Numeric.SpecFunctions:logBeta from math-functions-0.1.5.2, A

Percentage Accurate: 99.9% → 99.9%

Time: 5.2s

Alternatives: 14

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (+ (+ x y) z) (* z (log t))) (* (- a 0.5) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (((x + y) + z) - (z * log(t))) + ((a - 0.5) * b);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = (((x + y) + z) - (z * log(t))) + ((a - 0.5d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (((x + y) + z) - (z * Math.log(t))) + ((a - 0.5) * b);
}

Python

def code(x, y, z, t, a, b):
	return (((x + y) + z) - (z * math.log(t))) + ((a - 0.5) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(Float64(x + y) + z) - Float64(z * log(t))) + Float64(Float64(a - 0.5) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (((x + y) + z) - (z * log(t))) + ((a - 0.5) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(N[(x + y), $MachinePrecision] + z), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+ (- (+ (+ x y) z) (* z (log t))) (* (- a 0.5) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (((x + y) + z) - (z * log(t))) + ((a - 0.5) * b);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = (((x + y) + z) - (z * log(t))) + ((a - 0.5d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (((x + y) + z) - (z * Math.log(t))) + ((a - 0.5) * b);
}

Python

def code(x, y, z, t, a, b):
	return (((x + y) + z) - (z * math.log(t))) + ((a - 0.5) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(Float64(x + y) + z) - Float64(z * log(t))) + Float64(Float64(a - 0.5) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (((x + y) + z) - (z * log(t))) + ((a - 0.5) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(N[(x + y), $MachinePrecision] + z), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(a - 0.5, b, \left(\left(y + x\right) + z\right) - \log t \cdot z\right) \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (fma (- a 0.5) b (- (+ (+ y x) z) (* (log t) z))))

C

double code(double x, double y, double z, double t, double a, double b) {
	return fma((a - 0.5), b, (((y + x) + z) - (log(t) * z)));
}

Julia

function code(x, y, z, t, a, b)
	return fma(Float64(a - 0.5), b, Float64(Float64(Float64(y + x) + z) - Float64(log(t) * z)))
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(a - 0.5), $MachinePrecision] * b + N[(N[(N[(y + x), $MachinePrecision] + z), $MachinePrecision] - N[(N[Log[t], $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift--.f64 N/A

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

   3. lift-+.f64 N/A

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

   4. lift-+.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-log.f64 N/A

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

   7. lift--.f64 N/A

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

   8. lift-*.f64 N/A

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

   9. +-commutative N/A

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

   10. lower-fma.f64 N/A

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

   11. lift--.f64 N/A

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

   12. lower--.f64 N/A

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

   13. lift-+.f64 N/A

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

   14. +-commutative N/A

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

   15. lower-+.f64 N/A

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

   16. *-commutative N/A

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

   17. lower-*.f64 N/A

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

   18. lift-log.f64 99.9

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

3. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, \left(\left(y + x\right) + z\right) - \log t \cdot z\right)} \]
4. Add Preprocessing

