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

Percentage Accurate: 90.9% → 95.4%

Time: 9.5s

Alternatives: 8

Speedup: 0.7×

• 29.3% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (/ (- (* x y) (* (* z 9.0) t)) (* a 2.0)))

C

double code(double x, double y, double z, double t, double a) {
	return ((x * y) - ((z * 9.0) * t)) / (a * 2.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = ((x * y) - ((z * 9.0d0) * t)) / (a * 2.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((x * y) - ((z * 9.0) * t)) / (a * 2.0);
}

Python

def code(x, y, z, t, a):
	return ((x * y) - ((z * 9.0) * t)) / (a * 2.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(x * y) - Float64(Float64(z * 9.0) * t)) / Float64(a * 2.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((x * y) - ((z * 9.0) * t)) / (a * 2.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(x * y), $MachinePrecision] - N[(N[(z * 9.0), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision] / N[(a * 2.0), $MachinePrecision]), $MachinePrecision]

Initial Program: 90.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (/ (- (* x y) (* (* z 9.0) t)) (* a 2.0)))

C

double code(double x, double y, double z, double t, double a) {
	return ((x * y) - ((z * 9.0) * t)) / (a * 2.0);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    code = ((x * y) - ((z * 9.0d0) * t)) / (a * 2.0d0)
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	return ((x * y) - ((z * 9.0) * t)) / (a * 2.0);
}

Python

def code(x, y, z, t, a):
	return ((x * y) - ((z * 9.0) * t)) / (a * 2.0)

Julia

function code(x, y, z, t, a)
	return Float64(Float64(Float64(x * y) - Float64(Float64(z * 9.0) * t)) / Float64(a * 2.0))
end

MATLAB

function tmp = code(x, y, z, t, a)
	tmp = ((x * y) - ((z * 9.0) * t)) / (a * 2.0);
end

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(x * y), $MachinePrecision] - N[(N[(z * 9.0), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision] / N[(a * 2.0), $MachinePrecision]), $MachinePrecision]

Alternative 1: 95.4% accurate, 0.7× speedup

Math

\[\begin{array}{l} a\_m = \left|a\right| \\ a\_s = \mathsf{copysign}\left(1, a\right) \\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\\\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\ \\ a\_s \cdot \begin{array}{l} \mathbf{if}\;a\_m \leq 1.35 \cdot 10^{+33}:\\ \;\;\;\;\frac{\mathsf{fma}\left(0.5 \cdot y, x, \left(-4.5 \cdot t\right) \cdot z\right)}{a\_m}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t}{a\_m}, -4.5 \cdot z, \frac{y}{a\_m + a\_m} \cdot x\right)\\ \end{array} \end{array} \]

FPCore

a\_m = (fabs.f64 a)
a\_s = (copysign.f64 #s(literal 1 binary64) a)
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
(FPCore (a_s x y z t a_m)
 :precision binary64
 (*
  a_s
  (if (<= a_m 1.35e+33)
    (/ (fma (* 0.5 y) x (* (* -4.5 t) z)) a_m)
    (fma (/ t a_m) (* -4.5 z) (* (/ y (+ a_m a_m)) x)))))

C

a\_m = fabs(a);
a\_s = copysign(1.0, a);
assert(x < y && y < z && z < t && t < a_m);
assert(x < y && y < z && z < t && t < a_m);
double code(double a_s, double x, double y, double z, double t, double a_m) {
	double tmp;
	if (a_m <= 1.35e+33) {
		tmp = fma((0.5 * y), x, ((-4.5 * t) * z)) / a_m;
	} else {
		tmp = fma((t / a_m), (-4.5 * z), ((y / (a_m + a_m)) * x));
	}
	return a_s * tmp;
}

Julia

a\_m = abs(a)
a\_s = copysign(1.0, a)
x, y, z, t, a_m = sort([x, y, z, t, a_m])
x, y, z, t, a_m = sort([x, y, z, t, a_m])
function code(a_s, x, y, z, t, a_m)
	tmp = 0.0
	if (a_m <= 1.35e+33)
		tmp = Float64(fma(Float64(0.5 * y), x, Float64(Float64(-4.5 * t) * z)) / a_m);
	else
		tmp = fma(Float64(t / a_m), Float64(-4.5 * z), Float64(Float64(y / Float64(a_m + a_m)) * x));
	end
	return Float64(a_s * tmp)
end

Wolfram

a\_m = N[Abs[a], $MachinePrecision]
a\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
code[a$95$s_, x_, y_, z_, t_, a$95$m_] := N[(a$95$s * If[LessEqual[a$95$m, 1.35e+33], N[(N[(N[(0.5 * y), $MachinePrecision] * x + N[(N[(-4.5 * t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] / a$95$m), $MachinePrecision], N[(N[(t / a$95$m), $MachinePrecision] * N[(-4.5 * z), $MachinePrecision] + N[(N[(y / N[(a$95$m + a$95$m), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if a < 1.34999999999999996e33

   1. Initial program 90.3%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{-9}{2} \cdot \frac{t \cdot z}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \frac{x \cdot y}{a} + \color{blue}{\frac{-9}{2} \cdot \frac{t \cdot z}{a}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(x \cdot y\right)}{a} + \color{blue}{\frac{-9}{2}} \cdot \frac{t \cdot z}{a} \]

      3. associate-*r/ N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(x \cdot y\right)}{a} + \frac{\frac{-9}{2} \cdot \left(t \cdot z\right)}{\color{blue}{a}} \]

      4. div-add-rev N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(x \cdot y\right) + \frac{-9}{2} \cdot \left(t \cdot z\right)}{\color{blue}{a}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(x \cdot y\right) + \frac{-9}{2} \cdot \left(t \cdot z\right)}{\color{blue}{a}} \]

      6. *-commutative N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(y \cdot x\right) + \frac{-9}{2} \cdot \left(t \cdot z\right)}{a} \]

      7. associate-*r* N/A

         \[\leadsto \frac{\left(\frac{1}{2} \cdot y\right) \cdot x + \frac{-9}{2} \cdot \left(t \cdot z\right)}{a} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot y, x, \frac{-9}{2} \cdot \left(t \cdot z\right)\right)}{a} \]

      9. lower-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot y, x, \frac{-9}{2} \cdot \left(t \cdot z\right)\right)}{a} \]

      10. associate-*r* N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot y, x, \left(\frac{-9}{2} \cdot t\right) \cdot z\right)}{a} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot y, x, \left(\frac{-9}{2} \cdot t\right) \cdot z\right)}{a} \]

      12. lower-*.f64 90.3

          \[\leadsto \frac{\mathsf{fma}\left(0.5 \cdot y, x, \left(-4.5 \cdot t\right) \cdot z\right)}{a} \]

   5. Applied rewrites 90.3%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(0.5 \cdot y, x, \left(-4.5 \cdot t\right) \cdot z\right)}{a}} \]

   if 1.34999999999999996e33 < a

   1. Initial program 78.6%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot y - \left(z \cdot 9\right) \cdot t}}{a \cdot 2} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot y} - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{x \cdot y - \color{blue}{\left(z \cdot 9\right) \cdot t}}{a \cdot 2} \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \frac{\color{blue}{x \cdot y + \left(\mathsf{neg}\left(z \cdot 9\right)\right) \cdot t}}{a \cdot 2} \]

