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

Percentage Accurate: 98.4% → 98.4%

Time: 7.1s

Alternatives: 14

Speedup: 1.0×

• 52.0% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 * exp((((y * log(z)) + ((t - 1.0d0) * log(a))) - b))) / y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 98.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 * exp((((y * log(z)) + ((t - 1.0d0) * log(a))) - b))) / y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 93.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} t_1 := \left(t - 1\right) \cdot \log a\\ t_2 := \frac{x \cdot e^{\log a \cdot \left(t - 1\right) - b}}{y}\\ \mathbf{if}\;t\_1 \leq -2 \cdot 10^{+30}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+174}:\\ \;\;\;\;\frac{x \cdot e^{\left(y \cdot \log z + -1 \cdot \log a\right) - b}}{y}\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* (- t 1.0) (log a)))
       (t_2 (/ (* x (exp (- (* (log a) (- t 1.0)) b))) y)))
  (if (<= t_1 -2e+30)
    t_2
    (if (<= t_1 5e+174)
      (/ (* x (exp (- (+ (* y (log z)) (* -1.0 (log a))) b))) y)
      t_2))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (t - 1.0) * log(a);
	double t_2 = (x * exp(((log(a) * (t - 1.0)) - b))) / y;
	double tmp;
	if (t_1 <= -2e+30) {
		tmp = t_2;
	} else if (t_1 <= 5e+174) {
		tmp = (x * exp((((y * log(z)) + (-1.0 * log(a))) - b))) / y;
	} else {
		tmp = t_2;
	}
	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) :: t_2
    real(8) :: tmp
    t_1 = (t - 1.0d0) * log(a)
    t_2 = (x * exp(((log(a) * (t - 1.0d0)) - b))) / y
    if (t_1 <= (-2d+30)) then
        tmp = t_2
    else if (t_1 <= 5d+174) then
        tmp = (x * exp((((y * log(z)) + ((-1.0d0) * log(a))) - b))) / y
    else
        tmp = t_2
    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 = (t - 1.0) * Math.log(a);
	double t_2 = (x * Math.exp(((Math.log(a) * (t - 1.0)) - b))) / y;
	double tmp;
	if (t_1 <= -2e+30) {
		tmp = t_2;
	} else if (t_1 <= 5e+174) {
		tmp = (x * Math.exp((((y * Math.log(z)) + (-1.0 * Math.log(a))) - b))) / y;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (t - 1.0) * math.log(a)
	t_2 = (x * math.exp(((math.log(a) * (t - 1.0)) - b))) / y
	tmp = 0
	if t_1 <= -2e+30:
		tmp = t_2
	elif t_1 <= 5e+174:
		tmp = (x * math.exp((((y * math.log(z)) + (-1.0 * math.log(a))) - b))) / y
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(t - 1.0) * log(a))
	t_2 = Float64(Float64(x * exp(Float64(Float64(log(a) * Float64(t - 1.0)) - b))) / y)
	tmp = 0.0
	if (t_1 <= -2e+30)
		tmp = t_2;
	elseif (t_1 <= 5e+174)
		tmp = Float64(Float64(x * exp(Float64(Float64(Float64(y * log(z)) + Float64(-1.0 * log(a))) - b))) / y);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (t - 1.0) * log(a);
	t_2 = (x * exp(((log(a) * (t - 1.0)) - b))) / y;
	tmp = 0.0;
	if (t_1 <= -2e+30)
		tmp = t_2;
	elseif (t_1 <= 5e+174)
		tmp = (x * exp((((y * log(z)) + (-1.0 * log(a))) - b))) / y;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(t - 1.0), $MachinePrecision] * N[Log[a], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$2 = N[(N[(x * N[Exp[N[(N[(N[Log[a], $MachinePrecision] * N[(t - 1.0), $MachinePrecision]), $MachinePrecision] - b), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[t$95$1, -2e+30], t$95$2, If[LessEqual[t$95$1, 5e+174], N[(N[(x * N[Exp[N[(N[(N[(y * N[Log[z], $MachinePrecision]), $MachinePrecision] + N[(-1.0 * N[Log[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - b), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision], t$95$2]]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a)) < -2e30 or 4.9999999999999997e174 < (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a))

   1. Initial program 98.4%

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

   2. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 N/A

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

      3. lower--.f64 79.4%

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

   4. Applied rewrites 79.4%

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

   if -2e30 < (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a)) < 4.9999999999999997e174

   1. Initial program 98.4%

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

   2. Taylor expanded in t around 0

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

   4. Applied rewrites 80.6%

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

Alternative 2: 91.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} t_1 := \frac{x \cdot e^{y \cdot \log z - b}}{y}\\ \mathbf{if}\;y \leq -3.6 \cdot 10^{+33}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;y \leq 10.5:\\ \;\;\;\;\frac{x \cdot e^{\log a \cdot \left(t - 1\right) - b}}{y}\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (/ (* x (exp (- (* y (log z)) b))) y)))
  (if (<= y -3.6e+33)
    t_1
    (if (<= y 10.5)
      (/ (* x (exp (- (* (log a) (- t 1.0)) b))) y)
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x * exp(((y * log(z)) - b))) / y;
	double tmp;
	if (y <= -3.6e+33) {
		tmp = t_1;
	} else if (y <= 10.5) {
		tmp = (x * exp(((log(a) * (t - 1.0)) - b))) / y;
	} else {
		tmp = t_1;
	}
	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 * exp(((y * log(z)) - b))) / y
    if (y <= (-3.6d+33)) then
        tmp = t_1
    else if (y <= 10.5d0) then
        tmp = (x * exp(((log(a) * (t - 1.0d0)) - b))) / y
    else
        tmp = t_1
    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 * Math.exp(((y * Math.log(z)) - b))) / y;
	double tmp;
	if (y <= -3.6e+33) {
		tmp = t_1;
	} else if (y <= 10.5) {
		tmp = (x * Math.exp(((Math.log(a) * (t - 1.0)) - b))) / y;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x * math.exp(((y * math.log(z)) - b))) / y
	tmp = 0
	if y <= -3.6e+33:
		tmp = t_1
	elif y <= 10.5:
		tmp = (x * math.exp(((math.log(a) * (t - 1.0)) - b))) / y
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x * exp(Float64(Float64(y * log(z)) - b))) / y)
	tmp = 0.0
	if (y <= -3.6e+33)
		tmp = t_1;
	elseif (y <= 10.5)
		tmp = Float64(Float64(x * exp(Float64(Float64(log(a) * Float64(t - 1.0)) - b))) / y);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x * exp(((y * log(z)) - b))) / y;
	tmp = 0.0;
	if (y <= -3.6e+33)
		tmp = t_1;
	elseif (y <= 10.5)
		tmp = (x * exp(((log(a) * (t - 1.0)) - b))) / y;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x * N[Exp[N[(N[(y * N[Log[z], $MachinePrecision]), $MachinePrecision] - b), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[y, -3.6e+33], t$95$1, If[LessEqual[y, 10.5], N[(N[(x * N[Exp[N[(N[(N[Log[a], $MachinePrecision] * N[(t - 1.0), $MachinePrecision]), $MachinePrecision] - b), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if y < -3.6000000000000003e33 or 10.5 < y

   1. Initial program 98.4%

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

   2. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 N/A

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

      3. lower--.f64 79.4%

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

   4. Applied rewrites 79.4%

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

   5. Taylor expanded in y around inf

      \[\leadsto \frac{x \cdot e^{\color{blue}{y \cdot \log z} - b}}{y} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{x \cdot e^{y \cdot \color{blue}{\log z} - b}}{y} \]

      2. lower-log.f64 71.3%

         \[\leadsto \frac{x \cdot e^{y \cdot \log z - b}}{y} \]

