Data.HashTable.ST.Basic:computeOverhead from hashtables-1.2.0.2

Percentage Accurate: 85.8% → 99.4%

Time: 8.0s

Alternatives: 14

Speedup: 1.2×

• 22.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (/ x y) (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z))))

C

double code(double x, double y, double z, double t) {
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x / y) + ((2.0d0 + ((z * 2.0d0) * (1.0d0 - t))) / (t * z))
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
}

Python

def code(x, y, z, t):
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z))

Julia

function code(x, y, z, t)
	return Float64(Float64(x / y) + Float64(Float64(2.0 + Float64(Float64(z * 2.0) * Float64(1.0 - t))) / Float64(t * z)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 + N[(N[(z * 2.0), $MachinePrecision] * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 85.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (/ x y) (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z))))

C

double code(double x, double y, double z, double t) {
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = (x / y) + ((2.0d0 + ((z * 2.0d0) * (1.0d0 - t))) / (t * z))
end function

Java

public static double code(double x, double y, double z, double t) {
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
}

Python

def code(x, y, z, t):
	return (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z))

Julia

function code(x, y, z, t)
	return Float64(Float64(x / y) + Float64(Float64(2.0 + Float64(Float64(z * 2.0) * Float64(1.0 - t))) / Float64(t * z)))
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 + N[(N[(z * 2.0), $MachinePrecision] * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{2}{z} - -2\\ \mathbf{if}\;\frac{x}{y} \leq -2 \cdot 10^{+35}:\\ \;\;\;\;\frac{x}{y} + \frac{t\_1}{t}\\ \mathbf{elif}\;\frac{x}{y} \leq 5 \cdot 10^{+15}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, t\_1\right)}{t}\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} + \frac{\mathsf{fma}\left(z, 2, 2\right)}{t \cdot z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (/ 2.0 z) -2.0)))
   (if (<= (/ x y) -2e+35)
     (+ (/ x y) (/ t_1 t))
     (if (<= (/ x y) 5e+15)
       (/ (fma (- (/ x y) 2.0) t t_1) t)
       (+ (/ x y) (/ (fma z 2.0 2.0) (* t z)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (2.0 / z) - -2.0;
	double tmp;
	if ((x / y) <= -2e+35) {
		tmp = (x / y) + (t_1 / t);
	} else if ((x / y) <= 5e+15) {
		tmp = fma(((x / y) - 2.0), t, t_1) / t;
	} else {
		tmp = (x / y) + (fma(z, 2.0, 2.0) / (t * z));
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(2.0 / z) - -2.0)
	tmp = 0.0
	if (Float64(x / y) <= -2e+35)
		tmp = Float64(Float64(x / y) + Float64(t_1 / t));
	elseif (Float64(x / y) <= 5e+15)
		tmp = Float64(fma(Float64(Float64(x / y) - 2.0), t, t_1) / t);
	else
		tmp = Float64(Float64(x / y) + Float64(fma(z, 2.0, 2.0) / Float64(t * z)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -2e+35], N[(N[(x / y), $MachinePrecision] + N[(t$95$1 / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 5e+15], N[(N[(N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision] * t + t$95$1), $MachinePrecision] / t), $MachinePrecision], N[(N[(x / y), $MachinePrecision] + N[(N[(z * 2.0 + 2.0), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 x y) < -1.9999999999999999e35

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{2 \cdot z + 2}{\color{blue}{t} \cdot z} \]

      2. div-add N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{t \cdot z} + \color{blue}{\frac{2}{t \cdot z}}\right) \]

      3. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{z \cdot t} + \frac{2}{t \cdot z}\right) \]

      4. associate-/r* N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2 \cdot z}{z}}{t} + \frac{\color{blue}{2}}{t \cdot z}\right) \]

      5. associate-*l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2}{z} \cdot z}{t} + \frac{2}{t \cdot z}\right) \]

      6. mult-flip-rev N/A

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

      7. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{2}{z \cdot \color{blue}{t}}\right) \]

      8. associate-/l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{\frac{2}{z}}{\color{blue}{t}}\right) \]

      9. mult-flip-rev N/A

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

      10. div-add-rev N/A

          \[\leadsto \frac{x}{y} + \frac{\left(2 \cdot \frac{1}{z}\right) \cdot z + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

   4. Applied rewrites 78.9%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   if -1.9999999999999999e35 < (/.f64 x y) < 5e15

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

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

      1. lower-/.f64 N/A

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

      2. associate-+r+ N/A

         \[\leadsto \frac{\left(2 + 2 \cdot \frac{1}{z}\right) + t \cdot \left(\frac{x}{y} - 2\right)}{t} \]

      3. +-commutative N/A

         \[\leadsto \frac{t \cdot \left(\frac{x}{y} - 2\right) + \left(2 + 2 \cdot \frac{1}{z}\right)}{t} \]

      4. *-commutative N/A

         \[\leadsto \frac{\left(\frac{x}{y} - 2\right) \cdot t + \left(2 + 2 \cdot \frac{1}{z}\right)}{t} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, 2 + 2 \cdot \frac{1}{z}\right)}{t} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, 2 + 2 \cdot \frac{1}{z}\right)}{t} \]

      7. lift-/.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, 2 + 2 \cdot \frac{1}{z}\right)}{t} \]

      8. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, 2 \cdot \frac{1}{z} + 2\right)}{t} \]

      9. add-flip N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, 2 \cdot \frac{1}{z} - \left(\mathsf{neg}\left(2\right)\right)\right)}{t} \]

      10. metadata-eval N/A

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

      11. lower--.f64 N/A

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

      12. mult-flip-rev N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, \frac{2}{z} - -2\right)}{t} \]

      13. lower-/.f64 91.8

          \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, \frac{2}{z} - -2\right)}{t} \]

   4. Applied rewrites 91.8%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{x}{y} - 2, t, \frac{2}{z} - -2\right)}{t}} \]

   if 5e15 < (/.f64 x y)

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \frac{x}{y} + \frac{\color{blue}{2 + 2 \cdot z}}{t \cdot z} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{2 \cdot z + \color{blue}{2}}{t \cdot z} \]

      2. *-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{z \cdot 2 + 2}{t \cdot z} \]

      3. lower-fma.f64 78.8

         \[\leadsto \frac{x}{y} + \frac{\mathsf{fma}\left(z, \color{blue}{2}, 2\right)}{t \cdot z} \]

   4. Applied rewrites 78.8%

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

Alternative 2: 98.8% accurate, 0.5× speedup

Math

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

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ (/ x y) (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z)))))
   (if (<= t_1 INFINITY) t_1 (- (/ x y) 2.0))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
	double tmp;
	if (t_1 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = (x / y) - 2.0;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
	double tmp;
	if (t_1 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = (x / y) - 2.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z))
	tmp = 0
	if t_1 <= math.inf:
		tmp = t_1
	else:
		tmp = (x / y) - 2.0
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) + Float64(Float64(2.0 + Float64(Float64(z * 2.0) * Float64(1.0 - t))) / Float64(t * z)))
	tmp = 0.0
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = Float64(Float64(x / y) - 2.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) + ((2.0 + ((z * 2.0) * (1.0 - t))) / (t * z));
	tmp = 0.0;
	if (t_1 <= Inf)
		tmp = t_1;
	else
		tmp = (x / y) - 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 + N[(N[(z * 2.0), $MachinePrecision] * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, Infinity], t$95$1, N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (/.f64 x y) (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z))) < +inf.0

