Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, J

Percentage Accurate: 79.6% → 88.9%

Time: 8.8s

Alternatives: 19

Speedup: 0.7×

• 37.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = ((((x * 9.0d0) * y) - (((z * 4.0d0) * t) * a)) + b) / (z * c)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
}

Python

def code(x, y, z, t, a, b, c):
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c)

Julia

function code(x, y, z, t, a, b, c)
	return Float64(Float64(Float64(Float64(Float64(x * 9.0) * y) - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(N[(N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision] - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]

Initial Program: 79.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \end{array} \]

FPCore

(FPCore (x y z t a b c)
 :precision binary64
 (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)))

C

double code(double x, double y, double z, double t, double a, double b, double c) {
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    code = ((((x * 9.0d0) * y) - (((z * 4.0d0) * t) * a)) + b) / (z * c)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
}

Python

def code(x, y, z, t, a, b, c):
	return ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c)

Julia

function code(x, y, z, t, a, b, c)
	return Float64(Float64(Float64(Float64(Float64(x * 9.0) * y) - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c))
end

MATLAB

function tmp = code(x, y, z, t, a, b, c)
	tmp = ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
end

Wolfram

code[x_, y_, z_, t_, a_, b_, c_] := N[(N[(N[(N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision] - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]

Alternative 1: 88.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \frac{b \cdot \left(\frac{1}{z} + \frac{x \cdot \left(9 \cdot \frac{y}{z} - 4 \cdot \frac{a \cdot t}{x}\right)}{b}\right)}{c}\\ \mathbf{if}\;z \leq -4.4 \cdot 10^{+142}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 2.7 \cdot 10^{+39}:\\ \;\;\;\;\frac{-1 \cdot \left(y \cdot \mathsf{fma}\left(-9, x, 4 \cdot \frac{a \cdot \left(t \cdot z\right)}{y}\right)\right) + b}{z \cdot c}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1
         (/
          (*
           b
           (+ (/ 1.0 z) (/ (* x (- (* 9.0 (/ y z)) (* 4.0 (/ (* a t) x)))) b)))
          c)))
   (if (<= z -4.4e+142)
     t_1
     (if (<= z 2.7e+39)
       (/
        (+ (* -1.0 (* y (fma -9.0 x (* 4.0 (/ (* a (* t z)) y))))) b)
        (* z c))
       t_1))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (b * ((1.0 / z) + ((x * ((9.0 * (y / z)) - (4.0 * ((a * t) / x)))) / b))) / c;
	double tmp;
	if (z <= -4.4e+142) {
		tmp = t_1;
	} else if (z <= 2.7e+39) {
		tmp = ((-1.0 * (y * fma(-9.0, x, (4.0 * ((a * (t * z)) / y))))) + b) / (z * c);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(b * Float64(Float64(1.0 / z) + Float64(Float64(x * Float64(Float64(9.0 * Float64(y / z)) - Float64(4.0 * Float64(Float64(a * t) / x)))) / b))) / c)
	tmp = 0.0
	if (z <= -4.4e+142)
		tmp = t_1;
	elseif (z <= 2.7e+39)
		tmp = Float64(Float64(Float64(-1.0 * Float64(y * fma(-9.0, x, Float64(4.0 * Float64(Float64(a * Float64(t * z)) / y))))) + b) / Float64(z * c));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(b * N[(N[(1.0 / z), $MachinePrecision] + N[(N[(x * N[(N[(9.0 * N[(y / z), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(N[(a * t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]}, If[LessEqual[z, -4.4e+142], t$95$1, If[LessEqual[z, 2.7e+39], N[(N[(N[(-1.0 * N[(y * N[(-9.0 * x + N[(4.0 * N[(N[(a * N[(t * z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -4.39999999999999974e142 or 2.70000000000000003e39 < z

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in b around inf

      \[\leadsto \frac{b \cdot \left(\frac{1}{z} + \frac{x \cdot \left(9 \cdot \frac{y}{z} - 4 \cdot \frac{a \cdot t}{x}\right)}{b}\right)}{c} \]

   10. Applied rewrites 75.5%

       \[\leadsto \frac{b \cdot \left(\frac{1}{z} + \frac{x \cdot \left(9 \cdot \frac{y}{z} - 4 \cdot \frac{a \cdot t}{x}\right)}{b}\right)}{c} \]

   if -4.39999999999999974e142 < z < 2.70000000000000003e39

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in y around -inf

      \[\leadsto \frac{\color{blue}{-1 \cdot \left(y \cdot \left(-9 \cdot x + 4 \cdot \frac{a \cdot \left(t \cdot z\right)}{y}\right)\right)} + b}{z \cdot c} \]

   4. Applied rewrites 76.3%

      \[\leadsto \frac{\color{blue}{-1 \cdot \left(y \cdot \mathsf{fma}\left(-9, x, 4 \cdot \frac{a \cdot \left(t \cdot z\right)}{y}\right)\right)} + b}{z \cdot c} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 88.1% accurate, 0.2× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := \frac{\left(t\_1 - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c}\\ \mathbf{if}\;t\_2 \leq -5 \cdot 10^{-229}:\\ \;\;\;\;\frac{\left(t\_1 - \left(z \cdot 4\right) \cdot \left(a \cdot t\right)\right) + b}{z \cdot c}\\ \mathbf{elif}\;t\_2 \leq 0:\\ \;\;\;\;\frac{\mathsf{fma}\left(-4, \frac{a \cdot \left(t \cdot z\right)}{c}, \mathsf{fma}\left(9, \frac{x \cdot y}{c}, \frac{b}{c}\right)\right)}{z}\\ \mathbf{elif}\;t\_2 \leq \infty:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-4, a \cdot t, \frac{b}{z}\right)}{c}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y))
        (t_2 (/ (+ (- t_1 (* (* (* z 4.0) t) a)) b) (* z c))))
   (if (<= t_2 -5e-229)
     (/ (+ (- t_1 (* (* z 4.0) (* a t))) b) (* z c))
     (if (<= t_2 0.0)
       (/ (fma -4.0 (/ (* a (* t z)) c) (fma 9.0 (/ (* x y) c) (/ b c))) z)
       (if (<= t_2 INFINITY) t_2 (/ (fma -4.0 (* a t) (/ b z)) c))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = ((t_1 - (((z * 4.0) * t) * a)) + b) / (z * c);
	double tmp;
	if (t_2 <= -5e-229) {
		tmp = ((t_1 - ((z * 4.0) * (a * t))) + b) / (z * c);
	} else if (t_2 <= 0.0) {
		tmp = fma(-4.0, ((a * (t * z)) / c), fma(9.0, ((x * y) / c), (b / c))) / z;
	} else if (t_2 <= ((double) INFINITY)) {
		tmp = t_2;
	} else {
		tmp = fma(-4.0, (a * t), (b / z)) / c;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(Float64(Float64(t_1 - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c))
	tmp = 0.0
	if (t_2 <= -5e-229)
		tmp = Float64(Float64(Float64(t_1 - Float64(Float64(z * 4.0) * Float64(a * t))) + b) / Float64(z * c));
	elseif (t_2 <= 0.0)
		tmp = Float64(fma(-4.0, Float64(Float64(a * Float64(t * z)) / c), fma(9.0, Float64(Float64(x * y) / c), Float64(b / c))) / z);
	elseif (t_2 <= Inf)
		tmp = t_2;
	else
		tmp = Float64(fma(-4.0, Float64(a * t), Float64(b / z)) / c);
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(t$95$1 - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -5e-229], N[(N[(N[(t$95$1 - N[(N[(z * 4.0), $MachinePrecision] * N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 0.0], N[(N[(-4.0 * N[(N[(a * N[(t * z), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision] + N[(9.0 * N[(N[(x * y), $MachinePrecision] / c), $MachinePrecision] + N[(b / c), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$2, Infinity], t$95$2, N[(N[(-4.0 * N[(a * t), $MachinePrecision] + N[(b / z), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < -5.00000000000000016e-229