   7. Applied rewrites 71.3%

      \[\leadsto \frac{x \cdot e^{\color{blue}{y \cdot \log z} - b}}{y} \]

   if -3.6000000000000003e33 < y < 10.5

   1. Initial program 98.4%

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

   2. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 N/A

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

      3. lower--.f64 79.4%

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

   4. Applied rewrites 79.4%

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

Alternative 3: 83.9% accurate, 1.4× speedup

Math

\[\begin{array}{l} t_1 := {a}^{\left(t - 1\right)}\\ t_2 := \frac{x \cdot e^{y \cdot \log z - b}}{y}\\ \mathbf{if}\;b \leq -460:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;b \leq 6 \cdot 10^{-108}:\\ \;\;\;\;t\_1 \cdot \frac{x \cdot {z}^{y}}{y}\\ \mathbf{elif}\;b \leq 6 \cdot 10^{+54}:\\ \;\;\;\;\frac{t\_1 \cdot 1}{y + b \cdot y} \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (pow a (- t 1.0)))
       (t_2 (/ (* x (exp (- (* y (log z)) b))) y)))
  (if (<= b -460.0)
    t_2
    (if (<= b 6e-108)
      (* t_1 (/ (* x (pow z y)) y))
      (if (<= b 6e+54) (* (/ (* t_1 1.0) (+ y (* b y))) x) t_2)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = pow(a, (t - 1.0));
	double t_2 = (x * exp(((y * log(z)) - b))) / y;
	double tmp;
	if (b <= -460.0) {
		tmp = t_2;
	} else if (b <= 6e-108) {
		tmp = t_1 * ((x * pow(z, y)) / y);
	} else if (b <= 6e+54) {
		tmp = ((t_1 * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = t_2;
	}
	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) :: t_2
    real(8) :: tmp
    t_1 = a ** (t - 1.0d0)
    t_2 = (x * exp(((y * log(z)) - b))) / y
    if (b <= (-460.0d0)) then
        tmp = t_2
    else if (b <= 6d-108) then
        tmp = t_1 * ((x * (z ** y)) / y)
    else if (b <= 6d+54) then
        tmp = ((t_1 * 1.0d0) / (y + (b * y))) * x
    else
        tmp = t_2
    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 = Math.pow(a, (t - 1.0));
	double t_2 = (x * Math.exp(((y * Math.log(z)) - b))) / y;
	double tmp;
	if (b <= -460.0) {
		tmp = t_2;
	} else if (b <= 6e-108) {
		tmp = t_1 * ((x * Math.pow(z, y)) / y);
	} else if (b <= 6e+54) {
		tmp = ((t_1 * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = t_2;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = math.pow(a, (t - 1.0))
	t_2 = (x * math.exp(((y * math.log(z)) - b))) / y
	tmp = 0
	if b <= -460.0:
		tmp = t_2
	elif b <= 6e-108:
		tmp = t_1 * ((x * math.pow(z, y)) / y)
	elif b <= 6e+54:
		tmp = ((t_1 * 1.0) / (y + (b * y))) * x
	else:
		tmp = t_2
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = a ^ Float64(t - 1.0)
	t_2 = Float64(Float64(x * exp(Float64(Float64(y * log(z)) - b))) / y)
	tmp = 0.0
	if (b <= -460.0)
		tmp = t_2;
	elseif (b <= 6e-108)
		tmp = Float64(t_1 * Float64(Float64(x * (z ^ y)) / y));
	elseif (b <= 6e+54)
		tmp = Float64(Float64(Float64(t_1 * 1.0) / Float64(y + Float64(b * y))) * x);
	else
		tmp = t_2;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = a ^ (t - 1.0);
	t_2 = (x * exp(((y * log(z)) - b))) / y;
	tmp = 0.0;
	if (b <= -460.0)
		tmp = t_2;
	elseif (b <= 6e-108)
		tmp = t_1 * ((x * (z ^ y)) / y);
	elseif (b <= 6e+54)
		tmp = ((t_1 * 1.0) / (y + (b * y))) * x;
	else
		tmp = t_2;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[Power[a, N[(t - 1.0), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[(x * N[Exp[N[(N[(y * N[Log[z], $MachinePrecision]), $MachinePrecision] - b), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[b, -460.0], t$95$2, If[LessEqual[b, 6e-108], N[(t$95$1 * N[(N[(x * N[Power[z, y], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 6e+54], N[(N[(N[(t$95$1 * 1.0), $MachinePrecision] / N[(y + N[(b * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], t$95$2]]]]]

Derivation

1. Split input into 3 regimes

2. if b < -460 or 5.9999999999999998e54 < b

   1. Initial program 98.4%

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

   2. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 N/A

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

      3. lower--.f64 79.4%

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

   4. Applied rewrites 79.4%

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

   5. Taylor expanded in y around inf

      \[\leadsto \frac{x \cdot e^{\color{blue}{y \cdot \log z} - b}}{y} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{x \cdot e^{y \cdot \color{blue}{\log z} - b}}{y} \]

      2. lower-log.f64 71.3%

         \[\leadsto \frac{x \cdot e^{y \cdot \log z - b}}{y} \]

   7. Applied rewrites 71.3%

      \[\leadsto \frac{x \cdot e^{\color{blue}{y \cdot \log z} - b}}{y} \]

   if -460 < b < 5.9999999999999999e-108

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

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

      2. lift-/.f64 N/A

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

      3. lift-*.f64 N/A

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

      4. associate-/l* N/A

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

      5. associate-*l* N/A

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

      6. lower-*.f64 N/A

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

      7. associate-*l/ N/A

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

      8. lower-/.f64 N/A

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

      9. lower-*.f64 66.2%

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

   8. Applied rewrites 66.2%

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

   9. Taylor expanded in b around 0

      \[\leadsto {a}^{\left(t - 1\right)} \cdot \color{blue}{\frac{x \cdot {z}^{y}}{y}} \]
   10. Step-by-step derivation

