x) * x;
	elseif (z <= 4.3e-41)
		tmp = (1.0 - (y / a)) * x;
	else
		tmp = x + t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[z, -2.5e+37], N[(N[(t / x), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[z, 4.3e-41], N[(N[(1.0 - N[(y / a), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], N[(x + t), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -2.49999999999999994e37

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in z around inf

      \[\leadsto \frac{t}{x} \cdot x \]
   6. Step-by-step derivation

      1. lower-/.f64 22.0

         \[\leadsto \frac{t}{x} \cdot x \]

   7. Applied rewrites 22.0%

      \[\leadsto \frac{t}{x} \cdot x \]

   if -2.49999999999999994e37 < z < 4.2999999999999999e-41

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in z around 0

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

      1. lower--.f64 N/A

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

      2. lower-/.f64 36.9

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

   7. Applied rewrites 36.9%

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

   if 4.2999999999999999e-41 < z

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

      \[\leadsto x + t \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 16: 44.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -2.3 \cdot 10^{+206}:\\ \;\;\;\;y \cdot \frac{t - x}{a}\\ \mathbf{elif}\;y \leq -1.15 \cdot 10^{+139}:\\ \;\;\;\;x \cdot \frac{y}{z}\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{+122}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{t}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -2.3e+206)
   (* y (/ (- t x) a))
   (if (<= y -1.15e+139)
     (* x (/ y z))
     (if (<= y 1.5e+122) (+ x t) (* y (/ t (- a z)))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -2.3e+206) {
		tmp = y * ((t - x) / a);
	} else if (y <= -1.15e+139) {
		tmp = x * (y / z);
	} else if (y <= 1.5e+122) {
		tmp = x + t;
	} else {
		tmp = y * (t / (a - z));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 (y <= (-2.3d+206)) then
        tmp = y * ((t - x) / a)
    else if (y <= (-1.15d+139)) then
        tmp = x * (y / z)
    else if (y <= 1.5d+122) then
        tmp = x + t
    else
        tmp = y * (t / (a - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -2.3e+206) {
		tmp = y * ((t - x) / a);
	} else if (y <= -1.15e+139) {
		tmp = x * (y / z);
	} else if (y <= 1.5e+122) {
		tmp = x + t;
	} else {
		tmp = y * (t / (a - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -2.3e+206:
		tmp = y * ((t - x) / a)
	elif y <= -1.15e+139:
		tmp = x * (y / z)
	elif y <= 1.5e+122:
		tmp = x + t
	else:
		tmp = y * (t / (a - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -2.3e+206)
		tmp = Float64(y * Float64(Float64(t - x) / a));
	elseif (y <= -1.15e+139)
		tmp = Float64(x * Float64(y / z));
	elseif (y <= 1.5e+122)
		tmp = Float64(x + t);
	else
		tmp = Float64(y * Float64(t / Float64(a - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -2.3e+206)
		tmp = y * ((t - x) / a);
	elseif (y <= -1.15e+139)
		tmp = x * (y / z);
	elseif (y <= 1.5e+122)
		tmp = x + t;
	else
		tmp = y * (t / (a - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -2.3e+206], N[(y * N[(N[(t - x), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, -1.15e+139], N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.5e+122], N[(x + t), $MachinePrecision], N[(y * N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 4 regimes

2. if y < -2.30000000000000016e206

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in y around inf

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

      1. sub-div N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a - z}} \]

      2. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\frac{t - x}{a - z}} \]

      3. lift-/.f64 N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a - z}} \]

      4. lift--.f64 N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a} - z} \]

      5. lift--.f64 42.4

         \[\leadsto y \cdot \frac{t - x}{a - \color{blue}{z}} \]

   7. Applied rewrites 42.4%

      \[\leadsto \color{blue}{y \cdot \frac{t - x}{a - z}} \]