      5. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\left(\mathsf{neg}\left(z \cdot 9\right)\right) \cdot t + x \cdot y}}{a \cdot 2} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(\color{blue}{z \cdot 9}\right)\right) \cdot t + x \cdot y}{a \cdot 2} \]

      7. *-commutative N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(\color{blue}{9 \cdot z}\right)\right) \cdot t + x \cdot y}{a \cdot 2} \]

      8. distribute-lft-neg-in N/A

         \[\leadsto \frac{\color{blue}{\left(\left(\mathsf{neg}\left(9\right)\right) \cdot z\right)} \cdot t + x \cdot y}{a \cdot 2} \]

      9. metadata-eval N/A

         \[\leadsto \frac{\left(\color{blue}{-9} \cdot z\right) \cdot t + x \cdot y}{a \cdot 2} \]

      10. associate-*r* N/A

          \[\leadsto \frac{\color{blue}{-9 \cdot \left(z \cdot t\right)} + x \cdot y}{a \cdot 2} \]

      11. *-commutative N/A

          \[\leadsto \frac{-9 \cdot \color{blue}{\left(t \cdot z\right)} + x \cdot y}{a \cdot 2} \]

      12. *-commutative N/A

          \[\leadsto \frac{\color{blue}{\left(t \cdot z\right) \cdot -9} + x \cdot y}{a \cdot 2} \]

      13. lower-fma.f64 N/A

          \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(t \cdot z, -9, x \cdot y\right)}}{a \cdot 2} \]

      14. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{t \cdot z}, -9, x \cdot y\right)}{a \cdot 2} \]

      15. *-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(t \cdot z, -9, \color{blue}{y \cdot x}\right)}{a \cdot 2} \]

      16. lower-*.f64 78.6

          \[\leadsto \frac{\mathsf{fma}\left(t \cdot z, -9, \color{blue}{y \cdot x}\right)}{a \cdot 2} \]

   4. Applied rewrites 78.6%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(t \cdot z, -9, y \cdot x\right)}}{a \cdot 2} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t \cdot z, -9, y \cdot x\right)}{\color{blue}{a \cdot 2}} \]

      2. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(t \cdot z, -9, y \cdot x\right)}{a \cdot 2}} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{t \cdot z}, -9, y \cdot x\right)}{a \cdot 2} \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t \cdot z, -9, \color{blue}{y \cdot x}\right)}{a \cdot 2} \]

      5. lift-fma.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(t \cdot z\right) \cdot -9 + y \cdot x}}{a \cdot 2} \]

      6. div-add N/A

         \[\leadsto \color{blue}{\frac{\left(t \cdot z\right) \cdot -9}{a \cdot 2} + \frac{y \cdot x}{a \cdot 2}} \]

      7. associate-*l* N/A

         \[\leadsto \frac{\color{blue}{t \cdot \left(z \cdot -9\right)}}{a \cdot 2} + \frac{y \cdot x}{a \cdot 2} \]

      8. *-commutative N/A

         \[\leadsto \frac{t \cdot \color{blue}{\left(-9 \cdot z\right)}}{a \cdot 2} + \frac{y \cdot x}{a \cdot 2} \]

      9. times-frac N/A

         \[\leadsto \color{blue}{\frac{t}{a} \cdot \frac{-9 \cdot z}{2}} + \frac{y \cdot x}{a \cdot 2} \]

      10. *-commutative N/A

          \[\leadsto \frac{t}{a} \cdot \frac{-9 \cdot z}{2} + \frac{\color{blue}{x \cdot y}}{a \cdot 2} \]

      11. *-commutative N/A

          \[\leadsto \frac{t}{a} \cdot \frac{-9 \cdot z}{2} + \frac{x \cdot y}{\color{blue}{2 \cdot a}} \]

      12. associate-*r/ N/A

          \[\leadsto \frac{t}{a} \cdot \frac{-9 \cdot z}{2} + \color{blue}{x \cdot \frac{y}{2 \cdot a}} \]

      13. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{a}, \frac{-9 \cdot z}{2}, x \cdot \frac{y}{2 \cdot a}\right)} \]

      14. lift-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{t}{a}}, \frac{-9 \cdot z}{2}, x \cdot \frac{y}{2 \cdot a}\right) \]

      15. lower-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \color{blue}{\frac{-9 \cdot z}{2}}, x \cdot \frac{y}{2 \cdot a}\right) \]

      16. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \frac{\color{blue}{-9 \cdot z}}{2}, x \cdot \frac{y}{2 \cdot a}\right) \]

      17. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \frac{-9 \cdot z}{2}, \color{blue}{\frac{y}{2 \cdot a} \cdot x}\right) \]

      18. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \frac{-9 \cdot z}{2}, \color{blue}{\frac{y}{2 \cdot a} \cdot x}\right) \]

      19. lift-/.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \frac{-9 \cdot z}{2}, \color{blue}{\frac{y}{2 \cdot a}} \cdot x\right) \]

      20. lift-*.f64 96.1

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \frac{-9 \cdot z}{2}, \frac{y}{\color{blue}{2 \cdot a}} \cdot x\right) \]

   6. Applied rewrites 96.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{t}{a}, \frac{-9 \cdot z}{2}, \frac{y}{2 \cdot a} \cdot x\right)} \]

   7. Taylor expanded in z around 0

      \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \color{blue}{\frac{-9}{2} \cdot z}, \frac{y}{2 \cdot a} \cdot x\right) \]
   8. Step-by-step derivation

      1. lower-*.f64 96.1

         \[\leadsto \mathsf{fma}\left(\frac{t}{a}, -4.5 \cdot \color{blue}{z}, \frac{y}{2 \cdot a} \cdot x\right) \]

   9. Applied rewrites 96.1%

      \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \color{blue}{-4.5 \cdot z}, \frac{y}{2 \cdot a} \cdot x\right) \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \frac{-9}{2} \cdot z, \frac{y}{\color{blue}{2 \cdot a}} \cdot x\right) \]

       2. count-2-rev N/A

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, \frac{-9}{2} \cdot z, \frac{y}{\color{blue}{a + a}} \cdot x\right) \]

       3. lower-+.f64 96.1

          \[\leadsto \mathsf{fma}\left(\frac{t}{a}, -4.5 \cdot z, \frac{y}{\color{blue}{a + a}} \cdot x\right) \]

   11. Applied rewrites 96.1%

       \[\leadsto \mathsf{fma}\left(\frac{t}{a}, -4.5 \cdot z, \frac{y}{\color{blue}{a + a}} \cdot x\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 93.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} a\_m = \left|a\right| \\ a\_s = \mathsf{copysign}\left(1, a\right) \\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\\\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\ \\ \begin{array}{l} t_1 := \left(z \cdot 9\right) \cdot t\\ a\_s \cdot \begin{array}{l} \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;\left(\frac{t}{a\_m} \cdot -4.5\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;\frac{x \cdot y - t\_1}{a\_m + a\_m}\\ \end{array} \end{array} \end{array} \]

