Diagrams.Backend.Cairo.Internal:setTexture from diagrams-cairo-1.3.0.3

Percentage Accurate: 84.5% → 99.7%

Time: 21.4s

Alternatives: 5

Speedup: 1.0×

Specification

Math

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

FPCore

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

C

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

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 - z)) / y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 84.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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 - z)) / y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.7% accurate, 0.6× speedup

Math

\[\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{y}\right), y, x, z, x\right) \]

FPCore

(FPCore (x y z)
  :precision binary64
  (134-z0z1z2z3z4 (/ 1 y) y x z x))

Derivation

1. Initial program 84.5%

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

   1. lift-/.f64 N/A

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

   2. mult-flip N/A

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

   3. *-commutative N/A

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

   4. lift-*.f64 N/A

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

   5. lift--.f64 N/A

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

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

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

   7. lower-134-z0z1z2z3z4 N/A

      \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{y}\right), y, x, z, x\right)} \]

   8. lower-/.f64 99.7%

      \[\leadsto \mathsf{134\_z0z1z2z3z4}\left(\color{blue}{\left(\frac{1}{y}\right)}, y, x, z, x\right) \]

3. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\mathsf{134\_z0z1z2z3z4}\left(\left(\frac{1}{y}\right), y, x, z, x\right)} \]
4. Add Preprocessing

Alternative 2: 96.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return x - ((z / y) * 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 - ((z / y) * x)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 84.5%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. lift--.f64 N/A

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

   5. sub-flip N/A

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

   6. div-add N/A

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

   7. distribute-rgt-in N/A

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

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

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

   9. *-inverses N/A

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

   10. *-lft-identity N/A

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

   11. distribute-neg-frac N/A

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

   12. remove-double-neg N/A

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

   13. lower--.f64 N/A

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

   14. lower-*.f64 N/A

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

   15. lower-/.f64 96.3%

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

3. Applied rewrites 96.3%

   \[\leadsto \color{blue}{x - \frac{z}{y} \cdot x} \]
4. Add Preprocessing

Alternative 3: 96.3% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return ((y - z) / y) * 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 = ((y - z) / y) * x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 84.5%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

   6. lower-/.f64 96.3%

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

3. Applied rewrites 96.3%

   \[\leadsto \color{blue}{\frac{y - z}{y} \cdot x} \]
4. Add Preprocessing

Alternative 4: 91.8% accurate, 0.1× speedup

Math

\[\begin{array}{l} t_0 := \frac{\left|x\right|}{y} \cdot \left(y - z\right)\\ t_1 := \frac{\left|x\right| \cdot \left(y - z\right)}{y}\\ \mathsf{copysign}\left(1, x\right) \cdot \begin{array}{l} \mathbf{if}\;t\_1 \leq 0:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_1 \leq \frac{4230758200257591}{42307582002575910332922579714097346549017899709713998034217522897561970639123926132812109468141778230245837569601494931472384}:\\ \;\;\;\;1 \cdot \left|x\right|\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  (let* ((t_0 (* (/ (fabs x) y) (- y z)))
       (t_1 (/ (* (fabs x) (- y z)) y)))
  (*
   (copysign 1 x)
   (if (<= t_1 0)
     t_0
     (if (<=
          t_1
          4230758200257591/42307582002575910332922579714097346549017899709713998034217522897561970639123926132812109468141778230245837569601494931472384)
       (* 1 (fabs x))
       t_0)))))

C

double code(double x, double y, double z) {
	double t_0 = (fabs(x) / y) * (y - z);
	double t_1 = (fabs(x) * (y - z)) / y;
	double tmp;
	if (t_1 <= 0.0) {
		tmp = t_0;
	} else if (t_1 <= 1e-109) {
		tmp = 1.0 * fabs(x);
	} else {
		tmp = t_0;
	}
	return copysign(1.0, x) * tmp;
}