   8. Taylor expanded in z around 0

      \[\leadsto y \cdot \frac{t - x}{\color{blue}{a}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto y \cdot \frac{t - x}{a} \]

      2. lift--.f64 26.1

         \[\leadsto y \cdot \frac{t - x}{a} \]

   10. Applied rewrites 26.1%

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

   if -2.30000000000000016e206 < y < -1.15e139

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in a around 0

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

      1. associate-/l* N/A

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

      2. lower-*.f64 N/A

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

      3. lift-/.f64 19.2

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

   7. Applied rewrites 19.2%

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

   if -1.15e139 < y < 1.49999999999999993e122

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

      \[\leadsto x + t \]

   if 1.49999999999999993e122 < y

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in y around inf

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

      1. sub-div N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a - z}} \]

      2. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\frac{t - x}{a - z}} \]

      3. lift-/.f64 N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a - z}} \]

      4. lift--.f64 N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a} - z} \]

      5. lift--.f64 42.4

         \[\leadsto y \cdot \frac{t - x}{a - \color{blue}{z}} \]

   7. Applied rewrites 42.4%

      \[\leadsto \color{blue}{y \cdot \frac{t - x}{a - z}} \]

   8. Taylor expanded in x around 0

      \[\leadsto y \cdot \frac{t}{\color{blue}{a - z}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto y \cdot \frac{t}{a - \color{blue}{z}} \]

      2. lift--.f64 23.2

         \[\leadsto y \cdot \frac{t}{a - z} \]

   10. Applied rewrites 23.2%

       \[\leadsto y \cdot \frac{t}{\color{blue}{a - z}} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 17: 44.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right) \cdot \frac{t - x}{a - z}\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;t \cdot \frac{y - z}{a}\\ \mathbf{elif}\;t\_1 \leq -1 \cdot 10^{-266}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{-145}:\\ \;\;\;\;\frac{t}{x} \cdot x\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+307}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;\frac{t \cdot y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y z) (/ (- t x) (- a z))))))
   (if (<= t_1 (- INFINITY))
     (* t (/ (- y z) a))
     (if (<= t_1 -1e-266)
       (+ x t)
       (if (<= t_1 5e-145)
         (* (/ t x) x)
         (if (<= t_1 5e+307) (+ x t) (/ (* t y) a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = t * ((y - z) / a);
	} else if (t_1 <= -1e-266) {
		tmp = x + t;
	} else if (t_1 <= 5e-145) {
		tmp = (t / x) * x;
	} else if (t_1 <= 5e+307) {
		tmp = x + t;
	} else {
		tmp = (t * y) / a;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = t * ((y - z) / a);
	} else if (t_1 <= -1e-266) {
		tmp = x + t;
	} else if (t_1 <= 5e-145) {
		tmp = (t / x) * x;
	} else if (t_1 <= 5e+307) {
		tmp = x + t;
	} else {
		tmp = (t * y) / a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) * ((t - x) / (a - z)))
	tmp = 0
	if t_1 <= -math.inf:
		tmp = t * ((y - z) / a)
	elif t_1 <= -1e-266:
		tmp = x + t
	elif t_1 <= 5e-145:
		tmp = (t / x) * x
	elif t_1 <= 5e+307:
		tmp = x + t
	else:
		tmp = (t * y) / a
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / Float64(a - z))))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(t * Float64(Float64(y - z) / a));
	elseif (t_1 <= -1e-266)
		tmp = Float64(x + t);
	elseif (t_1 <= 5e-145)
		tmp = Float64(Float64(t / x) * x);
	elseif (t_1 <= 5e+307)
		tmp = Float64(x + t);
	else
		tmp = Float64(Float64(t * y) / a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y - z) * ((t - x) / (a - z)));
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = t * ((y - z) / a);
	elseif (t_1 <= -1e-266)
		tmp = x + t;
	elseif (t_1 <= 5e-145)
		tmp = (t / x) * x;
	elseif (t_1 <= 5e+307)
		tmp = x + t;
	else
		tmp = (t * y) / a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(t * N[(N[(y - z), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -1e-266], N[(x + t), $MachinePrecision], If[LessEqual[t$95$1, 5e-145], N[(N[(t / x), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[t$95$1, 5e+307], N[(x + t), $MachinePrecision], N[(N[(t * y), $MachinePrecision] / a), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -inf.0