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   3. Applied rewrites 80.4%

      \[\leadsto \frac{\left(\left(x \cdot 9\right) \cdot y - \color{blue}{\left(z \cdot 4\right) \cdot \left(a \cdot t\right)}\right) + b}{z \cdot c} \]

   if -5.00000000000000016e-229 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < 0.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in z around 0

      \[\leadsto \color{blue}{\frac{-4 \cdot \frac{a \cdot \left(t \cdot z\right)}{c} + \left(9 \cdot \frac{x \cdot y}{c} + \frac{b}{c}\right)}{z}} \]

   4. Applied rewrites 75.9%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(-4, \frac{a \cdot \left(t \cdot z\right)}{c}, \mathsf{fma}\left(9, \frac{x \cdot y}{c}, \frac{b}{c}\right)\right)}{z}} \]

   if 0.0 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < +inf.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   if +inf.0 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c))

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 64.2%

       \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   11. Taylor expanded in t around 0

       \[\leadsto \frac{-4 \cdot \left(a \cdot t\right) + \frac{b}{z}}{c} \]

   13. Applied rewrites 64.2%

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

Alternative 3: 88.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{c}\\ \mathbf{if}\;z \leq -1.65 \cdot 10^{+40}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 7.5 \cdot 10^{+127}:\\ \;\;\;\;\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1
         (/
          (* x (- (fma 9.0 (/ y z) (/ b (* x z))) (* 4.0 (/ (* a t) x))))
          c)))
   (if (<= z -1.65e+40)
     t_1
     (if (<= z 7.5e+127)
       (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c))
       t_1))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * (fma(9.0, (y / z), (b / (x * z))) - (4.0 * ((a * t) / x)))) / c;
	double tmp;
	if (z <= -1.65e+40) {
		tmp = t_1;
	} else if (z <= 7.5e+127) {
		tmp = ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * Float64(fma(9.0, Float64(y / z), Float64(b / Float64(x * z))) - Float64(4.0 * Float64(Float64(a * t) / x)))) / c)
	tmp = 0.0
	if (z <= -1.65e+40)
		tmp = t_1;
	elseif (z <= 7.5e+127)
		tmp = Float64(Float64(Float64(Float64(Float64(x * 9.0) * y) - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * N[(N[(9.0 * N[(y / z), $MachinePrecision] + N[(b / N[(x * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(4.0 * N[(N[(a * t), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]}, If[LessEqual[z, -1.65e+40], t$95$1, If[LessEqual[z, 7.5e+127], N[(N[(N[(N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision] - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.6499999999999999e40 or 7.4999999999999996e127 < z

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   if -1.6499999999999999e40 < z < 7.4999999999999996e127

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 86.3% accurate, 0.2× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := \frac{\left(t\_1 - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c}\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{-265}:\\ \;\;\;\;\frac{\left(t\_1 - \left(z \cdot 4\right) \cdot \left(a \cdot t\right)\right) + b}{z \cdot c}\\ \mathbf{elif}\;t\_2 \leq 0:\\ \;\;\;\;\frac{x \cdot \mathsf{fma}\left(9, \frac{y}{c}, \frac{b}{c \cdot x}\right)}{z}\\ \mathbf{elif}\;t\_2 \leq \infty:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-4, a \cdot t, \frac{b}{z}\right)}{c}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y))
        (t_2 (/ (+ (- t_1 (* (* (* z 4.0) t) a)) b) (* z c))))
   (if (<= t_2 -2e-265)
     (/ (+ (- t_1 (* (* z 4.0) (* a t))) b) (* z c))
     (if (<= t_2 0.0)
       (/ (* x (fma 9.0 (/ y c) (/ b (* c x)))) z)
       (if (<= t_2 INFINITY) t_2 (/ (fma -4.0 (* a t) (/ b z)) c))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = ((t_1 - (((z * 4.0) * t) * a)) + b) / (z * c);
	double tmp;
	if (t_2 <= -2e-265) {
		tmp = ((t_1 - ((z * 4.0) * (a * t))) + b) / (z * c);
	} else if (t_2 <= 0.0) {
		tmp = (x * fma(9.0, (y / c), (b / (c * x)))) / z;
	} else if (t_2 <= ((double) INFINITY)) {
		tmp = t_2;
	} else {
		tmp = fma(-4.0, (a * t), (b / z)) / c;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(Float64(Float64(t_1 - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c))
	tmp = 0.0
	if (t_2 <= -2e-265)
		tmp = Float64(Float64(Float64(t_1 - Float64(Float64(z * 4.0) * Float64(a * t))) + b) / Float64(z * c));
	elseif (t_2 <= 0.0)
		tmp = Float64(Float64(x * fma(9.0, Float64(y / c), Float64(b / Float64(c * x)))) / z);
	elseif (t_2 <= Inf)
		tmp = t_2;
	else
		tmp = Float64(fma(-4.0, Float64(a * t), Float64(b / z)) / c);
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(t$95$1 - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -2e-265], N[(N[(N[(t$95$1 - N[(N[(z * 4.0), $MachinePrecision] * N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$2, 0.0], N[(N[(x * N[(9.0 * N[(y / c), $MachinePrecision] + N[(b / N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$2, Infinity], t$95$2, N[(N[(-4.0 * N[(a * t), $MachinePrecision] + N[(b / z), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < -1.99999999999999997e-265

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   3. Applied rewrites 80.4%

      \[\leadsto \frac{\left(\left(x \cdot 9\right) \cdot y - \color{blue}{\left(z \cdot 4\right) \cdot \left(a \cdot t\right)}\right) + b}{z \cdot c} \]

   if -1.99999999999999997e-265 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < 0.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in z around 0

      \[\leadsto \frac{x \cdot \left(9 \cdot \frac{y}{c} + \frac{b}{c \cdot x}\right)}{\color{blue}{z}} \]

   7. Applied rewrites 56.5%

      \[\leadsto \frac{x \cdot \mathsf{fma}\left(9, \frac{y}{c}, \frac{b}{c \cdot x}\right)}{\color{blue}{z}} \]

   if 0.0 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < +inf.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   if +inf.0 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c))

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 64.2%

       \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   11. Taylor expanded in t around 0

       \[\leadsto \frac{-4 \cdot \left(a \cdot t\right) + \frac{b}{z}}{c} \]

