Diagrams.TwoD.Segment.Bernstein:evaluateBernstein from diagrams-lib-1.3.0.3

Percentage Accurate: 88.2% → 99.9%

Time: 2.8s

Alternatives: 10

Speedup: 0.7×

• 79.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 88.2% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 0.6× speedup

Math

\[\begin{array}{l} t_0 := \mathsf{fma}\left(\frac{y}{z}, x, -x\right)\\ \mathbf{if}\;z \leq -3.9 \cdot 10^{+15}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 1200000000:\\ \;\;\;\;\frac{\mathsf{fma}\left(x, y, x\right)}{z} - x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fma (/ y z) x (- x))))
   (if (<= z -3.9e+15)
     t_0
     (if (<= z 1200000000.0) (- (/ (fma x y x) z) x) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fma((y / z), x, -x);
	double tmp;
	if (z <= -3.9e+15) {
		tmp = t_0;
	} else if (z <= 1200000000.0) {
		tmp = (fma(x, y, x) / z) - x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = fma(Float64(y / z), x, Float64(-x))
	tmp = 0.0
	if (z <= -3.9e+15)
		tmp = t_0;
	elseif (z <= 1200000000.0)
		tmp = Float64(Float64(fma(x, y, x) / z) - x);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(y / z), $MachinePrecision] * x + (-x)), $MachinePrecision]}, If[LessEqual[z, -3.9e+15], t$95$0, If[LessEqual[z, 1200000000.0], N[(N[(N[(x * y + x), $MachinePrecision] / z), $MachinePrecision] - x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -3.9e15 or 1.2e9 < z

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   5. Applied rewrites 95.7%

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

   6. Taylor expanded in y around inf

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

      1. lower-/.f64 71.2%

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

   8. Applied rewrites 71.2%

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

   if -3.9e15 < z < 1.2e9

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   5. Applied rewrites 95.9%

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

Alternative 2: 99.9% accurate, 0.5× speedup

Math

\[\mathsf{copysign}\left(1, x\right) \cdot \begin{array}{l} \mathbf{if}\;\left|x\right| \leq 5 \cdot 10^{-20}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left|x\right|, y, \left|x\right|\right)}{z} - \left|x\right|\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{y - -1}{z}, \left|x\right|, -\left|x\right|\right)\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (*
  (copysign 1.0 x)
  (if (<= (fabs x) 5e-20)
    (- (/ (fma (fabs x) y (fabs x)) z) (fabs x))
    (fma (/ (- y -1.0) z) (fabs x) (- (fabs x))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (fabs(x) <= 5e-20) {
		tmp = (fma(fabs(x), y, fabs(x)) / z) - fabs(x);
	} else {
		tmp = fma(((y - -1.0) / z), fabs(x), -fabs(x));
	}
	return copysign(1.0, x) * tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (abs(x) <= 5e-20)
		tmp = Float64(Float64(fma(abs(x), y, abs(x)) / z) - abs(x));
	else
		tmp = fma(Float64(Float64(y - -1.0) / z), abs(x), Float64(-abs(x)));
	end
	return Float64(copysign(1.0, x) * tmp)
end

Wolfram

code[x_, y_, z_] := N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[N[Abs[x], $MachinePrecision], 5e-20], N[(N[(N[(N[Abs[x], $MachinePrecision] * y + N[Abs[x], $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision] - N[Abs[x], $MachinePrecision]), $MachinePrecision], N[(N[(N[(y - -1.0), $MachinePrecision] / z), $MachinePrecision] * N[Abs[x], $MachinePrecision] + (-N[Abs[x], $MachinePrecision])), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 4.9999999999999999e-20

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   5. Applied rewrites 95.9%

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

   if 4.9999999999999999e-20 < x

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   5. Applied rewrites 95.7%

