Language.Haskell.HsColour.ColourHighlight:unbase from hscolour-1.23

Percentage Accurate: 99.9% → 99.9%
Time: 5.4s
Alternatives: 6
Speedup: 1.0×

Specification

?
\[\begin{array}{l} \\ \left(x \cdot y + z\right) \cdot y + t \end{array} \]
(FPCore (x y z t) :precision binary64 (+ (* (+ (* x y) z) y) t))
double code(double x, double y, double z, double t) {
	return (((x * y) + z) * y) + t;
}

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

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

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

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

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

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

\begin{array}{l}

\\
\left(x \cdot y + z\right) \cdot y + t
\end{array}

Sampling outcomes in binary64 precision:

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Accuracy vs Speed?

Herbie found 6 alternatives:

AlternativeAccuracySpeedup
The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Initial Program: 99.9% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \left(x \cdot y + z\right) \cdot y + t \end{array} \]
(FPCore (x y z t) :precision binary64 (+ (* (+ (* x y) z) y) t))
double code(double x, double y, double z, double t) {
	return (((x * y) + z) * y) + t;
}

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

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

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

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

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

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

\begin{array}{l}

\\
\left(x \cdot y + z\right) \cdot y + t
\end{array}

Alternative 1: 99.9% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \left(x \cdot y + z\right) \cdot y + t \end{array} \]
(FPCore (x y z t) :precision binary64 (+ (* (+ (* x y) z) y) t))
double code(double x, double y, double z, double t) {
	return (((x * y) + z) * y) + t;
}

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

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

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

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

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

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

\begin{array}{l}

\\
\left(x \cdot y + z\right) \cdot y + t
\end{array}
Derivation

  1. Initial program 99.9%

    \[\left(x \cdot y + z\right) \cdot y + t \]
  2. Add Preprocessing
  3. Add Preprocessing

Alternative 2: 90.2% accurate, 0.3× speedup?

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(x \cdot y + z\right) \cdot y\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+219} \lor \neg \left(t\_1 \leq 2 \cdot 10^{+167}\right):\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z, y, t\right)\\ \end{array} \end{array} \]
(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (+ (* x y) z) y)))
   (if (or (<= t_1 -5e+219) (not (<= t_1 2e+167)))
     (* (fma y x z) y)
     (fma z y t))))
double code(double x, double y, double z, double t) {
	double t_1 = ((x * y) + z) * y;
	double tmp;
	if ((t_1 <= -5e+219) || !(t_1 <= 2e+167)) {
		tmp = fma(y, x, z) * y;
	} else {
		tmp = fma(z, y, t);
	}
	return tmp;
}

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(x * y) + z) * y)
	tmp = 0.0
	if ((t_1 <= -5e+219) || !(t_1 <= 2e+167))
		tmp = Float64(fma(y, x, z) * y);
	else
		tmp = fma(z, y, t);
	end
	return tmp
end

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision] * y), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -5e+219], N[Not[LessEqual[t$95$1, 2e+167]], $MachinePrecision]], N[(N[(y * x + z), $MachinePrecision] * y), $MachinePrecision], N[(z * y + t), $MachinePrecision]]]

\begin{array}{l}

\\
\begin{array}{l}
t_1 := \left(x \cdot y + z\right) \cdot y\\
\mathbf{if}\;t\_1 \leq -5 \cdot 10^{+219} \lor \neg \left(t\_1 \leq 2 \cdot 10^{+167}\right):\\
\;\;\;\;\mathsf{fma}\left(y, x, z\right) \cdot y\\

\mathbf{else}:\\
\;\;\;\;\mathsf{fma}\left(z, y, t\right)\\


\end{array}
\end{array}
Derivation
  1. Split input into 2 regimes

  2. if (*.f64 (+.f64 (*.f64 x y) z) y) < -5e219 or 2.0000000000000001e167 < (*.f64 (+.f64 (*.f64 x y) z) y)

    1. Initial program 100.0%

      \[\left(x \cdot y + z\right) \cdot y + t \]
    2. Add Preprocessing

    3. Taylor expanded in t around 0

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

      1. +-commutativeN/A

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

      2. *-commutativeN/A

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

      3. lower-*.f64N/A

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

      4. *-commutativeN/A

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

      5. lower-fma.f6498.9

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

    5. Applied rewrites98.9%

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

    if -5e219 < (*.f64 (+.f64 (*.f64 x y) z) y) < 2.0000000000000001e167

    1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
    2. Add Preprocessing

    3. Taylor expanded in x around 0

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

      1. +-commutativeN/A

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

      2. *-commutativeN/A

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

      3. lower-fma.f6486.0

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

    5. Applied rewrites86.0%

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

  4. Final simplification91.3%

    \[\leadsto \begin{array}{l} \mathbf{if}\;\left(x \cdot y + z\right) \cdot y \leq -5 \cdot 10^{+219} \lor \neg \left(\left(x \cdot y + z\right) \cdot y \leq 2 \cdot 10^{+167}\right):\\ \;\;\;\;\mathsf{fma}\left(y, x, z\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z, y, t\right)\\ \end{array} \]
  5. Add Preprocessing

Alternative 3: 80.8% accurate, 0.3× speedup?