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   if +inf.0 < (+.f64 (/.f64 x y) (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z)))

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \frac{x}{y} - \color{blue}{2} \]

      2. lift-/.f64 54.3

         \[\leadsto \frac{x}{y} - 2 \]

   4. Applied rewrites 54.3%

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

Alternative 3: 97.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{2}{z} - -2}{t}\\ \mathbf{if}\;\frac{x}{y} \leq -2 \cdot 10^{+35}:\\ \;\;\;\;\frac{x}{y} + t\_1\\ \mathbf{elif}\;\frac{x}{y} \leq 5 \cdot 10^{-9}:\\ \;\;\;\;t\_1 - 2\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} + \frac{\mathsf{fma}\left(z, 2, 2\right)}{t \cdot z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- (/ 2.0 z) -2.0) t)))
   (if (<= (/ x y) -2e+35)
     (+ (/ x y) t_1)
     (if (<= (/ x y) 5e-9)
       (- t_1 2.0)
       (+ (/ x y) (/ (fma z 2.0 2.0) (* t z)))))))

C

double code(double x, double y, double z, double t) {
	double t_1 = ((2.0 / z) - -2.0) / t;
	double tmp;
	if ((x / y) <= -2e+35) {
		tmp = (x / y) + t_1;
	} else if ((x / y) <= 5e-9) {
		tmp = t_1 - 2.0;
	} else {
		tmp = (x / y) + (fma(z, 2.0, 2.0) / (t * z));
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(2.0 / z) - -2.0) / t)
	tmp = 0.0
	if (Float64(x / y) <= -2e+35)
		tmp = Float64(Float64(x / y) + t_1);
	elseif (Float64(x / y) <= 5e-9)
		tmp = Float64(t_1 - 2.0);
	else
		tmp = Float64(Float64(x / y) + Float64(fma(z, 2.0, 2.0) / Float64(t * z)));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision] / t), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -2e+35], N[(N[(x / y), $MachinePrecision] + t$95$1), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 5e-9], N[(t$95$1 - 2.0), $MachinePrecision], N[(N[(x / y), $MachinePrecision] + N[(N[(z * 2.0 + 2.0), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 x y) < -1.9999999999999999e35

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{2 \cdot z + 2}{\color{blue}{t} \cdot z} \]

      2. div-add N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{t \cdot z} + \color{blue}{\frac{2}{t \cdot z}}\right) \]

      3. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{z \cdot t} + \frac{2}{t \cdot z}\right) \]

      4. associate-/r* N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2 \cdot z}{z}}{t} + \frac{\color{blue}{2}}{t \cdot z}\right) \]

      5. associate-*l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2}{z} \cdot z}{t} + \frac{2}{t \cdot z}\right) \]

      6. mult-flip-rev N/A

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

      7. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{2}{z \cdot \color{blue}{t}}\right) \]

      8. associate-/l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{\frac{2}{z}}{\color{blue}{t}}\right) \]

      9. mult-flip-rev N/A

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

      10. div-add-rev N/A

          \[\leadsto \frac{x}{y} + \frac{\left(2 \cdot \frac{1}{z}\right) \cdot z + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

   4. Applied rewrites 78.9%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   if -1.9999999999999999e35 < (/.f64 x y) < 5.0000000000000001e-9

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]

      2. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\frac{1 - t}{t} + \color{blue}{\frac{1 - t}{t}}\right) \]

      3. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \frac{\color{blue}{1 - t}}{t}\right) \]

      4. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \left(\frac{1}{t} - \color{blue}{\frac{t}{t}}\right)\right) \]

      5. distribute-sub-out N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} + \frac{1}{t}\right) - \color{blue}{\left(\frac{t}{t} + \frac{t}{t}\right)}\right) \]

      6. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(\color{blue}{\frac{t}{t}} + \frac{t}{t}\right)\right) \]

      7. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + \frac{\color{blue}{t}}{t}\right)\right) \]

      8. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + 1\right)\right) \]

      9. metadata-eval N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - 2\right) \]

      10. associate--l+ N/A

          \[\leadsto \left(2 \cdot \frac{1}{t \cdot z} + 2 \cdot \frac{1}{t}\right) - \color{blue}{2} \]

      11. +-commutative N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + 2 \cdot \frac{1}{t \cdot z}\right) - 2 \]

      12. mult-flip-rev N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - 2 \]

      13. lower--.f64 N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - \color{blue}{2} \]

   4. Applied rewrites 67.3%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t} - 2} \]

   if 5.0000000000000001e-9 < (/.f64 x y)

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \frac{x}{y} + \frac{\color{blue}{2 + 2 \cdot z}}{t \cdot z} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{2 \cdot z + \color{blue}{2}}{t \cdot z} \]

      2. *-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{z \cdot 2 + 2}{t \cdot z} \]

      3. lower-fma.f64 78.8

         \[\leadsto \frac{x}{y} + \frac{\mathsf{fma}\left(z, \color{blue}{2}, 2\right)}{t \cdot z} \]

   4. Applied rewrites 78.8%

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

Alternative 4: 97.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{2}{z} - -2}{t}\\ t_2 := \frac{x}{y} + t\_1\\ \mathbf{if}\;\frac{x}{y} \leq -5.7 \cdot 10^{+34}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;\frac{x}{y} \leq 7.5 \cdot 10^{-8}:\\ \;\;\;\;t\_1 - 2\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- (/ 2.0 z) -2.0) t)) (t_2 (+ (/ x y) t_1)))
   (if (<= (/ x y) -5.7e+34) t_2 (if (<= (/ x y) 7.5e-8) (- t_1 2.0) t_2))))

C

double code(double x, double y, double z, double t) {
	double t_1 = ((2.0 / z) - -2.0) / t;
	double t_2 = (x / y) + t_1;
	double tmp;
	if ((x / y) <= -5.7e+34) {
		tmp = t_2;
	} else if ((x / y) <= 7.5e-8) {
		tmp = t_1 - 2.0;
	} else {
		tmp = t_2;
	}
	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)
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) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = ((2.0d0 / z) - (-2.0d0)) / t
    t_2 = (x / y) + t_1
    if ((x / y) <= (-5.7d+34)) then
        tmp = t_2
    else if ((x / y) <= 7.5d-8) then
        tmp = t_1 - 2.0d0
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = ((2.0 / z) - -2.0) / t;
	double t_2 = (x / y) + t_1;
	double tmp;
	if ((x / y) <= -5.7e+34) {
		tmp = t_2;
	} else if ((x / y) <= 7.5e-8) {
		tmp = t_1 - 2.0;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = ((2.0 / z) - -2.0) / t
	t_2 = (x / y) + t_1
	tmp = 0
	if (x / y) <= -5.7e+34:
		tmp = t_2
	elif (x / y) <= 7.5e-8:
		tmp = t_1 - 2.0
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(2.0 / z) - -2.0) / t)
	t_2 = Float64(Float64(x / y) + t_1)
	tmp = 0.0
	if (Float64(x / y) <= -5.7e+34)
		tmp = t_2;
	elseif (Float64(x / y) <= 7.5e-8)
		tmp = Float64(t_1 - 2.0);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = ((2.0 / z) - -2.0) / t;
	t_2 = (x / y) + t_1;
	tmp = 0.0;
	if ((x / y) <= -5.7e+34)
		tmp = t_2;
	elseif ((x / y) <= 7.5e-8)
		tmp = t_1 - 2.0;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision] / t), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x / y), $MachinePrecision] + t$95$1), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -5.7e+34], t$95$2, If[LessEqual[N[(x / y), $MachinePrecision], 7.5e-8], N[(t$95$1 - 2.0), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x y) < -5.69999999999999975e34 or 7.4999999999999997e-8 < (/.f64 x y)