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in t around inf

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

      1. lower-*.f64 N/A

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

      2. lower-*.f64 N/A

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

      3. sub-div N/A

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

      4. lower-/.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lift--.f64 42.4

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

   7. Applied rewrites 42.4%

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

   8. Taylor expanded in a around inf

      \[\leadsto t \cdot \frac{y - z}{a} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto t \cdot \frac{y - z}{a} \]

      2. lift--.f64 23.1

         \[\leadsto t \cdot \frac{y - z}{a} \]

   10. Applied rewrites 23.1%

       \[\leadsto t \cdot \frac{y - z}{a} \]

   if -inf.0 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -9.9999999999999998e-267 or 4.9999999999999998e-145 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 5e307

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

      \[\leadsto x + t \]

   if -9.9999999999999998e-267 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 4.9999999999999998e-145

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in z around inf

      \[\leadsto \frac{t}{x} \cdot x \]
   6. Step-by-step derivation

      1. lower-/.f64 22.0

         \[\leadsto \frac{t}{x} \cdot x \]

   7. Applied rewrites 22.0%

      \[\leadsto \frac{t}{x} \cdot x \]

   if 5e307 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z))))

   1. Initial program 80.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lift--.f64 N/A

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

      13. lift--.f64 80.1

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

   3. Applied rewrites 80.1%

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

   4. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 39.3

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

   6. Applied rewrites 39.3%

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

   7. Taylor expanded in z around 0

      \[\leadsto \frac{t \cdot y}{\color{blue}{a}} \]
   8. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot y}{a} \]

      2. lower-*.f64 16.7

         \[\leadsto \frac{t \cdot y}{a} \]

   9. Applied rewrites 16.7%

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

Alternative 18: 43.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right) \cdot \frac{t - x}{a - z}\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;\frac{x \cdot y}{z}\\ \mathbf{elif}\;t\_1 \leq -1 \cdot 10^{-266}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{-145}:\\ \;\;\;\;\frac{t}{x} \cdot x\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+307}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;\frac{t \cdot y}{a}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y z) (/ (- t x) (- a z))))))
   (if (<= t_1 (- INFINITY))
     (/ (* x y) z)
     (if (<= t_1 -1e-266)
       (+ x t)
       (if (<= t_1 5e-145)
         (* (/ t x) x)
         (if (<= t_1 5e+307) (+ x t) (/ (* t y) a)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = (x * y) / z;
	} else if (t_1 <= -1e-266) {
		tmp = x + t;
	} else if (t_1 <= 5e-145) {
		tmp = (t / x) * x;
	} else if (t_1 <= 5e+307) {
		tmp = x + t;
	} else {
		tmp = (t * y) / a;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = (x * y) / z;
	} else if (t_1 <= -1e-266) {
		tmp = x + t;
	} else if (t_1 <= 5e-145) {
		tmp = (t / x) * x;
	} else if (t_1 <= 5e+307) {
		tmp = x + t;
	} else {
		tmp = (t * y) / a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) * ((t - x) / (a - z)))
	tmp = 0
	if t_1 <= -math.inf:
		tmp = (x * y) / z
	elif t_1 <= -1e-266:
		tmp = x + t
	elif t_1 <= 5e-145:
		tmp = (t / x) * x
	elif t_1 <= 5e+307:
		tmp = x + t
	else:
		tmp = (t * y) / a
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / Float64(a - z))))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(x * y) / z);
	elseif (t_1 <= -1e-266)
		tmp = Float64(x + t);
	elseif (t_1 <= 5e-145)
		tmp = Float64(Float64(t / x) * x);
	elseif (t_1 <= 5e+307)
		tmp = Float64(x + t);
	else
		tmp = Float64(Float64(t * y) / a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y - z) * ((t - x) / (a - z)));
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = (x * y) / z;
	elseif (t_1 <= -1e-266)
		tmp = x + t;
	elseif (t_1 <= 5e-145)
		tmp = (t / x) * x;
	elseif (t_1 <= 5e+307)
		tmp = x + t;
	else
		tmp = (t * y) / a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$1, -1e-266], N[(x + t), $MachinePrecision], If[LessEqual[t$95$1, 5e-145], N[(N[(t / x), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[t$95$1, 5e+307], N[(x + t), $MachinePrecision], N[(N[(t * y), $MachinePrecision] / a), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -inf.0