FPCore

a\_m = (fabs.f64 a)
a\_s = (copysign.f64 #s(literal 1 binary64) a)
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
(FPCore (a_s x y z t a_m)
 :precision binary64
 (let* ((t_1 (* (* z 9.0) t)))
   (*
    a_s
    (if (<= t_1 (- INFINITY))
      (* (* (/ t a_m) -4.5) z)
      (/ (- (* x y) t_1) (+ a_m a_m))))))

C

a\_m = fabs(a);
a\_s = copysign(1.0, a);
assert(x < y && y < z && z < t && t < a_m);
assert(x < y && y < z && z < t && t < a_m);
double code(double a_s, double x, double y, double z, double t, double a_m) {
	double t_1 = (z * 9.0) * t;
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = ((t / a_m) * -4.5) * z;
	} else {
		tmp = ((x * y) - t_1) / (a_m + a_m);
	}
	return a_s * tmp;
}

Java

a\_m = Math.abs(a);
a\_s = Math.copySign(1.0, a);
assert x < y && y < z && z < t && t < a_m;
assert x < y && y < z && z < t && t < a_m;
public static double code(double a_s, double x, double y, double z, double t, double a_m) {
	double t_1 = (z * 9.0) * t;
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = ((t / a_m) * -4.5) * z;
	} else {
		tmp = ((x * y) - t_1) / (a_m + a_m);
	}
	return a_s * tmp;
}

Python

a\_m = math.fabs(a)
a\_s = math.copysign(1.0, a)
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
def code(a_s, x, y, z, t, a_m):
	t_1 = (z * 9.0) * t
	tmp = 0
	if t_1 <= -math.inf:
		tmp = ((t / a_m) * -4.5) * z
	else:
		tmp = ((x * y) - t_1) / (a_m + a_m)
	return a_s * tmp

Julia

a\_m = abs(a)
a\_s = copysign(1.0, a)
x, y, z, t, a_m = sort([x, y, z, t, a_m])
x, y, z, t, a_m = sort([x, y, z, t, a_m])
function code(a_s, x, y, z, t, a_m)
	t_1 = Float64(Float64(z * 9.0) * t)
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = Float64(Float64(Float64(t / a_m) * -4.5) * z);
	else
		tmp = Float64(Float64(Float64(x * y) - t_1) / Float64(a_m + a_m));
	end
	return Float64(a_s * tmp)
end

MATLAB

a\_m = abs(a);
a\_s = sign(a) * abs(1.0);
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
function tmp_2 = code(a_s, x, y, z, t, a_m)
	t_1 = (z * 9.0) * t;
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = ((t / a_m) * -4.5) * z;
	else
		tmp = ((x * y) - t_1) / (a_m + a_m);
	end
	tmp_2 = a_s * tmp;
end

Wolfram

a\_m = N[Abs[a], $MachinePrecision]
a\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
code[a$95$s_, x_, y_, z_, t_, a$95$m_] := Block[{t$95$1 = N[(N[(z * 9.0), $MachinePrecision] * t), $MachinePrecision]}, N[(a$95$s * If[LessEqual[t$95$1, (-Infinity)], N[(N[(N[(t / a$95$m), $MachinePrecision] * -4.5), $MachinePrecision] * z), $MachinePrecision], N[(N[(N[(x * y), $MachinePrecision] - t$95$1), $MachinePrecision] / N[(a$95$m + a$95$m), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 z #s(literal 9 binary64)) t) < -inf.0

   1. Initial program 41.3%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      4. *-commutative N/A

         \[\leadsto \left(\frac{x \cdot y}{a \cdot z} \cdot \frac{1}{2} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      5. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{x \cdot y}{a \cdot z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      6. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      7. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      8. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      11. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      12. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      13. lower-/.f64 90.3

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z \]

   5. Applied rewrites 90.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z} \]

   6. Taylor expanded in x around 0

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      3. lift-*.f64 95.0

         \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

   8. Applied rewrites 95.0%

      \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

   if -inf.0 < (*.f64 (*.f64 z #s(literal 9 binary64)) t)

   1. Initial program 91.8%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{\color{blue}{a \cdot 2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{\color{blue}{2 \cdot a}} \]

      3. count-2-rev N/A

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

      4. lower-+.f64 91.8

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

   4. Applied rewrites 91.8%

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

Alternative 3: 93.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} a\_m = \left|a\right| \\ a\_s = \mathsf{copysign}\left(1, a\right) \\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\\\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\ \\ a\_s \cdot \begin{array}{l} \mathbf{if}\;\left(z \cdot 9\right) \cdot t \leq -\infty:\\ \;\;\;\;\left(\frac{t}{a\_m} \cdot -4.5\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(0.5 \cdot y, x, \left(-4.5 \cdot t\right) \cdot z\right)}{a\_m}\\ \end{array} \end{array} \]

FPCore

a\_m = (fabs.f64 a)
a\_s = (copysign.f64 #s(literal 1 binary64) a)
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
(FPCore (a_s x y z t a_m)
 :precision binary64
 (*
  a_s
  (if (<= (* (* z 9.0) t) (- INFINITY))
    (* (* (/ t a_m) -4.5) z)
    (/ (fma (* 0.5 y) x (* (* -4.5 t) z)) a_m))))

C

a\_m = fabs(a);
a\_s = copysign(1.0, a);
assert(x < y && y < z && z < t && t < a_m);
assert(x < y && y < z && z < t && t < a_m);
double code(double a_s, double x, double y, double z, double t, double a_m) {
	double tmp;
	if (((z * 9.0) * t) <= -((double) INFINITY)) {
		tmp = ((t / a_m) * -4.5) * z;
	} else {
		tmp = fma((0.5 * y), x, ((-4.5 * t) * z)) / a_m;
	}
	return a_s * tmp;
}

Julia

a\_m = abs(a)
a\_s = copysign(1.0, a)
x, y, z, t, a_m = sort([x, y, z, t, a_m])
x, y, z, t, a_m = sort([x, y, z, t, a_m])
function code(a_s, x, y, z, t, a_m)
	tmp = 0.0
	if (Float64(Float64(z * 9.0) * t) <= Float64(-Inf))
		tmp = Float64(Float64(Float64(t / a_m) * -4.5) * z);
	else
		tmp = Float64(fma(Float64(0.5 * y), x, Float64(Float64(-4.5 * t) * z)) / a_m);
	end
	return Float64(a_s * tmp)
end

Wolfram

a\_m = N[Abs[a], $MachinePrecision]
a\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
code[a$95$s_, x_, y_, z_, t_, a$95$m_] := N[(a$95$s * If[LessEqual[N[(N[(z * 9.0), $MachinePrecision] * t), $MachinePrecision], (-Infinity)], N[(N[(N[(t / a$95$m), $MachinePrecision] * -4.5), $MachinePrecision] * z), $MachinePrecision], N[(N[(N[(0.5 * y), $MachinePrecision] * x + N[(N[(-4.5 * t), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision] / a$95$m), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 z #s(literal 9 binary64)) t) < -inf.0