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{2 \cdot z + 2}{\color{blue}{t} \cdot z} \]

      2. div-add N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{t \cdot z} + \color{blue}{\frac{2}{t \cdot z}}\right) \]

      3. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{z \cdot t} + \frac{2}{t \cdot z}\right) \]

      4. associate-/r* N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2 \cdot z}{z}}{t} + \frac{\color{blue}{2}}{t \cdot z}\right) \]

      5. associate-*l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2}{z} \cdot z}{t} + \frac{2}{t \cdot z}\right) \]

      6. mult-flip-rev N/A

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

      7. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{2}{z \cdot \color{blue}{t}}\right) \]

      8. associate-/l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{\frac{2}{z}}{\color{blue}{t}}\right) \]

      9. mult-flip-rev N/A

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

      10. div-add-rev N/A

          \[\leadsto \frac{x}{y} + \frac{\left(2 \cdot \frac{1}{z}\right) \cdot z + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

   4. Applied rewrites 78.9%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   if -5.69999999999999975e34 < (/.f64 x y) < 7.4999999999999997e-8

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]

      2. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\frac{1 - t}{t} + \color{blue}{\frac{1 - t}{t}}\right) \]

      3. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \frac{\color{blue}{1 - t}}{t}\right) \]

      4. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \left(\frac{1}{t} - \color{blue}{\frac{t}{t}}\right)\right) \]

      5. distribute-sub-out N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} + \frac{1}{t}\right) - \color{blue}{\left(\frac{t}{t} + \frac{t}{t}\right)}\right) \]

      6. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(\color{blue}{\frac{t}{t}} + \frac{t}{t}\right)\right) \]

      7. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + \frac{\color{blue}{t}}{t}\right)\right) \]

      8. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + 1\right)\right) \]

      9. metadata-eval N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - 2\right) \]

      10. associate--l+ N/A

          \[\leadsto \left(2 \cdot \frac{1}{t \cdot z} + 2 \cdot \frac{1}{t}\right) - \color{blue}{2} \]

      11. +-commutative N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + 2 \cdot \frac{1}{t \cdot z}\right) - 2 \]

      12. mult-flip-rev N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - 2 \]

      13. lower--.f64 N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - \color{blue}{2} \]

   4. Applied rewrites 67.3%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t} - 2} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 92.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{y} \leq -7.8 \cdot 10^{+50}:\\ \;\;\;\;\frac{x}{y} + \frac{\frac{2}{z}}{t}\\ \mathbf{elif}\;\frac{x}{y} \leq 36000000000:\\ \;\;\;\;\frac{\frac{2}{z} - -2}{t} - 2\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{y} + \frac{2}{t \cdot z}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= (/ x y) -7.8e+50)
   (+ (/ x y) (/ (/ 2.0 z) t))
   (if (<= (/ x y) 36000000000.0)
     (- (/ (- (/ 2.0 z) -2.0) t) 2.0)
     (+ (/ x y) (/ 2.0 (* t z))))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((x / y) <= -7.8e+50) {
		tmp = (x / y) + ((2.0 / z) / t);
	} else if ((x / y) <= 36000000000.0) {
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	} else {
		tmp = (x / y) + (2.0 / (t * 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)
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) :: tmp
    if ((x / y) <= (-7.8d+50)) then
        tmp = (x / y) + ((2.0d0 / z) / t)
    else if ((x / y) <= 36000000000.0d0) then
        tmp = (((2.0d0 / z) - (-2.0d0)) / t) - 2.0d0
    else
        tmp = (x / y) + (2.0d0 / (t * z))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((x / y) <= -7.8e+50) {
		tmp = (x / y) + ((2.0 / z) / t);
	} else if ((x / y) <= 36000000000.0) {
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	} else {
		tmp = (x / y) + (2.0 / (t * z));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (x / y) <= -7.8e+50:
		tmp = (x / y) + ((2.0 / z) / t)
	elif (x / y) <= 36000000000.0:
		tmp = (((2.0 / z) - -2.0) / t) - 2.0
	else:
		tmp = (x / y) + (2.0 / (t * z))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (Float64(x / y) <= -7.8e+50)
		tmp = Float64(Float64(x / y) + Float64(Float64(2.0 / z) / t));
	elseif (Float64(x / y) <= 36000000000.0)
		tmp = Float64(Float64(Float64(Float64(2.0 / z) - -2.0) / t) - 2.0);
	else
		tmp = Float64(Float64(x / y) + Float64(2.0 / Float64(t * z)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((x / y) <= -7.8e+50)
		tmp = (x / y) + ((2.0 / z) / t);
	elseif ((x / y) <= 36000000000.0)
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	else
		tmp = (x / y) + (2.0 / (t * z));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[N[(x / y), $MachinePrecision], -7.8e+50], N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 / z), $MachinePrecision] / t), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[(x / y), $MachinePrecision], 36000000000.0], N[(N[(N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision] / t), $MachinePrecision] - 2.0), $MachinePrecision], N[(N[(x / y), $MachinePrecision] + N[(2.0 / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 x y) < -7.79999999999999935e50

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{2 \cdot z + 2}{\color{blue}{t} \cdot z} \]

      2. div-add N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{t \cdot z} + \color{blue}{\frac{2}{t \cdot z}}\right) \]

      3. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{z \cdot t} + \frac{2}{t \cdot z}\right) \]

      4. associate-/r* N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2 \cdot z}{z}}{t} + \frac{\color{blue}{2}}{t \cdot z}\right) \]

      5. associate-*l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2}{z} \cdot z}{t} + \frac{2}{t \cdot z}\right) \]

      6. mult-flip-rev N/A

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

      7. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{2}{z \cdot \color{blue}{t}}\right) \]

      8. associate-/l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{\frac{2}{z}}{\color{blue}{t}}\right) \]

      9. mult-flip-rev N/A

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

      10. div-add-rev N/A

          \[\leadsto \frac{x}{y} + \frac{\left(2 \cdot \frac{1}{z}\right) \cdot z + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