   13. Applied rewrites 64.2%

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

Alternative 5: 86.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := \frac{\left(t\_1 - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c}\\ t_3 := \frac{\left(t\_1 - \left(z \cdot 4\right) \cdot \left(a \cdot t\right)\right) + b}{z \cdot c}\\ \mathbf{if}\;t\_2 \leq -2 \cdot 10^{-265}:\\ \;\;\;\;t\_3\\ \mathbf{elif}\;t\_2 \leq 0:\\ \;\;\;\;\frac{x \cdot \mathsf{fma}\left(9, \frac{y}{c}, \frac{b}{c \cdot x}\right)}{z}\\ \mathbf{elif}\;t\_2 \leq \infty:\\ \;\;\;\;t\_3\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-4, a \cdot t, \frac{b}{z}\right)}{c}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y))
        (t_2 (/ (+ (- t_1 (* (* (* z 4.0) t) a)) b) (* z c)))
        (t_3 (/ (+ (- t_1 (* (* z 4.0) (* a t))) b) (* z c))))
   (if (<= t_2 -2e-265)
     t_3
     (if (<= t_2 0.0)
       (/ (* x (fma 9.0 (/ y c) (/ b (* c x)))) z)
       (if (<= t_2 INFINITY) t_3 (/ (fma -4.0 (* a t) (/ b z)) c))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = ((t_1 - (((z * 4.0) * t) * a)) + b) / (z * c);
	double t_3 = ((t_1 - ((z * 4.0) * (a * t))) + b) / (z * c);
	double tmp;
	if (t_2 <= -2e-265) {
		tmp = t_3;
	} else if (t_2 <= 0.0) {
		tmp = (x * fma(9.0, (y / c), (b / (c * x)))) / z;
	} else if (t_2 <= ((double) INFINITY)) {
		tmp = t_3;
	} else {
		tmp = fma(-4.0, (a * t), (b / z)) / c;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(Float64(Float64(t_1 - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c))
	t_3 = Float64(Float64(Float64(t_1 - Float64(Float64(z * 4.0) * Float64(a * t))) + b) / Float64(z * c))
	tmp = 0.0
	if (t_2 <= -2e-265)
		tmp = t_3;
	elseif (t_2 <= 0.0)
		tmp = Float64(Float64(x * fma(9.0, Float64(y / c), Float64(b / Float64(c * x)))) / z);
	elseif (t_2 <= Inf)
		tmp = t_3;
	else
		tmp = Float64(fma(-4.0, Float64(a * t), Float64(b / z)) / c);
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(t$95$1 - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(t$95$1 - N[(N[(z * 4.0), $MachinePrecision] * N[(a * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -2e-265], t$95$3, If[LessEqual[t$95$2, 0.0], N[(N[(x * N[(9.0 * N[(y / c), $MachinePrecision] + N[(b / N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$2, Infinity], t$95$3, N[(N[(-4.0 * N[(a * t), $MachinePrecision] + N[(b / z), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]]]]]]]

Derivation

1. Split input into 3 regimes

2. if (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < -1.99999999999999997e-265 or 0.0 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < +inf.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   3. Applied rewrites 80.4%

      \[\leadsto \frac{\left(\left(x \cdot 9\right) \cdot y - \color{blue}{\left(z \cdot 4\right) \cdot \left(a \cdot t\right)}\right) + b}{z \cdot c} \]

   if -1.99999999999999997e-265 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c)) < 0.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in z around 0

      \[\leadsto \frac{x \cdot \left(9 \cdot \frac{y}{c} + \frac{b}{c \cdot x}\right)}{\color{blue}{z}} \]

   7. Applied rewrites 56.5%

      \[\leadsto \frac{x \cdot \mathsf{fma}\left(9, \frac{y}{c}, \frac{b}{c \cdot x}\right)}{\color{blue}{z}} \]

   if +inf.0 < (/.f64 (+.f64 (-.f64 (*.f64 (*.f64 x #s(literal 9 binary64)) y) (*.f64 (*.f64 (*.f64 z #s(literal 4 binary64)) t) a)) b) (*.f64 z c))

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 64.2%

       \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   11. Taylor expanded in t around 0

       \[\leadsto \frac{-4 \cdot \left(a \cdot t\right) + \frac{b}{z}}{c} \]

   13. Applied rewrites 64.2%

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

Alternative 6: 86.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \mathsf{fma}\left(-4, \frac{a \cdot t}{c}, \mathsf{fma}\left(9, \frac{x \cdot y}{c \cdot z}, \frac{b}{c \cdot z}\right)\right)\\ \mathbf{if}\;z \leq -1.1 \cdot 10^{+137}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{+53}:\\ \;\;\;\;\frac{-1 \cdot \left(y \cdot \mathsf{fma}\left(-9, x, 4 \cdot \frac{a \cdot \left(t \cdot z\right)}{y}\right)\right) + b}{z \cdot c}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1
         (fma -4.0 (/ (* a t) c) (fma 9.0 (/ (* x y) (* c z)) (/ b (* c z))))))
   (if (<= z -1.1e+137)
     t_1
     (if (<= z 9.5e+53)
       (/
        (+ (* -1.0 (* y (fma -9.0 x (* 4.0 (/ (* a (* t z)) y))))) b)
        (* z c))
       t_1))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = fma(-4.0, ((a * t) / c), fma(9.0, ((x * y) / (c * z)), (b / (c * z))));
	double tmp;
	if (z <= -1.1e+137) {
		tmp = t_1;
	} else if (z <= 9.5e+53) {
		tmp = ((-1.0 * (y * fma(-9.0, x, (4.0 * ((a * (t * z)) / y))))) + b) / (z * c);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = fma(-4.0, Float64(Float64(a * t) / c), fma(9.0, Float64(Float64(x * y) / Float64(c * z)), Float64(b / Float64(c * z))))
	tmp = 0.0
	if (z <= -1.1e+137)
		tmp = t_1;
	elseif (z <= 9.5e+53)
		tmp = Float64(Float64(Float64(-1.0 * Float64(y * fma(-9.0, x, Float64(4.0 * Float64(Float64(a * Float64(t * z)) / y))))) + b) / Float64(z * c));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(-4.0 * N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision] + N[(9.0 * N[(N[(x * y), $MachinePrecision] / N[(c * z), $MachinePrecision]), $MachinePrecision] + N[(b / N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.1e+137], t$95$1, If[LessEqual[z, 9.5e+53], N[(N[(N[(-1.0 * N[(y * N[(-9.0 * x + N[(4.0 * N[(N[(a * N[(t * z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.10000000000000008e137 or 9.5000000000000006e53 < z

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{a \cdot t}{c} + \left(9 \cdot \frac{x \cdot y}{c \cdot z} + \frac{b}{c \cdot z}\right)} \]

   4. Applied rewrites 78.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-4, \frac{a \cdot t}{c}, \mathsf{fma}\left(9, \frac{x \cdot y}{c \cdot z}, \frac{b}{c \cdot z}\right)\right)} \]

   if -1.10000000000000008e137 < z < 9.5000000000000006e53

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in y around -inf

      \[\leadsto \frac{\color{blue}{-1 \cdot \left(y \cdot \left(-9 \cdot x + 4 \cdot \frac{a \cdot \left(t \cdot z\right)}{y}\right)\right)} + b}{z \cdot c} \]

   4. Applied rewrites 76.3%

      \[\leadsto \frac{\color{blue}{-1 \cdot \left(y \cdot \mathsf{fma}\left(-9, x, 4 \cdot \frac{a \cdot \left(t \cdot z\right)}{y}\right)\right)} + b}{z \cdot c} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 86.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \mathsf{fma}\left(-4, \frac{a \cdot t}{c}, \mathsf{fma}\left(9, \frac{x \cdot y}{c \cdot z}, \frac{b}{c \cdot z}\right)\right)\\ \mathbf{if}\;z \leq -1.1 \cdot 10^{+137}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 9.5 \cdot 10^{+53}:\\ \;\;\;\;\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1
         (fma -4.0 (/ (* a t) c) (fma 9.0 (/ (* x y) (* c z)) (/ b (* c z))))))
   (if (<= z -1.1e+137)
     t_1
     (if (<= z 9.5e+53)
       (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c))
       t_1))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = fma(-4.0, ((a * t) / c), fma(9.0, ((x * y) / (c * z)), (b / (c * z))));
	double tmp;
	if (z <= -1.1e+137) {
		tmp = t_1;
	} else if (z <= 9.5e+53) {
		tmp = ((((x * 9.0) * y) - (((z * 4.0) * t) * a)) + b) / (z * c);
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = fma(-4.0, Float64(Float64(a * t) / c), fma(9.0, Float64(Float64(x * y) / Float64(c * z)), Float64(b / Float64(c * z))))
	tmp = 0.0
	if (z <= -1.1e+137)
		tmp = t_1;
	elseif (z <= 9.5e+53)
		tmp = Float64(Float64(Float64(Float64(Float64(x * 9.0) * y) - Float64(Float64(Float64(z * 4.0) * t) * a)) + b) / Float64(z * c));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(-4.0 * N[(N[(a * t), $MachinePrecision] / c), $MachinePrecision] + N[(9.0 * N[(N[(x * y), $MachinePrecision] / N[(c * z), $MachinePrecision]), $MachinePrecision] + N[(b / N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[z, -1.1e+137], t$95$1, If[LessEqual[z, 9.5e+53], N[(N[(N[(N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision] - N[(N[(N[(z * 4.0), $MachinePrecision] * t), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] + b), $MachinePrecision] / N[(z * c), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -1.10000000000000008e137 or 9.5000000000000006e53 < z