Alternative 2: 33.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b\\ \mathbf{if}\;t\_1 \leq -1 \cdot 10^{+306}:\\ \;\;\;\;b \cdot a\\ \mathbf{elif}\;t\_1 \leq -2 \cdot 10^{-30}:\\ \;\;\;\;x\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+307}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;b \cdot a\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (+ (- (+ (+ x y) z) (* z (log t))) (* (- a 0.5) b))))
   (if (<= t_1 -1e+306)
     (* b a)
     (if (<= t_1 -2e-30) x (if (<= t_1 5e+307) y (* b a))))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (((x + y) + z) - (z * log(t))) + ((a - 0.5) * b);
	double tmp;
	if (t_1 <= -1e+306) {
		tmp = b * a;
	} else if (t_1 <= -2e-30) {
		tmp = x;
	} else if (t_1 <= 5e+307) {
		tmp = y;
	} else {
		tmp = b * a;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: t_1
    real(8) :: tmp
    t_1 = (((x + y) + z) - (z * log(t))) + ((a - 0.5d0) * b)
    if (t_1 <= (-1d+306)) then
        tmp = b * a
    else if (t_1 <= (-2d-30)) then
        tmp = x
    else if (t_1 <= 5d+307) then
        tmp = y
    else
        tmp = b * a
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (((x + y) + z) - (z * Math.log(t))) + ((a - 0.5) * b);
	double tmp;
	if (t_1 <= -1e+306) {
		tmp = b * a;
	} else if (t_1 <= -2e-30) {
		tmp = x;
	} else if (t_1 <= 5e+307) {
		tmp = y;
	} else {
		tmp = b * a;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (((x + y) + z) - (z * math.log(t))) + ((a - 0.5) * b)
	tmp = 0
	if t_1 <= -1e+306:
		tmp = b * a
	elif t_1 <= -2e-30:
		tmp = x
	elif t_1 <= 5e+307:
		tmp = y
	else:
		tmp = b * a
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(Float64(Float64(x + y) + z) - Float64(z * log(t))) + Float64(Float64(a - 0.5) * b))
	tmp = 0.0
	if (t_1 <= -1e+306)
		tmp = Float64(b * a);
	elseif (t_1 <= -2e-30)
		tmp = x;
	elseif (t_1 <= 5e+307)
		tmp = y;
	else
		tmp = Float64(b * a);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (((x + y) + z) - (z * log(t))) + ((a - 0.5) * b);
	tmp = 0.0;
	if (t_1 <= -1e+306)
		tmp = b * a;
	elseif (t_1 <= -2e-30)
		tmp = x;
	elseif (t_1 <= 5e+307)
		tmp = y;
	else
		tmp = b * a;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[(N[(x + y), $MachinePrecision] + z), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -1e+306], N[(b * a), $MachinePrecision], If[LessEqual[t$95$1, -2e-30], x, If[LessEqual[t$95$1, 5e+307], y, N[(b * a), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 (-.f64 (+.f64 (+.f64 x y) z) (*.f64 z (log.f64 t))) (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)) < -1.00000000000000002e306 or 5e307 < (+.f64 (-.f64 (+.f64 (+.f64 x y) z) (*.f64 z (log.f64 t))) (*.f64 (-.f64 a #s(literal 1/2 binary64)) b))

   1. Initial program 100.0%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in a around inf

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

      1. *-commutative N/A

         \[\leadsto b \cdot \color{blue}{a} \]

      2. lower-*.f64 92.4

         \[\leadsto b \cdot \color{blue}{a} \]

   4. Applied rewrites 92.4%

      \[\leadsto \color{blue}{b \cdot a} \]

   if -1.00000000000000002e306 < (+.f64 (-.f64 (+.f64 (+.f64 x y) z) (*.f64 z (log.f64 t))) (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)) < -2e-30

   1. Initial program 99.8%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 24.5%

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

   if -2e-30 < (+.f64 (-.f64 (+.f64 (+.f64 x y) z) (*.f64 z (log.f64 t))) (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)) < 5e307

   1. Initial program 99.8%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in y around inf

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

   4. Applied rewrites 23.5%

      \[\leadsto \color{blue}{y} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 89.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a - 0.5\right) \cdot b\\ t_2 := \mathsf{fma}\left(a - 0.5, b, x + y\right)\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+147}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+26}:\\ \;\;\;\;\left(\left(y + x\right) + z\right) - \log t \cdot z\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (- a 0.5) b)) (t_2 (fma (- a 0.5) b (+ x y))))
   (if (<= t_1 -2e+147)
     t_2
     (if (<= t_1 5e+26) (- (+ (+ y x) z) (* (log t) z)) t_2))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (a - 0.5) * b;
	double t_2 = fma((a - 0.5), b, (x + y));
	double tmp;
	if (t_1 <= -2e+147) {
		tmp = t_2;
	} else if (t_1 <= 5e+26) {
		tmp = ((y + x) + z) - (log(t) * z);
	} else {
		tmp = t_2;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(a - 0.5) * b)
	t_2 = fma(Float64(a - 0.5), b, Float64(x + y))
	tmp = 0.0
	if (t_1 <= -2e+147)
		tmp = t_2;
	elseif (t_1 <= 5e+26)
		tmp = Float64(Float64(Float64(y + x) + z) - Float64(log(t) * z));
	else
		tmp = t_2;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]}, Block[{t$95$2 = N[(N[(a - 0.5), $MachinePrecision] * b + N[(x + y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+147], t$95$2, If[LessEqual[t$95$1, 5e+26], N[(N[(N[(y + x), $MachinePrecision] + z), $MachinePrecision] - N[(N[Log[t], $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < -2e147 or 5.0000000000000001e26 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 87.7

         \[\leadsto \mathsf{fma}\left(a - 0.5, b, x + \color{blue}{y}\right) \]