       1. lower-/.f64 N/A

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

       2. lower-*.f64 N/A

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

       3. lower-pow.f64 66.9%

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

   11. Applied rewrites 66.9%

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

   if 5.9999999999999999e-108 < b < 5.9999999999999998e54

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

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

   7. Taylor expanded in b around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 52.1%

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

   9. Applied rewrites 52.1%

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

Alternative 4: 79.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} t_1 := \frac{x \cdot e^{y \cdot \log z - b}}{y}\\ \mathbf{if}\;b \leq -1:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq 5 \cdot 10^{-245}:\\ \;\;\;\;\frac{\frac{1}{a} \cdot {z}^{y}}{\left(1 + b\right) \cdot y} \cdot x\\ \mathbf{elif}\;b \leq 6 \cdot 10^{+54}:\\ \;\;\;\;\frac{{a}^{\left(t - 1\right)} \cdot 1}{y + b \cdot y} \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (/ (* x (exp (- (* y (log z)) b))) y)))
  (if (<= b -1.0)
    t_1
    (if (<= b 5e-245)
      (* (/ (* (/ 1.0 a) (pow z y)) (* (+ 1.0 b) y)) x)
      (if (<= b 6e+54)
        (* (/ (* (pow a (- t 1.0)) 1.0) (+ y (* b y))) x)
        t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (x * exp(((y * log(z)) - b))) / y;
	double tmp;
	if (b <= -1.0) {
		tmp = t_1;
	} else if (b <= 5e-245) {
		tmp = (((1.0 / a) * pow(z, y)) / ((1.0 + b) * y)) * x;
	} else if (b <= 6e+54) {
		tmp = ((pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = t_1;
	}
	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 * exp(((y * log(z)) - b))) / y
    if (b <= (-1.0d0)) then
        tmp = t_1
    else if (b <= 5d-245) then
        tmp = (((1.0d0 / a) * (z ** y)) / ((1.0d0 + b) * y)) * x
    else if (b <= 6d+54) then
        tmp = (((a ** (t - 1.0d0)) * 1.0d0) / (y + (b * y))) * x
    else
        tmp = t_1
    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 * Math.exp(((y * Math.log(z)) - b))) / y;
	double tmp;
	if (b <= -1.0) {
		tmp = t_1;
	} else if (b <= 5e-245) {
		tmp = (((1.0 / a) * Math.pow(z, y)) / ((1.0 + b) * y)) * x;
	} else if (b <= 6e+54) {
		tmp = ((Math.pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (x * math.exp(((y * math.log(z)) - b))) / y
	tmp = 0
	if b <= -1.0:
		tmp = t_1
	elif b <= 5e-245:
		tmp = (((1.0 / a) * math.pow(z, y)) / ((1.0 + b) * y)) * x
	elif b <= 6e+54:
		tmp = ((math.pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(x * exp(Float64(Float64(y * log(z)) - b))) / y)
	tmp = 0.0
	if (b <= -1.0)
		tmp = t_1;
	elseif (b <= 5e-245)
		tmp = Float64(Float64(Float64(Float64(1.0 / a) * (z ^ y)) / Float64(Float64(1.0 + b) * y)) * x);
	elseif (b <= 6e+54)
		tmp = Float64(Float64(Float64((a ^ Float64(t - 1.0)) * 1.0) / Float64(y + Float64(b * y))) * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (x * exp(((y * log(z)) - b))) / y;
	tmp = 0.0;
	if (b <= -1.0)
		tmp = t_1;
	elseif (b <= 5e-245)
		tmp = (((1.0 / a) * (z ^ y)) / ((1.0 + b) * y)) * x;
	elseif (b <= 6e+54)
		tmp = (((a ^ (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(x * N[Exp[N[(N[(y * N[Log[z], $MachinePrecision]), $MachinePrecision] - b), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, If[LessEqual[b, -1.0], t$95$1, If[LessEqual[b, 5e-245], N[(N[(N[(N[(1.0 / a), $MachinePrecision] * N[Power[z, y], $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 + b), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[b, 6e+54], N[(N[(N[(N[Power[a, N[(t - 1.0), $MachinePrecision]], $MachinePrecision] * 1.0), $MachinePrecision] / N[(y + N[(b * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if b < -1 or 5.9999999999999998e54 < b

   1. Initial program 98.4%

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

   2. Taylor expanded in y around 0

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

      1. lower-*.f64 N/A

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

      2. lower-log.f64 N/A

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

      3. lower--.f64 79.4%

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

   4. Applied rewrites 79.4%

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

   5. Taylor expanded in y around inf

      \[\leadsto \frac{x \cdot e^{\color{blue}{y \cdot \log z} - b}}{y} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \frac{x \cdot e^{y \cdot \color{blue}{\log z} - b}}{y} \]

      2. lower-log.f64 71.3%

         \[\leadsto \frac{x \cdot e^{y \cdot \log z - b}}{y} \]

   7. Applied rewrites 71.3%

      \[\leadsto \frac{x \cdot e^{\color{blue}{y \cdot \log z} - b}}{y} \]

   if -1 < b < 4.9999999999999997e-245

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in b around 0

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

      1. lower-+.f64 58.3%

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

   6. Applied rewrites 58.3%

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

   7. Taylor expanded in t around 0

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

      1. lower-/.f64 49.5%

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

   9. Applied rewrites 49.5%

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

   if 4.9999999999999997e-245 < b < 5.9999999999999998e54

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

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

   7. Taylor expanded in b around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 52.1%

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

   9. Applied rewrites 52.1%

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

Alternative 5: 73.6% accurate, 2.2× speedup

Math

\[\begin{array}{l} t_1 := \frac{e^{-b}}{y} \cdot x\\ \mathbf{if}\;b \leq -1.25 \cdot 10^{+47}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq 5 \cdot 10^{-245}:\\ \;\;\;\;\frac{\frac{1}{a} \cdot {z}^{y}}{\left(1 + b\right) \cdot y} \cdot x\\ \mathbf{elif}\;b \leq 2.95 \cdot 10^{+72}:\\ \;\;\;\;\frac{{a}^{\left(t - 1\right)} \cdot 1}{y + b \cdot y} \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* (/ (exp (- b)) y) x)))
  (if (<= b -1.25e+47)
    t_1
    (if (<= b 5e-245)
      (* (/ (* (/ 1.0 a) (pow z y)) (* (+ 1.0 b) y)) x)
      (if (<= b 2.95e+72)
        (* (/ (* (pow a (- t 1.0)) 1.0) (+ y (* b y))) x)
        t_1)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (exp(-b) / y) * x;
	double tmp;
	if (b <= -1.25e+47) {
		tmp = t_1;
	} else if (b <= 5e-245) {
		tmp = (((1.0 / a) * pow(z, y)) / ((1.0 + b) * y)) * x;
	} else if (b <= 2.95e+72) {
		tmp = ((pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = t_1;
	}
	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 = (exp(-b) / y) * x
    if (b <= (-1.25d+47)) then
        tmp = t_1
    else if (b <= 5d-245) then
        tmp = (((1.0d0 / a) * (z ** y)) / ((1.0d0 + b) * y)) * x
    else if (b <= 2.95d+72) then
        tmp = (((a ** (t - 1.0d0)) * 1.0d0) / (y + (b * y))) * x
    else
        tmp = t_1
    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 = (Math.exp(-b) / y) * x;
	double tmp;
	if (b <= -1.25e+47) {
		tmp = t_1;
	} else if (b <= 5e-245) {
		tmp = (((1.0 / a) * Math.pow(z, y)) / ((1.0 + b) * y)) * x;
	} else if (b <= 2.95e+72) {
		tmp = ((Math.pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (math.exp(-b) / y) * x
	tmp = 0
	if b <= -1.25e+47:
		tmp = t_1
	elif b <= 5e-245:
		tmp = (((1.0 / a) * math.pow(z, y)) / ((1.0 + b) * y)) * x
	elif b <= 2.95e+72:
		tmp = ((math.pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(exp(Float64(-b)) / y) * x)
	tmp = 0.0
	if (b <= -1.25e+47)
		tmp = t_1;
	elseif (b <= 5e-245)
		tmp = Float64(Float64(Float64(Float64(1.0 / a) * (z ^ y)) / Float64(Float64(1.0 + b) * y)) * x);
	elseif (b <= 2.95e+72)
		tmp = Float64(Float64(Float64((a ^ Float64(t - 1.0)) * 1.0) / Float64(y + Float64(b * y))) * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (exp(-b) / y) * x;
	tmp = 0.0;
	if (b <= -1.25e+47)
		tmp = t_1;
	elseif (b <= 5e-245)
		tmp = (((1.0 / a) * (z ^ y)) / ((1.0 + b) * y)) * x;
	elseif (b <= 2.95e+72)
		tmp = (((a ^ (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[Exp[(-b)], $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[b, -1.25e+47], t$95$1, If[LessEqual[b, 5e-245], N[(N[(N[(N[(1.0 / a), $MachinePrecision] * N[Power[z, y], $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 + b), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[b, 2.95e+72], N[(N[(N[(N[Power[a, N[(t - 1.0), $MachinePrecision]], $MachinePrecision] * 1.0), $MachinePrecision] / N[(y + N[(b * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], t$95$1]]]]

Derivation

1. Split input into 3 regimes

2. if b < -1.2500000000000001e47 or 2.9500000000000001e72 < b

   1. Initial program 98.4%

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

   2. Taylor expanded in b around inf

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   3. Step-by-step derivation

      1. lower-*.f64 47.4%

         \[\leadsto \frac{x \cdot e^{-1 \cdot \color{blue}{b}}}{y} \]