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in z around inf

      \[\leadsto \color{blue}{-4 \cdot \frac{a \cdot t}{c} + \left(9 \cdot \frac{x \cdot y}{c \cdot z} + \frac{b}{c \cdot z}\right)} \]

   4. Applied rewrites 78.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-4, \frac{a \cdot t}{c}, \mathsf{fma}\left(9, \frac{x \cdot y}{c \cdot z}, \frac{b}{c \cdot z}\right)\right)} \]

   if -1.10000000000000008e137 < z < 9.5000000000000006e53

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 77.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := \frac{\mathsf{fma}\left(a \cdot t, -4, \frac{t\_1}{z}\right)}{c}\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+246}:\\ \;\;\;\;x \cdot \frac{\mathsf{fma}\left(9, \frac{y}{c}, \frac{b}{c \cdot x}\right)}{z}\\ \mathbf{elif}\;t\_1 \leq -5 \cdot 10^{+114}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{-91}:\\ \;\;\;\;\frac{\frac{b}{z} - \left(t \cdot 4\right) \cdot a}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y)) (t_2 (/ (fma (* a t) -4.0 (/ t_1 z)) c)))
   (if (<= t_1 -5e+246)
     (* x (/ (fma 9.0 (/ y c) (/ b (* c x))) z))
     (if (<= t_1 -5e+114)
       t_2
       (if (<= t_1 5e-91) (/ (- (/ b z) (* (* t 4.0) a)) c) t_2)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = fma((a * t), -4.0, (t_1 / z)) / c;
	double tmp;
	if (t_1 <= -5e+246) {
		tmp = x * (fma(9.0, (y / c), (b / (c * x))) / z);
	} else if (t_1 <= -5e+114) {
		tmp = t_2;
	} else if (t_1 <= 5e-91) {
		tmp = ((b / z) - ((t * 4.0) * a)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(fma(Float64(a * t), -4.0, Float64(t_1 / z)) / c)
	tmp = 0.0
	if (t_1 <= -5e+246)
		tmp = Float64(x * Float64(fma(9.0, Float64(y / c), Float64(b / Float64(c * x))) / z));
	elseif (t_1 <= -5e+114)
		tmp = t_2;
	elseif (t_1 <= 5e-91)
		tmp = Float64(Float64(Float64(b / z) - Float64(Float64(t * 4.0) * a)) / c);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(a * t), $MachinePrecision] * -4.0 + N[(t$95$1 / z), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+246], N[(x * N[(N[(9.0 * N[(y / c), $MachinePrecision] + N[(b / N[(c * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -5e+114], t$95$2, If[LessEqual[t$95$1, 5e-91], N[(N[(N[(b / z), $MachinePrecision] - N[(N[(t * 4.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -4.99999999999999976e246

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in z around 0

      \[\leadsto x \cdot \frac{9 \cdot \frac{y}{c} + \frac{b}{c \cdot x}}{\color{blue}{z}} \]

   7. Applied rewrites 55.3%

      \[\leadsto x \cdot \frac{\mathsf{fma}\left(9, \frac{y}{c}, \frac{b}{c \cdot x}\right)}{\color{blue}{z}} \]

   if -4.99999999999999976e246 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -5.0000000000000001e114 or 4.99999999999999997e-91 < (*.f64 (*.f64 x #s(literal 9 binary64)) y)

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{\frac{9 \cdot \left(x \cdot y\right) - 4 \cdot \left(a \cdot \left(t \cdot z\right)\right)}{c \cdot z}} \]

   4. Applied rewrites 55.4%

      \[\leadsto \color{blue}{\frac{9 \cdot \left(x \cdot y\right) - 4 \cdot \left(a \cdot \left(t \cdot z\right)\right)}{c \cdot z}} \]

   5. Taylor expanded in x around 0

      \[\leadsto -4 \cdot \frac{a \cdot t}{c} + \color{blue}{9 \cdot \frac{x \cdot y}{c \cdot z}} \]

   7. Applied rewrites 60.4%

      \[\leadsto \mathsf{fma}\left(-4, \color{blue}{\frac{a \cdot t}{c}}, 9 \cdot \frac{x \cdot y}{c \cdot z}\right) \]

   9. Applied rewrites 62.3%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(a \cdot t, -4, \frac{\left(x \cdot 9\right) \cdot y}{z}\right)}{c}} \]

   if -5.0000000000000001e114 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < 4.99999999999999997e-91

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 64.2%

       \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   12. Applied rewrites 64.2%

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

Alternative 9: 77.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := \frac{\mathsf{fma}\left(a \cdot t, -4, \frac{t\_1}{z}\right)}{c}\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+246}:\\ \;\;\;\;x \cdot \left(\frac{y}{c} \cdot \frac{9}{z}\right)\\ \mathbf{elif}\;t\_1 \leq -5 \cdot 10^{+114}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{-91}:\\ \;\;\;\;\frac{\frac{b}{z} - \left(t \cdot 4\right) \cdot a}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y)) (t_2 (/ (fma (* a t) -4.0 (/ t_1 z)) c)))
   (if (<= t_1 -5e+246)
     (* x (* (/ y c) (/ 9.0 z)))
     (if (<= t_1 -5e+114)
       t_2
       (if (<= t_1 5e-91) (/ (- (/ b z) (* (* t 4.0) a)) c) t_2)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = fma((a * t), -4.0, (t_1 / z)) / c;
	double tmp;
	if (t_1 <= -5e+246) {
		tmp = x * ((y / c) * (9.0 / z));
	} else if (t_1 <= -5e+114) {
		tmp = t_2;
	} else if (t_1 <= 5e-91) {
		tmp = ((b / z) - ((t * 4.0) * a)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(fma(Float64(a * t), -4.0, Float64(t_1 / z)) / c)
	tmp = 0.0
	if (t_1 <= -5e+246)
		tmp = Float64(x * Float64(Float64(y / c) * Float64(9.0 / z)));
	elseif (t_1 <= -5e+114)
		tmp = t_2;
	elseif (t_1 <= 5e-91)
		tmp = Float64(Float64(Float64(b / z) - Float64(Float64(t * 4.0) * a)) / c);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(a * t), $MachinePrecision] * -4.0 + N[(t$95$1 / z), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+246], N[(x * N[(N[(y / c), $MachinePrecision] * N[(9.0 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, -5e+114], t$95$2, If[LessEqual[t$95$1, 5e-91], N[(N[(N[(b / z), $MachinePrecision] - N[(N[(t * 4.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -4.99999999999999976e246

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   7. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   9. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(\frac{y}{c} \cdot \frac{9}{\color{blue}{z}}\right) \]

   if -4.99999999999999976e246 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -5.0000000000000001e114 or 4.99999999999999997e-91 < (*.f64 (*.f64 x #s(literal 9 binary64)) y)