   6. Applied rewrites 87.7%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, \color{blue}{x + y}\right) \]

   if -2e147 < (*.f64 (-.f64 a #s(literal 1/2 binary64)) b) < 5.0000000000000001e26

   1. Initial program 99.8%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in b around 0

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

      1. associate-+r+ N/A

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

      2. lower--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. +-commutative N/A

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

      5. lower-+.f64 N/A

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

      6. *-commutative N/A

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

      7. lower-*.f64 N/A

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

      8. lift-log.f64 90.9

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

   4. Applied rewrites 90.9%

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

Alternative 4: 21.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \leq -2 \cdot 10^{-30}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= (+ (- (+ (+ x y) z) (* z (log t))) (* (- a 0.5) b)) -2e-30) x y))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (((((x + y) + z) - (z * log(t))) + ((a - 0.5) * b)) <= -2e-30) {
		tmp = x;
	} else {
		tmp = y;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (((((x + y) + z) - (z * log(t))) + ((a - 0.5d0) * b)) <= (-2d-30)) then
        tmp = x
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (((((x + y) + z) - (z * Math.log(t))) + ((a - 0.5) * b)) <= -2e-30) {
		tmp = x;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if ((((x + y) + z) - (z * math.log(t))) + ((a - 0.5) * b)) <= -2e-30:
		tmp = x
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (Float64(Float64(Float64(Float64(x + y) + z) - Float64(z * log(t))) + Float64(Float64(a - 0.5) * b)) <= -2e-30)
		tmp = x;
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (((((x + y) + z) - (z * log(t))) + ((a - 0.5) * b)) <= -2e-30)
		tmp = x;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[N[(N[(N[(N[(x + y), $MachinePrecision] + z), $MachinePrecision] - N[(z * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision], -2e-30], x, y]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (-.f64 (+.f64 (+.f64 x y) z) (*.f64 z (log.f64 t))) (*.f64 (-.f64 a #s(literal 1/2 binary64)) b)) < -2e-30

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 21.6%

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

   if -2e-30 < (+.f64 (-.f64 (+.f64 (+.f64 x y) z) (*.f64 z (log.f64 t))) (*.f64 (-.f64 a #s(literal 1/2 binary64)) b))

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in y around inf

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

   4. Applied rewrites 20.9%

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

Alternative 5: 84.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2.2 \cdot 10^{+60}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, x + y\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{fma}\left(a - 0.5, b, z\right) + y\right) - \log t \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= x -2.2e+60)
   (fma (- a 0.5) b (+ x y))
   (- (+ (fma (- a 0.5) b z) y) (* (log t) z))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (x <= -2.2e+60) {
		tmp = fma((a - 0.5), b, (x + y));
	} else {
		tmp = (fma((a - 0.5), b, z) + y) - (log(t) * z);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (x <= -2.2e+60)
		tmp = fma(Float64(a - 0.5), b, Float64(x + y));
	else
		tmp = Float64(Float64(fma(Float64(a - 0.5), b, z) + y) - Float64(log(t) * z));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[x, -2.2e+60], N[(N[(a - 0.5), $MachinePrecision] * b + N[(x + y), $MachinePrecision]), $MachinePrecision], N[(N[(N[(N[(a - 0.5), $MachinePrecision] * b + z), $MachinePrecision] + y), $MachinePrecision] - N[(N[Log[t], $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.19999999999999996e60

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 86.1

         \[\leadsto \mathsf{fma}\left(a - 0.5, b, x + \color{blue}{y}\right) \]