   1. Initial program 41.3%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      4. *-commutative N/A

         \[\leadsto \left(\frac{x \cdot y}{a \cdot z} \cdot \frac{1}{2} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      5. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{x \cdot y}{a \cdot z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      6. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      7. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      8. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      11. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      12. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      13. lower-/.f64 90.3

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z \]

   5. Applied rewrites 90.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z} \]

   6. Taylor expanded in x around 0

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      3. lift-*.f64 95.0

         \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

   8. Applied rewrites 95.0%

      \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

   if -inf.0 < (*.f64 (*.f64 z #s(literal 9 binary64)) t)

   1. Initial program 91.8%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{\frac{-9}{2} \cdot \frac{t \cdot z}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \frac{x \cdot y}{a} + \color{blue}{\frac{-9}{2} \cdot \frac{t \cdot z}{a}} \]

      2. associate-*r/ N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(x \cdot y\right)}{a} + \color{blue}{\frac{-9}{2}} \cdot \frac{t \cdot z}{a} \]

      3. associate-*r/ N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(x \cdot y\right)}{a} + \frac{\frac{-9}{2} \cdot \left(t \cdot z\right)}{\color{blue}{a}} \]

      4. div-add-rev N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(x \cdot y\right) + \frac{-9}{2} \cdot \left(t \cdot z\right)}{\color{blue}{a}} \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(x \cdot y\right) + \frac{-9}{2} \cdot \left(t \cdot z\right)}{\color{blue}{a}} \]

      6. *-commutative N/A

         \[\leadsto \frac{\frac{1}{2} \cdot \left(y \cdot x\right) + \frac{-9}{2} \cdot \left(t \cdot z\right)}{a} \]

      7. associate-*r* N/A

         \[\leadsto \frac{\left(\frac{1}{2} \cdot y\right) \cdot x + \frac{-9}{2} \cdot \left(t \cdot z\right)}{a} \]

      8. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot y, x, \frac{-9}{2} \cdot \left(t \cdot z\right)\right)}{a} \]

      9. lower-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot y, x, \frac{-9}{2} \cdot \left(t \cdot z\right)\right)}{a} \]

      10. associate-*r* N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot y, x, \left(\frac{-9}{2} \cdot t\right) \cdot z\right)}{a} \]

      11. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot y, x, \left(\frac{-9}{2} \cdot t\right) \cdot z\right)}{a} \]

      12. lower-*.f64 91.8

          \[\leadsto \frac{\mathsf{fma}\left(0.5 \cdot y, x, \left(-4.5 \cdot t\right) \cdot z\right)}{a} \]

   5. Applied rewrites 91.8%

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(0.5 \cdot y, x, \left(-4.5 \cdot t\right) \cdot z\right)}{a}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 93.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} a\_m = \left|a\right| \\ a\_s = \mathsf{copysign}\left(1, a\right) \\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\\\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\ \\ a\_s \cdot \begin{array}{l} \mathbf{if}\;\left(z \cdot 9\right) \cdot t \leq -2 \cdot 10^{+300}:\\ \;\;\;\;\left(\frac{t}{a\_m} \cdot -4.5\right) \cdot z\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(t \cdot z, -9, y \cdot x\right)}{a\_m + a\_m}\\ \end{array} \end{array} \]

FPCore

a\_m = (fabs.f64 a)
a\_s = (copysign.f64 #s(literal 1 binary64) a)
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
(FPCore (a_s x y z t a_m)
 :precision binary64
 (*
  a_s
  (if (<= (* (* z 9.0) t) -2e+300)
    (* (* (/ t a_m) -4.5) z)
    (/ (fma (* t z) -9.0 (* y x)) (+ a_m a_m)))))

C

a\_m = fabs(a);
a\_s = copysign(1.0, a);
assert(x < y && y < z && z < t && t < a_m);
assert(x < y && y < z && z < t && t < a_m);
double code(double a_s, double x, double y, double z, double t, double a_m) {
	double tmp;
	if (((z * 9.0) * t) <= -2e+300) {
		tmp = ((t / a_m) * -4.5) * z;
	} else {
		tmp = fma((t * z), -9.0, (y * x)) / (a_m + a_m);
	}
	return a_s * tmp;
}

Julia

a\_m = abs(a)
a\_s = copysign(1.0, a)
x, y, z, t, a_m = sort([x, y, z, t, a_m])
x, y, z, t, a_m = sort([x, y, z, t, a_m])
function code(a_s, x, y, z, t, a_m)
	tmp = 0.0
	if (Float64(Float64(z * 9.0) * t) <= -2e+300)
		tmp = Float64(Float64(Float64(t / a_m) * -4.5) * z);
	else
		tmp = Float64(fma(Float64(t * z), -9.0, Float64(y * x)) / Float64(a_m + a_m));
	end
	return Float64(a_s * tmp)
end

Wolfram

a\_m = N[Abs[a], $MachinePrecision]
a\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
code[a$95$s_, x_, y_, z_, t_, a$95$m_] := N[(a$95$s * If[LessEqual[N[(N[(z * 9.0), $MachinePrecision] * t), $MachinePrecision], -2e+300], N[(N[(N[(t / a$95$m), $MachinePrecision] * -4.5), $MachinePrecision] * z), $MachinePrecision], N[(N[(N[(t * z), $MachinePrecision] * -9.0 + N[(y * x), $MachinePrecision]), $MachinePrecision] / N[(a$95$m + a$95$m), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 z #s(literal 9 binary64)) t) < -2.0000000000000001e300

   1. Initial program 43.9%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      4. *-commutative N/A

         \[\leadsto \left(\frac{x \cdot y}{a \cdot z} \cdot \frac{1}{2} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      5. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{x \cdot y}{a \cdot z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      6. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      7. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      8. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      11. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      12. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      13. lower-/.f64 90.7

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z \]

   5. Applied rewrites 90.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z} \]

   6. Taylor expanded in x around 0

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      3. lift-*.f64 95.2

         \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

   8. Applied rewrites 95.2%

      \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

   if -2.0000000000000001e300 < (*.f64 (*.f64 z #s(literal 9 binary64)) t)

   1. Initial program 91.8%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot y - \left(z \cdot 9\right) \cdot t}}{a \cdot 2} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot y} - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{x \cdot y - \color{blue}{\left(z \cdot 9\right) \cdot t}}{a \cdot 2} \]

      4. fp-cancel-sub-sign-inv N/A

         \[\leadsto \frac{\color{blue}{x \cdot y + \left(\mathsf{neg}\left(z \cdot 9\right)\right) \cdot t}}{a \cdot 2} \]

      5. +-commutative N/A

         \[\leadsto \frac{\color{blue}{\left(\mathsf{neg}\left(z \cdot 9\right)\right) \cdot t + x \cdot y}}{a \cdot 2} \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(\color{blue}{z \cdot 9}\right)\right) \cdot t + x \cdot y}{a \cdot 2} \]