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around 0

      \[\leadsto \color{blue}{\frac{9 \cdot \left(x \cdot y\right) - 4 \cdot \left(a \cdot \left(t \cdot z\right)\right)}{c \cdot z}} \]

   4. Applied rewrites 55.4%

      \[\leadsto \color{blue}{\frac{9 \cdot \left(x \cdot y\right) - 4 \cdot \left(a \cdot \left(t \cdot z\right)\right)}{c \cdot z}} \]

   5. Taylor expanded in x around 0

      \[\leadsto -4 \cdot \frac{a \cdot t}{c} + \color{blue}{9 \cdot \frac{x \cdot y}{c \cdot z}} \]

   7. Applied rewrites 60.4%

      \[\leadsto \mathsf{fma}\left(-4, \color{blue}{\frac{a \cdot t}{c}}, 9 \cdot \frac{x \cdot y}{c \cdot z}\right) \]

   9. Applied rewrites 62.3%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(a \cdot t, -4, \frac{\left(x \cdot 9\right) \cdot y}{z}\right)}{c}} \]

   if -5.0000000000000001e114 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < 4.99999999999999997e-91

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 64.2%

       \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   12. Applied rewrites 64.2%

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

Alternative 10: 76.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := x \cdot \left(\frac{y}{c} \cdot \frac{9}{z}\right)\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+148}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 10^{+132}:\\ \;\;\;\;\frac{\frac{b}{z} - \left(t \cdot 4\right) \cdot a}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y)) (t_2 (* x (* (/ y c) (/ 9.0 z)))))
   (if (<= t_1 -2e+148)
     t_2
     (if (<= t_1 1e+132) (/ (- (/ b z) (* (* t 4.0) a)) c) t_2))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = x * ((y / c) * (9.0 / z));
	double tmp;
	if (t_1 <= -2e+148) {
		tmp = t_2;
	} else if (t_1 <= 1e+132) {
		tmp = ((b / z) - ((t * 4.0) * a)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x * 9.0d0) * y
    t_2 = x * ((y / c) * (9.0d0 / z))
    if (t_1 <= (-2d+148)) then
        tmp = t_2
    else if (t_1 <= 1d+132) then
        tmp = ((b / z) - ((t * 4.0d0) * a)) / c
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = x * ((y / c) * (9.0 / z));
	double tmp;
	if (t_1 <= -2e+148) {
		tmp = t_2;
	} else if (t_1 <= 1e+132) {
		tmp = ((b / z) - ((t * 4.0) * a)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	t_1 = (x * 9.0) * y
	t_2 = x * ((y / c) * (9.0 / z))
	tmp = 0
	if t_1 <= -2e+148:
		tmp = t_2
	elif t_1 <= 1e+132:
		tmp = ((b / z) - ((t * 4.0) * a)) / c
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(x * Float64(Float64(y / c) * Float64(9.0 / z)))
	tmp = 0.0
	if (t_1 <= -2e+148)
		tmp = t_2;
	elseif (t_1 <= 1e+132)
		tmp = Float64(Float64(Float64(b / z) - Float64(Float64(t * 4.0) * a)) / c);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (x * 9.0) * y;
	t_2 = x * ((y / c) * (9.0 / z));
	tmp = 0.0;
	if (t_1 <= -2e+148)
		tmp = t_2;
	elseif (t_1 <= 1e+132)
		tmp = ((b / z) - ((t * 4.0) * a)) / c;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(N[(y / c), $MachinePrecision] * N[(9.0 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+148], t$95$2, If[LessEqual[t$95$1, 1e+132], N[(N[(N[(b / z), $MachinePrecision] - N[(N[(t * 4.0), $MachinePrecision] * a), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -2.0000000000000001e148 or 9.99999999999999991e131 < (*.f64 (*.f64 x #s(literal 9 binary64)) y)

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   7. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   9. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(\frac{y}{c} \cdot \frac{9}{\color{blue}{z}}\right) \]

   if -2.0000000000000001e148 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < 9.99999999999999991e131

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 64.2%

       \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   12. Applied rewrites 64.2%

       \[\leadsto \frac{\frac{b}{z} - \left(t \cdot 4\right) \cdot a}{c} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 76.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := x \cdot \left(\frac{y}{c} \cdot \frac{9}{z}\right)\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+148}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 10^{+132}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-4, a \cdot t, \frac{b}{z}\right)}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y)) (t_2 (* x (* (/ y c) (/ 9.0 z)))))
   (if (<= t_1 -2e+148)
     t_2
     (if (<= t_1 1e+132) (/ (fma -4.0 (* a t) (/ b z)) c) t_2))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = x * ((y / c) * (9.0 / z));
	double tmp;
	if (t_1 <= -2e+148) {
		tmp = t_2;
	} else if (t_1 <= 1e+132) {
		tmp = fma(-4.0, (a * t), (b / z)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(x * Float64(Float64(y / c) * Float64(9.0 / z)))
	tmp = 0.0
	if (t_1 <= -2e+148)
		tmp = t_2;
	elseif (t_1 <= 1e+132)
		tmp = Float64(fma(-4.0, Float64(a * t), Float64(b / z)) / c);
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(N[(y / c), $MachinePrecision] * N[(9.0 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+148], t$95$2, If[LessEqual[t$95$1, 1e+132], N[(N[(-4.0 * N[(a * t), $MachinePrecision] + N[(b / z), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -2.0000000000000001e148 or 9.99999999999999991e131 < (*.f64 (*.f64 x #s(literal 9 binary64)) y)

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   7. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   9. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(\frac{y}{c} \cdot \frac{9}{\color{blue}{z}}\right) \]

   if -2.0000000000000001e148 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < 9.99999999999999991e131

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in x around 0

      \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 64.2%

       \[\leadsto \frac{\frac{b}{z} - 4 \cdot \left(a \cdot t\right)}{c} \]

   11. Taylor expanded in t around 0

       \[\leadsto \frac{-4 \cdot \left(a \cdot t\right) + \frac{b}{z}}{c} \]

   13. Applied rewrites 64.2%

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

Alternative 12: 52.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := x \cdot \left(\frac{y}{c} \cdot \frac{9}{z}\right)\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+114}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 0:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \mathbf{elif}\;t\_1 \leq 10^{+155}:\\ \;\;\;\;\frac{-4 \cdot \left(a \cdot t\right)}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y)) (t_2 (* x (* (/ y c) (/ 9.0 z)))))
   (if (<= t_1 -5e+114)
     t_2
     (if (<= t_1 0.0)
       (/ (/ b c) z)
       (if (<= t_1 1e+155) (/ (* -4.0 (* a t)) c) t_2)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = x * ((y / c) * (9.0 / z));
	double tmp;
	if (t_1 <= -5e+114) {
		tmp = t_2;
	} else if (t_1 <= 0.0) {
		tmp = (b / c) / z;
	} else if (t_1 <= 1e+155) {
		tmp = (-4.0 * (a * t)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x * 9.0d0) * y
    t_2 = x * ((y / c) * (9.0d0 / z))
    if (t_1 <= (-5d+114)) then
        tmp = t_2
    else if (t_1 <= 0.0d0) then
        tmp = (b / c) / z
    else if (t_1 <= 1d+155) then
        tmp = ((-4.0d0) * (a * t)) / c
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = x * ((y / c) * (9.0 / z));
	double tmp;
	if (t_1 <= -5e+114) {
		tmp = t_2;
	} else if (t_1 <= 0.0) {
		tmp = (b / c) / z;
	} else if (t_1 <= 1e+155) {
		tmp = (-4.0 * (a * t)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	t_1 = (x * 9.0) * y
	t_2 = x * ((y / c) * (9.0 / z))
	tmp = 0
	if t_1 <= -5e+114:
		tmp = t_2
	elif t_1 <= 0.0:
		tmp = (b / c) / z
	elif t_1 <= 1e+155:
		tmp = (-4.0 * (a * t)) / c
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(x * Float64(Float64(y / c) * Float64(9.0 / z)))
	tmp = 0.0
	if (t_1 <= -5e+114)
		tmp = t_2;
	elseif (t_1 <= 0.0)
		tmp = Float64(Float64(b / c) / z);
	elseif (t_1 <= 1e+155)
		tmp = Float64(Float64(-4.0 * Float64(a * t)) / c);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (x * 9.0) * y;
	t_2 = x * ((y / c) * (9.0 / z));
	tmp = 0.0;
	if (t_1 <= -5e+114)
		tmp = t_2;
	elseif (t_1 <= 0.0)
		tmp = (b / c) / z;
	elseif (t_1 <= 1e+155)
		tmp = (-4.0 * (a * t)) / c;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(N[(y / c), $MachinePrecision] * N[(9.0 / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+114], t$95$2, If[LessEqual[t$95$1, 0.0], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$1, 1e+155], N[(N[(-4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -5.0000000000000001e114 or 1.00000000000000001e155 < (*.f64 (*.f64 x #s(literal 9 binary64)) y)