   6. Applied rewrites 86.1%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, \color{blue}{x + y}\right) \]

   if -2.19999999999999996e60 < x

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. +-commutative N/A

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

      5. *-commutative N/A

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

      6. lower-fma.f64 N/A

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

      7. lift--.f64 N/A

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

      8. *-commutative N/A

         \[\leadsto \left(\mathsf{fma}\left(a - \frac{1}{2}, b, z\right) + y\right) - \log t \cdot \color{blue}{z} \]

      9. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(a - \frac{1}{2}, b, z\right) + y\right) - \log t \cdot \color{blue}{z} \]

      10. lift-log.f64 84.4

          \[\leadsto \left(\mathsf{fma}\left(a - 0.5, b, z\right) + y\right) - \log t \cdot z \]

   4. Applied rewrites 84.4%

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

Alternative 6: 83.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.1 \cdot 10^{+268}:\\ \;\;\;\;\left(1 - \log t\right) \cdot z\\ \mathbf{elif}\;z \leq 10^{+194}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, x + y\right)\\ \mathbf{else}:\\ \;\;\;\;\left(z + y\right) - \log t \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -6.1e+268)
   (* (- 1.0 (log t)) z)
   (if (<= z 1e+194) (fma (- a 0.5) b (+ x y)) (- (+ z y) (* (log t) z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -6.1e+268) {
		tmp = (1.0 - log(t)) * z;
	} else if (z <= 1e+194) {
		tmp = fma((a - 0.5), b, (x + y));
	} else {
		tmp = (z + y) - (log(t) * z);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -6.1e+268)
		tmp = Float64(Float64(1.0 - log(t)) * z);
	elseif (z <= 1e+194)
		tmp = fma(Float64(a - 0.5), b, Float64(x + y));
	else
		tmp = Float64(Float64(z + y) - Float64(log(t) * z));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -6.1e+268], N[(N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[z, 1e+194], N[(N[(a - 0.5), $MachinePrecision] * b + N[(x + y), $MachinePrecision]), $MachinePrecision], N[(N[(z + y), $MachinePrecision] - N[(N[Log[t], $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -6.10000000000000002e268

   1. Initial program 99.6%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \left(1 - \log t\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(1 - \log t\right) \cdot \color{blue}{z} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 - \log t\right) \cdot \color{blue}{z} \]

      3. lower--.f64 N/A

         \[\leadsto \left(1 - \log t\right) \cdot z \]

      4. lift-log.f64 80.6

         \[\leadsto \left(1 - \log t\right) \cdot z \]

   4. Applied rewrites 80.6%

      \[\leadsto \color{blue}{\left(1 - \log t\right) \cdot z} \]

   if -6.10000000000000002e268 < z < 9.99999999999999945e193

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 85.1

         \[\leadsto \mathsf{fma}\left(a - 0.5, b, x + \color{blue}{y}\right) \]

   6. Applied rewrites 85.1%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, \color{blue}{x + y}\right) \]

   if 9.99999999999999945e193 < z

   1. Initial program 99.6%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. +-commutative N/A

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

      3. lower-+.f64 N/A

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

      4. +-commutative N/A

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

      5. *-commutative N/A

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

      6. lower-fma.f64 N/A

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

      7. lift--.f64 N/A

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

      8. *-commutative N/A

         \[\leadsto \left(\mathsf{fma}\left(a - \frac{1}{2}, b, z\right) + y\right) - \log t \cdot \color{blue}{z} \]

      9. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{fma}\left(a - \frac{1}{2}, b, z\right) + y\right) - \log t \cdot \color{blue}{z} \]

      10. lift-log.f64 93.6

          \[\leadsto \left(\mathsf{fma}\left(a - 0.5, b, z\right) + y\right) - \log t \cdot z \]

   4. Applied rewrites 93.6%

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(a - 0.5, b, z\right) + y\right) - \log t \cdot z} \]