   4. Applied rewrites 47.4%

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x \cdot e^{-1 \cdot b}}{y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot e^{-1 \cdot b}}}{y} \]

      3. *-commutative N/A

         \[\leadsto \frac{\color{blue}{e^{-1 \cdot b} \cdot x}}{y} \]

      4. associate-/l* N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      6. lift-*.f64 N/A

         \[\leadsto e^{-1 \cdot \color{blue}{b}} \cdot \frac{x}{y} \]

      7. mul-1-neg N/A

         \[\leadsto e^{\mathsf{neg}\left(b\right)} \cdot \frac{x}{y} \]

      8. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \frac{x}{y} \]

      9. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \mathsf{Rewrite=>}\left(lower-/.f64, \left(\frac{x}{y}\right)\right) \]

   6. Applied rewrites 43.1%

      \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{x}{y} \cdot e^{-b}} \]

      3. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x}{y}} \cdot e^{-b} \]

      4. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{x}{\frac{y}{e^{-b}}}} \]

      5. div-flip-rev N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\frac{y}{e^{-b}}}{x}}} \]

      6. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{e^{-b}}} \cdot x} \]

      7. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{e^{-b}}} \cdot x} \]

      8. div-flip-rev N/A

         \[\leadsto \color{blue}{\frac{e^{-b}}{y}} \cdot x \]

      9. lower-/.f64 47.4%

         \[\leadsto \color{blue}{\frac{e^{-b}}{y}} \cdot x \]

   8. Applied rewrites 47.4%

      \[\leadsto \color{blue}{\frac{e^{-b}}{y} \cdot x} \]

   if -1.2500000000000001e47 < b < 4.9999999999999997e-245

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in b around 0

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

      1. lower-+.f64 58.3%

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

   6. Applied rewrites 58.3%

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

   7. Taylor expanded in t around 0

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

      1. lower-/.f64 49.5%

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

   9. Applied rewrites 49.5%

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

   if 4.9999999999999997e-245 < b < 2.9500000000000001e72

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

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

   7. Taylor expanded in b around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 52.1%

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

   9. Applied rewrites 52.1%

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

Alternative 6: 73.3% accurate, 2.3× speedup

Math

\[\begin{array}{l} \mathbf{if}\;b \leq -8.6 \cdot 10^{-25}:\\ \;\;\;\;\frac{1}{a} \cdot \left(\frac{1}{e^{b} \cdot y} \cdot x\right)\\ \mathbf{elif}\;b \leq 2.95 \cdot 10^{+72}:\\ \;\;\;\;\frac{{a}^{\left(t - 1\right)} \cdot 1}{y + b \cdot y} \cdot x\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{-b}}{y} \cdot x\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (if (<= b -8.6e-25)
  (* (/ 1.0 a) (* (/ 1.0 (* (exp b) y)) x))
  (if (<= b 2.95e+72)
    (* (/ (* (pow a (- t 1.0)) 1.0) (+ y (* b y))) x)
    (* (/ (exp (- b)) y) x))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double tmp;
	if (b <= -8.6e-25) {
		tmp = (1.0 / a) * ((1.0 / (exp(b) * y)) * x);
	} else if (b <= 2.95e+72) {
		tmp = ((pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = (exp(-b) / y) * x;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 (b <= (-8.6d-25)) then
        tmp = (1.0d0 / a) * ((1.0d0 / (exp(b) * y)) * x)
    else if (b <= 2.95d+72) then
        tmp = (((a ** (t - 1.0d0)) * 1.0d0) / (y + (b * y))) * x
    else
        tmp = (exp(-b) / y) * x
    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 (b <= -8.6e-25) {
		tmp = (1.0 / a) * ((1.0 / (Math.exp(b) * y)) * x);
	} else if (b <= 2.95e+72) {
		tmp = ((Math.pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = (Math.exp(-b) / y) * x;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	tmp = 0
	if b <= -8.6e-25:
		tmp = (1.0 / a) * ((1.0 / (math.exp(b) * y)) * x)
	elif b <= 2.95e+72:
		tmp = ((math.pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x
	else:
		tmp = (math.exp(-b) / y) * x
	return tmp

Julia

function code(x, y, z, t, a, b)
	tmp = 0.0
	if (b <= -8.6e-25)
		tmp = Float64(Float64(1.0 / a) * Float64(Float64(1.0 / Float64(exp(b) * y)) * x));
	elseif (b <= 2.95e+72)
		tmp = Float64(Float64(Float64((a ^ Float64(t - 1.0)) * 1.0) / Float64(y + Float64(b * y))) * x);
	else
		tmp = Float64(Float64(exp(Float64(-b)) / y) * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	tmp = 0.0;
	if (b <= -8.6e-25)
		tmp = (1.0 / a) * ((1.0 / (exp(b) * y)) * x);
	elseif (b <= 2.95e+72)
		tmp = (((a ^ (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	else
		tmp = (exp(-b) / y) * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := If[LessEqual[b, -8.6e-25], N[(N[(1.0 / a), $MachinePrecision] * N[(N[(1.0 / N[(N[Exp[b], $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 2.95e+72], N[(N[(N[(N[Power[a, N[(t - 1.0), $MachinePrecision]], $MachinePrecision] * 1.0), $MachinePrecision] / N[(y + N[(b * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], N[(N[(N[Exp[(-b)], $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b < -8.5999999999999995e-25

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

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

   7. Taylor expanded in t around 0

      \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]
   8. Step-by-step derivation

      1. lower-/.f64 58.3%

         \[\leadsto \frac{\frac{1}{\color{blue}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

   9. Applied rewrites 58.3%

      \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \color{blue}{\frac{\frac{1}{a} \cdot 1}{e^{b} \cdot y} \cdot x} \]

       2. lift-/.f64 N/A

          \[\leadsto \color{blue}{\frac{\frac{1}{a} \cdot 1}{e^{b} \cdot y}} \cdot x \]

       3. lift-*.f64 N/A

          \[\leadsto \frac{\color{blue}{\frac{1}{a} \cdot 1}}{e^{b} \cdot y} \cdot x \]

       4. associate-/l* N/A

          \[\leadsto \color{blue}{\left(\frac{1}{a} \cdot \frac{1}{e^{b} \cdot y}\right)} \cdot x \]

       5. associate-*l* N/A

          \[\leadsto \color{blue}{\frac{1}{a} \cdot \left(\frac{1}{e^{b} \cdot y} \cdot x\right)} \]

       6. lower-*.f64 N/A

          \[\leadsto \color{blue}{\frac{1}{a} \cdot \left(\frac{1}{e^{b} \cdot y} \cdot x\right)} \]

       7. lower-*.f64 N/A

          \[\leadsto \frac{1}{a} \cdot \color{blue}{\left(\frac{1}{e^{b} \cdot y} \cdot x\right)} \]

       8. lower-/.f64 58.8%

          \[\leadsto \frac{1}{a} \cdot \left(\color{blue}{\frac{1}{e^{b} \cdot y}} \cdot x\right) \]

   11. Applied rewrites 58.8%

       \[\leadsto \color{blue}{\frac{1}{a} \cdot \left(\frac{1}{e^{b} \cdot y} \cdot x\right)} \]

   if -8.5999999999999995e-25 < b < 2.9500000000000001e72

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

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

   7. Taylor expanded in b around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 52.1%

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

   9. Applied rewrites 52.1%

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

   if 2.9500000000000001e72 < b

   1. Initial program 98.4%

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

   2. Taylor expanded in b around inf

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   3. Step-by-step derivation

      1. lower-*.f64 47.4%

         \[\leadsto \frac{x \cdot e^{-1 \cdot \color{blue}{b}}}{y} \]

   4. Applied rewrites 47.4%

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x \cdot e^{-1 \cdot b}}{y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot e^{-1 \cdot b}}}{y} \]