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   7. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   9. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(\frac{y}{c} \cdot \frac{9}{\color{blue}{z}}\right) \]

   if -5.0000000000000001e114 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -0.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   4. Applied rewrites 36.1%

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   6. Applied rewrites 35.7%

      \[\leadsto \frac{\frac{b}{c}}{\color{blue}{z}} \]

   if -0.0 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < 1.00000000000000001e155

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in z around inf

      \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 37.9%

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

Alternative 13: 51.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+114}:\\ \;\;\;\;x \cdot \left(9 \cdot \frac{y}{c \cdot z}\right)\\ \mathbf{elif}\;t\_1 \leq 0:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \mathbf{elif}\;t\_1 \leq 10^{+155}:\\ \;\;\;\;\frac{-4 \cdot \left(a \cdot t\right)}{c}\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{y \cdot 9}{c \cdot z}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y)))
   (if (<= t_1 -5e+114)
     (* x (* 9.0 (/ y (* c z))))
     (if (<= t_1 0.0)
       (/ (/ b c) z)
       (if (<= t_1 1e+155)
         (/ (* -4.0 (* a t)) c)
         (* x (/ (* y 9.0) (* c z))))))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double tmp;
	if (t_1 <= -5e+114) {
		tmp = x * (9.0 * (y / (c * z)));
	} else if (t_1 <= 0.0) {
		tmp = (b / c) / z;
	} else if (t_1 <= 1e+155) {
		tmp = (-4.0 * (a * t)) / c;
	} else {
		tmp = x * ((y * 9.0) / (c * z));
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (x * 9.0d0) * y
    if (t_1 <= (-5d+114)) then
        tmp = x * (9.0d0 * (y / (c * z)))
    else if (t_1 <= 0.0d0) then
        tmp = (b / c) / z
    else if (t_1 <= 1d+155) then
        tmp = ((-4.0d0) * (a * t)) / c
    else
        tmp = x * ((y * 9.0d0) / (c * z))
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double tmp;
	if (t_1 <= -5e+114) {
		tmp = x * (9.0 * (y / (c * z)));
	} else if (t_1 <= 0.0) {
		tmp = (b / c) / z;
	} else if (t_1 <= 1e+155) {
		tmp = (-4.0 * (a * t)) / c;
	} else {
		tmp = x * ((y * 9.0) / (c * z));
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	t_1 = (x * 9.0) * y
	tmp = 0
	if t_1 <= -5e+114:
		tmp = x * (9.0 * (y / (c * z)))
	elif t_1 <= 0.0:
		tmp = (b / c) / z
	elif t_1 <= 1e+155:
		tmp = (-4.0 * (a * t)) / c
	else:
		tmp = x * ((y * 9.0) / (c * z))
	return tmp

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	tmp = 0.0
	if (t_1 <= -5e+114)
		tmp = Float64(x * Float64(9.0 * Float64(y / Float64(c * z))));
	elseif (t_1 <= 0.0)
		tmp = Float64(Float64(b / c) / z);
	elseif (t_1 <= 1e+155)
		tmp = Float64(Float64(-4.0 * Float64(a * t)) / c);
	else
		tmp = Float64(x * Float64(Float64(y * 9.0) / Float64(c * z)));
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (x * 9.0) * y;
	tmp = 0.0;
	if (t_1 <= -5e+114)
		tmp = x * (9.0 * (y / (c * z)));
	elseif (t_1 <= 0.0)
		tmp = (b / c) / z;
	elseif (t_1 <= 1e+155)
		tmp = (-4.0 * (a * t)) / c;
	else
		tmp = x * ((y * 9.0) / (c * z));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+114], N[(x * N[(9.0 * N[(y / N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 0.0], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$1, 1e+155], N[(N[(-4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], N[(x * N[(N[(y * 9.0), $MachinePrecision] / N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -5.0000000000000001e114

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   7. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   if -5.0000000000000001e114 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -0.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   4. Applied rewrites 36.1%

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   6. Applied rewrites 35.7%

      \[\leadsto \frac{\frac{b}{c}}{\color{blue}{z}} \]

   if -0.0 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < 1.00000000000000001e155

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in z around inf

      \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 37.9%

       \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   if 1.00000000000000001e155 < (*.f64 (*.f64 x #s(literal 9 binary64)) y)

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   7. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   9. Applied rewrites 36.7%

      \[\leadsto \color{blue}{x \cdot \frac{y \cdot 9}{c \cdot z}} \]
3. Recombined 4 regimes into one program.
4. Add Preprocessing

Alternative 14: 51.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := x \cdot \left(9 \cdot \frac{y}{c \cdot z}\right)\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+114}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 0:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \mathbf{elif}\;t\_1 \leq 10^{+155}:\\ \;\;\;\;\frac{-4 \cdot \left(a \cdot t\right)}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y)) (t_2 (* x (* 9.0 (/ y (* c z))))))
   (if (<= t_1 -5e+114)
     t_2
     (if (<= t_1 0.0)
       (/ (/ b c) z)
       (if (<= t_1 1e+155) (/ (* -4.0 (* a t)) c) t_2)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = x * (9.0 * (y / (c * z)));
	double tmp;
	if (t_1 <= -5e+114) {
		tmp = t_2;
	} else if (t_1 <= 0.0) {
		tmp = (b / c) / z;
	} else if (t_1 <= 1e+155) {
		tmp = (-4.0 * (a * t)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x * 9.0d0) * y
    t_2 = x * (9.0d0 * (y / (c * z)))
    if (t_1 <= (-5d+114)) then
        tmp = t_2
    else if (t_1 <= 0.0d0) then
        tmp = (b / c) / z
    else if (t_1 <= 1d+155) then
        tmp = ((-4.0d0) * (a * t)) / c
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = x * (9.0 * (y / (c * z)));
	double tmp;
	if (t_1 <= -5e+114) {
		tmp = t_2;
	} else if (t_1 <= 0.0) {
		tmp = (b / c) / z;
	} else if (t_1 <= 1e+155) {
		tmp = (-4.0 * (a * t)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	t_1 = (x * 9.0) * y
	t_2 = x * (9.0 * (y / (c * z)))
	tmp = 0
	if t_1 <= -5e+114:
		tmp = t_2
	elif t_1 <= 0.0:
		tmp = (b / c) / z
	elif t_1 <= 1e+155:
		tmp = (-4.0 * (a * t)) / c
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(x * Float64(9.0 * Float64(y / Float64(c * z))))
	tmp = 0.0
	if (t_1 <= -5e+114)
		tmp = t_2;
	elseif (t_1 <= 0.0)
		tmp = Float64(Float64(b / c) / z);
	elseif (t_1 <= 1e+155)
		tmp = Float64(Float64(-4.0 * Float64(a * t)) / c);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (x * 9.0) * y;
	t_2 = x * (9.0 * (y / (c * z)));
	tmp = 0.0;
	if (t_1 <= -5e+114)
		tmp = t_2;
	elseif (t_1 <= 0.0)
		tmp = (b / c) / z;
	elseif (t_1 <= 1e+155)
		tmp = (-4.0 * (a * t)) / c;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(x * N[(9.0 * N[(y / N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+114], t$95$2, If[LessEqual[t$95$1, 0.0], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$1, 1e+155], N[(N[(-4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -5.0000000000000001e114 or 1.00000000000000001e155 < (*.f64 (*.f64 x #s(literal 9 binary64)) y)

