Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendInside from plot-0.2.3.4

Percentage Accurate: 99.9% → 99.9%

Time: 4.5s

Alternatives: 7

Speedup: 1.3×

Specification

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (+ (+ (+ (+ x y) y) x) z) x))

C

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

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((((x + y) + y) + x) + z) + x
end function

Java

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

Python

def code(x, y, z):
	return ((((x + y) + y) + x) + z) + x

Julia

function code(x, y, z)
	return Float64(Float64(Float64(Float64(Float64(x + y) + y) + x) + z) + x)
end

MATLAB

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

Wolfram

code[x_, y_, z_] := N[(N[(N[(N[(N[(x + y), $MachinePrecision] + y), $MachinePrecision] + x), $MachinePrecision] + z), $MachinePrecision] + x), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (+ (+ (+ (+ x y) y) x) z) x))

C

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

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((((x + y) + y) + x) + z) + x
end function

Java

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

Python

def code(x, y, z):
	return ((((x + y) + y) + x) + z) + x

Julia

function code(x, y, z)
	return Float64(Float64(Float64(Float64(Float64(x + y) + y) + x) + z) + x)
end

MATLAB

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

Wolfram

code[x_, y_, z_] := N[(N[(N[(N[(N[(x + y), $MachinePrecision] + y), $MachinePrecision] + x), $MachinePrecision] + z), $MachinePrecision] + x), $MachinePrecision]

Alternative 1: 99.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{x}{\mathsf{fma}\left(2, y + x, z\right)} + 1\\ \mathsf{fma}\left(t\_0, 2 \cdot \left(y + x\right), t\_0 \cdot z\right) \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ (/ x (fma 2.0 (+ y x) z)) 1.0)))
   (fma t_0 (* 2.0 (+ y x)) (* t_0 z))))

C

double code(double x, double y, double z) {
	double t_0 = (x / fma(2.0, (y + x), z)) + 1.0;
	return fma(t_0, (2.0 * (y + x)), (t_0 * z));
}

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x / fma(2.0, Float64(y + x), z)) + 1.0)
	return fma(t_0, Float64(2.0 * Float64(y + x)), Float64(t_0 * z))
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x / N[(2.0 * N[(y + x), $MachinePrecision] + z), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]}, N[(t$95$0 * N[(2.0 * N[(y + x), $MachinePrecision]), $MachinePrecision] + N[(t$95$0 * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. sum-to-mult N/A

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

   3. lift-+.f64 N/A

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

   4. distribute-lft-in N/A

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

   5. lower-fma.f64 N/A

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

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{x}{\mathsf{fma}\left(2, y + x, z\right)} + 1, 2 \cdot \left(y + x\right), \left(\frac{x}{\mathsf{fma}\left(2, y + x, z\right)} + 1\right) \cdot z\right)} \]
4. Add Preprocessing

Alternative 2: 99.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ z + \mathsf{fma}\left(2, y + x, x\right) \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ z (fma 2.0 (+ y x) x)))

C

double code(double x, double y, double z) {
	return z + fma(2.0, (y + x), x);
}

Julia

function code(x, y, z)
	return Float64(z + fma(2.0, Float64(y + x), x))
end

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift-+.f64 N/A

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

   3. +-commutative N/A

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

   4. associate-+l+ N/A

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

   5. lower-+.f64 N/A

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

   6. lift-+.f64 N/A

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

   7. lift-+.f64 N/A

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

   8. associate-+l+ N/A

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

   9. +-commutative N/A

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

   10. lift-+.f64 N/A

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

   11. count-2 N/A

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

   12. lower-fma.f64 99.9

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

   13. lift-+.f64 N/A

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

   14. +-commutative N/A

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

   15. lower-+.f64 99.9

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

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{z + \mathsf{fma}\left(2, y + x, x\right)} \]
4. Add Preprocessing

Alternative 3: 87.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(y + x, 2, z\right)\\ \mathbf{if}\;y \leq -3.1 \cdot 10^{+31}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-14}:\\ \;\;\;\;3 \cdot x + z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fma (+ y x) 2.0 z)))
   (if (<= y -3.1e+31) t_0 (if (<= y 2.25e-14) (+ (* 3.0 x) z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fma((y + x), 2.0, z);
	double tmp;
	if (y <= -3.1e+31) {
		tmp = t_0;
	} else if (y <= 2.25e-14) {
		tmp = (3.0 * x) + z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = fma(Float64(y + x), 2.0, z)
	tmp = 0.0
	if (y <= -3.1e+31)
		tmp = t_0;
	elseif (y <= 2.25e-14)
		tmp = Float64(Float64(3.0 * x) + z);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(y + x), $MachinePrecision] * 2.0 + z), $MachinePrecision]}, If[LessEqual[y, -3.1e+31], t$95$0, If[LessEqual[y, 2.25e-14], N[(N[(3.0 * x), $MachinePrecision] + z), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.1000000000000002e31 or 2.2499999999999999e-14 < y