Java

public static double code(double x, double y, double z) {
	double t_0 = (Math.abs(x) / y) * (y - z);
	double t_1 = (Math.abs(x) * (y - z)) / y;
	double tmp;
	if (t_1 <= 0.0) {
		tmp = t_0;
	} else if (t_1 <= 1e-109) {
		tmp = 1.0 * Math.abs(x);
	} else {
		tmp = t_0;
	}
	return Math.copySign(1.0, x) * tmp;
}

Python

def code(x, y, z):
	t_0 = (math.fabs(x) / y) * (y - z)
	t_1 = (math.fabs(x) * (y - z)) / y
	tmp = 0
	if t_1 <= 0.0:
		tmp = t_0
	elif t_1 <= 1e-109:
		tmp = 1.0 * math.fabs(x)
	else:
		tmp = t_0
	return math.copysign(1.0, x) * tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(abs(x) / y) * Float64(y - z))
	t_1 = Float64(Float64(abs(x) * Float64(y - z)) / y)
	tmp = 0.0
	if (t_1 <= 0.0)
		tmp = t_0;
	elseif (t_1 <= 1e-109)
		tmp = Float64(1.0 * abs(x));
	else
		tmp = t_0;
	end
	return Float64(copysign(1.0, x) * tmp)
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (abs(x) / y) * (y - z);
	t_1 = (abs(x) * (y - z)) / y;
	tmp = 0.0;
	if (t_1 <= 0.0)
		tmp = t_0;
	elseif (t_1 <= 1e-109)
		tmp = 1.0 * abs(x);
	else
		tmp = t_0;
	end
	tmp_2 = (sign(x) * abs(1.0)) * tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(N[Abs[x], $MachinePrecision] / y), $MachinePrecision] * N[(y - z), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(N[Abs[x], $MachinePrecision] * N[(y - z), $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$1, 0], t$95$0, If[LessEqual[t$95$1, 4230758200257591/42307582002575910332922579714097346549017899709713998034217522897561970639123926132812109468141778230245837569601494931472384], N[(1 * N[Abs[x], $MachinePrecision]), $MachinePrecision], t$95$0]]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 x (-.f64 y z)) y) < -0.0 or 9.9999999999999999e-110 < (/.f64 (*.f64 x (-.f64 y z)) y)

   1. Initial program 84.5%

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

      1. lift-/.f64 N/A

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

      2. mult-flip N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. associate-*l* N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. mult-flip-rev N/A

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

      9. lower-/.f64 84.2%

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

   3. Applied rewrites 84.2%

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

   if -0.0 < (/.f64 (*.f64 x (-.f64 y z)) y) < 9.9999999999999999e-110

   1. Initial program 84.5%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 96.3%

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

   3. Applied rewrites 96.3%

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

   4. Taylor expanded in y around inf

      \[\leadsto \color{blue}{1} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 51.6%

      \[\leadsto \color{blue}{1} \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 51.6% accurate, 3.3× speedup

Math

\[1 \cdot x \]

FPCore

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

C

double code(double x, double y, double z) {
	return 1.0 * 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 = 1.0d0 * x
end function

Java

public static double code(double x, double y, double z) {
	return 1.0 * x;
}

Python

def code(x, y, z):
	return 1.0 * x

Julia

function code(x, y, z)
	return Float64(1.0 * x)
end

MATLAB

function tmp = code(x, y, z)
	tmp = 1.0 * x;
end

Wolfram

code[x_, y_, z_] := N[(1 * x), $MachinePrecision]

Derivation

1. Initial program 84.5%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

   6. lower-/.f64 96.3%

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

3. Applied rewrites 96.3%

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

4. Taylor expanded in y around inf

   \[\leadsto \color{blue}{1} \cdot x \]
5. Step-by-step derivation

6. Applied rewrites 51.6%

   \[\leadsto \color{blue}{1} \cdot x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2025271 -o generate:evaluate
(FPCore (x y z)
  :name "Diagrams.Backend.Cairo.Internal:setTexture from diagrams-cairo-1.3.0.3"
  :precision binary64
  (/ (* x (- y z)) y))