   5. Taylor expanded in z around inf

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

   7. Applied rewrites 71.8%

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

Alternative 7: 82.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.1 \cdot 10^{+268}:\\ \;\;\;\;\left(1 - \log t\right) \cdot z\\ \mathbf{elif}\;z \leq 2.9 \cdot 10^{+118}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, x + y\right)\\ \mathbf{else}:\\ \;\;\;\;\left(z + x\right) - \log t \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= z -6.1e+268)
   (* (- 1.0 (log t)) z)
   (if (<= z 2.9e+118) (fma (- a 0.5) b (+ x y)) (- (+ z x) (* (log t) z)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (z <= -6.1e+268) {
		tmp = (1.0 - log(t)) * z;
	} else if (z <= 2.9e+118) {
		tmp = fma((a - 0.5), b, (x + y));
	} else {
		tmp = (z + x) - (log(t) * z);
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (z <= -6.1e+268)
		tmp = Float64(Float64(1.0 - log(t)) * z);
	elseif (z <= 2.9e+118)
		tmp = fma(Float64(a - 0.5), b, Float64(x + y));
	else
		tmp = Float64(Float64(z + x) - Float64(log(t) * z));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[z, -6.1e+268], N[(N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision], If[LessEqual[z, 2.9e+118], N[(N[(a - 0.5), $MachinePrecision] * b + N[(x + y), $MachinePrecision]), $MachinePrecision], N[(N[(z + x), $MachinePrecision] - N[(N[Log[t], $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -6.10000000000000002e268

   1. Initial program 99.6%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \left(1 - \log t\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(1 - \log t\right) \cdot \color{blue}{z} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 - \log t\right) \cdot \color{blue}{z} \]

      3. lower--.f64 N/A

         \[\leadsto \left(1 - \log t\right) \cdot z \]

      4. lift-log.f64 80.6

         \[\leadsto \left(1 - \log t\right) \cdot z \]

   4. Applied rewrites 80.6%

      \[\leadsto \color{blue}{\left(1 - \log t\right) \cdot z} \]

   if -6.10000000000000002e268 < z < 2.90000000000000016e118

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 86.4

         \[\leadsto \mathsf{fma}\left(a - 0.5, b, x + \color{blue}{y}\right) \]

   6. Applied rewrites 86.4%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, \color{blue}{x + y}\right) \]

   if 2.90000000000000016e118 < z

   1. Initial program 99.7%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.7

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

   3. Applied rewrites 99.7%

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

   4. Taylor expanded in b around 0

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

      1. +-commutative N/A

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

      2. associate--l+ N/A

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

      3. +-commutative N/A

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

      4. *-commutative N/A

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

      5. associate--l+ N/A

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

      6. lower--.f64 N/A

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

      7. +-commutative N/A

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

      8. lower-+.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-+.f64 N/A

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

      11. *-commutative N/A

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

      12. lift-log.f64 N/A

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

      13. lift-*.f64 72.5

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

   6. Applied rewrites 72.5%

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

   7. Taylor expanded in y around 0

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

   9. Applied rewrites 61.5%

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

Alternative 8: 83.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(1 - \log t\right) \cdot z\\ \mathbf{if}\;z \leq -6.1 \cdot 10^{+268}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;z \leq 3.7 \cdot 10^{+194}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, x + y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (- 1.0 (log t)) z)))
   (if (<= z -6.1e+268)
     t_1
     (if (<= z 3.7e+194) (fma (- a 0.5) b (+ x y)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (1.0 - log(t)) * z;
	double tmp;
	if (z <= -6.1e+268) {
		tmp = t_1;
	} else if (z <= 3.7e+194) {
		tmp = fma((a - 0.5), b, (x + y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(1.0 - log(t)) * z)
	tmp = 0.0
	if (z <= -6.1e+268)
		tmp = t_1;
	elseif (z <= 3.7e+194)
		tmp = fma(Float64(a - 0.5), b, Float64(x + y));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(1.0 - N[Log[t], $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision]}, If[LessEqual[z, -6.1e+268], t$95$1, If[LessEqual[z, 3.7e+194], N[(N[(a - 0.5), $MachinePrecision] * b + N[(x + y), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if z < -6.10000000000000002e268 or 3.7000000000000003e194 < z

   1. Initial program 99.6%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in z around inf

      \[\leadsto \color{blue}{z \cdot \left(1 - \log t\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(1 - \log t\right) \cdot \color{blue}{z} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(1 - \log t\right) \cdot \color{blue}{z} \]

      3. lower--.f64 N/A

         \[\leadsto \left(1 - \log t\right) \cdot z \]