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

Alternative 3: 99.8% accurate, 0.5× speedup

Math

\[\mathsf{copysign}\left(1, x\right) \cdot \begin{array}{l} \mathbf{if}\;\left|x\right| \leq 10^{-28}:\\ \;\;\;\;\frac{\mathsf{fma}\left(\left|x\right|, y, \left|x\right|\right)}{z} - \left|x\right|\\ \mathbf{else}:\\ \;\;\;\;\frac{\left|x\right|}{z} \cdot \left(\left(y - z\right) - -1\right)\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (*
  (copysign 1.0 x)
  (if (<= (fabs x) 1e-28)
    (- (/ (fma (fabs x) y (fabs x)) z) (fabs x))
    (* (/ (fabs x) z) (- (- y z) -1.0)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (fabs(x) <= 1e-28) {
		tmp = (fma(fabs(x), y, fabs(x)) / z) - fabs(x);
	} else {
		tmp = (fabs(x) / z) * ((y - z) - -1.0);
	}
	return copysign(1.0, x) * tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (abs(x) <= 1e-28)
		tmp = Float64(Float64(fma(abs(x), y, abs(x)) / z) - abs(x));
	else
		tmp = Float64(Float64(abs(x) / z) * Float64(Float64(y - z) - -1.0));
	end
	return Float64(copysign(1.0, x) * tmp)
end

Wolfram

code[x_, y_, z_] := N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[N[Abs[x], $MachinePrecision], 1e-28], N[(N[(N[(N[Abs[x], $MachinePrecision] * y + N[Abs[x], $MachinePrecision]), $MachinePrecision] / z), $MachinePrecision] - N[Abs[x], $MachinePrecision]), $MachinePrecision], N[(N[(N[Abs[x], $MachinePrecision] / z), $MachinePrecision] * N[(N[(y - z), $MachinePrecision] - -1.0), $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 9.9999999999999997e-29

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   5. Applied rewrites 95.9%

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

   if 9.9999999999999997e-29 < x

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. associate-/l* N/A

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

      5. *-commutative N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 88.8%

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

      8. lift-+.f64 N/A

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

      9. add-flip N/A

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

      10. lower--.f64 N/A

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

      11. metadata-eval 88.8%

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

   3. Applied rewrites 88.8%

      \[\leadsto \color{blue}{\frac{x}{z} \cdot \left(\left(y - z\right) - -1\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 97.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_0 := \mathsf{fma}\left(\frac{y}{z}, x, -x\right)\\ \mathbf{if}\;z \leq -3.9 \cdot 10^{+15}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;z \leq 5.6 \cdot 10^{-30}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, x, x\right)}{z}\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fma (/ y z) x (- x))))
   (if (<= z -3.9e+15) t_0 (if (<= z 5.6e-30) (/ (fma y x x) z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fma((y / z), x, -x);
	double tmp;
	if (z <= -3.9e+15) {
		tmp = t_0;
	} else if (z <= 5.6e-30) {
		tmp = fma(y, x, x) / z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = fma(Float64(y / z), x, Float64(-x))
	tmp = 0.0
	if (z <= -3.9e+15)
		tmp = t_0;
	elseif (z <= 5.6e-30)
		tmp = Float64(fma(y, x, x) / z);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(y / z), $MachinePrecision] * x + (-x)), $MachinePrecision]}, If[LessEqual[z, -3.9e+15], t$95$0, If[LessEqual[z, 5.6e-30], N[(N[(y * x + x), $MachinePrecision] / z), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if z < -3.9e15 or 5.5999999999999998e-30 < z

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   5. Applied rewrites 95.7%