      3. *-commutative N/A

         \[\leadsto \frac{\color{blue}{e^{-1 \cdot b} \cdot x}}{y} \]

      4. associate-/l* N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      6. lift-*.f64 N/A

         \[\leadsto e^{-1 \cdot \color{blue}{b}} \cdot \frac{x}{y} \]

      7. mul-1-neg N/A

         \[\leadsto e^{\mathsf{neg}\left(b\right)} \cdot \frac{x}{y} \]

      8. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \frac{x}{y} \]

      9. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \mathsf{Rewrite=>}\left(lower-/.f64, \left(\frac{x}{y}\right)\right) \]

   6. Applied rewrites 43.1%

      \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{x}{y} \cdot e^{-b}} \]

      3. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x}{y}} \cdot e^{-b} \]

      4. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{x}{\frac{y}{e^{-b}}}} \]

      5. div-flip-rev N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\frac{y}{e^{-b}}}{x}}} \]

      6. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{e^{-b}}} \cdot x} \]

      7. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{e^{-b}}} \cdot x} \]

      8. div-flip-rev N/A

         \[\leadsto \color{blue}{\frac{e^{-b}}{y}} \cdot x \]

      9. lower-/.f64 47.4%

         \[\leadsto \color{blue}{\frac{e^{-b}}{y}} \cdot x \]

   8. Applied rewrites 47.4%

      \[\leadsto \color{blue}{\frac{e^{-b}}{y} \cdot x} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 73.0% accurate, 2.3× speedup

Math

\[\begin{array}{l} t_1 := \frac{e^{-b}}{y} \cdot x\\ \mathbf{if}\;b \leq -1.45 \cdot 10^{+15}:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;b \leq 2.95 \cdot 10^{+72}:\\ \;\;\;\;\frac{{a}^{\left(t - 1\right)} \cdot 1}{y + b \cdot y} \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1 (* (/ (exp (- b)) y) x)))
  (if (<= b -1.45e+15)
    t_1
    (if (<= b 2.95e+72)
      (* (/ (* (pow a (- t 1.0)) 1.0) (+ y (* b y))) x)
      t_1))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (exp(-b) / y) * x;
	double tmp;
	if (b <= -1.45e+15) {
		tmp = t_1;
	} else if (b <= 2.95e+72) {
		tmp = ((pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = t_1;
	}
	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 = (exp(-b) / y) * x
    if (b <= (-1.45d+15)) then
        tmp = t_1
    else if (b <= 2.95d+72) then
        tmp = (((a ** (t - 1.0d0)) * 1.0d0) / (y + (b * y))) * x
    else
        tmp = t_1
    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 = (Math.exp(-b) / y) * x;
	double tmp;
	if (b <= -1.45e+15) {
		tmp = t_1;
	} else if (b <= 2.95e+72) {
		tmp = ((Math.pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (math.exp(-b) / y) * x
	tmp = 0
	if b <= -1.45e+15:
		tmp = t_1
	elif b <= 2.95e+72:
		tmp = ((math.pow(a, (t - 1.0)) * 1.0) / (y + (b * y))) * x
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(exp(Float64(-b)) / y) * x)
	tmp = 0.0
	if (b <= -1.45e+15)
		tmp = t_1;
	elseif (b <= 2.95e+72)
		tmp = Float64(Float64(Float64((a ^ Float64(t - 1.0)) * 1.0) / Float64(y + Float64(b * y))) * x);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (exp(-b) / y) * x;
	tmp = 0.0;
	if (b <= -1.45e+15)
		tmp = t_1;
	elseif (b <= 2.95e+72)
		tmp = (((a ^ (t - 1.0)) * 1.0) / (y + (b * y))) * x;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[Exp[(-b)], $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]}, If[LessEqual[b, -1.45e+15], t$95$1, If[LessEqual[b, 2.95e+72], N[(N[(N[(N[Power[a, N[(t - 1.0), $MachinePrecision]], $MachinePrecision] * 1.0), $MachinePrecision] / N[(y + N[(b * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if b < -1.45e15 or 2.9500000000000001e72 < b

   1. Initial program 98.4%

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

   2. Taylor expanded in b around inf

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   3. Step-by-step derivation

      1. lower-*.f64 47.4%

         \[\leadsto \frac{x \cdot e^{-1 \cdot \color{blue}{b}}}{y} \]

   4. Applied rewrites 47.4%

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x \cdot e^{-1 \cdot b}}{y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot e^{-1 \cdot b}}}{y} \]

      3. *-commutative N/A

         \[\leadsto \frac{\color{blue}{e^{-1 \cdot b} \cdot x}}{y} \]

      4. associate-/l* N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      6. lift-*.f64 N/A

         \[\leadsto e^{-1 \cdot \color{blue}{b}} \cdot \frac{x}{y} \]

      7. mul-1-neg N/A

         \[\leadsto e^{\mathsf{neg}\left(b\right)} \cdot \frac{x}{y} \]

      8. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \frac{x}{y} \]

      9. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \mathsf{Rewrite=>}\left(lower-/.f64, \left(\frac{x}{y}\right)\right) \]

   6. Applied rewrites 43.1%

      \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{x}{y} \cdot e^{-b}} \]

      3. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x}{y}} \cdot e^{-b} \]

      4. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{x}{\frac{y}{e^{-b}}}} \]

      5. div-flip-rev N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\frac{y}{e^{-b}}}{x}}} \]

      6. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{e^{-b}}} \cdot x} \]

      7. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{e^{-b}}} \cdot x} \]

      8. div-flip-rev N/A

         \[\leadsto \color{blue}{\frac{e^{-b}}{y}} \cdot x \]

      9. lower-/.f64 47.4%

         \[\leadsto \color{blue}{\frac{e^{-b}}{y}} \cdot x \]

   8. Applied rewrites 47.4%

      \[\leadsto \color{blue}{\frac{e^{-b}}{y} \cdot x} \]