      7. *-commutative N/A

         \[\leadsto \frac{\left(\mathsf{neg}\left(\color{blue}{9 \cdot z}\right)\right) \cdot t + x \cdot y}{a \cdot 2} \]

      8. distribute-lft-neg-in N/A

         \[\leadsto \frac{\color{blue}{\left(\left(\mathsf{neg}\left(9\right)\right) \cdot z\right)} \cdot t + x \cdot y}{a \cdot 2} \]

      9. metadata-eval N/A

         \[\leadsto \frac{\left(\color{blue}{-9} \cdot z\right) \cdot t + x \cdot y}{a \cdot 2} \]

      10. associate-*r* N/A

          \[\leadsto \frac{\color{blue}{-9 \cdot \left(z \cdot t\right)} + x \cdot y}{a \cdot 2} \]

      11. *-commutative N/A

          \[\leadsto \frac{-9 \cdot \color{blue}{\left(t \cdot z\right)} + x \cdot y}{a \cdot 2} \]

      12. *-commutative N/A

          \[\leadsto \frac{\color{blue}{\left(t \cdot z\right) \cdot -9} + x \cdot y}{a \cdot 2} \]

      13. lower-fma.f64 N/A

          \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(t \cdot z, -9, x \cdot y\right)}}{a \cdot 2} \]

      14. lower-*.f64 N/A

          \[\leadsto \frac{\mathsf{fma}\left(\color{blue}{t \cdot z}, -9, x \cdot y\right)}{a \cdot 2} \]

      15. *-commutative N/A

          \[\leadsto \frac{\mathsf{fma}\left(t \cdot z, -9, \color{blue}{y \cdot x}\right)}{a \cdot 2} \]

      16. lower-*.f64 91.8

          \[\leadsto \frac{\mathsf{fma}\left(t \cdot z, -9, \color{blue}{y \cdot x}\right)}{a \cdot 2} \]

   4. Applied rewrites 91.8%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(t \cdot z, -9, y \cdot x\right)}}{a \cdot 2} \]
   5. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(t \cdot z, -9, y \cdot x\right)}{\color{blue}{a \cdot 2}} \]

      2. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(t \cdot z, -9, y \cdot x\right)}{\color{blue}{2 \cdot a}} \]

      3. count-2-rev N/A

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

      4. lower-+.f64 91.8

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

   6. Applied rewrites 91.8%

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

Alternative 5: 71.2% accurate, 0.8× speedup

Math

\[\begin{array}{l} a\_m = \left|a\right| \\ a\_s = \mathsf{copysign}\left(1, a\right) \\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\\\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\ \\ a\_s \cdot \begin{array}{l} \mathbf{if}\;x \cdot y \leq -2 \cdot 10^{+73} \lor \neg \left(x \cdot y \leq 5 \cdot 10^{+24}\right):\\ \;\;\;\;\frac{y \cdot x}{a\_m \cdot 2}\\ \mathbf{else}:\\ \;\;\;\;\frac{-4.5 \cdot t}{a\_m} \cdot z\\ \end{array} \end{array} \]

FPCore

a\_m = (fabs.f64 a)
a\_s = (copysign.f64 #s(literal 1 binary64) a)
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
(FPCore (a_s x y z t a_m)
 :precision binary64
 (*
  a_s
  (if (or (<= (* x y) -2e+73) (not (<= (* x y) 5e+24)))
    (/ (* y x) (* a_m 2.0))
    (* (/ (* -4.5 t) a_m) z))))

C

a\_m = fabs(a);
a\_s = copysign(1.0, a);
assert(x < y && y < z && z < t && t < a_m);
assert(x < y && y < z && z < t && t < a_m);
double code(double a_s, double x, double y, double z, double t, double a_m) {
	double tmp;
	if (((x * y) <= -2e+73) || !((x * y) <= 5e+24)) {
		tmp = (y * x) / (a_m * 2.0);
	} else {
		tmp = ((-4.5 * t) / a_m) * z;
	}
	return a_s * tmp;
}

Fortran

a\_m =     private
a\_s =     private
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m 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(a_s, x, y, z, t, a_m)
use fmin_fmax_functions
    real(8), intent (in) :: a_s
    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_m
    real(8) :: tmp
    if (((x * y) <= (-2d+73)) .or. (.not. ((x * y) <= 5d+24))) then
        tmp = (y * x) / (a_m * 2.0d0)
    else
        tmp = (((-4.5d0) * t) / a_m) * z
    end if
    code = a_s * tmp
end function

Java

a\_m = Math.abs(a);
a\_s = Math.copySign(1.0, a);
assert x < y && y < z && z < t && t < a_m;
assert x < y && y < z && z < t && t < a_m;
public static double code(double a_s, double x, double y, double z, double t, double a_m) {
	double tmp;
	if (((x * y) <= -2e+73) || !((x * y) <= 5e+24)) {
		tmp = (y * x) / (a_m * 2.0);
	} else {
		tmp = ((-4.5 * t) / a_m) * z;
	}
	return a_s * tmp;
}

Python

a\_m = math.fabs(a)
a\_s = math.copysign(1.0, a)
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
def code(a_s, x, y, z, t, a_m):
	tmp = 0
	if ((x * y) <= -2e+73) or not ((x * y) <= 5e+24):
		tmp = (y * x) / (a_m * 2.0)
	else:
		tmp = ((-4.5 * t) / a_m) * z
	return a_s * tmp

Julia

a\_m = abs(a)
a\_s = copysign(1.0, a)
x, y, z, t, a_m = sort([x, y, z, t, a_m])
x, y, z, t, a_m = sort([x, y, z, t, a_m])
function code(a_s, x, y, z, t, a_m)
	tmp = 0.0
	if ((Float64(x * y) <= -2e+73) || !(Float64(x * y) <= 5e+24))
		tmp = Float64(Float64(y * x) / Float64(a_m * 2.0));
	else
		tmp = Float64(Float64(Float64(-4.5 * t) / a_m) * z);
	end
	return Float64(a_s * tmp)
end

MATLAB

a\_m = abs(a);
a\_s = sign(a) * abs(1.0);
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
function tmp_2 = code(a_s, x, y, z, t, a_m)
	tmp = 0.0;
	if (((x * y) <= -2e+73) || ~(((x * y) <= 5e+24)))
		tmp = (y * x) / (a_m * 2.0);
	else
		tmp = ((-4.5 * t) / a_m) * z;
	end
	tmp_2 = a_s * tmp;
end

Wolfram

a\_m = N[Abs[a], $MachinePrecision]
a\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
code[a$95$s_, x_, y_, z_, t_, a$95$m_] := N[(a$95$s * If[Or[LessEqual[N[(x * y), $MachinePrecision], -2e+73], N[Not[LessEqual[N[(x * y), $MachinePrecision], 5e+24]], $MachinePrecision]], N[(N[(y * x), $MachinePrecision] / N[(a$95$m * 2.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(-4.5 * t), $MachinePrecision] / a$95$m), $MachinePrecision] * z), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x y) < -1.99999999999999997e73 or 5.00000000000000045e24 < (*.f64 x y)