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   7. Applied rewrites 36.7%

      \[\leadsto x \cdot \left(9 \cdot \color{blue}{\frac{y}{c \cdot z}}\right) \]

   if -5.0000000000000001e114 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -0.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   4. Applied rewrites 36.1%

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   6. Applied rewrites 35.7%

      \[\leadsto \frac{\frac{b}{c}}{\color{blue}{z}} \]

   if -0.0 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < 1.00000000000000001e155

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in z around inf

      \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 37.9%

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

Alternative 15: 49.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \left(x \cdot 9\right) \cdot y\\ t_2 := 9 \cdot \frac{x \cdot y}{c \cdot z}\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+114}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 0:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \mathbf{elif}\;t\_1 \leq 10^{+155}:\\ \;\;\;\;\frac{-4 \cdot \left(a \cdot t\right)}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (* (* x 9.0) y)) (t_2 (* 9.0 (/ (* x y) (* c z)))))
   (if (<= t_1 -5e+114)
     t_2
     (if (<= t_1 0.0)
       (/ (/ b c) z)
       (if (<= t_1 1e+155) (/ (* -4.0 (* a t)) c) t_2)))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = 9.0 * ((x * y) / (c * z));
	double tmp;
	if (t_1 <= -5e+114) {
		tmp = t_2;
	} else if (t_1 <= 0.0) {
		tmp = (b / c) / z;
	} else if (t_1 <= 1e+155) {
		tmp = (-4.0 * (a * t)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_1 = (x * 9.0d0) * y
    t_2 = 9.0d0 * ((x * y) / (c * z))
    if (t_1 <= (-5d+114)) then
        tmp = t_2
    else if (t_1 <= 0.0d0) then
        tmp = (b / c) / z
    else if (t_1 <= 1d+155) then
        tmp = ((-4.0d0) * (a * t)) / c
    else
        tmp = t_2
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (x * 9.0) * y;
	double t_2 = 9.0 * ((x * y) / (c * z));
	double tmp;
	if (t_1 <= -5e+114) {
		tmp = t_2;
	} else if (t_1 <= 0.0) {
		tmp = (b / c) / z;
	} else if (t_1 <= 1e+155) {
		tmp = (-4.0 * (a * t)) / c;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	t_1 = (x * 9.0) * y
	t_2 = 9.0 * ((x * y) / (c * z))
	tmp = 0
	if t_1 <= -5e+114:
		tmp = t_2
	elif t_1 <= 0.0:
		tmp = (b / c) / z
	elif t_1 <= 1e+155:
		tmp = (-4.0 * (a * t)) / c
	else:
		tmp = t_2
	return tmp

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(x * 9.0) * y)
	t_2 = Float64(9.0 * Float64(Float64(x * y) / Float64(c * z)))
	tmp = 0.0
	if (t_1 <= -5e+114)
		tmp = t_2;
	elseif (t_1 <= 0.0)
		tmp = Float64(Float64(b / c) / z);
	elseif (t_1 <= 1e+155)
		tmp = Float64(Float64(-4.0 * Float64(a * t)) / c);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (x * 9.0) * y;
	t_2 = 9.0 * ((x * y) / (c * z));
	tmp = 0.0;
	if (t_1 <= -5e+114)
		tmp = t_2;
	elseif (t_1 <= 0.0)
		tmp = (b / c) / z;
	elseif (t_1 <= 1e+155)
		tmp = (-4.0 * (a * t)) / c;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(x * 9.0), $MachinePrecision] * y), $MachinePrecision]}, Block[{t$95$2 = N[(9.0 * N[(N[(x * y), $MachinePrecision] / N[(c * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -5e+114], t$95$2, If[LessEqual[t$95$1, 0.0], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision], If[LessEqual[t$95$1, 1e+155], N[(N[(-4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -5.0000000000000001e114 or 1.00000000000000001e155 < (*.f64 (*.f64 x #s(literal 9 binary64)) y)

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{9 \cdot \frac{x \cdot y}{c \cdot z}} \]

   4. Applied rewrites 34.7%

      \[\leadsto \color{blue}{9 \cdot \frac{x \cdot y}{c \cdot z}} \]

   if -5.0000000000000001e114 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < -0.0

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   4. Applied rewrites 36.1%

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   6. Applied rewrites 35.7%

      \[\leadsto \frac{\frac{b}{c}}{\color{blue}{z}} \]

   if -0.0 < (*.f64 (*.f64 x #s(literal 9 binary64)) y) < 1.00000000000000001e155

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in z around inf

      \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 37.9%

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

Alternative 16: 46.5% accurate, 1.5× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \frac{-4 \cdot \left(a \cdot t\right)}{c}\\ \mathbf{if}\;a \leq -6.6 \cdot 10^{-64}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 2.4 \cdot 10^{+54}:\\ \;\;\;\;\frac{\frac{1}{z} \cdot b}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (/ (* -4.0 (* a t)) c)))
   (if (<= a -6.6e-64) t_1 (if (<= a 2.4e+54) (/ (* (/ 1.0 z) b) c) t_1))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (-4.0 * (a * t)) / c;
	double tmp;
	if (a <= -6.6e-64) {
		tmp = t_1;
	} else if (a <= 2.4e+54) {
		tmp = ((1.0 / z) * b) / c;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((-4.0d0) * (a * t)) / c
    if (a <= (-6.6d-64)) then
        tmp = t_1
    else if (a <= 2.4d+54) then
        tmp = ((1.0d0 / z) * b) / c
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (-4.0 * (a * t)) / c;
	double tmp;
	if (a <= -6.6e-64) {
		tmp = t_1;
	} else if (a <= 2.4e+54) {
		tmp = ((1.0 / z) * b) / c;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	t_1 = (-4.0 * (a * t)) / c
	tmp = 0
	if a <= -6.6e-64:
		tmp = t_1
	elif a <= 2.4e+54:
		tmp = ((1.0 / z) * b) / c
	else:
		tmp = t_1
	return tmp

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(-4.0 * Float64(a * t)) / c)
	tmp = 0.0
	if (a <= -6.6e-64)
		tmp = t_1;
	elseif (a <= 2.4e+54)
		tmp = Float64(Float64(Float64(1.0 / z) * b) / c);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (-4.0 * (a * t)) / c;
	tmp = 0.0;
	if (a <= -6.6e-64)
		tmp = t_1;
	elseif (a <= 2.4e+54)
		tmp = ((1.0 / z) * b) / c;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(-4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]}, If[LessEqual[a, -6.6e-64], t$95$1, If[LessEqual[a, 2.4e+54], N[(N[(N[(1.0 / z), $MachinePrecision] * b), $MachinePrecision] / c), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -6.5999999999999999e-64 or 2.39999999999999998e54 < a