   if -1.45e15 < b < 2.9500000000000001e72

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

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

   7. Taylor expanded in b around 0

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

      1. lower-+.f64 N/A

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

      2. lower-*.f64 52.1%

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

   9. Applied rewrites 52.1%

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

Alternative 8: 55.1% accurate, 0.4× speedup

Math

\[\begin{array}{l} t_1 := \frac{\left|x\right| \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y}\\ t_2 := \frac{e^{-b}}{y} \cdot \left|x\right|\\ \mathsf{copysign}\left(1, x\right) \cdot \begin{array}{l} \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+150}:\\ \;\;\;\;\frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot b\right)\right)\right) \cdot y} \cdot \left|x\right|\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1
        (/
         (*
          (fabs x)
          (exp (- (+ (* y (log z)) (* (- t 1.0) (log a))) b)))
         y))
       (t_2 (* (/ (exp (- b)) y) (fabs x))))
  (*
   (copysign 1.0 x)
   (if (<= t_1 (- INFINITY))
     t_2
     (if (<= t_1 5e+150)
       (*
        (/
         (* (/ 1.0 a) 1.0)
         (*
          (+
           1.0
           (* b (+ 1.0 (* b (+ 0.5 (* 0.16666666666666666 b))))))
          y))
        (fabs x))
       t_2)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (fabs(x) * exp((((y * log(z)) + ((t - 1.0) * log(a))) - b))) / y;
	double t_2 = (exp(-b) / y) * fabs(x);
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = t_2;
	} else if (t_1 <= 5e+150) {
		tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * fabs(x);
	} else {
		tmp = t_2;
	}
	return copysign(1.0, x) * tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (Math.abs(x) * Math.exp((((y * Math.log(z)) + ((t - 1.0) * Math.log(a))) - b))) / y;
	double t_2 = (Math.exp(-b) / y) * Math.abs(x);
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else if (t_1 <= 5e+150) {
		tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * Math.abs(x);
	} else {
		tmp = t_2;
	}
	return Math.copySign(1.0, x) * tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (math.fabs(x) * math.exp((((y * math.log(z)) + ((t - 1.0) * math.log(a))) - b))) / y
	t_2 = (math.exp(-b) / y) * math.fabs(x)
	tmp = 0
	if t_1 <= -math.inf:
		tmp = t_2
	elif t_1 <= 5e+150:
		tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * math.fabs(x)
	else:
		tmp = t_2
	return math.copysign(1.0, x) * tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(abs(x) * exp(Float64(Float64(Float64(y * log(z)) + Float64(Float64(t - 1.0) * log(a))) - b))) / y)
	t_2 = Float64(Float64(exp(Float64(-b)) / y) * abs(x))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = t_2;
	elseif (t_1 <= 5e+150)
		tmp = Float64(Float64(Float64(Float64(1.0 / a) * 1.0) / Float64(Float64(1.0 + Float64(b * Float64(1.0 + Float64(b * Float64(0.5 + Float64(0.16666666666666666 * b)))))) * y)) * abs(x));
	else
		tmp = t_2;
	end
	return Float64(copysign(1.0, x) * tmp)
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (abs(x) * exp((((y * log(z)) + ((t - 1.0) * log(a))) - b))) / y;
	t_2 = (exp(-b) / y) * abs(x);
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = t_2;
	elseif (t_1 <= 5e+150)
		tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * abs(x);
	else
		tmp = t_2;
	end
	tmp_2 = (sign(x) * abs(1.0)) * tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[Abs[x], $MachinePrecision] * N[Exp[N[(N[(N[(y * N[Log[z], $MachinePrecision]), $MachinePrecision] + N[(N[(t - 1.0), $MachinePrecision] * N[Log[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - b), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[Exp[(-b)], $MachinePrecision] / y), $MachinePrecision] * N[Abs[x], $MachinePrecision]), $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$1, (-Infinity)], t$95$2, If[LessEqual[t$95$1, 5e+150], N[(N[(N[(N[(1.0 / a), $MachinePrecision] * 1.0), $MachinePrecision] / N[(N[(1.0 + N[(b * N[(1.0 + N[(b * N[(0.5 + N[(0.16666666666666666 * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] * N[Abs[x], $MachinePrecision]), $MachinePrecision], t$95$2]]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 x (exp.f64 (-.f64 (+.f64 (*.f64 y (log.f64 z)) (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a))) b))) y) < -inf.0 or 5.0000000000000001e150 < (/.f64 (*.f64 x (exp.f64 (-.f64 (+.f64 (*.f64 y (log.f64 z)) (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a))) b))) y)

   1. Initial program 98.4%

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

   2. Taylor expanded in b around inf

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   3. Step-by-step derivation

      1. lower-*.f64 47.4%

         \[\leadsto \frac{x \cdot e^{-1 \cdot \color{blue}{b}}}{y} \]

   4. Applied rewrites 47.4%

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x \cdot e^{-1 \cdot b}}{y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot e^{-1 \cdot b}}}{y} \]

      3. *-commutative N/A

         \[\leadsto \frac{\color{blue}{e^{-1 \cdot b} \cdot x}}{y} \]

      4. associate-/l* N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      6. lift-*.f64 N/A

         \[\leadsto e^{-1 \cdot \color{blue}{b}} \cdot \frac{x}{y} \]

      7. mul-1-neg N/A

         \[\leadsto e^{\mathsf{neg}\left(b\right)} \cdot \frac{x}{y} \]

      8. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \frac{x}{y} \]

      9. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \mathsf{Rewrite=>}\left(lower-/.f64, \left(\frac{x}{y}\right)\right) \]

   6. Applied rewrites 43.1%

      \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]
   7. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\frac{x}{y} \cdot e^{-b}} \]

      3. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x}{y}} \cdot e^{-b} \]

      4. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{x}{\frac{y}{e^{-b}}}} \]

      5. div-flip-rev N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{\frac{y}{e^{-b}}}{x}}} \]

      6. associate-/r/ N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{e^{-b}}} \cdot x} \]

      7. lower-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{\frac{y}{e^{-b}}} \cdot x} \]

      8. div-flip-rev N/A

         \[\leadsto \color{blue}{\frac{e^{-b}}{y}} \cdot x \]

      9. lower-/.f64 47.4%

         \[\leadsto \color{blue}{\frac{e^{-b}}{y}} \cdot x \]

   8. Applied rewrites 47.4%

      \[\leadsto \color{blue}{\frac{e^{-b}}{y} \cdot x} \]

   if -inf.0 < (/.f64 (*.f64 x (exp.f64 (-.f64 (+.f64 (*.f64 y (log.f64 z)) (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a))) b))) y) < 5.0000000000000001e150

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

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

   7. Taylor expanded in t around 0

      \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]
   8. Step-by-step derivation

      1. lower-/.f64 58.3%

         \[\leadsto \frac{\frac{1}{\color{blue}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

   9. Applied rewrites 58.3%

      \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

   10. Taylor expanded in b around 0

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{\left(1 + b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)\right)} \cdot y} \cdot x \]
   11. Step-by-step derivation

       1. lower-+.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + \color{blue}{b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)}\right) \cdot y} \cdot x \]

       2. lower-*.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \color{blue}{\left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)}\right) \cdot y} \cdot x \]

       3. lower-+.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + \color{blue}{b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)}\right)\right) \cdot y} \cdot x \]

       4. lower-*.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \color{blue}{\left(\frac{1}{2} + \frac{1}{6} \cdot b\right)}\right)\right) \cdot y} \cdot x \]

       5. lower-+.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \color{blue}{\frac{1}{6} \cdot b}\right)\right)\right) \cdot y} \cdot x \]

       6. lower-*.f64 39.6%

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot \color{blue}{b}\right)\right)\right) \cdot y} \cdot x \]

   12. Applied rewrites 39.6%

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot b\right)\right)\right)} \cdot y} \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 53.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} t_1 := \frac{\left|x\right| \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y}\\ t_2 := e^{-b} \cdot \frac{\left|x\right|}{y}\\ \mathsf{copysign}\left(1, x\right) \cdot \begin{array}{l} \mathbf{if}\;t\_1 \leq -\infty:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;t\_1 \leq 5 \cdot 10^{+150}:\\ \;\;\;\;\frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot b\right)\right)\right) \cdot y} \cdot \left|x\right|\\ \mathbf{else}:\\ \;\;\;\;t\_2\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (let* ((t_1
        (/
         (*
          (fabs x)
          (exp (- (+ (* y (log z)) (* (- t 1.0) (log a))) b)))
         y))
       (t_2 (* (exp (- b)) (/ (fabs x) y))))
  (*
   (copysign 1.0 x)
   (if (<= t_1 (- INFINITY))
     t_2
     (if (<= t_1 5e+150)
       (*
        (/
         (* (/ 1.0 a) 1.0)
         (*
          (+
           1.0
           (* b (+ 1.0 (* b (+ 0.5 (* 0.16666666666666666 b))))))
          y))
        (fabs x))
       t_2)))))

C

double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (fabs(x) * exp((((y * log(z)) + ((t - 1.0) * log(a))) - b))) / y;
	double t_2 = exp(-b) * (fabs(x) / y);
	double tmp;
	if (t_1 <= -((double) INFINITY)) {
		tmp = t_2;
	} else if (t_1 <= 5e+150) {
		tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * fabs(x);
	} else {
		tmp = t_2;
	}
	return copysign(1.0, x) * tmp;
}

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	double t_1 = (Math.abs(x) * Math.exp((((y * Math.log(z)) + ((t - 1.0) * Math.log(a))) - b))) / y;
	double t_2 = Math.exp(-b) * (Math.abs(x) / y);
	double tmp;
	if (t_1 <= -Double.POSITIVE_INFINITY) {
		tmp = t_2;
	} else if (t_1 <= 5e+150) {
		tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * Math.abs(x);
	} else {
		tmp = t_2;
	}
	return Math.copySign(1.0, x) * tmp;
}