   1. Initial program 83.8%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

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

      1. *-commutative N/A

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

      2. lower-*.f64 72.6

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

   5. Applied rewrites 72.6%

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

   if -1.99999999999999997e73 < (*.f64 x y) < 5.00000000000000045e24

   1. Initial program 90.8%

      \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
   2. Add Preprocessing

   3. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

      3. +-commutative N/A

         \[\leadsto \left(\frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      4. *-commutative N/A

         \[\leadsto \left(\frac{x \cdot y}{a \cdot z} \cdot \frac{1}{2} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      5. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{x \cdot y}{a \cdot z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      6. associate-/r* N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      7. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      8. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      11. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      12. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      13. lower-/.f64 88.0

          \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z \]

   5. Applied rewrites 88.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z} \]

   6. Taylor expanded in x around 0

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]
   7. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      3. lift-*.f64 72.9

         \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

   8. Applied rewrites 72.9%

      \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      2. lift-/.f64 N/A

         \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

      3. *-commutative N/A

         \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

      4. associate-*r/ N/A

         \[\leadsto \frac{\frac{-9}{2} \cdot t}{a} \cdot z \]

      5. lower-/.f64 N/A

         \[\leadsto \frac{\frac{-9}{2} \cdot t}{a} \cdot z \]

      6. lift-*.f64 72.9

         \[\leadsto \frac{-4.5 \cdot t}{a} \cdot z \]

   10. Applied rewrites 72.9%

       \[\leadsto \frac{-4.5 \cdot t}{a} \cdot z \]
3. Recombined 2 regimes into one program.

4. Final simplification 72.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot y \leq -2 \cdot 10^{+73} \lor \neg \left(x \cdot y \leq 5 \cdot 10^{+24}\right):\\ \;\;\;\;\frac{y \cdot x}{a \cdot 2}\\ \mathbf{else}:\\ \;\;\;\;\frac{-4.5 \cdot t}{a} \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 51.5% accurate, 1.6× speedup

Math

\[\begin{array}{l} a\_m = \left|a\right| \\ a\_s = \mathsf{copysign}\left(1, a\right) \\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\\\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\ \\ a\_s \cdot \left(\left(-4.5 \cdot t\right) \cdot \frac{z}{a\_m}\right) \end{array} \]

FPCore

a\_m = (fabs.f64 a)
a\_s = (copysign.f64 #s(literal 1 binary64) a)
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
(FPCore (a_s x y z t a_m) :precision binary64 (* a_s (* (* -4.5 t) (/ z a_m))))

C

a\_m = fabs(a);
a\_s = copysign(1.0, a);
assert(x < y && y < z && z < t && t < a_m);
assert(x < y && y < z && z < t && t < a_m);
double code(double a_s, double x, double y, double z, double t, double a_m) {
	return a_s * ((-4.5 * t) * (z / a_m));
}

Fortran

a\_m =     private
a\_s =     private
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m 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(a_s, x, y, z, t, a_m)
use fmin_fmax_functions
    real(8), intent (in) :: a_s
    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_m
    code = a_s * (((-4.5d0) * t) * (z / a_m))
end function

Java

a\_m = Math.abs(a);
a\_s = Math.copySign(1.0, a);
assert x < y && y < z && z < t && t < a_m;
assert x < y && y < z && z < t && t < a_m;
public static double code(double a_s, double x, double y, double z, double t, double a_m) {
	return a_s * ((-4.5 * t) * (z / a_m));
}

Python

a\_m = math.fabs(a)
a\_s = math.copysign(1.0, a)
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
def code(a_s, x, y, z, t, a_m):
	return a_s * ((-4.5 * t) * (z / a_m))

Julia

a\_m = abs(a)
a\_s = copysign(1.0, a)
x, y, z, t, a_m = sort([x, y, z, t, a_m])
x, y, z, t, a_m = sort([x, y, z, t, a_m])
function code(a_s, x, y, z, t, a_m)
	return Float64(a_s * Float64(Float64(-4.5 * t) * Float64(z / a_m)))
end

MATLAB

a\_m = abs(a);
a\_s = sign(a) * abs(1.0);
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
function tmp = code(a_s, x, y, z, t, a_m)
	tmp = a_s * ((-4.5 * t) * (z / a_m));
end

Wolfram

a\_m = N[Abs[a], $MachinePrecision]
a\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
code[a$95$s_, x_, y_, z_, t_, a$95$m_] := N[(a$95$s * N[(N[(-4.5 * t), $MachinePrecision] * N[(z / a$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.7%

   \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{-9}{2} \cdot \frac{t \cdot z}{a}} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{t \cdot z}{a} \cdot \color{blue}{\frac{-9}{2}} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{t \cdot z}{a} \cdot \color{blue}{\frac{-9}{2}} \]

   3. lower-/.f64 N/A

      \[\leadsto \frac{t \cdot z}{a} \cdot \frac{-9}{2} \]

   4. lower-*.f64 46.2

      \[\leadsto \frac{t \cdot z}{a} \cdot -4.5 \]

5. Applied rewrites 46.2%

   \[\leadsto \color{blue}{\frac{t \cdot z}{a} \cdot -4.5} \]
6. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \frac{t \cdot z}{a} \cdot \color{blue}{\frac{-9}{2}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{t \cdot z}{a} \cdot \frac{-9}{2} \]

   3. lift-/.f64 N/A

      \[\leadsto \frac{t \cdot z}{a} \cdot \frac{-9}{2} \]

   4. associate-*l/ N/A

      \[\leadsto \frac{\left(t \cdot z\right) \cdot \frac{-9}{2}}{\color{blue}{a}} \]

   5. *-commutative N/A

      \[\leadsto \frac{\frac{-9}{2} \cdot \left(t \cdot z\right)}{a} \]

   6. associate-*r* N/A

      \[\leadsto \frac{\left(\frac{-9}{2} \cdot t\right) \cdot z}{a} \]

   7. lift-*.f64 N/A

      \[\leadsto \frac{\left(\frac{-9}{2} \cdot t\right) \cdot z}{a} \]

   8. lift-*.f64 N/A

      \[\leadsto \frac{\left(\frac{-9}{2} \cdot t\right) \cdot z}{a} \]

   9. lower-/.f64 46.3

      \[\leadsto \frac{\left(-4.5 \cdot t\right) \cdot z}{\color{blue}{a}} \]

7. Applied rewrites 46.3%

   \[\leadsto \frac{\left(-4.5 \cdot t\right) \cdot z}{\color{blue}{a}} \]
8. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \frac{\left(\frac{-9}{2} \cdot t\right) \cdot z}{\color{blue}{a}} \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{\left(\frac{-9}{2} \cdot t\right) \cdot z}{a} \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{\left(\frac{-9}{2} \cdot t\right) \cdot z}{a} \]

   4. associate-/l* N/A

      \[\leadsto \left(\frac{-9}{2} \cdot t\right) \cdot \color{blue}{\frac{z}{a}} \]