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(x \cdot y + z\right) \cdot y\\ \mathbf{if}\;t\_1 \leq -1 \cdot 10^{+287} \lor \neg \left(t\_1 \leq 10^{+199}\right):\\ \;\;\;\;\left(y \cdot x\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z, y, t\right)\\ \end{array} \end{array} \]
(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (+ (* x y) z) y)))
   (if (or (<= t_1 -1e+287) (not (<= t_1 1e+199))) (* (* y x) y) (fma z y t))))
double code(double x, double y, double z, double t) {
	double t_1 = ((x * y) + z) * y;
	double tmp;
	if ((t_1 <= -1e+287) || !(t_1 <= 1e+199)) {
		tmp = (y * x) * y;
	} else {
		tmp = fma(z, y, t);
	}
	return tmp;
}

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(x * y) + z) * y)
	tmp = 0.0
	if ((t_1 <= -1e+287) || !(t_1 <= 1e+199))
		tmp = Float64(Float64(y * x) * y);
	else
		tmp = fma(z, y, t);
	end
	return tmp
end

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision] * y), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -1e+287], N[Not[LessEqual[t$95$1, 1e+199]], $MachinePrecision]], N[(N[(y * x), $MachinePrecision] * y), $MachinePrecision], N[(z * y + t), $MachinePrecision]]]

\begin{array}{l}

\\
\begin{array}{l}
t_1 := \left(x \cdot y + z\right) \cdot y\\
\mathbf{if}\;t\_1 \leq -1 \cdot 10^{+287} \lor \neg \left(t\_1 \leq 10^{+199}\right):\\
\;\;\;\;\left(y \cdot x\right) \cdot y\\

\mathbf{else}:\\
\;\;\;\;\mathsf{fma}\left(z, y, t\right)\\


\end{array}
\end{array}
Derivation
  1. Split input into 2 regimes

  2. if (*.f64 (+.f64 (*.f64 x y) z) y) < -1.0000000000000001e287 or 1.0000000000000001e199 < (*.f64 (+.f64 (*.f64 x y) z) y)

    1. Initial program 100.0%

      \[\left(x \cdot y + z\right) \cdot y + t \]
    2. Add Preprocessing

    3. Taylor expanded in t around 0

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

      1. +-commutativeN/A

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

      2. *-commutativeN/A

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

      3. lower-*.f64N/A

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

      4. *-commutativeN/A

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

      5. lower-fma.f6499.7

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

    5. Applied rewrites99.7%

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

    6. Taylor expanded in x around inf

      \[\leadsto \left(x \cdot y\right) \cdot y \]
    7. Step-by-step derivation

      1. *-commutativeN/A

        \[\leadsto \left(y \cdot x\right) \cdot y \]

      2. lift-*.f6482.7

        \[\leadsto \left(y \cdot x\right) \cdot y \]

    8. Applied rewrites82.7%

      \[\leadsto \left(y \cdot x\right) \cdot y \]

    if -1.0000000000000001e287 < (*.f64 (+.f64 (*.f64 x y) z) y) < 1.0000000000000001e199

    1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
    2. Add Preprocessing

    3. Taylor expanded in x around 0

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

      1. +-commutativeN/A

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

      2. *-commutativeN/A

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

      3. lower-fma.f6484.9

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

    5. Applied rewrites84.9%

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

  4. Final simplification84.1%

    \[\leadsto \begin{array}{l} \mathbf{if}\;\left(x \cdot y + z\right) \cdot y \leq -1 \cdot 10^{+287} \lor \neg \left(\left(x \cdot y + z\right) \cdot y \leq 10^{+199}\right):\\ \;\;\;\;\left(y \cdot x\right) \cdot y\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(z, y, t\right)\\ \end{array} \]
  5. Add Preprocessing

Alternative 4: 51.6% accurate, 0.4× speedup?

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(x \cdot y + z\right) \cdot y\\ \mathbf{if}\;t\_1 \leq -5 \cdot 10^{+26} \lor \neg \left(t\_1 \leq 2 \cdot 10^{+167}\right):\\ \;\;\;\;z \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]
(FPCore (x y z t)
 :precision binary64
 (let* ((t_1 (* (+ (* x y) z) y)))
   (if (or (<= t_1 -5e+26) (not (<= t_1 2e+167))) (* z y) t)))
double code(double x, double y, double z, double t) {
	double t_1 = ((x * y) + z) * y;
	double tmp;
	if ((t_1 <= -5e+26) || !(t_1 <= 2e+167)) {
		tmp = z * y;
	} else {
		tmp = t;
	}
	return tmp;
}