Python

def code(x, y, z, t, a, b):
	t_1 = (math.fabs(x) * math.exp((((y * math.log(z)) + ((t - 1.0) * math.log(a))) - b))) / y
	t_2 = math.exp(-b) * (math.fabs(x) / y)
	tmp = 0
	if t_1 <= -math.inf:
		tmp = t_2
	elif t_1 <= 5e+150:
		tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * math.fabs(x)
	else:
		tmp = t_2
	return math.copysign(1.0, x) * tmp

Julia

function code(x, y, z, t, a, b)
	t_1 = Float64(Float64(abs(x) * exp(Float64(Float64(Float64(y * log(z)) + Float64(Float64(t - 1.0) * log(a))) - b))) / y)
	t_2 = Float64(exp(Float64(-b)) * Float64(abs(x) / y))
	tmp = 0.0
	if (t_1 <= Float64(-Inf))
		tmp = t_2;
	elseif (t_1 <= 5e+150)
		tmp = Float64(Float64(Float64(Float64(1.0 / a) * 1.0) / Float64(Float64(1.0 + Float64(b * Float64(1.0 + Float64(b * Float64(0.5 + Float64(0.16666666666666666 * b)))))) * y)) * abs(x));
	else
		tmp = t_2;
	end
	return Float64(copysign(1.0, x) * tmp)
end

MATLAB

function tmp_2 = code(x, y, z, t, a, b)
	t_1 = (abs(x) * exp((((y * log(z)) + ((t - 1.0) * log(a))) - b))) / y;
	t_2 = exp(-b) * (abs(x) / y);
	tmp = 0.0;
	if (t_1 <= -Inf)
		tmp = t_2;
	elseif (t_1 <= 5e+150)
		tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * abs(x);
	else
		tmp = t_2;
	end
	tmp_2 = (sign(x) * abs(1.0)) * tmp;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := Block[{t$95$1 = N[(N[(N[Abs[x], $MachinePrecision] * N[Exp[N[(N[(N[(y * N[Log[z], $MachinePrecision]), $MachinePrecision] + N[(N[(t - 1.0), $MachinePrecision] * N[Log[a], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - b), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / y), $MachinePrecision]}, Block[{t$95$2 = N[(N[Exp[(-b)], $MachinePrecision] * N[(N[Abs[x], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]}, N[(N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision] * If[LessEqual[t$95$1, (-Infinity)], t$95$2, If[LessEqual[t$95$1, 5e+150], N[(N[(N[(N[(1.0 / a), $MachinePrecision] * 1.0), $MachinePrecision] / N[(N[(1.0 + N[(b * N[(1.0 + N[(b * N[(0.5 + N[(0.16666666666666666 * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] * N[Abs[x], $MachinePrecision]), $MachinePrecision], t$95$2]]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (*.f64 x (exp.f64 (-.f64 (+.f64 (*.f64 y (log.f64 z)) (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a))) b))) y) < -inf.0 or 5.0000000000000001e150 < (/.f64 (*.f64 x (exp.f64 (-.f64 (+.f64 (*.f64 y (log.f64 z)) (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a))) b))) y)

   1. Initial program 98.4%

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

   2. Taylor expanded in b around inf

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   3. Step-by-step derivation

      1. lower-*.f64 47.4%

         \[\leadsto \frac{x \cdot e^{-1 \cdot \color{blue}{b}}}{y} \]

   4. Applied rewrites 47.4%

      \[\leadsto \frac{x \cdot e^{\color{blue}{-1 \cdot b}}}{y} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{x \cdot e^{-1 \cdot b}}{y}} \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\color{blue}{x \cdot e^{-1 \cdot b}}}{y} \]

      3. *-commutative N/A

         \[\leadsto \frac{\color{blue}{e^{-1 \cdot b} \cdot x}}{y} \]

      4. associate-/l* N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      5. lower-*.f64 N/A

         \[\leadsto \color{blue}{e^{-1 \cdot b} \cdot \frac{x}{y}} \]

      6. lift-*.f64 N/A

         \[\leadsto e^{-1 \cdot \color{blue}{b}} \cdot \frac{x}{y} \]

      7. mul-1-neg N/A

         \[\leadsto e^{\mathsf{neg}\left(b\right)} \cdot \frac{x}{y} \]

      8. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \frac{x}{y} \]

      9. lift-neg.f64 N/A

         \[\leadsto e^{-b} \cdot \mathsf{Rewrite=>}\left(lower-/.f64, \left(\frac{x}{y}\right)\right) \]

   6. Applied rewrites 43.1%

      \[\leadsto \color{blue}{e^{-b} \cdot \frac{x}{y}} \]

   if -inf.0 < (/.f64 (*.f64 x (exp.f64 (-.f64 (+.f64 (*.f64 y (log.f64 z)) (*.f64 (-.f64 t #s(literal 1 binary64)) (log.f64 a))) b))) y) < 5.0000000000000001e150

   1. Initial program 98.4%

      \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
   2. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. lift-*.f64 N/A

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

      3. associate-/l* N/A

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

      4. *-commutative N/A

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

      5. lower-*.f64 N/A

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

   3. Applied rewrites 69.8%

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

   4. Taylor expanded in y around 0

      \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
   5. Step-by-step derivation

   6. Applied rewrites 68.0%

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

   7. Taylor expanded in t around 0

      \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]
   8. Step-by-step derivation

      1. lower-/.f64 58.3%

         \[\leadsto \frac{\frac{1}{\color{blue}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

   9. Applied rewrites 58.3%

      \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

   10. Taylor expanded in b around 0

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{\left(1 + b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)\right)} \cdot y} \cdot x \]
   11. Step-by-step derivation

       1. lower-+.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + \color{blue}{b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)}\right) \cdot y} \cdot x \]

       2. lower-*.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \color{blue}{\left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)}\right) \cdot y} \cdot x \]

       3. lower-+.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + \color{blue}{b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)}\right)\right) \cdot y} \cdot x \]

       4. lower-*.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \color{blue}{\left(\frac{1}{2} + \frac{1}{6} \cdot b\right)}\right)\right) \cdot y} \cdot x \]

       5. lower-+.f64 N/A

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \color{blue}{\frac{1}{6} \cdot b}\right)\right)\right) \cdot y} \cdot x \]

       6. lower-*.f64 39.6%

          \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot \color{blue}{b}\right)\right)\right) \cdot y} \cdot x \]

   12. Applied rewrites 39.6%

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot b\right)\right)\right)} \cdot y} \cdot x \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 39.6% accurate, 5.4× speedup

Math

\[\frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot b\right)\right)\right) \cdot y} \cdot x \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (*
 (/
  (* (/ 1.0 a) 1.0)
  (* (+ 1.0 (* b (+ 1.0 (* b (+ 0.5 (* 0.16666666666666666 b)))))) y))
 x))

C

double code(double x, double y, double z, double t, double a, double b) {
	return (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * x;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 = (((1.0d0 / a) * 1.0d0) / ((1.0d0 + (b * (1.0d0 + (b * (0.5d0 + (0.16666666666666666d0 * b)))))) * y)) * x
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * x;
}

Python

def code(x, y, z, t, a, b):
	return (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * x

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(Float64(1.0 / a) * 1.0) / Float64(Float64(1.0 + Float64(b * Float64(1.0 + Float64(b * Float64(0.5 + Float64(0.16666666666666666 * b)))))) * y)) * x)
end

MATLAB

function tmp = code(x, y, z, t, a, b)
	tmp = (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (b * (0.5 + (0.16666666666666666 * b)))))) * y)) * x;
end