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in y around inf

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

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. associate-*r* N/A

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

      4. mul-1-neg N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{a - z} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{a - z} \]

      6. lower-neg.f64 N/A

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

      7. lift--.f64 21.6

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

   7. Applied rewrites 21.6%

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

   8. Taylor expanded in z around inf

      \[\leadsto \frac{x \cdot y}{z} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-*.f64 16.6

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

   10. Applied rewrites 16.6%

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

   if -inf.0 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -9.9999999999999998e-267 or 4.9999999999999998e-145 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 5e307

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

      \[\leadsto x + t \]

   if -9.9999999999999998e-267 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 4.9999999999999998e-145

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in z around inf

      \[\leadsto \frac{t}{x} \cdot x \]
   6. Step-by-step derivation

      1. lower-/.f64 22.0

         \[\leadsto \frac{t}{x} \cdot x \]

   7. Applied rewrites 22.0%

      \[\leadsto \frac{t}{x} \cdot x \]

   if 5e307 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z))))

   1. Initial program 80.1%

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

      1. lift-+.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. lift--.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. +-commutative N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-/.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lift--.f64 N/A

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

      13. lift--.f64 80.1

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

   3. Applied rewrites 80.1%

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

   4. Taylor expanded in x around 0

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

      1. lower-/.f64 N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. lift--.f64 N/A

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

      5. lift--.f64 39.3

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

   6. Applied rewrites 39.3%

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

   7. Taylor expanded in z around 0

      \[\leadsto \frac{t \cdot y}{\color{blue}{a}} \]
   8. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{t \cdot y}{a} \]

      2. lower-*.f64 16.7

         \[\leadsto \frac{t \cdot y}{a} \]