   4. Applied rewrites 78.9%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   5. Taylor expanded in z around inf

      \[\leadsto \frac{x}{y} + \frac{2}{t} \]
   6. Step-by-step derivation

   7. Applied rewrites 52.1%

      \[\leadsto \frac{x}{y} + \frac{2}{t} \]

   8. Taylor expanded in z around 0

      \[\leadsto \frac{x}{y} + \frac{\frac{2}{z}}{t} \]
   9. Step-by-step derivation

      1. lift-/.f64 61.3

         \[\leadsto \frac{x}{y} + \frac{\frac{2}{z}}{t} \]

   10. Applied rewrites 61.3%

       \[\leadsto \frac{x}{y} + \frac{\frac{2}{z}}{t} \]

   if -7.79999999999999935e50 < (/.f64 x y) < 3.6e10

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]

      2. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\frac{1 - t}{t} + \color{blue}{\frac{1 - t}{t}}\right) \]

      3. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \frac{\color{blue}{1 - t}}{t}\right) \]

      4. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \left(\frac{1}{t} - \color{blue}{\frac{t}{t}}\right)\right) \]

      5. distribute-sub-out N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} + \frac{1}{t}\right) - \color{blue}{\left(\frac{t}{t} + \frac{t}{t}\right)}\right) \]

      6. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(\color{blue}{\frac{t}{t}} + \frac{t}{t}\right)\right) \]

      7. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + \frac{\color{blue}{t}}{t}\right)\right) \]

      8. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + 1\right)\right) \]

      9. metadata-eval N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - 2\right) \]

      10. associate--l+ N/A

          \[\leadsto \left(2 \cdot \frac{1}{t \cdot z} + 2 \cdot \frac{1}{t}\right) - \color{blue}{2} \]

      11. +-commutative N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + 2 \cdot \frac{1}{t \cdot z}\right) - 2 \]

      12. mult-flip-rev N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - 2 \]

      13. lower--.f64 N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - \color{blue}{2} \]

   4. Applied rewrites 67.3%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t} - 2} \]

   if 3.6e10 < (/.f64 x y)

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in z around 0

      \[\leadsto \frac{x}{y} + \frac{\color{blue}{2}}{t \cdot z} \]
   3. Step-by-step derivation

   4. Applied rewrites 61.3%

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

Alternative 6: 92.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} + \frac{2}{t \cdot z}\\ \mathbf{if}\;\frac{x}{y} \leq -7.8 \cdot 10^{+50}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;\frac{x}{y} \leq 36000000000:\\ \;\;\;\;\frac{\frac{2}{z} - -2}{t} - 2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ (/ x y) (/ 2.0 (* t z)))))
   (if (<= (/ x y) -7.8e+50)
     t_1
     (if (<= (/ x y) 36000000000.0) (- (/ (- (/ 2.0 z) -2.0) t) 2.0) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + (2.0 / (t * z));
	double tmp;
	if ((x / y) <= -7.8e+50) {
		tmp = t_1;
	} else if ((x / y) <= 36000000000.0) {
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	} 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)
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) :: t_1
    real(8) :: tmp
    t_1 = (x / y) + (2.0d0 / (t * z))
    if ((x / y) <= (-7.8d+50)) then
        tmp = t_1
    else if ((x / y) <= 36000000000.0d0) then
        tmp = (((2.0d0 / z) - (-2.0d0)) / t) - 2.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + (2.0 / (t * z));
	double tmp;
	if ((x / y) <= -7.8e+50) {
		tmp = t_1;
	} else if ((x / y) <= 36000000000.0) {
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) + (2.0 / (t * z))
	tmp = 0
	if (x / y) <= -7.8e+50:
		tmp = t_1
	elif (x / y) <= 36000000000.0:
		tmp = (((2.0 / z) - -2.0) / t) - 2.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) + Float64(2.0 / Float64(t * z)))
	tmp = 0.0
	if (Float64(x / y) <= -7.8e+50)
		tmp = t_1;
	elseif (Float64(x / y) <= 36000000000.0)
		tmp = Float64(Float64(Float64(Float64(2.0 / z) - -2.0) / t) - 2.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) + (2.0 / (t * z));
	tmp = 0.0;
	if ((x / y) <= -7.8e+50)
		tmp = t_1;
	elseif ((x / y) <= 36000000000.0)
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] + N[(2.0 / N[(t * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -7.8e+50], t$95$1, If[LessEqual[N[(x / y), $MachinePrecision], 36000000000.0], N[(N[(N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision] / t), $MachinePrecision] - 2.0), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x y) < -7.79999999999999935e50 or 3.6e10 < (/.f64 x y)

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in z around 0

      \[\leadsto \frac{x}{y} + \frac{\color{blue}{2}}{t \cdot z} \]
   3. Step-by-step derivation

   4. Applied rewrites 61.3%

      \[\leadsto \frac{x}{y} + \frac{\color{blue}{2}}{t \cdot z} \]

   if -7.79999999999999935e50 < (/.f64 x y) < 3.6e10

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]

      2. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\frac{1 - t}{t} + \color{blue}{\frac{1 - t}{t}}\right) \]

      3. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \frac{\color{blue}{1 - t}}{t}\right) \]

      4. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \left(\frac{1}{t} - \color{blue}{\frac{t}{t}}\right)\right) \]

      5. distribute-sub-out N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} + \frac{1}{t}\right) - \color{blue}{\left(\frac{t}{t} + \frac{t}{t}\right)}\right) \]

      6. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(\color{blue}{\frac{t}{t}} + \frac{t}{t}\right)\right) \]

      7. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + \frac{\color{blue}{t}}{t}\right)\right) \]

      8. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + 1\right)\right) \]

      9. metadata-eval N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - 2\right) \]

      10. associate--l+ N/A

          \[\leadsto \left(2 \cdot \frac{1}{t \cdot z} + 2 \cdot \frac{1}{t}\right) - \color{blue}{2} \]

      11. +-commutative N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + 2 \cdot \frac{1}{t \cdot z}\right) - 2 \]

      12. mult-flip-rev N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - 2 \]

      13. lower--.f64 N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - \color{blue}{2} \]