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(N[(1.0 / a), $MachinePrecision] * 1.0), $MachinePrecision] / N[(N[(1.0 + N[(b * N[(1.0 + N[(b * N[(0.5 + N[(0.16666666666666666 * b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 98.4%

   \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

3. Applied rewrites 69.8%

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

4. Taylor expanded in y around 0

   \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
5. Step-by-step derivation

6. Applied rewrites 68.0%

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

7. Taylor expanded in t around 0

   \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]
8. Step-by-step derivation

   1. lower-/.f64 58.3%

      \[\leadsto \frac{\frac{1}{\color{blue}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

9. Applied rewrites 58.3%

   \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

10. Taylor expanded in b around 0

    \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{\left(1 + b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)\right)} \cdot y} \cdot x \]
11. Step-by-step derivation

    1. lower-+.f64 N/A

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + \color{blue}{b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)}\right) \cdot y} \cdot x \]

    2. lower-*.f64 N/A

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \color{blue}{\left(1 + b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)\right)}\right) \cdot y} \cdot x \]

    3. lower-+.f64 N/A

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + \color{blue}{b \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot b\right)}\right)\right) \cdot y} \cdot x \]

    4. lower-*.f64 N/A

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \color{blue}{\left(\frac{1}{2} + \frac{1}{6} \cdot b\right)}\right)\right) \cdot y} \cdot x \]

    5. lower-+.f64 N/A

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(\frac{1}{2} + \color{blue}{\frac{1}{6} \cdot b}\right)\right)\right) \cdot y} \cdot x \]

    6. lower-*.f64 39.6%

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot \color{blue}{b}\right)\right)\right) \cdot y} \cdot x \]

12. Applied rewrites 39.6%

    \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{\left(1 + b \cdot \left(1 + b \cdot \left(0.5 + 0.16666666666666666 \cdot b\right)\right)\right)} \cdot y} \cdot x \]
13. Add Preprocessing

Alternative 11: 38.3% accurate, 6.2× speedup

Math

\[\frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + 0.5 \cdot b\right)\right) \cdot y} \cdot x \]

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 = (((1.0d0 / a) * 1.0d0) / ((1.0d0 + (b * (1.0d0 + (0.5d0 * b)))) * y)) * x
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (0.5 * b)))) * y)) * x;
}

Python

def code(x, y, z, t, a, b):
	return (((1.0 / a) * 1.0) / ((1.0 + (b * (1.0 + (0.5 * b)))) * y)) * x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.4%

   \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

3. Applied rewrites 69.8%

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

4. Taylor expanded in y around 0

   \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
5. Step-by-step derivation

6. Applied rewrites 68.0%

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

7. Taylor expanded in t around 0

   \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]
8. Step-by-step derivation

   1. lower-/.f64 58.3%

      \[\leadsto \frac{\frac{1}{\color{blue}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

9. Applied rewrites 58.3%

   \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

10. Taylor expanded in b around 0

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

    1. lower-+.f64 N/A

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

    2. lower-*.f64 N/A

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

    3. lower-+.f64 N/A

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

    4. lower-*.f64 38.3%

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{\left(1 + b \cdot \left(1 + 0.5 \cdot \color{blue}{b}\right)\right) \cdot y} \cdot x \]

12. Applied rewrites 38.3%

    \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{\left(1 + b \cdot \left(1 + 0.5 \cdot b\right)\right)} \cdot y} \cdot x \]
13. Add Preprocessing

Alternative 12: 37.1% accurate, 6.2× speedup

Math

\[\frac{\frac{1}{a} \cdot 1}{y + b \cdot \left(y + 0.5 \cdot \left(b \cdot y\right)\right)} \cdot x \]

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 = (((1.0d0 / a) * 1.0d0) / (y + (b * (y + (0.5d0 * (b * y)))))) * x
end function

Java

public static double code(double x, double y, double z, double t, double a, double b) {
	return (((1.0 / a) * 1.0) / (y + (b * (y + (0.5 * (b * y)))))) * x;
}

Python

def code(x, y, z, t, a, b):
	return (((1.0 / a) * 1.0) / (y + (b * (y + (0.5 * (b * y)))))) * x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 98.4%

   \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

3. Applied rewrites 69.8%

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

4. Taylor expanded in y around 0

   \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
5. Step-by-step derivation

6. Applied rewrites 68.0%

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

7. Taylor expanded in t around 0

   \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]
8. Step-by-step derivation

   1. lower-/.f64 58.3%

      \[\leadsto \frac{\frac{1}{\color{blue}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

9. Applied rewrites 58.3%

   \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

10. Taylor expanded in b around 0

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

    1. lower-+.f64 N/A

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

    2. lower-*.f64 N/A

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

    3. lower-+.f64 N/A

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

    4. lower-*.f64 N/A

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

    5. lower-*.f64 37.1%

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{y + b \cdot \left(y + 0.5 \cdot \left(b \cdot \color{blue}{y}\right)\right)} \cdot x \]

12. Applied rewrites 37.1%

    \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{y + b \cdot \left(y + 0.5 \cdot \left(b \cdot y\right)\right)}} \cdot x \]
13. Add Preprocessing

Alternative 13: 31.3% accurate, 8.2× speedup

Math

\[\frac{\frac{1}{a} \cdot 1}{y + b \cdot y} \cdot x \]

FPCore

(FPCore (x y z t a b)
  :precision binary64
  (* (/ (* (/ 1.0 a) 1.0) (+ y (* b y))) x))

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(x, y, z, 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 = (((1.0d0 / a) * 1.0d0) / (y + (b * y))) * x
end function

Java

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

Python

def code(x, y, z, t, a, b):
	return (((1.0 / a) * 1.0) / (y + (b * y))) * x

Julia

function code(x, y, z, t, a, b)
	return Float64(Float64(Float64(Float64(1.0 / a) * 1.0) / Float64(y + Float64(b * y))) * x)
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_, b_] := N[(N[(N[(N[(1.0 / a), $MachinePrecision] * 1.0), $MachinePrecision] / N[(y + N[(b * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 98.4%

   \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y} \]
2. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-/l* N/A

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

   4. *-commutative N/A

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

   5. lower-*.f64 N/A

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

3. Applied rewrites 69.8%

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

4. Taylor expanded in y around 0

   \[\leadsto \frac{{a}^{\left(t - 1\right)} \cdot \color{blue}{1}}{e^{b} \cdot y} \cdot x \]
5. Step-by-step derivation

6. Applied rewrites 68.0%

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

7. Taylor expanded in t around 0

   \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]
8. Step-by-step derivation

   1. lower-/.f64 58.3%

      \[\leadsto \frac{\frac{1}{\color{blue}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

9. Applied rewrites 58.3%

   \[\leadsto \frac{\color{blue}{\frac{1}{a}} \cdot 1}{e^{b} \cdot y} \cdot x \]

10. Taylor expanded in b around 0

    \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{y + b \cdot y}} \cdot x \]
11. Step-by-step derivation

    1. lower-+.f64 N/A

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{y + \color{blue}{b \cdot y}} \cdot x \]

    2. lower-*.f64 31.3%

       \[\leadsto \frac{\frac{1}{a} \cdot 1}{y + b \cdot \color{blue}{y}} \cdot x \]

12. Applied rewrites 31.3%

    \[\leadsto \frac{\frac{1}{a} \cdot 1}{\color{blue}{y + b \cdot y}} \cdot x \]
13. Add Preprocessing

Reproduce

herbie shell --seed 2025258 
(FPCore (x y z t a b)
  :name "Numeric.SpecFunctions:incompleteBetaWorker from math-functions-0.1.5.2, A"
  :precision binary64
  (/ (* x (exp (- (+ (* y (log z)) (* (- t 1.0) (log a))) b))) y))