      4. lift-log.f64 69.2

         \[\leadsto \left(1 - \log t\right) \cdot z \]

   4. Applied rewrites 69.2%

      \[\leadsto \color{blue}{\left(1 - \log t\right) \cdot z} \]

   if -6.10000000000000002e268 < z < 3.7000000000000003e194

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 85.1

         \[\leadsto \mathsf{fma}\left(a - 0.5, b, x + \color{blue}{y}\right) \]

   6. Applied rewrites 85.1%

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

Alternative 9: 41.4% accurate, 4.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x + y \leq -3 \cdot 10^{+142}:\\ \;\;\;\;x\\ \mathbf{elif}\;x + y \leq 2 \cdot 10^{+148}:\\ \;\;\;\;\left(a - 0.5\right) \cdot b\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= (+ x y) -3e+142) x (if (<= (+ x y) 2e+148) (* (- a 0.5) b) y)))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((x + y) <= -3e+142) {
		tmp = x;
	} else if ((x + y) <= 2e+148) {
		tmp = (a - 0.5) * b;
	} else {
		tmp = y;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((x + y) <= (-3d+142)) then
        tmp = x
    else if ((x + y) <= 2d+148) then
        tmp = (a - 0.5d0) * b
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if ((x + y) <= -3e+142) {
		tmp = x;
	} else if ((x + y) <= 2e+148) {
		tmp = (a - 0.5) * b;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if (x + y) <= -3e+142:
		tmp = x
	elif (x + y) <= 2e+148:
		tmp = (a - 0.5) * b
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (Float64(x + y) <= -3e+142)
		tmp = x;
	elseif (Float64(x + y) <= 2e+148)
		tmp = Float64(Float64(a - 0.5) * b);
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if ((x + y) <= -3e+142)
		tmp = x;
	elseif ((x + y) <= 2e+148)
		tmp = (a - 0.5) * b;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[N[(x + y), $MachinePrecision], -3e+142], x, If[LessEqual[N[(x + y), $MachinePrecision], 2e+148], N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision], y]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 x y) < -2.99999999999999975e142

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in x around inf

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

   4. Applied rewrites 32.9%

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

   if -2.99999999999999975e142 < (+.f64 x y) < 2.0000000000000001e148

   1. Initial program 99.8%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(a - \frac{1}{2}\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(a - \frac{1}{2}\right) \cdot \color{blue}{b} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(a - \frac{1}{2}\right) \cdot \color{blue}{b} \]

      3. lift--.f64 49.1

         \[\leadsto \left(a - 0.5\right) \cdot b \]

   4. Applied rewrites 49.1%

      \[\leadsto \color{blue}{\left(a - 0.5\right) \cdot b} \]

   if 2.0000000000000001e148 < (+.f64 x y)

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in y around inf

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

   4. Applied rewrites 32.1%

      \[\leadsto \color{blue}{y} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 71.2% accurate, 5.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_1 := \left(a - 0.5\right) \cdot b\\ \mathbf{if}\;b \leq -6.2 \cdot 10^{+176}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq 2.5 \cdot 10^{+180}:\\ \;\;\;\;\mathsf{fma}\left(a, b, x + y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (let* ((t_1 (* (- a 0.5) b)))
   (if (<= b -6.2e+176) t_1 (if (<= b 2.5e+180) (fma a b (+ x y)) t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (a - 0.5) * b;
	double tmp;
	if (b <= -6.2e+176) {
		tmp = t_1;
	} else if (b <= 2.5e+180) {
		tmp = fma(a, b, (x + y));
	} else {
		tmp = t_1;
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(a - 0.5) * b)
	tmp = 0.0
	if (b <= -6.2e+176)
		tmp = t_1;
	elseif (b <= 2.5e+180)
		tmp = fma(a, b, Float64(x + y));
	else
		tmp = t_1;
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]}, If[LessEqual[b, -6.2e+176], t$95$1, If[LessEqual[b, 2.5e+180], N[(a * b + N[(x + y), $MachinePrecision]), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if b < -6.1999999999999998e176 or 2.4999999999999998e180 < b

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

   2. Taylor expanded in b around inf

      \[\leadsto \color{blue}{b \cdot \left(a - \frac{1}{2}\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(a - \frac{1}{2}\right) \cdot \color{blue}{b} \]