   5. lower-*.f64 N/A

      \[\leadsto \left(\frac{-9}{2} \cdot t\right) \cdot \color{blue}{\frac{z}{a}} \]

   6. lift-*.f64 N/A

      \[\leadsto \left(\frac{-9}{2} \cdot t\right) \cdot \frac{\color{blue}{z}}{a} \]

   7. lower-/.f64 50.0

      \[\leadsto \left(-4.5 \cdot t\right) \cdot \frac{z}{\color{blue}{a}} \]

9. Applied rewrites 50.0%

   \[\leadsto \left(-4.5 \cdot t\right) \cdot \color{blue}{\frac{z}{a}} \]
10. Add Preprocessing

Alternative 7: 51.8% accurate, 1.6× speedup

Math

\[\begin{array}{l} a\_m = \left|a\right| \\ a\_s = \mathsf{copysign}\left(1, a\right) \\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\\\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\ \\ a\_s \cdot \left(\frac{-4.5 \cdot t}{a\_m} \cdot z\right) \end{array} \]

FPCore

a\_m = (fabs.f64 a)
a\_s = (copysign.f64 #s(literal 1 binary64) a)
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
(FPCore (a_s x y z t a_m) :precision binary64 (* a_s (* (/ (* -4.5 t) a_m) z)))

C

a\_m = fabs(a);
a\_s = copysign(1.0, a);
assert(x < y && y < z && z < t && t < a_m);
assert(x < y && y < z && z < t && t < a_m);
double code(double a_s, double x, double y, double z, double t, double a_m) {
	return a_s * (((-4.5 * t) / a_m) * z);
}

Fortran

a\_m =     private
a\_s =     private
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m 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(a_s, x, y, z, t, a_m)
use fmin_fmax_functions
    real(8), intent (in) :: a_s
    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_m
    code = a_s * ((((-4.5d0) * t) / a_m) * z)
end function

Java

a\_m = Math.abs(a);
a\_s = Math.copySign(1.0, a);
assert x < y && y < z && z < t && t < a_m;
assert x < y && y < z && z < t && t < a_m;
public static double code(double a_s, double x, double y, double z, double t, double a_m) {
	return a_s * (((-4.5 * t) / a_m) * z);
}

Python

a\_m = math.fabs(a)
a\_s = math.copysign(1.0, a)
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
def code(a_s, x, y, z, t, a_m):
	return a_s * (((-4.5 * t) / a_m) * z)

Julia

a\_m = abs(a)
a\_s = copysign(1.0, a)
x, y, z, t, a_m = sort([x, y, z, t, a_m])
x, y, z, t, a_m = sort([x, y, z, t, a_m])
function code(a_s, x, y, z, t, a_m)
	return Float64(a_s * Float64(Float64(Float64(-4.5 * t) / a_m) * z))
end

MATLAB

a\_m = abs(a);
a\_s = sign(a) * abs(1.0);
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
function tmp = code(a_s, x, y, z, t, a_m)
	tmp = a_s * (((-4.5 * t) / a_m) * z);
end

Wolfram

a\_m = N[Abs[a], $MachinePrecision]
a\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
code[a$95$s_, x_, y_, z_, t_, a$95$m_] := N[(a$95$s * N[(N[(N[(-4.5 * t), $MachinePrecision] / a$95$m), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.7%

   \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
2. Add Preprocessing

3. Taylor expanded in z around inf

   \[\leadsto \color{blue}{z \cdot \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

   2. lower-*.f64 N/A

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

   3. +-commutative N/A

      \[\leadsto \left(\frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   4. *-commutative N/A

      \[\leadsto \left(\frac{x \cdot y}{a \cdot z} \cdot \frac{1}{2} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   5. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{x \cdot y}{a \cdot z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   6. associate-/r* N/A

      \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   7. lower-/.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   8. lower-/.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   9. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   10. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   11. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   12. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   13. lower-/.f64 79.0

       \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z \]

5. Applied rewrites 79.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z} \]

6. Taylor expanded in x around 0

   \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]
7. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   2. lift-/.f64 N/A

      \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   3. lift-*.f64 50.3

      \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

8. Applied rewrites 50.3%

   \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]
9. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   2. lift-/.f64 N/A

      \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   3. *-commutative N/A

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   4. associate-*r/ N/A

      \[\leadsto \frac{\frac{-9}{2} \cdot t}{a} \cdot z \]

   5. lower-/.f64 N/A

      \[\leadsto \frac{\frac{-9}{2} \cdot t}{a} \cdot z \]

   6. lift-*.f64 50.2

      \[\leadsto \frac{-4.5 \cdot t}{a} \cdot z \]

10. Applied rewrites 50.2%

    \[\leadsto \frac{-4.5 \cdot t}{a} \cdot z \]
11. Add Preprocessing

Alternative 8: 51.8% accurate, 1.6× speedup

Math

\[\begin{array}{l} a\_m = \left|a\right| \\ a\_s = \mathsf{copysign}\left(1, a\right) \\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\\\ [x, y, z, t, a_m] = \mathsf{sort}([x, y, z, t, a_m])\\ \\ a\_s \cdot \left(\left(\frac{t}{a\_m} \cdot -4.5\right) \cdot z\right) \end{array} \]

FPCore

a\_m = (fabs.f64 a)
a\_s = (copysign.f64 #s(literal 1 binary64) a)
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
(FPCore (a_s x y z t a_m) :precision binary64 (* a_s (* (* (/ t a_m) -4.5) z)))

C

a\_m = fabs(a);
a\_s = copysign(1.0, a);
assert(x < y && y < z && z < t && t < a_m);
assert(x < y && y < z && z < t && t < a_m);
double code(double a_s, double x, double y, double z, double t, double a_m) {
	return a_s * (((t / a_m) * -4.5) * z);
}

Fortran

a\_m =     private
a\_s =     private
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m 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(a_s, x, y, z, t, a_m)
use fmin_fmax_functions
    real(8), intent (in) :: a_s
    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_m
    code = a_s * (((t / a_m) * (-4.5d0)) * z)
end function

Java

a\_m = Math.abs(a);
a\_s = Math.copySign(1.0, a);
assert x < y && y < z && z < t && t < a_m;
assert x < y && y < z && z < t && t < a_m;
public static double code(double a_s, double x, double y, double z, double t, double a_m) {
	return a_s * (((t / a_m) * -4.5) * z);
}

Python

a\_m = math.fabs(a)
a\_s = math.copysign(1.0, a)
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
[x, y, z, t, a_m] = sort([x, y, z, t, a_m])
def code(a_s, x, y, z, t, a_m):
	return a_s * (((t / a_m) * -4.5) * z)

Julia

a\_m = abs(a)
a\_s = copysign(1.0, a)
x, y, z, t, a_m = sort([x, y, z, t, a_m])
x, y, z, t, a_m = sort([x, y, z, t, a_m])
function code(a_s, x, y, z, t, a_m)
	return Float64(a_s * Float64(Float64(Float64(t / a_m) * -4.5) * z))
end