   4. Applied rewrites 67.3%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t} - 2} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 89.0% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} + \left(\frac{2}{t} - 2\right)\\ \mathbf{if}\;z \leq -5.2 \cdot 10^{-62}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 2.12 \cdot 10^{+15}:\\ \;\;\;\;\frac{\frac{2}{z} - -2}{t} - 2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ (/ x y) (- (/ 2.0 t) 2.0))))
   (if (<= z -5.2e-62)
     t_1
     (if (<= z 2.12e+15) (- (/ (- (/ 2.0 z) -2.0) t) 2.0) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + ((2.0 / t) - 2.0);
	double tmp;
	if (z <= -5.2e-62) {
		tmp = t_1;
	} else if (z <= 2.12e+15) {
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	} 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)
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) :: t_1
    real(8) :: tmp
    t_1 = (x / y) + ((2.0d0 / t) - 2.0d0)
    if (z <= (-5.2d-62)) then
        tmp = t_1
    else if (z <= 2.12d+15) then
        tmp = (((2.0d0 / z) - (-2.0d0)) / t) - 2.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + ((2.0 / t) - 2.0);
	double tmp;
	if (z <= -5.2e-62) {
		tmp = t_1;
	} else if (z <= 2.12e+15) {
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) + ((2.0 / t) - 2.0)
	tmp = 0
	if z <= -5.2e-62:
		tmp = t_1
	elif z <= 2.12e+15:
		tmp = (((2.0 / z) - -2.0) / t) - 2.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) + Float64(Float64(2.0 / t) - 2.0))
	tmp = 0.0
	if (z <= -5.2e-62)
		tmp = t_1;
	elseif (z <= 2.12e+15)
		tmp = Float64(Float64(Float64(Float64(2.0 / z) - -2.0) / t) - 2.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) + ((2.0 / t) - 2.0);
	tmp = 0.0;
	if (z <= -5.2e-62)
		tmp = t_1;
	elseif (z <= 2.12e+15)
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] + N[(N[(2.0 / t), $MachinePrecision] - 2.0), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -5.2e-62], t$95$1, If[LessEqual[z, 2.12e+15], N[(N[(N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision] / t), $MachinePrecision] - 2.0), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -5.1999999999999999e-62 or 2.12e15 < z

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in z around inf

      \[\leadsto \frac{x}{y} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]
   3. Step-by-step derivation

      1. count-2-rev N/A

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

      2. div-sub N/A

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

      3. div-sub N/A

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

      4. distribute-sub-out N/A

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

      5. count-2-rev N/A

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

      6. *-inverses N/A

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

      7. *-inverses N/A

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

      8. metadata-eval N/A

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

      9. lower--.f64 N/A

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

      10. mult-flip-rev N/A

          \[\leadsto \frac{x}{y} + \left(\frac{2}{t} - 2\right) \]

      11. lower-/.f64 71.8

          \[\leadsto \frac{x}{y} + \left(\frac{2}{t} - 2\right) \]

   4. Applied rewrites 71.8%

      \[\leadsto \frac{x}{y} + \color{blue}{\left(\frac{2}{t} - 2\right)} \]

   if -5.1999999999999999e-62 < z < 2.12e15

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]

      2. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\frac{1 - t}{t} + \color{blue}{\frac{1 - t}{t}}\right) \]

      3. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \frac{\color{blue}{1 - t}}{t}\right) \]

      4. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \left(\frac{1}{t} - \color{blue}{\frac{t}{t}}\right)\right) \]

      5. distribute-sub-out N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} + \frac{1}{t}\right) - \color{blue}{\left(\frac{t}{t} + \frac{t}{t}\right)}\right) \]

      6. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(\color{blue}{\frac{t}{t}} + \frac{t}{t}\right)\right) \]

      7. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + \frac{\color{blue}{t}}{t}\right)\right) \]

      8. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + 1\right)\right) \]

      9. metadata-eval N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - 2\right) \]

      10. associate--l+ N/A

          \[\leadsto \left(2 \cdot \frac{1}{t \cdot z} + 2 \cdot \frac{1}{t}\right) - \color{blue}{2} \]

      11. +-commutative N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + 2 \cdot \frac{1}{t \cdot z}\right) - 2 \]

      12. mult-flip-rev N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - 2 \]

      13. lower--.f64 N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - \color{blue}{2} \]

   4. Applied rewrites 67.3%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t} - 2} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 85.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} + \frac{2}{t}\\ \mathbf{if}\;\frac{x}{y} \leq -1.3 \cdot 10^{+35}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;\frac{x}{y} \leq 9.2 \cdot 10^{+84}:\\ \;\;\;\;\frac{\frac{2}{z} - -2}{t} - 2\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (+ (/ x y) (/ 2.0 t))))
   (if (<= (/ x y) -1.3e+35)
     t_1
     (if (<= (/ x y) 9.2e+84) (- (/ (- (/ 2.0 z) -2.0) t) 2.0) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + (2.0 / t);
	double tmp;
	if ((x / y) <= -1.3e+35) {
		tmp = t_1;
	} else if ((x / y) <= 9.2e+84) {
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	} 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)
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) :: t_1
    real(8) :: tmp
    t_1 = (x / y) + (2.0d0 / t)
    if ((x / y) <= (-1.3d+35)) then
        tmp = t_1
    else if ((x / y) <= 9.2d+84) then
        tmp = (((2.0d0 / z) - (-2.0d0)) / t) - 2.0d0
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) + (2.0 / t);
	double tmp;
	if ((x / y) <= -1.3e+35) {
		tmp = t_1;
	} else if ((x / y) <= 9.2e+84) {
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) + (2.0 / t)
	tmp = 0
	if (x / y) <= -1.3e+35:
		tmp = t_1
	elif (x / y) <= 9.2e+84:
		tmp = (((2.0 / z) - -2.0) / t) - 2.0
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) + Float64(2.0 / t))
	tmp = 0.0
	if (Float64(x / y) <= -1.3e+35)
		tmp = t_1;
	elseif (Float64(x / y) <= 9.2e+84)
		tmp = Float64(Float64(Float64(Float64(2.0 / z) - -2.0) / t) - 2.0);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) + (2.0 / t);
	tmp = 0.0;
	if ((x / y) <= -1.3e+35)
		tmp = t_1;
	elseif ((x / y) <= 9.2e+84)
		tmp = (((2.0 / z) - -2.0) / t) - 2.0;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] + N[(2.0 / t), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(x / y), $MachinePrecision], -1.3e+35], t$95$1, If[LessEqual[N[(x / y), $MachinePrecision], 9.2e+84], N[(N[(N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision] / t), $MachinePrecision] - 2.0), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 x y) < -1.30000000000000003e35 or 9.1999999999999996e84 < (/.f64 x y)

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{2 + 2 \cdot z}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{x}{y} + \frac{2 \cdot z + 2}{\color{blue}{t} \cdot z} \]

      2. div-add N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{t \cdot z} + \color{blue}{\frac{2}{t \cdot z}}\right) \]

      3. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{2 \cdot z}{z \cdot t} + \frac{2}{t \cdot z}\right) \]

      4. associate-/r* N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2 \cdot z}{z}}{t} + \frac{\color{blue}{2}}{t \cdot z}\right) \]

      5. associate-*l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\frac{2}{z} \cdot z}{t} + \frac{2}{t \cdot z}\right) \]

      6. mult-flip-rev N/A

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

      7. *-commutative N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{2}{z \cdot \color{blue}{t}}\right) \]

      8. associate-/l/ N/A

         \[\leadsto \frac{x}{y} + \left(\frac{\left(2 \cdot \frac{1}{z}\right) \cdot z}{t} + \frac{\frac{2}{z}}{\color{blue}{t}}\right) \]

      9. mult-flip-rev N/A

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

      10. div-add-rev N/A

          \[\leadsto \frac{x}{y} + \frac{\left(2 \cdot \frac{1}{z}\right) \cdot z + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