   9. Applied rewrites 16.7%

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

Alternative 19: 42.6% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x + \left(y - z\right) \cdot \frac{t - x}{a - z}\\ \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;\frac{x \cdot y}{z}\\ \mathbf{elif}\;t\_1 \leq -1 \cdot 10^{-266}:\\ \;\;\;\;x + t\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{-145}:\\ \;\;\;\;\frac{t}{x} \cdot x\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+307}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{y}{z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (+ x (* (- y z) (/ (- t x) (- a z))))))
   (if (<= t_1 (- INFINITY))
     (/ (* x y) z)
     (if (<= t_1 -1e-266)
       (+ x t)
       (if (<= t_1 5e-145)
         (* (/ t x) x)
         (if (<= t_1 5e+307) (+ x t) (* x (/ y z))))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = (x * y) / z;
	} else if (t_1 <= -1e-266) {
		tmp = x + t;
	} else if (t_1 <= 5e-145) {
		tmp = (t / x) * x;
	} else if (t_1 <= 5e+307) {
		tmp = x + t;
	} else {
		tmp = x * (y / z);
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = x + ((y - z) * ((t - x) / (a - z)));
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = (x * y) / z;
	} else if (t_1 <= -1e-266) {
		tmp = x + t;
	} else if (t_1 <= 5e-145) {
		tmp = (t / x) * x;
	} else if (t_1 <= 5e+307) {
		tmp = x + t;
	} else {
		tmp = x * (y / z);
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x + ((y - z) * ((t - x) / (a - z)))
	tmp = 0
	if t_1 <= -math.inf:
		tmp = (x * y) / z
	elif t_1 <= -1e-266:
		tmp = x + t
	elif t_1 <= 5e-145:
		tmp = (t / x) * x
	elif t_1 <= 5e+307:
		tmp = x + t
	else:
		tmp = x * (y / z)
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x + Float64(Float64(y - z) * Float64(Float64(t - x) / Float64(a - z))))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(x * y) / z);
	elseif (t_1 <= -1e-266)
		tmp = Float64(x + t);
	elseif (t_1 <= 5e-145)
		tmp = Float64(Float64(t / x) * x);
	elseif (t_1 <= 5e+307)
		tmp = Float64(x + t);
	else
		tmp = Float64(x * Float64(y / z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x + ((y - z) * ((t - x) / (a - z)));
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = (x * y) / z;
	elseif (t_1 <= -1e-266)
		tmp = x + t;
	elseif (t_1 <= 5e-145)
		tmp = (t / x) * x;
	elseif (t_1 <= 5e+307)
		tmp = x + t;
	else
		tmp = x * (y / z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x + N[(N[(y - z), $MachinePrecision] * N[(N[(t - x), $MachinePrecision] / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, (-Infinity)], N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$1, -1e-266], N[(x + t), $MachinePrecision], If[LessEqual[t$95$1, 5e-145], N[(N[(t / x), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[t$95$1, 5e+307], N[(x + t), $MachinePrecision], N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -inf.0

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in y around inf

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

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. associate-*r* N/A

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

      4. mul-1-neg N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{a - z} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{a - z} \]

      6. lower-neg.f64 N/A

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

      7. lift--.f64 21.6

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

   7. Applied rewrites 21.6%

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

   8. Taylor expanded in z around inf

      \[\leadsto \frac{x \cdot y}{z} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-*.f64 16.6

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

   10. Applied rewrites 16.6%

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

   if -inf.0 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < -9.9999999999999998e-267 or 4.9999999999999998e-145 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 5e307

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

      \[\leadsto x + t \]

   if -9.9999999999999998e-267 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z)))) < 4.9999999999999998e-145

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in z around inf

      \[\leadsto \frac{t}{x} \cdot x \]
   6. Step-by-step derivation

      1. lower-/.f64 22.0

         \[\leadsto \frac{t}{x} \cdot x \]

   7. Applied rewrites 22.0%

      \[\leadsto \frac{t}{x} \cdot x \]

   if 5e307 < (+.f64 x (*.f64 (-.f64 y z) (/.f64 (-.f64 t x) (-.f64 a z))))

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in a around 0

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

      1. associate-/l* N/A

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

      2. lower-*.f64 N/A

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

      3. lift-/.f64 19.2

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

   7. Applied rewrites 19.2%

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

Alternative 20: 41.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.15 \cdot 10^{+139}:\\ \;\;\;\;x \cdot \frac{y}{z}\\ \mathbf{elif}\;y \leq 1.5 \cdot 10^{+122}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;y \cdot \frac{t}{a - z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (<= y -1.15e+139)
   (* x (/ y z))
   (if (<= y 1.5e+122) (+ x t) (* y (/ t (- a z))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.15e+139) {
		tmp = x * (y / z);
	} else if (y <= 1.5e+122) {
		tmp = x + t;
	} else {
		tmp = y * (t / (a - z));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 (y <= (-1.15d+139)) then
        tmp = x * (y / z)
    else if (y <= 1.5d+122) then
        tmp = x + t
    else
        tmp = y * (t / (a - z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (y <= -1.15e+139) {
		tmp = x * (y / z);
	} else if (y <= 1.5e+122) {
		tmp = x + t;
	} else {
		tmp = y * (t / (a - z));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if y <= -1.15e+139:
		tmp = x * (y / z)
	elif y <= 1.5e+122:
		tmp = x + t
	else:
		tmp = y * (t / (a - z))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (y <= -1.15e+139)
		tmp = Float64(x * Float64(y / z));
	elseif (y <= 1.5e+122)
		tmp = Float64(x + t);
	else
		tmp = Float64(y * Float64(t / Float64(a - z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (y <= -1.15e+139)
		tmp = x * (y / z);
	elseif (y <= 1.5e+122)
		tmp = x + t;
	else
		tmp = y * (t / (a - z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[y, -1.15e+139], N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 1.5e+122], N[(x + t), $MachinePrecision], N[(y * N[(t / N[(a - z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.15e139