      2. lift-*.f64 N/A

         \[\leadsto \left(a - \frac{1}{2}\right) \cdot \color{blue}{b} \]

      3. lift--.f64 82.3

         \[\leadsto \left(a - 0.5\right) \cdot b \]

   4. Applied rewrites 82.3%

      \[\leadsto \color{blue}{\left(a - 0.5\right) \cdot b} \]

   if -6.1999999999999998e176 < b < 2.4999999999999998e180

   1. Initial program 99.8%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 74.4

         \[\leadsto \mathsf{fma}\left(a - 0.5, b, x + \color{blue}{y}\right) \]

   6. Applied rewrites 74.4%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, \color{blue}{x + y}\right) \]

   7. Taylor expanded in a around inf

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

   9. Applied rewrites 68.2%

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

Alternative 11: 65.4% accurate, 7.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq 3.3 \cdot 10^{+18}:\\ \;\;\;\;\mathsf{fma}\left(a - 0.5, b, x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(a, b, x + y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (if (<= y 3.3e+18) (fma (- a 0.5) b x) (fma a b (+ x y))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (y <= 3.3e+18) {
		tmp = fma((a - 0.5), b, x);
	} else {
		tmp = fma(a, b, (x + y));
	}
	return tmp;
}

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (y <= 3.3e+18)
		tmp = fma(Float64(a - 0.5), b, x);
	else
		tmp = fma(a, b, Float64(x + y));
	end
	return tmp
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[y, 3.3e+18], N[(N[(a - 0.5), $MachinePrecision] * b + x), $MachinePrecision], N[(a * b + N[(x + y), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < 3.3e18

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in x around inf

      \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, \color{blue}{x}\right) \]
   5. Step-by-step derivation

   6. Applied rewrites 62.3%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, \color{blue}{x}\right) \]

   if 3.3e18 < y

   1. Initial program 99.9%

      \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
   2. Step-by-step derivation

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. lift-+.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-log.f64 N/A

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

      7. lift--.f64 N/A

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

      8. lift-*.f64 N/A

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

      9. +-commutative N/A

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

      10. lower-fma.f64 N/A

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

      11. lift--.f64 N/A

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

      12. lower--.f64 N/A

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

      13. lift-+.f64 N/A

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

      14. +-commutative N/A

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

      15. lower-+.f64 N/A

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

      16. *-commutative N/A

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

      17. lower-*.f64 N/A

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

      18. lift-log.f64 99.9

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

   3. Applied rewrites 99.9%

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

   4. Taylor expanded in z around 0

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

      1. lower-+.f64 85.1

         \[\leadsto \mathsf{fma}\left(a - 0.5, b, x + \color{blue}{y}\right) \]

   6. Applied rewrites 85.1%

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, \color{blue}{x + y}\right) \]

   7. Taylor expanded in a around inf

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

   9. Applied rewrites 76.3%

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

Alternative 12: 78.4% accurate, 9.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(a - 0.5, b, x + y\right) \end{array} \]

FPCore

(FPCore (x y z t a b) :precision binary64 (fma (- a 0.5) b (+ x y)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return fma((a - 0.5), b, (x + y));
}

Julia

function code(x, y, z, t, a, b)
	return fma(Float64(a - 0.5), b, Float64(x + y))
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(a - 0.5), $MachinePrecision] * b + N[(x + y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]
2. Step-by-step derivation

   1. lift-+.f64 N/A

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

   2. lift--.f64 N/A

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

   3. lift-+.f64 N/A

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

   4. lift-+.f64 N/A

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

   5. lift-*.f64 N/A

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

   6. lift-log.f64 N/A

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

   7. lift--.f64 N/A

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

   8. lift-*.f64 N/A

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

   9. +-commutative N/A

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

   10. lower-fma.f64 N/A

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

   11. lift--.f64 N/A

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

   12. lower--.f64 N/A

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

   13. lift-+.f64 N/A

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

   14. +-commutative N/A

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

   15. lower-+.f64 N/A

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

   16. *-commutative N/A

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

   17. lower-*.f64 N/A

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

   18. lift-log.f64 99.9

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

3. Applied rewrites 99.9%

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

4. Taylor expanded in z around 0

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

   1. lower-+.f64 78.4

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, x + \color{blue}{y}\right) \]