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: t_1
    real(8) :: tmp
    t_1 = ((x * y) + z) * y
    if ((t_1 <= (-5d+26)) .or. (.not. (t_1 <= 2d+167))) then
        tmp = z * y
    else
        tmp = t
    end if
    code = tmp
end function

public static double code(double x, double y, double z, double t) {
	double t_1 = ((x * y) + z) * y;
	double tmp;
	if ((t_1 <= -5e+26) || !(t_1 <= 2e+167)) {
		tmp = z * y;
	} else {
		tmp = t;
	}
	return tmp;
}

def code(x, y, z, t):
	t_1 = ((x * y) + z) * y
	tmp = 0
	if (t_1 <= -5e+26) or not (t_1 <= 2e+167):
		tmp = z * y
	else:
		tmp = t
	return tmp

function code(x, y, z, t)
	t_1 = Float64(Float64(Float64(x * y) + z) * y)
	tmp = 0.0
	if ((t_1 <= -5e+26) || !(t_1 <= 2e+167))
		tmp = Float64(z * y);
	else
		tmp = t;
	end
	return tmp
end

function tmp_2 = code(x, y, z, t)
	t_1 = ((x * y) + z) * y;
	tmp = 0.0;
	if ((t_1 <= -5e+26) || ~((t_1 <= 2e+167)))
		tmp = z * y;
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

code[x_, y_, z_, t_] := Block[{t$95$1 = N[(N[(N[(x * y), $MachinePrecision] + z), $MachinePrecision] * y), $MachinePrecision]}, If[Or[LessEqual[t$95$1, -5e+26], N[Not[LessEqual[t$95$1, 2e+167]], $MachinePrecision]], N[(z * y), $MachinePrecision], t]]

\begin{array}{l}

\\
\begin{array}{l}
t_1 := \left(x \cdot y + z\right) \cdot y\\
\mathbf{if}\;t\_1 \leq -5 \cdot 10^{+26} \lor \neg \left(t\_1 \leq 2 \cdot 10^{+167}\right):\\
\;\;\;\;z \cdot y\\

\mathbf{else}:\\
\;\;\;\;t\\


\end{array}
\end{array}
Derivation
  1. Split input into 2 regimes

  2. if (*.f64 (+.f64 (*.f64 x y) z) y) < -5.0000000000000001e26 or 2.0000000000000001e167 < (*.f64 (+.f64 (*.f64 x y) z) y)

    1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
    2. Add Preprocessing

    3. Taylor expanded in z around inf

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

      1. *-commutativeN/A

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

      2. lower-*.f6440.4

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

    5. Applied rewrites40.4%

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

    if -5.0000000000000001e26 < (*.f64 (+.f64 (*.f64 x y) z) y) < 2.0000000000000001e167

    1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
    2. Add Preprocessing

    3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{t} \]
    4. Step-by-step derivation

      1. Applied rewrites75.2%

        \[\leadsto \color{blue}{t} \]
    5. Recombined 2 regimes into one program.

    6. Final simplification57.5%

      \[\leadsto \begin{array}{l} \mathbf{if}\;\left(x \cdot y + z\right) \cdot y \leq -5 \cdot 10^{+26} \lor \neg \left(\left(x \cdot y + z\right) \cdot y \leq 2 \cdot 10^{+167}\right):\\ \;\;\;\;z \cdot y\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]
    7. Add Preprocessing

    Alternative 5: 65.9% accurate, 2.4× speedup?

    \[\begin{array}{l} \\ \mathsf{fma}\left(z, y, t\right) \end{array} \]
    (FPCore (x y z t) :precision binary64 (fma z y t))
    double code(double x, double y, double z, double t) {
	return fma(z, y, t);
}

    function code(x, y, z, t)
	return fma(z, y, t)
end

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

    \begin{array}{l}

\\
\mathsf{fma}\left(z, y, t\right)
\end{array}
    Derivation

    1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
    2. Add Preprocessing

    3. Taylor expanded in x around 0

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

      1. +-commutativeN/A

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

      2. *-commutativeN/A

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

      3. lower-fma.f6466.5

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

    5. Applied rewrites66.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, y, t\right)} \]
    6. Add Preprocessing

    Alternative 6: 38.3% accurate, 17.0× speedup?

    \[\begin{array}{l} \\ t \end{array} \]
    (FPCore (x y z t) :precision binary64 t)
    double code(double x, double y, double z, double t) {
	return t;
}

    module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

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

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

    function code(x, y, z, t)
	return t
end

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

    code[x_, y_, z_, t_] := t

    \begin{array}{l}

\\
t
\end{array}
    Derivation

    1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
    2. Add Preprocessing

    3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{t} \]
    4. Step-by-step derivation

      1. Applied rewrites41.3%

        \[\leadsto \color{blue}{t} \]
      2. Add Preprocessing

      Reproduce

      ?
      herbie shell --seed 2025043 
(FPCore (x y z t)
  :name "Language.Haskell.HsColour.ColourHighlight:unbase from hscolour-1.23"
  :precision binary64
  (+ (* (+ (* x y) z) y) t))