   4. Applied rewrites 78.9%

      \[\leadsto \frac{x}{y} + \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   5. Taylor expanded in z around inf

      \[\leadsto \frac{x}{y} + \frac{2}{t} \]
   6. Step-by-step derivation

   7. Applied rewrites 52.1%

      \[\leadsto \frac{x}{y} + \frac{2}{t} \]

   if -1.30000000000000003e35 < (/.f64 x y) < 9.1999999999999996e84

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in x around 0

      \[\leadsto \color{blue}{2 \cdot \frac{1 - t}{t} + 2 \cdot \frac{1}{t \cdot z}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \color{blue}{2 \cdot \frac{1 - t}{t}} \]

      2. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\frac{1 - t}{t} + \color{blue}{\frac{1 - t}{t}}\right) \]

      3. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \frac{\color{blue}{1 - t}}{t}\right) \]

      4. div-sub N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} - \frac{t}{t}\right) + \left(\frac{1}{t} - \color{blue}{\frac{t}{t}}\right)\right) \]

      5. distribute-sub-out N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(\left(\frac{1}{t} + \frac{1}{t}\right) - \color{blue}{\left(\frac{t}{t} + \frac{t}{t}\right)}\right) \]

      6. count-2-rev N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(\color{blue}{\frac{t}{t}} + \frac{t}{t}\right)\right) \]

      7. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + \frac{\color{blue}{t}}{t}\right)\right) \]

      8. *-inverses N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - \left(1 + 1\right)\right) \]

      9. metadata-eval N/A

         \[\leadsto 2 \cdot \frac{1}{t \cdot z} + \left(2 \cdot \frac{1}{t} - 2\right) \]

      10. associate--l+ N/A

          \[\leadsto \left(2 \cdot \frac{1}{t \cdot z} + 2 \cdot \frac{1}{t}\right) - \color{blue}{2} \]

      11. +-commutative N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + 2 \cdot \frac{1}{t \cdot z}\right) - 2 \]

      12. mult-flip-rev N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - 2 \]

      13. lower--.f64 N/A

          \[\leadsto \left(2 \cdot \frac{1}{t} + \frac{2}{t \cdot z}\right) - \color{blue}{2} \]

   4. Applied rewrites 67.3%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t} - 2} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 84.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z}\\ t_2 := \frac{x}{y} - 2\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+25}:\\ \;\;\;\;\frac{\frac{2}{z} - -2}{t}\\ \mathbf{elif}\;t\_1 \leq 4 \cdot 10^{+65}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq \infty:\\ \;\;\;\;\frac{\mathsf{fma}\left(z, 2, 2\right)}{t \cdot z}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z)))
        (t_2 (- (/ x y) 2.0)))
   (if (<= t_1 -2e+25)
     (/ (- (/ 2.0 z) -2.0) t)
     (if (<= t_1 4e+65)
       t_2
       (if (<= t_1 INFINITY) (/ (fma z 2.0 2.0) (* t z)) t_2)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (2.0 + ((z * 2.0) * (1.0 - t))) / (t * z);
	double t_2 = (x / y) - 2.0;
	double tmp;
	if (t_1 <= -2e+25) {
		tmp = ((2.0 / z) - -2.0) / t;
	} else if (t_1 <= 4e+65) {
		tmp = t_2;
	} else if (t_1 <= ((double) INFINITY)) {
		tmp = fma(z, 2.0, 2.0) / (t * z);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(2.0 + Float64(Float64(z * 2.0) * Float64(1.0 - t))) / Float64(t * z))
	t_2 = Float64(Float64(x / y) - 2.0)
	tmp = 0.0
	if (t_1 <= -2e+25)
		tmp = Float64(Float64(Float64(2.0 / z) - -2.0) / t);
	elseif (t_1 <= 4e+65)
		tmp = t_2;
	elseif (t_1 <= Inf)
		tmp = Float64(fma(z, 2.0, 2.0) / Float64(t * z));
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(2.0 + N[(N[(z * 2.0), $MachinePrecision] * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+25], N[(N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision] / t), $MachinePrecision], If[LessEqual[t$95$1, 4e+65], t$95$2, If[LessEqual[t$95$1, Infinity], N[(N[(z * 2.0 + 2.0), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z)) < -2.00000000000000018e25

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{2 + 2 \cdot \frac{1}{z}}{t}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{2 + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} + 2}{t} \]

      3. add-flip N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - \left(\mathsf{neg}\left(2\right)\right)}{t} \]

      4. metadata-eval N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      5. lower--.f64 N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

      7. lower-/.f64 47.8

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

   4. Applied rewrites 47.8%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   if -2.00000000000000018e25 < (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z)) < 4e65 or +inf.0 < (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z))

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \frac{x}{y} - \color{blue}{2} \]

      2. lift-/.f64 54.3

         \[\leadsto \frac{x}{y} - 2 \]

   4. Applied rewrites 54.3%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if 4e65 < (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z)) < +inf.0

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{2 + 2 \cdot \frac{1}{z}}{t}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{2 + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} + 2}{t} \]

      3. add-flip N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - \left(\mathsf{neg}\left(2\right)\right)}{t} \]

      4. metadata-eval N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      5. lower--.f64 N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

      7. lower-/.f64 47.8

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

   4. Applied rewrites 47.8%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   5. Taylor expanded in z around 0

      \[\leadsto \frac{2 \cdot \frac{z}{t} + 2 \cdot \frac{1}{t}}{\color{blue}{z}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{2 \cdot \frac{1}{t} + 2 \cdot \frac{z}{t}}{z} \]

      2. mult-flip-rev N/A

         \[\leadsto \frac{\frac{2}{t} + 2 \cdot \frac{z}{t}}{z} \]

      3. associate-*r/ N/A

         \[\leadsto \frac{\frac{2}{t} + \frac{2 \cdot z}{t}}{z} \]

      4. div-add-rev N/A

         \[\leadsto \frac{\frac{2 + 2 \cdot z}{t}}{z} \]

      5. associate-/r* N/A

         \[\leadsto \frac{2 + 2 \cdot z}{t \cdot \color{blue}{z}} \]

      6. lower-/.f64 N/A

         \[\leadsto \frac{2 + 2 \cdot z}{t \cdot \color{blue}{z}} \]

      7. +-commutative N/A

         \[\leadsto \frac{2 \cdot z + 2}{t \cdot z} \]

      8. *-commutative N/A

         \[\leadsto \frac{z \cdot 2 + 2}{t \cdot z} \]

      9. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(z, 2, 2\right)}{t \cdot z} \]

      10. lower-*.f64 47.7

          \[\leadsto \frac{\mathsf{fma}\left(z, 2, 2\right)}{t \cdot z} \]

   7. Applied rewrites 47.7%

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

Alternative 10: 84.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{\frac{2}{z} - -2}{t}\\ t_2 := \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z}\\ t_3 := \frac{x}{y} - 2\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{+25}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t\_2 \leq 4 \cdot 10^{+65}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_2 \leq \infty:\\ \;\;\;\;t\_1\\ \mathbf{else}:\\ \;\;\;\;t\_3\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (/ (- (/ 2.0 z) -2.0) t))
        (t_2 (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z)))
        (t_3 (- (/ x y) 2.0)))
   (if (<= t_2 -2e+25)
     t_1
     (if (<= t_2 4e+65) t_3 (if (<= t_2 INFINITY) t_1 t_3)))))