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

   6. Taylor expanded in y around inf

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

      1. lower-/.f64 71.2%

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

   8. Applied rewrites 71.2%

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

   if -3.9e15 < z < 5.5999999999999998e-30

   1. Initial program 88.2%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. distribute-lft1-in N/A

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

      5. lower-fma.f64 88.2%

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

   3. Applied rewrites 88.2%

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

   4. Taylor expanded in z around 0

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 61.9%

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

   6. Applied rewrites 61.9%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 61.9%

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

   8. Applied rewrites 61.9%

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

Alternative 5: 86.1% accurate, 0.7× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq -1.2 \cdot 10^{+26}:\\ \;\;\;\;-x\\ \mathbf{elif}\;z \leq 6 \cdot 10^{+81}:\\ \;\;\;\;\frac{x}{z} \cdot \left(y - -1\right)\\ \mathbf{else}:\\ \;\;\;\;-x\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1.2e+26) (- x) (if (<= z 6e+81) (* (/ x z) (- y -1.0)) (- x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.2e+26) {
		tmp = -x;
	} else if (z <= 6e+81) {
		tmp = (x / z) * (y - -1.0);
	} else {
		tmp = -x;
	}
	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)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-1.2d+26)) then
        tmp = -x
    else if (z <= 6d+81) then
        tmp = (x / z) * (y - (-1.0d0))
    else
        tmp = -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -1.2e+26) {
		tmp = -x;
	} else if (z <= 6e+81) {
		tmp = (x / z) * (y - -1.0);
	} else {
		tmp = -x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1.2e+26:
		tmp = -x
	elif z <= 6e+81:
		tmp = (x / z) * (y - -1.0)
	else:
		tmp = -x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1.2e+26)
		tmp = Float64(-x);
	elseif (z <= 6e+81)
		tmp = Float64(Float64(x / z) * Float64(y - -1.0));
	else
		tmp = Float64(-x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -1.2e+26)
		tmp = -x;
	elseif (z <= 6e+81)
		tmp = (x / z) * (y - -1.0);
	else
		tmp = -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1.2e+26], (-x), If[LessEqual[z, 6e+81], N[(N[(x / z), $MachinePrecision] * N[(y - -1.0), $MachinePrecision]), $MachinePrecision], (-x)]]

Derivation

1. Split input into 2 regimes

2. if z < -1.2e26 or 5.9999999999999999e81 < z

   1. Initial program 88.2%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 37.9%

         \[\leadsto -1 \cdot \color{blue}{x} \]

   4. Applied rewrites 37.9%

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

      1. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{x} \]

      2. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(x\right) \]

      3. lower-neg.f64 37.9%

         \[\leadsto -x \]

   6. Applied rewrites 37.9%

      \[\leadsto -x \]

   if -1.2e26 < z < 5.9999999999999999e81

   1. Initial program 88.2%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. distribute-lft1-in N/A

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

      5. lower-fma.f64 88.2%

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

   3. Applied rewrites 88.2%

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

   4. Taylor expanded in z around 0

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 61.9%

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

   6. Applied rewrites 61.9%

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

      1. lift-/.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. distribute-rgt1-in N/A

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

      6. metadata-eval N/A

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

      7. sub-flip N/A

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

      8. associate-/l* N/A

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

      9. lift-/.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lower--.f64 62.1%

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

   8. Applied rewrites 62.1%

      \[\leadsto \frac{x}{z} \cdot \color{blue}{\left(y - -1\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 85.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \mathbf{if}\;z \leq -6 \cdot 10^{+15}:\\ \;\;\;\;-x\\ \mathbf{elif}\;z \leq 6 \cdot 10^{+81}:\\ \;\;\;\;\frac{\mathsf{fma}\left(y, x, x\right)}{z}\\ \mathbf{else}:\\ \;\;\;\;-x\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -6e+15) (- x) (if (<= z 6e+81) (/ (fma y x x) z) (- x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -6e+15) {
		tmp = -x;
	} else if (z <= 6e+81) {
		tmp = fma(y, x, x) / z;
	} else {
		tmp = -x;
	}
	return tmp;
}

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -6e+15)
		tmp = Float64(-x);
	elseif (z <= 6e+81)
		tmp = Float64(fma(y, x, x) / z);
	else
		tmp = Float64(-x);
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -6e+15], (-x), If[LessEqual[z, 6e+81], N[(N[(y * x + x), $MachinePrecision] / z), $MachinePrecision], (-x)]]