   1. Initial program 99.9%

      \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. sum-to-mult N/A

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

      3. lift-+.f64 N/A

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

      4. distribute-lft-in N/A

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

      5. lower-fma.f64 N/A

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

   3. Applied rewrites 99.9%

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

      1. lift-fma.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. distribute-lft-out N/A

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

      4. lift-+.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-/.f64 N/A

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

      7. lift-fma.f64 N/A

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

      8. count-2-rev N/A

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

      9. lift-+.f64 N/A

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

      10. +-commutative N/A

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

      11. lift-+.f64 N/A

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

      12. associate-+l+ N/A

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

      13. lift-*.f64 N/A

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

      14. count-2-rev N/A

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

      15. lift-+.f64 N/A

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

      16. +-commutative N/A

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

      17. lift-+.f64 N/A

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

      18. associate-+l+ N/A

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

   5. Applied rewrites 99.9%

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

   6. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(y + x, 2, \color{blue}{z}\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 72.6%

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

   if -3.1000000000000002e31 < y < 2.2499999999999999e-14

   1. Initial program 99.9%

      \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate-+l+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-+.f64 N/A

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

      7. lift-+.f64 N/A

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

      8. associate-+l+ N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. count-2 N/A

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

      12. lower-fma.f64 99.9

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in y around 0

      \[\leadsto z + \color{blue}{\left(x + 2 \cdot x\right)} \]
   5. Step-by-step derivation

      1. lower-+.f64 N/A

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

      2. lower-*.f64 66.5

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

   6. Applied rewrites 66.5%

      \[\leadsto z + \color{blue}{\left(x + 2 \cdot x\right)} \]
   7. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 66.5

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. distribute-rgt1-in N/A

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

      7. metadata-eval N/A

         \[\leadsto 3 \cdot x + z \]

      8. lower-*.f64 66.5

         \[\leadsto 3 \cdot \color{blue}{x} + z \]

   8. Applied rewrites 66.5%

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

Alternative 4: 86.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := z + \mathsf{fma}\left(2, y, x\right)\\ \mathbf{if}\;y \leq -8.2 \cdot 10^{+32}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 2.25 \cdot 10^{-14}:\\ \;\;\;\;3 \cdot x + z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (+ z (fma 2.0 y x))))
   (if (<= y -8.2e+32) t_0 (if (<= y 2.25e-14) (+ (* 3.0 x) z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = z + fma(2.0, y, x);
	double tmp;
	if (y <= -8.2e+32) {
		tmp = t_0;
	} else if (y <= 2.25e-14) {
		tmp = (3.0 * x) + z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(z + fma(2.0, y, x))
	tmp = 0.0
	if (y <= -8.2e+32)
		tmp = t_0;
	elseif (y <= 2.25e-14)
		tmp = Float64(Float64(3.0 * x) + z);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(z + N[(2.0 * y + x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -8.2e+32], t$95$0, If[LessEqual[y, 2.25e-14], N[(N[(3.0 * x), $MachinePrecision] + z), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -8.19999999999999961e32 or 2.2499999999999999e-14 < y

   1. Initial program 99.9%

      \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate-+l+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-+.f64 N/A

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

      7. lift-+.f64 N/A

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

      8. associate-+l+ N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. count-2 N/A

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

      12. lower-fma.f64 99.9

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in x around 0

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

   6. Applied rewrites 71.8%

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

   if -8.19999999999999961e32 < y < 2.2499999999999999e-14

   1. Initial program 99.9%

      \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate-+l+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-+.f64 N/A

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

      7. lift-+.f64 N/A

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

      8. associate-+l+ N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. count-2 N/A

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

      12. lower-fma.f64 99.9

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in y around 0

      \[\leadsto z + \color{blue}{\left(x + 2 \cdot x\right)} \]
   5. Step-by-step derivation

      1. lower-+.f64 N/A

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

      2. lower-*.f64 66.5

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

   6. Applied rewrites 66.5%

      \[\leadsto z + \color{blue}{\left(x + 2 \cdot x\right)} \]
   7. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 66.5

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. distribute-rgt1-in N/A

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

      7. metadata-eval N/A

         \[\leadsto 3 \cdot x + z \]

      8. lower-*.f64 66.5

         \[\leadsto 3 \cdot \color{blue}{x} + z \]

   8. Applied rewrites 66.5%

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

Alternative 5: 85.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.8 \cdot 10^{+65}:\\ \;\;\;\;\mathsf{fma}\left(y, 2, z\right)\\ \mathbf{elif}\;y \leq 2.75 \cdot 10^{-14}:\\ \;\;\;\;3 \cdot x + z\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(y, 2, z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= y -7.8e+65)
   (fma y 2.0 z)
   (if (<= y 2.75e-14) (+ (* 3.0 x) z) (fma y 2.0 z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (y <= -7.8e+65) {
		tmp = fma(y, 2.0, z);
	} else if (y <= 2.75e-14) {
		tmp = (3.0 * x) + z;
	} else {
		tmp = fma(y, 2.0, z);
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (y <= -7.8e+65)
		tmp = fma(y, 2.0, z);
	elseif (y <= 2.75e-14)
		tmp = Float64(Float64(3.0 * x) + z);
	else
		tmp = fma(y, 2.0, z);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[y, -7.8e+65], N[(y * 2.0 + z), $MachinePrecision], If[LessEqual[y, 2.75e-14], N[(N[(3.0 * x), $MachinePrecision] + z), $MachinePrecision], N[(y * 2.0 + z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if y < -7.7999999999999996e65 or 2.74999999999999996e-14 < y