C

double code(double x, double y, double z, double t) {
	double t_1 = ((2.0 / z) - -2.0) / t;
	double t_2 = (2.0 + ((z * 2.0) * (1.0 - t))) / (t * z);
	double t_3 = (x / y) - 2.0;
	double tmp;
	if (t_2 <= -2e+25) {
		tmp = t_1;
	} else if (t_2 <= 4e+65) {
		tmp = t_3;
	} else if (t_2 <= ((double) INFINITY)) {
		tmp = t_1;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = ((2.0 / z) - -2.0) / t;
	double t_2 = (2.0 + ((z * 2.0) * (1.0 - t))) / (t * z);
	double t_3 = (x / y) - 2.0;
	double tmp;
	if (t_2 <= -2e+25) {
		tmp = t_1;
	} else if (t_2 <= 4e+65) {
		tmp = t_3;
	} else if (t_2 <= Double.POSITIVE_INFINITY) {
		tmp = t_1;
	} else {
		tmp = t_3;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = ((2.0 / z) - -2.0) / t
	t_2 = (2.0 + ((z * 2.0) * (1.0 - t))) / (t * z)
	t_3 = (x / y) - 2.0
	tmp = 0
	if t_2 <= -2e+25:
		tmp = t_1
	elif t_2 <= 4e+65:
		tmp = t_3
	elif t_2 <= math.inf:
		tmp = t_1
	else:
		tmp = t_3
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(2.0 / z) - -2.0) / t)
	t_2 = Float64(Float64(2.0 + Float64(Float64(z * 2.0) * Float64(1.0 - t))) / Float64(t * z))
	t_3 = Float64(Float64(x / y) - 2.0)
	tmp = 0.0
	if (t_2 <= -2e+25)
		tmp = t_1;
	elseif (t_2 <= 4e+65)
		tmp = t_3;
	elseif (t_2 <= Inf)
		tmp = t_1;
	else
		tmp = t_3;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = ((2.0 / z) - -2.0) / t;
	t_2 = (2.0 + ((z * 2.0) * (1.0 - t))) / (t * z);
	t_3 = (x / y) - 2.0;
	tmp = 0.0;
	if (t_2 <= -2e+25)
		tmp = t_1;
	elseif (t_2 <= 4e+65)
		tmp = t_3;
	elseif (t_2 <= Inf)
		tmp = t_1;
	else
		tmp = t_3;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(2.0 / z), $MachinePrecision] - -2.0), $MachinePrecision] / t), $MachinePrecision]}, Block[{t$95$2 = N[(N[(2.0 + N[(N[(z * 2.0), $MachinePrecision] * N[(1.0 - t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(t * z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]}, If[LessEqual[t$95$2, -2e+25], t$95$1, If[LessEqual[t$95$2, 4e+65], t$95$3, If[LessEqual[t$95$2, Infinity], t$95$1, t$95$3]]]]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z)) < -2.00000000000000018e25 or 4e65 < (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z)) < +inf.0

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{2 + 2 \cdot \frac{1}{z}}{t}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{2 + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} + 2}{t} \]

      3. add-flip N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - \left(\mathsf{neg}\left(2\right)\right)}{t} \]

      4. metadata-eval N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      5. lower--.f64 N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

      7. lower-/.f64 47.8

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

   4. Applied rewrites 47.8%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   if -2.00000000000000018e25 < (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z)) < 4e65 or +inf.0 < (/.f64 (+.f64 #s(literal 2 binary64) (*.f64 (*.f64 z #s(literal 2 binary64)) (-.f64 #s(literal 1 binary64) t))) (*.f64 t z))

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \frac{x}{y} - \color{blue}{2} \]

      2. lift-/.f64 54.3

         \[\leadsto \frac{x}{y} - 2 \]

   4. Applied rewrites 54.3%

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

Alternative 11: 63.7% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} - 2\\ \mathbf{if}\;z \leq -5 \cdot 10^{-66}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 1.35 \cdot 10^{-55}:\\ \;\;\;\;\frac{2}{t \cdot z}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (/ x y) 2.0)))
   (if (<= z -5e-66) t_1 (if (<= z 1.35e-55) (/ 2.0 (* t z)) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double tmp;
	if (z <= -5e-66) {
		tmp = t_1;
	} else if (z <= 1.35e-55) {
		tmp = 2.0 / (t * z);
	} 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)
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) :: t_1
    real(8) :: tmp
    t_1 = (x / y) - 2.0d0
    if (z <= (-5d-66)) then
        tmp = t_1
    else if (z <= 1.35d-55) then
        tmp = 2.0d0 / (t * z)
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double tmp;
	if (z <= -5e-66) {
		tmp = t_1;
	} else if (z <= 1.35e-55) {
		tmp = 2.0 / (t * z);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) - 2.0
	tmp = 0
	if z <= -5e-66:
		tmp = t_1
	elif z <= 1.35e-55:
		tmp = 2.0 / (t * z)
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) - 2.0)
	tmp = 0.0
	if (z <= -5e-66)
		tmp = t_1;
	elseif (z <= 1.35e-55)
		tmp = Float64(2.0 / Float64(t * z));
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) - 2.0;
	tmp = 0.0;
	if (z <= -5e-66)
		tmp = t_1;
	elseif (z <= 1.35e-55)
		tmp = 2.0 / (t * z);
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]}, If[LessEqual[z, -5e-66], t$95$1, If[LessEqual[z, 1.35e-55], N[(2.0 / N[(t * z), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -4.99999999999999962e-66 or 1.35000000000000002e-55 < z

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \frac{x}{y} - \color{blue}{2} \]

      2. lift-/.f64 54.3

         \[\leadsto \frac{x}{y} - 2 \]

   4. Applied rewrites 54.3%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if -4.99999999999999962e-66 < z < 1.35000000000000002e-55

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\frac{2}{t \cdot z}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{2}{\color{blue}{t \cdot z}} \]

      2. lift-*.f64 30.4

         \[\leadsto \frac{2}{t \cdot \color{blue}{z}} \]

   4. Applied rewrites 30.4%

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

Alternative 12: 61.7% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \frac{x}{y} - 2\\ \mathbf{if}\;t \leq -9.5 \cdot 10^{-103}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;t \leq 6.2 \cdot 10^{-131}:\\ \;\;\;\;\frac{2}{t}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (- (/ x y) 2.0)))
   (if (<= t -9.5e-103) t_1 (if (<= t 6.2e-131) (/ 2.0 t) t_1))))