Derivation

1. Split input into 2 regimes

2. if z < -6e15 or 5.9999999999999999e81 < z

   1. Initial program 88.2%

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

   2. Taylor expanded in z around inf

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

      1. lower-*.f64 37.9%

         \[\leadsto -1 \cdot \color{blue}{x} \]

   4. Applied rewrites 37.9%

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

      1. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{x} \]

      2. mul-1-neg N/A

         \[\leadsto \mathsf{neg}\left(x\right) \]

      3. lower-neg.f64 37.9%

         \[\leadsto -x \]

   6. Applied rewrites 37.9%

      \[\leadsto -x \]

   if -6e15 < z < 5.9999999999999999e81

   1. Initial program 88.2%

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

      1. lift-*.f64 N/A

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

      2. *-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. distribute-lft1-in N/A

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

      5. lower-fma.f64 88.2%

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

   3. Applied rewrites 88.2%

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

   4. Taylor expanded in z around 0

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

      1. lower-/.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-*.f64 61.9%

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

   6. Applied rewrites 61.9%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-*.f64 N/A

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

      4. *-commutative N/A

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

      5. lower-fma.f64 61.9%

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

   8. Applied rewrites 61.9%

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

Alternative 7: 85.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} t_0 := \frac{x \cdot y}{z}\\ \mathbf{if}\;y \leq -1 \cdot 10^{+36}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{+36}:\\ \;\;\;\;\frac{x}{z} - x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (/ (* x y) z)))
   (if (<= y -1e+36) t_0 (if (<= y 4.8e+36) (- (/ x z) x) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (x * y) / z;
	double tmp;
	if (y <= -1e+36) {
		tmp = t_0;
	} else if (y <= 4.8e+36) {
		tmp = (x / z) - x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x * y) / z
    if (y <= (-1d+36)) then
        tmp = t_0
    else if (y <= 4.8d+36) then
        tmp = (x / z) - x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (x * y) / z;
	double tmp;
	if (y <= -1e+36) {
		tmp = t_0;
	} else if (y <= 4.8e+36) {
		tmp = (x / z) - x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (x * y) / z
	tmp = 0
	if y <= -1e+36:
		tmp = t_0
	elif y <= 4.8e+36:
		tmp = (x / z) - x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(x * y) / z)
	tmp = 0.0
	if (y <= -1e+36)
		tmp = t_0;
	elseif (y <= 4.8e+36)
		tmp = Float64(Float64(x / z) - x);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (x * y) / z;
	tmp = 0.0;
	if (y <= -1e+36)
		tmp = t_0;
	elseif (y <= 4.8e+36)
		tmp = (x / z) - x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(x * y), $MachinePrecision] / z), $MachinePrecision]}, If[LessEqual[y, -1e+36], t$95$0, If[LessEqual[y, 4.8e+36], N[(N[(x / z), $MachinePrecision] - x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1e36 or 4.7999999999999998e36 < y

   1. Initial program 88.2%

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

   2. Taylor expanded in y around inf

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

      1. lower-*.f64 38.1%

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

   4. Applied rewrites 38.1%

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

   if -1e36 < y < 4.7999999999999998e36

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   5. Applied rewrites 95.7%

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

   6. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\frac{x}{z} - x} \]
   7. Step-by-step derivation

      1. lower--.f64 N/A

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

      2. lower-/.f64 65.6%

         \[\leadsto \frac{x}{z} - x \]