   1. Initial program 99.9%

      \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]

   2. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 67.0

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

   4. Applied rewrites 67.0%

      \[\leadsto \color{blue}{z + 2 \cdot y} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

         \[\leadsto 2 \cdot y + z \]

      4. *-commutative N/A

         \[\leadsto y \cdot 2 + z \]

      5. lower-fma.f64 67.0

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

   6. Applied rewrites 67.0%

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

   if -7.7999999999999996e65 < y < 2.74999999999999996e-14

   1. Initial program 99.9%

      \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. associate-+l+ N/A

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

      5. lower-+.f64 N/A

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

      6. lift-+.f64 N/A

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

      7. lift-+.f64 N/A

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

      8. associate-+l+ N/A

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

      9. +-commutative N/A

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

      10. lift-+.f64 N/A

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

      11. count-2 N/A

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

      12. lower-fma.f64 99.9

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in y around 0

      \[\leadsto z + \color{blue}{\left(x + 2 \cdot x\right)} \]
   5. Step-by-step derivation

      1. lower-+.f64 N/A

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

      2. lower-*.f64 66.5

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

   6. Applied rewrites 66.5%

      \[\leadsto z + \color{blue}{\left(x + 2 \cdot x\right)} \]
   7. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 66.5

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. distribute-rgt1-in N/A

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

      7. metadata-eval N/A

         \[\leadsto 3 \cdot x + z \]

      8. lower-*.f64 66.5

         \[\leadsto 3 \cdot \color{blue}{x} + z \]

   8. Applied rewrites 66.5%

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

Alternative 6: 79.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3 \cdot 10^{+122}:\\ \;\;\;\;3 \cdot x\\ \mathbf{elif}\;x \leq 4.5 \cdot 10^{+181}:\\ \;\;\;\;\mathsf{fma}\left(y, 2, z\right)\\ \mathbf{else}:\\ \;\;\;\;3 \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -3e+122) (* 3.0 x) (if (<= x 4.5e+181) (fma y 2.0 z) (* 3.0 x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -3e+122) {
		tmp = 3.0 * x;
	} else if (x <= 4.5e+181) {
		tmp = fma(y, 2.0, z);
	} else {
		tmp = 3.0 * x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -3e+122)
		tmp = Float64(3.0 * x);
	elseif (x <= 4.5e+181)
		tmp = fma(y, 2.0, z);
	else
		tmp = Float64(3.0 * x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -3e+122], N[(3.0 * x), $MachinePrecision], If[LessEqual[x, 4.5e+181], N[(y * 2.0 + z), $MachinePrecision], N[(3.0 * x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -2.99999999999999986e122 or 4.5e181 < x

   1. Initial program 99.9%

      \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]

   2. Taylor expanded in x around inf

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

      1. lower-*.f64 33.9

         \[\leadsto 3 \cdot \color{blue}{x} \]

   4. Applied rewrites 33.9%

      \[\leadsto \color{blue}{3 \cdot x} \]

   if -2.99999999999999986e122 < x < 4.5e181

   1. Initial program 99.9%

      \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]

   2. Taylor expanded in x around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 67.0

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

   4. Applied rewrites 67.0%

      \[\leadsto \color{blue}{z + 2 \cdot y} \]
   5. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

         \[\leadsto 2 \cdot y + z \]

      4. *-commutative N/A

         \[\leadsto y \cdot 2 + z \]

      5. lower-fma.f64 67.0

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

   6. Applied rewrites 67.0%

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

Alternative 7: 33.9% accurate, 3.7× speedup

Math

\[\begin{array}{l} \\ 3 \cdot x \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (* 3.0 x))

C

double code(double x, double y, double z) {
	return 3.0 * x;
}

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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 3.0d0 * x
end function

Java

public static double code(double x, double y, double z) {
	return 3.0 * x;
}

Python

def code(x, y, z):
	return 3.0 * x

Julia

function code(x, y, z)
	return Float64(3.0 * x)
end

MATLAB

function tmp = code(x, y, z)
	tmp = 3.0 * x;
end

Wolfram

code[x_, y_, z_] := N[(3.0 * x), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(\left(x + y\right) + y\right) + x\right) + z\right) + x \]

2. Taylor expanded in x around inf

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

   1. lower-*.f64 33.9

      \[\leadsto 3 \cdot \color{blue}{x} \]

4. Applied rewrites 33.9%

   \[\leadsto \color{blue}{3 \cdot x} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2025142 
(FPCore (x y z)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendInside from plot-0.2.3.4"
  :precision binary64
  (+ (+ (+ (+ (+ x y) y) x) z) x))