C

double code(double x, double y, double z, double t) {
	double t_1 = (x / y) - 2.0;
	double tmp;
	if (t <= -9.5e-103) {
		tmp = t_1;
	} else if (t <= 6.2e-131) {
		tmp = 2.0 / 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)
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) :: t_1
    real(8) :: tmp
    t_1 = (x / y) - 2.0d0
    if (t <= (-9.5d-103)) then
        tmp = t_1
    else if (t <= 6.2d-131) then
        tmp = 2.0d0 / 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 t_1 = (x / y) - 2.0;
	double tmp;
	if (t <= -9.5e-103) {
		tmp = t_1;
	} else if (t <= 6.2e-131) {
		tmp = 2.0 / t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	t_1 = (x / y) - 2.0
	tmp = 0
	if t <= -9.5e-103:
		tmp = t_1
	elif t <= 6.2e-131:
		tmp = 2.0 / t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t)
	t_1 = Float64(Float64(x / y) - 2.0)
	tmp = 0.0
	if (t <= -9.5e-103)
		tmp = t_1;
	elseif (t <= 6.2e-131)
		tmp = Float64(2.0 / t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	t_1 = (x / y) - 2.0;
	tmp = 0.0;
	if (t <= -9.5e-103)
		tmp = t_1;
	elseif (t <= 6.2e-131)
		tmp = 2.0 / t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(x / y), $MachinePrecision] - 2.0), $MachinePrecision]}, If[LessEqual[t, -9.5e-103], t$95$1, If[LessEqual[t, 6.2e-131], N[(2.0 / t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if t < -9.50000000000000065e-103 or 6.20000000000000041e-131 < t

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \frac{x}{y} - \color{blue}{2} \]

      2. lift-/.f64 54.3

         \[\leadsto \frac{x}{y} - 2 \]

   4. Applied rewrites 54.3%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   if -9.50000000000000065e-103 < t < 6.20000000000000041e-131

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{2 + 2 \cdot \frac{1}{z}}{t}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{2 + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} + 2}{t} \]

      3. add-flip N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - \left(\mathsf{neg}\left(2\right)\right)}{t} \]

      4. metadata-eval N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      5. lower--.f64 N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

      7. lower-/.f64 47.8

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

   4. Applied rewrites 47.8%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   5. Taylor expanded in z around inf

      \[\leadsto \frac{2}{t} \]
   6. Step-by-step derivation

   7. Applied rewrites 19.6%

      \[\leadsto \frac{2}{t} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 38.3% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;t \leq -1.07 \cdot 10^{-7}:\\ \;\;\;\;-2\\ \mathbf{elif}\;t \leq 0.86:\\ \;\;\;\;\frac{2}{t}\\ \mathbf{else}:\\ \;\;\;\;-2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (<= t -1.07e-7) -2.0 (if (<= t 0.86) (/ 2.0 t) -2.0)))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -1.07e-7) {
		tmp = -2.0;
	} else if (t <= 0.86) {
		tmp = 2.0 / t;
	} else {
		tmp = -2.0;
	}
	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)
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) :: tmp
    if (t <= (-1.07d-7)) then
        tmp = -2.0d0
    else if (t <= 0.86d0) then
        tmp = 2.0d0 / t
    else
        tmp = -2.0d0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if (t <= -1.07e-7) {
		tmp = -2.0;
	} else if (t <= 0.86) {
		tmp = 2.0 / t;
	} else {
		tmp = -2.0;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if t <= -1.07e-7:
		tmp = -2.0
	elif t <= 0.86:
		tmp = 2.0 / t
	else:
		tmp = -2.0
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if (t <= -1.07e-7)
		tmp = -2.0;
	elseif (t <= 0.86)
		tmp = Float64(2.0 / t);
	else
		tmp = -2.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if (t <= -1.07e-7)
		tmp = -2.0;
	elseif (t <= 0.86)
		tmp = 2.0 / t;
	else
		tmp = -2.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[LessEqual[t, -1.07e-7], -2.0, If[LessEqual[t, 0.86], N[(2.0 / t), $MachinePrecision], -2.0]]

Derivation

1. Split input into 2 regimes

2. if t < -1.07000000000000005e-7 or 0.859999999999999987 < t

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around inf

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]
   3. Step-by-step derivation

      1. lower--.f64 N/A

         \[\leadsto \frac{x}{y} - \color{blue}{2} \]

      2. lift-/.f64 54.3

         \[\leadsto \frac{x}{y} - 2 \]

   4. Applied rewrites 54.3%

      \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

   5. Taylor expanded in x around 0

      \[\leadsto -2 \]
   6. Step-by-step derivation

   7. Applied rewrites 21.4%

      \[\leadsto -2 \]

   if -1.07000000000000005e-7 < t < 0.859999999999999987

   1. Initial program 85.8%

      \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

   2. Taylor expanded in t around 0

      \[\leadsto \color{blue}{\frac{2 + 2 \cdot \frac{1}{z}}{t}} \]
   3. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{2 + 2 \cdot \frac{1}{z}}{\color{blue}{t}} \]

      2. +-commutative N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} + 2}{t} \]

      3. add-flip N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - \left(\mathsf{neg}\left(2\right)\right)}{t} \]

      4. metadata-eval N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      5. lower--.f64 N/A

         \[\leadsto \frac{2 \cdot \frac{1}{z} - -2}{t} \]

      6. mult-flip-rev N/A

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

      7. lower-/.f64 47.8

         \[\leadsto \frac{\frac{2}{z} - -2}{t} \]

   4. Applied rewrites 47.8%

      \[\leadsto \color{blue}{\frac{\frac{2}{z} - -2}{t}} \]

   5. Taylor expanded in z around inf

      \[\leadsto \frac{2}{t} \]
   6. Step-by-step derivation

   7. Applied rewrites 19.6%

      \[\leadsto \frac{2}{t} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 21.4% accurate, 24.7× speedup

Math

\[\begin{array}{l} \\ -2 \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 -2.0)

C

double code(double x, double y, double z, double t) {
	return -2.0;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = -2.0d0
end function

Java

public static double code(double x, double y, double z, double t) {
	return -2.0;
}

Python

def code(x, y, z, t):
	return -2.0

Julia

function code(x, y, z, t)
	return -2.0
end

MATLAB

function tmp = code(x, y, z, t)
	tmp = -2.0;
end

Wolfram

code[x_, y_, z_, t_] := -2.0

Derivation

1. Initial program 85.8%

   \[\frac{x}{y} + \frac{2 + \left(z \cdot 2\right) \cdot \left(1 - t\right)}{t \cdot z} \]

2. Taylor expanded in t around inf

   \[\leadsto \color{blue}{\frac{x}{y} - 2} \]
3. Step-by-step derivation

   1. lower--.f64 N/A

      \[\leadsto \frac{x}{y} - \color{blue}{2} \]

   2. lift-/.f64 54.3

      \[\leadsto \frac{x}{y} - 2 \]

4. Applied rewrites 54.3%

   \[\leadsto \color{blue}{\frac{x}{y} - 2} \]

5. Taylor expanded in x around 0

   \[\leadsto -2 \]
6. Step-by-step derivation

7. Applied rewrites 21.4%

   \[\leadsto -2 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025136 
(FPCore (x y z t)
  :name "Data.HashTable.ST.Basic:computeOverhead from hashtables-1.2.0.2"
  :precision binary64
  (+ (/ x y) (/ (+ 2.0 (* (* z 2.0) (- 1.0 t))) (* t z))))