   8. Applied rewrites 65.6%

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

Alternative 8: 83.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} t_0 := \frac{y}{z} \cdot x\\ \mathbf{if}\;y \leq -1 \cdot 10^{+36}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 4.8 \cdot 10^{+36}:\\ \;\;\;\;\frac{x}{z} - x\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (/ y z) x)))
   (if (<= y -1e+36) t_0 (if (<= y 4.8e+36) (- (/ x z) x) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (y / z) * x;
	double tmp;
	if (y <= -1e+36) {
		tmp = t_0;
	} else if (y <= 4.8e+36) {
		tmp = (x / z) - x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (y / z) * x
    if (y <= (-1d+36)) then
        tmp = t_0
    else if (y <= 4.8d+36) then
        tmp = (x / z) - x
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (y / z) * x;
	double tmp;
	if (y <= -1e+36) {
		tmp = t_0;
	} else if (y <= 4.8e+36) {
		tmp = (x / z) - x;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (y / z) * x
	tmp = 0
	if y <= -1e+36:
		tmp = t_0
	elif y <= 4.8e+36:
		tmp = (x / z) - x
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(y / z) * x)
	tmp = 0.0
	if (y <= -1e+36)
		tmp = t_0;
	elseif (y <= 4.8e+36)
		tmp = Float64(Float64(x / z) - x);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (y / z) * x;
	tmp = 0.0;
	if (y <= -1e+36)
		tmp = t_0;
	elseif (y <= 4.8e+36)
		tmp = (x / z) - x;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(y / z), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[y, -1e+36], t$95$0, If[LessEqual[y, 4.8e+36], N[(N[(x / z), $MachinePrecision] - x), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1e36 or 4.7999999999999998e36 < y

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   4. Taylor expanded in y around inf

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

      1. lower-/.f64 36.0%

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

   6. Applied rewrites 36.0%

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

   if -1e36 < y < 4.7999999999999998e36

   1. Initial program 88.2%

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

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

      6. lower-/.f64 95.7%

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

      7. lift-+.f64 N/A

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

      8. add-flip N/A

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

      9. lower--.f64 N/A

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

      10. metadata-eval 95.7%

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

   3. Applied rewrites 95.7%

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

   5. Applied rewrites 95.7%

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

   6. Taylor expanded in y around 0

      \[\leadsto \color{blue}{\frac{x}{z} - x} \]
   7. Step-by-step derivation

      1. lower--.f64 N/A

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

      2. lower-/.f64 65.6%

         \[\leadsto \frac{x}{z} - x \]

   8. Applied rewrites 65.6%

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

Alternative 9: 65.6% accurate, 1.8× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 88.2%

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

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

   6. lower-/.f64 95.7%

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

   7. lift-+.f64 N/A

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

   8. add-flip N/A

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

   9. lower--.f64 N/A

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

   10. metadata-eval 95.7%

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

3. Applied rewrites 95.7%

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

5. Applied rewrites 95.7%

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

6. Taylor expanded in y around 0

   \[\leadsto \color{blue}{\frac{x}{z} - x} \]
7. Step-by-step derivation

   1. lower--.f64 N/A

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

   2. lower-/.f64 65.6%

      \[\leadsto \frac{x}{z} - x \]

8. Applied rewrites 65.6%

   \[\leadsto \color{blue}{\frac{x}{z} - x} \]
9. Add Preprocessing

Alternative 10: 37.9% accurate, 6.4× speedup

Math

\[-x \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := (-x)

Derivation

1. Initial program 88.2%

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

2. Taylor expanded in z around inf

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

   1. lower-*.f64 37.9%

      \[\leadsto -1 \cdot \color{blue}{x} \]

4. Applied rewrites 37.9%

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

   1. lift-*.f64 N/A

      \[\leadsto -1 \cdot \color{blue}{x} \]

   2. mul-1-neg N/A

      \[\leadsto \mathsf{neg}\left(x\right) \]

   3. lower-neg.f64 37.9%

      \[\leadsto -x \]

6. Applied rewrites 37.9%

   \[\leadsto -x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2025191 
(FPCore (x y z)
  :name "Diagrams.TwoD.Segment.Bernstein:evaluateBernstein from diagrams-lib-1.3.0.3"
  :precision binary64
  (/ (* x (+ (- y z) 1.0)) z))