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in a around 0

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

      1. associate-/l* N/A

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

      2. lower-*.f64 N/A

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

      3. lift-/.f64 19.2

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

   7. Applied rewrites 19.2%

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

   if -1.15e139 < y < 1.49999999999999993e122

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

      \[\leadsto x + t \]

   if 1.49999999999999993e122 < y

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

   4. Applied rewrites 69.1%

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

   5. Taylor expanded in y around inf

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

      1. sub-div N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a - z}} \]

      2. lower-*.f64 N/A

         \[\leadsto y \cdot \color{blue}{\frac{t - x}{a - z}} \]

      3. lift-/.f64 N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a - z}} \]

      4. lift--.f64 N/A

         \[\leadsto y \cdot \frac{t - x}{\color{blue}{a} - z} \]

      5. lift--.f64 42.4

         \[\leadsto y \cdot \frac{t - x}{a - \color{blue}{z}} \]

   7. Applied rewrites 42.4%

      \[\leadsto \color{blue}{y \cdot \frac{t - x}{a - z}} \]

   8. Taylor expanded in x around 0

      \[\leadsto y \cdot \frac{t}{\color{blue}{a - z}} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto y \cdot \frac{t}{a - \color{blue}{z}} \]

      2. lift--.f64 23.2

         \[\leadsto y \cdot \frac{t}{a - z} \]

   10. Applied rewrites 23.2%

       \[\leadsto y \cdot \frac{t}{\color{blue}{a - z}} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 21: 39.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := x \cdot \frac{y}{z}\\ \mathbf{if}\;y \leq -1.15 \cdot 10^{+139}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+128}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (* x (/ y z))))
   (if (<= y -1.15e+139) t_1 (if (<= y 2.5e+128) (+ x t) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = x * (y / z);
	double tmp;
	if (y <= -1.15e+139) {
		tmp = t_1;
	} else if (y <= 2.5e+128) {
		tmp = x + t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 * (y / z)
    if (y <= (-1.15d+139)) then
        tmp = t_1
    else if (y <= 2.5d+128) then
        tmp = x + 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 * (y / z);
	double tmp;
	if (y <= -1.15e+139) {
		tmp = t_1;
	} else if (y <= 2.5e+128) {
		tmp = x + t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = x * (y / z)
	tmp = 0
	if y <= -1.15e+139:
		tmp = t_1
	elif y <= 2.5e+128:
		tmp = x + t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(x * Float64(y / z))
	tmp = 0.0
	if (y <= -1.15e+139)
		tmp = t_1;
	elseif (y <= 2.5e+128)
		tmp = Float64(x + t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = x * (y / z);
	tmp = 0.0;
	if (y <= -1.15e+139)
		tmp = t_1;
	elseif (y <= 2.5e+128)
		tmp = x + t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(x * N[(y / z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.15e+139], t$95$1, If[LessEqual[y, 2.5e+128], N[(x + t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.15e139 or 2.5e128 < y