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in z around inf

      \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 37.9%

       \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   if -6.5999999999999999e-64 < a < 2.39999999999999998e54

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   4. Applied rewrites 36.1%

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   6. Applied rewrites 34.3%

      \[\leadsto \frac{\frac{b}{z}}{\color{blue}{c}} \]

   8. Applied rewrites 34.2%

      \[\leadsto \frac{\frac{1}{z} \cdot b}{c} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 17: 46.5% accurate, 1.5× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} t_1 := \frac{-4 \cdot \left(a \cdot t\right)}{c}\\ \mathbf{if}\;a \leq -6.6 \cdot 10^{-64}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 2.4 \cdot 10^{+54}:\\ \;\;\;\;\frac{\frac{b}{z}}{c}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (let* ((t_1 (/ (* -4.0 (* a t)) c)))
   (if (<= a -6.6e-64) t_1 (if (<= a 2.4e+54) (/ (/ b z) c) t_1))))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (-4.0 * (a * t)) / c;
	double tmp;
	if (a <= -6.6e-64) {
		tmp = t_1;
	} else if (a <= 2.4e+54) {
		tmp = (b / z) / c;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((-4.0d0) * (a * t)) / c
    if (a <= (-6.6d-64)) then
        tmp = t_1
    else if (a <= 2.4d+54) then
        tmp = (b / z) / c
    else
        tmp = t_1
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double t_1 = (-4.0 * (a * t)) / c;
	double tmp;
	if (a <= -6.6e-64) {
		tmp = t_1;
	} else if (a <= 2.4e+54) {
		tmp = (b / z) / c;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	t_1 = (-4.0 * (a * t)) / c
	tmp = 0
	if a <= -6.6e-64:
		tmp = t_1
	elif a <= 2.4e+54:
		tmp = (b / z) / c
	else:
		tmp = t_1
	return tmp

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	t_1 = Float64(Float64(-4.0 * Float64(a * t)) / c)
	tmp = 0.0
	if (a <= -6.6e-64)
		tmp = t_1;
	elseif (a <= 2.4e+54)
		tmp = Float64(Float64(b / z) / c);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp_2 = code(x, y, z, t, a, b, c)
	t_1 = (-4.0 * (a * t)) / c;
	tmp = 0.0;
	if (a <= -6.6e-64)
		tmp = t_1;
	elseif (a <= 2.4e+54)
		tmp = (b / z) / c;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := Block[{t$95$1 = N[(N[(-4.0 * N[(a * t), $MachinePrecision]), $MachinePrecision] / c), $MachinePrecision]}, If[LessEqual[a, -6.6e-64], t$95$1, If[LessEqual[a, 2.4e+54], N[(N[(b / z), $MachinePrecision] / c), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -6.5999999999999999e-64 or 2.39999999999999998e54 < a

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in x around inf

      \[\leadsto \color{blue}{x \cdot \left(\left(9 \cdot \frac{y}{c \cdot z} + \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   4. Applied rewrites 65.5%

      \[\leadsto \color{blue}{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{c \cdot z}, \frac{b}{c \cdot \left(x \cdot z\right)}\right) - 4 \cdot \frac{a \cdot t}{c \cdot x}\right)} \]

   5. Taylor expanded in c around 0

      \[\leadsto \frac{x \cdot \left(\left(9 \cdot \frac{y}{z} + \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   7. Applied rewrites 74.2%

      \[\leadsto \frac{x \cdot \left(\mathsf{fma}\left(9, \frac{y}{z}, \frac{b}{x \cdot z}\right) - 4 \cdot \frac{a \cdot t}{x}\right)}{\color{blue}{c}} \]

   8. Taylor expanded in z around inf

      \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   10. Applied rewrites 37.9%

       \[\leadsto \frac{-4 \cdot \left(a \cdot t\right)}{c} \]

   if -6.5999999999999999e-64 < a < 2.39999999999999998e54

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   4. Applied rewrites 36.1%

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   6. Applied rewrites 34.3%

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

Alternative 18: 36.1% accurate, 2.4× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \begin{array}{l} \mathbf{if}\;x \leq -6.1 \cdot 10^{+66}:\\ \;\;\;\;\frac{b}{c \cdot z}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{b}{c}}{z}\\ \end{array} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c)
 :precision binary64
 (if (<= x -6.1e+66) (/ b (* c z)) (/ (/ b c) z)))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (x <= -6.1e+66) {
		tmp = b / (c * z);
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b, c)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: c
    real(8) :: tmp
    if (x <= (-6.1d+66)) then
        tmp = b / (c * z)
    else
        tmp = (b / c) / z
    end if
    code = tmp
end function

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	double tmp;
	if (x <= -6.1e+66) {
		tmp = b / (c * z);
	} else {
		tmp = (b / c) / z;
	}
	return tmp;
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	tmp = 0
	if x <= -6.1e+66:
		tmp = b / (c * z)
	else:
		tmp = (b / c) / z
	return tmp

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	tmp = 0.0
	if (x <= -6.1e+66)
		tmp = Float64(b / Float64(c * z));
	else
		tmp = Float64(Float64(b / c) / z);
	end
	return tmp
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp_2 = code(x, y, z, t, a, b, c)
	tmp = 0.0;
	if (x <= -6.1e+66)
		tmp = b / (c * z);
	else
		tmp = (b / c) / z;
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := If[LessEqual[x, -6.1e+66], N[(b / N[(c * z), $MachinePrecision]), $MachinePrecision], N[(N[(b / c), $MachinePrecision] / z), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -6.10000000000000021e66

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   4. Applied rewrites 36.1%

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   if -6.10000000000000021e66 < x

   1. Initial program 79.6%

      \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   4. Applied rewrites 36.1%

      \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

   6. Applied rewrites 35.7%

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

Alternative 19: 35.9% accurate, 3.8× speedup

Math

\[\begin{array}{l} [x, y, z, t, a, b, c] = \mathsf{sort}([x, y, z, t, a, b, c])\\ \\ \frac{b}{c \cdot z} \end{array} \]

FPCore

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
(FPCore (x y z t a b c) :precision binary64 (/ b (* c z)))

C

assert(x < y && y < z && z < t && t < a && a < b && b < c);
double code(double x, double y, double z, double t, double a, double b, double c) {
	return b / (c * z);
}

Fortran

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

assert x < y && y < z && z < t && t < a && a < b && b < c;
public static double code(double x, double y, double z, double t, double a, double b, double c) {
	return b / (c * z);
}

Python

[x, y, z, t, a, b, c] = sort([x, y, z, t, a, b, c])
def code(x, y, z, t, a, b, c):
	return b / (c * z)

Julia

x, y, z, t, a, b, c = sort([x, y, z, t, a, b, c])
function code(x, y, z, t, a, b, c)
	return Float64(b / Float64(c * z))
end

MATLAB

x, y, z, t, a, b, c = num2cell(sort([x, y, z, t, a, b, c])){:}
function tmp = code(x, y, z, t, a, b, c)
	tmp = b / (c * z);
end

Wolfram

NOTE: x, y, z, t, a, b, and c should be sorted in increasing order before calling this function.
code[x_, y_, z_, t_, a_, b_, c_] := N[(b / N[(c * z), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 79.6%

   \[\frac{\left(\left(x \cdot 9\right) \cdot y - \left(\left(z \cdot 4\right) \cdot t\right) \cdot a\right) + b}{z \cdot c} \]

2. Taylor expanded in b around inf

   \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]

4. Applied rewrites 36.1%

   \[\leadsto \color{blue}{\frac{b}{c \cdot z}} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025159 
(FPCore (x y z t a b c)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, J"
  :precision binary64
  (/ (+ (- (* (* x 9.0) y) (* (* (* z 4.0) t) a)) b) (* z c)))