6. Applied rewrites 78.4%

   \[\leadsto \mathsf{fma}\left(a - 0.5, b, \color{blue}{x + y}\right) \]
7. Add Preprocessing

Alternative 13: 78.4% accurate, 9.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(a - 0.5, b, y\right) + x \end{array} \]

FPCore

(FPCore (x y z t a b) :precision binary64 (+ (fma (- a 0.5) b y) x))

C

double code(double x, double y, double z, double t, double a, double b) {
	return fma((a - 0.5), b, y) + x;
}

Julia

function code(x, y, z, t, a, b)
	return Float64(fma(Float64(a - 0.5), b, y) + x)
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(a - 0.5), $MachinePrecision] * b + y), $MachinePrecision] + x), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

2. Taylor expanded in z around 0

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

   1. +-commutative N/A

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

   2. lower-+.f64 N/A

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

   3. +-commutative N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(a - \frac{1}{2}, b, y\right) + x \]

   6. lift--.f64 78.4

      \[\leadsto \mathsf{fma}\left(a - 0.5, b, y\right) + x \]

4. Applied rewrites 78.4%

   \[\leadsto \color{blue}{\mathsf{fma}\left(a - 0.5, b, y\right) + x} \]
5. Add Preprocessing

Alternative 14: 22.5% accurate, 126.0× speedup

Math

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

FPCore

(FPCore (x y z t a b) :precision binary64 x)

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_, b_] := x

Derivation

1. Initial program 99.9%

   \[\left(\left(\left(x + y\right) + z\right) - z \cdot \log t\right) + \left(a - 0.5\right) \cdot b \]

2. Taylor expanded in x around inf

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

4. Applied rewrites 22.5%

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

Developer Target 1: 99.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \left(\left(x + y\right) + \frac{\left(1 - {\log t}^{2}\right) \cdot z}{1 + \log t}\right) + \left(a - 0.5\right) \cdot b \end{array} \]

FPCore

(FPCore (x y z t a b)
 :precision binary64
 (+
  (+ (+ x y) (/ (* (- 1.0 (pow (log t) 2.0)) z) (+ 1.0 (log t))))
  (* (- a 0.5) b)))

C

double code(double x, double y, double z, double t, double a, double b) {
	return ((x + y) + (((1.0 - pow(log(t), 2.0)) * z) / (1.0 + log(t)))) + ((a - 0.5) * b);
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, t, a, b)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = ((x + y) + (((1.0d0 - (log(t) ** 2.0d0)) * z) / (1.0d0 + log(t)))) + ((a - 0.5d0) * b)
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return ((x + y) + (((1.0 - Math.pow(Math.log(t), 2.0)) * z) / (1.0 + Math.log(t)))) + ((a - 0.5) * b);
}

Python

def code(x, y, z, t, a, b):
	return ((x + y) + (((1.0 - math.pow(math.log(t), 2.0)) * z) / (1.0 + math.log(t)))) + ((a - 0.5) * b)

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(x + y) + Float64(Float64(Float64(1.0 - (log(t) ^ 2.0)) * z) / Float64(1.0 + log(t)))) + Float64(Float64(a - 0.5) * b))
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = ((x + y) + (((1.0 - (log(t) ^ 2.0)) * z) / (1.0 + log(t)))) + ((a - 0.5) * b);
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(x + y), $MachinePrecision] + N[(N[(N[(1.0 - N[Power[N[Log[t], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] * z), $MachinePrecision] / N[(1.0 + N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(a - 0.5), $MachinePrecision] * b), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2025095 
(FPCore (x y z t a b)
  :name "Numeric.SpecFunctions:logBeta from math-functions-0.1.5.2, A"
  :precision binary64

  :alt
  (! :herbie-platform default (+ (+ (+ x y) (/ (* (- 1 (pow (log t) 2)) z) (+ 1 (log t)))) (* (- a 1/2) b)))

  (+ (- (+ (+ x y) z) (* z (log t))) (* (- a 0.5) b)))