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in a around 0

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

      1. associate-/l* N/A

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

      2. lower-*.f64 N/A

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

      3. lift-/.f64 19.2

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

   7. Applied rewrites 19.2%

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

   if -1.15e139 < y < 2.5e128

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

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

Alternative 22: 37.4% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x \cdot y}{z}\\ \mathbf{if}\;y \leq -4.4 \cdot 10^{+186}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 2.5 \cdot 10^{+128}:\\ \;\;\;\;x + t\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (let* ((t_1 (/ (* x y) z)))
   (if (<= y -4.4e+186) t_1 (if (<= y 2.5e+128) (+ x t) t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = (x * y) / z;
	double tmp;
	if (y <= -4.4e+186) {
		tmp = t_1;
	} else if (y <= 2.5e+128) {
		tmp = x + t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 * y) / z
    if (y <= (-4.4d+186)) then
        tmp = t_1
    else if (y <= 2.5d+128) then
        tmp = x + 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 * y) / z;
	double tmp;
	if (y <= -4.4e+186) {
		tmp = t_1;
	} else if (y <= 2.5e+128) {
		tmp = x + t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = (x * y) / z
	tmp = 0
	if y <= -4.4e+186:
		tmp = t_1
	elif y <= 2.5e+128:
		tmp = x + t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(x * y) / z)
	tmp = 0.0
	if (y <= -4.4e+186)
		tmp = t_1;
	elseif (y <= 2.5e+128)
		tmp = Float64(x + t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = (x * y) / z;
	tmp = 0.0;
	if (y <= -4.4e+186)
		tmp = t_1;
	elseif (y <= 2.5e+128)
		tmp = x + t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision]}, If[LessEqual[y, -4.4e+186], t$95$1, If[LessEqual[y, 2.5e+128], N[(x + t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -4.3999999999999997e186 or 2.5e128 < y

   1. Initial program 80.1%

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

   2. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 N/A

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

      3. +-commutative N/A

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

      4. lower-+.f64 N/A

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

      5. sub-div N/A

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

      6. mul-1-neg N/A

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

      7. lower-neg.f64 N/A

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

      8. sub-div N/A

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

      9. lower-/.f64 N/A

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

      10. lift--.f64 N/A

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

      11. lift--.f64 43.3

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

   4. Applied rewrites 43.3%

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

   5. Taylor expanded in y around inf

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

      1. associate-*r/ N/A

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

      2. lower-/.f64 N/A

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

      3. associate-*r* N/A

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

      4. mul-1-neg N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{a - z} \]

      5. lower-*.f64 N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(x\right)\right) \cdot y}{a - z} \]

      6. lower-neg.f64 N/A

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

      7. lift--.f64 21.6

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

   7. Applied rewrites 21.6%

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

   8. Taylor expanded in z around inf

      \[\leadsto \frac{x \cdot y}{z} \]
   9. Step-by-step derivation

      1. lower-/.f64 N/A

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

      2. lower-*.f64 16.6

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

   10. Applied rewrites 16.6%

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

   if -4.3999999999999997e186 < y < 2.5e128

   1. Initial program 80.1%

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

   2. Taylor expanded in z around inf

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

      1. lift--.f64 19.3

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

   4. Applied rewrites 19.3%

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

   5. Taylor expanded in x around 0

      \[\leadsto x + t \]
   6. Step-by-step derivation

   7. Applied rewrites 34.1%

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

Alternative 23: 34.1% accurate, 4.8× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    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 + t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(x + t), $MachinePrecision]

Derivation

1. Initial program 80.1%

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

2. Taylor expanded in z around inf

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

   1. lift--.f64 19.3

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

4. Applied rewrites 19.3%

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

5. Taylor expanded in x around 0

   \[\leadsto x + t \]
6. Step-by-step derivation

7. Applied rewrites 34.1%

   \[\leadsto x + t \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025139 
(FPCore (x y z t a)
  :name "Numeric.Signal:interpolate   from hsignal-0.2.7.1"
  :precision binary64
  (+ x (* (- y z) (/ (- t x) (- a z)))))