MATLAB

a\_m = abs(a);
a\_s = sign(a) * abs(1.0);
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
x, y, z, t, a_m = num2cell(sort([x, y, z, t, a_m])){:}
function tmp = code(a_s, x, y, z, t, a_m)
	tmp = a_s * (((t / a_m) * -4.5) * z);
end

Wolfram

a\_m = N[Abs[a], $MachinePrecision]
a\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[a]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
NOTE: x, y, z, t, and a_m should be sorted in increasing order before calling this function.
code[a$95$s_, x_, y_, z_, t_, a$95$m_] := N[(a$95$s * N[(N[(N[(t / a$95$m), $MachinePrecision] * -4.5), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 87.7%

   \[\frac{x \cdot y - \left(z \cdot 9\right) \cdot t}{a \cdot 2} \]
2. Add Preprocessing

3. Taylor expanded in z around inf

   \[\leadsto \color{blue}{z \cdot \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

   2. lower-*.f64 N/A

      \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a} + \frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z}\right) \cdot \color{blue}{z} \]

   3. +-commutative N/A

      \[\leadsto \left(\frac{1}{2} \cdot \frac{x \cdot y}{a \cdot z} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   4. *-commutative N/A

      \[\leadsto \left(\frac{x \cdot y}{a \cdot z} \cdot \frac{1}{2} + \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   5. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{x \cdot y}{a \cdot z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   6. associate-/r* N/A

      \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   7. lower-/.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   8. lower-/.f64 N/A

      \[\leadsto \mathsf{fma}\left(\frac{\frac{x \cdot y}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   9. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   10. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]

   11. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   12. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, \frac{1}{2}, \frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   13. lower-/.f64 79.0

       \[\leadsto \mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z \]

5. Applied rewrites 79.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\frac{y \cdot x}{a}}{z}, 0.5, \frac{t}{a} \cdot -4.5\right) \cdot z} \]

6. Taylor expanded in x around 0

   \[\leadsto \left(\frac{-9}{2} \cdot \frac{t}{a}\right) \cdot z \]
7. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   2. lift-/.f64 N/A

      \[\leadsto \left(\frac{t}{a} \cdot \frac{-9}{2}\right) \cdot z \]

   3. lift-*.f64 50.3

      \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]

8. Applied rewrites 50.3%

   \[\leadsto \left(\frac{t}{a} \cdot -4.5\right) \cdot z \]
9. Add Preprocessing

Developer Target 1: 94.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a < -2.090464557976709 \cdot 10^{+86}:\\ \;\;\;\;0.5 \cdot \frac{y \cdot x}{a} - 4.5 \cdot \frac{t}{\frac{a}{z}}\\ \mathbf{elif}\;a < 2.144030707833976 \cdot 10^{+99}:\\ \;\;\;\;\frac{x \cdot y - z \cdot \left(9 \cdot t\right)}{a \cdot 2}\\ \mathbf{else}:\\ \;\;\;\;\frac{y}{a} \cdot \left(x \cdot 0.5\right) - \frac{t}{a} \cdot \left(z \cdot 4.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a)
 :precision binary64
 (if (< a -2.090464557976709e+86)
   (- (* 0.5 (/ (* y x) a)) (* 4.5 (/ t (/ a z))))
   (if (< a 2.144030707833976e+99)
     (/ (- (* x y) (* z (* 9.0 t))) (* a 2.0))
     (- (* (/ y a) (* x 0.5)) (* (/ t a) (* z 4.5))))))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a < -2.090464557976709e+86) {
		tmp = (0.5 * ((y * x) / a)) - (4.5 * (t / (a / z)));
	} else if (a < 2.144030707833976e+99) {
		tmp = ((x * y) - (z * (9.0 * t))) / (a * 2.0);
	} else {
		tmp = ((y / a) * (x * 0.5)) - ((t / a) * (z * 4.5));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8) :: tmp
    if (a < (-2.090464557976709d+86)) then
        tmp = (0.5d0 * ((y * x) / a)) - (4.5d0 * (t / (a / z)))
    else if (a < 2.144030707833976d+99) then
        tmp = ((x * y) - (z * (9.0d0 * t))) / (a * 2.0d0)
    else
        tmp = ((y / a) * (x * 0.5d0)) - ((t / a) * (z * 4.5d0))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (a < -2.090464557976709e+86) {
		tmp = (0.5 * ((y * x) / a)) - (4.5 * (t / (a / z)));
	} else if (a < 2.144030707833976e+99) {
		tmp = ((x * y) - (z * (9.0 * t))) / (a * 2.0);
	} else {
		tmp = ((y / a) * (x * 0.5)) - ((t / a) * (z * 4.5));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if a < -2.090464557976709e+86:
		tmp = (0.5 * ((y * x) / a)) - (4.5 * (t / (a / z)))
	elif a < 2.144030707833976e+99:
		tmp = ((x * y) - (z * (9.0 * t))) / (a * 2.0)
	else:
		tmp = ((y / a) * (x * 0.5)) - ((t / a) * (z * 4.5))
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (a < -2.090464557976709e+86)
		tmp = Float64(Float64(0.5 * Float64(Float64(y * x) / a)) - Float64(4.5 * Float64(t / Float64(a / z))));
	elseif (a < 2.144030707833976e+99)
		tmp = Float64(Float64(Float64(x * y) - Float64(z * Float64(9.0 * t))) / Float64(a * 2.0));
	else
		tmp = Float64(Float64(Float64(y / a) * Float64(x * 0.5)) - Float64(Float64(t / a) * Float64(z * 4.5)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (a < -2.090464557976709e+86)
		tmp = (0.5 * ((y * x) / a)) - (4.5 * (t / (a / z)));
	elseif (a < 2.144030707833976e+99)
		tmp = ((x * y) - (z * (9.0 * t))) / (a * 2.0);
	else
		tmp = ((y / a) * (x * 0.5)) - ((t / a) * (z * 4.5));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[Less[a, -2.090464557976709e+86], N[(N[(0.5 * N[(N[(y * x), $MachinePrecision] / a), $MachinePrecision]), $MachinePrecision] - N[(4.5 * N[(t / N[(a / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[Less[a, 2.144030707833976e+99], N[(N[(N[(x * y), $MachinePrecision] - N[(z * N[(9.0 * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(a * 2.0), $MachinePrecision]), $MachinePrecision], N[(N[(N[(y / a), $MachinePrecision] * N[(x * 0.5), $MachinePrecision]), $MachinePrecision] - N[(N[(t / a), $MachinePrecision] * N[(z * 4.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Reproduce

herbie shell --seed 2025064 
(FPCore (x y z t a)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, I"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< a -209046455797670900000000000000000000000000000000000000000000000000000000000000000000000) (- (* 1/2 (/ (* y x) a)) (* 9/2 (/ t (/ a z)))) (if (< a 2144030707833976000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (/ (- (* x y) (* z (* 9 t))) (* a 2)) (- (* (/ y a) (* x 1/2)) (* (/ t a) (* z 9/2))))))

  (/ (- (* x y) (* (* z 9.0) t)) (* a 2.0)))