Numeric.SpecFunctions:logGammaL from math-functions-0.1.5.2

Percentage Accurate: 99.6% → 99.6%

Time: 34.0s

Alternatives: 11

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (x y z t a)
  :precision binary64
  (+ (- (+ (log (+ x y)) (log z)) t) (* (- a 1/2) (log t))))

C

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

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)
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
    code = ((log((x + y)) + log(z)) - t) + ((a - 0.5d0) * log(t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 1/2), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.6% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z t a)
  :precision binary64
  (+ (- (+ (log (+ x y)) (log z)) t) (* (- a 1/2) (log t))))

C

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

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)
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
    code = ((log((x + y)) + log(z)) - t) + ((a - 0.5d0) * log(t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[(N[Log[N[(x + y), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 1/2), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.3% accurate, 0.8× speedup

Math

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

FPCore

(FPCore (x y z t a)
  :precision binary64
  (- (+ (log (fmax x y)) (+ (log z) (* (log t) (- a 1/2)))) t))

C

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

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)
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
    code = (log(fmax(x, y)) + (log(z) + (log(t) * (a - 0.5d0)))) - t
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(N[Log[N[Max[x, y], $MachinePrecision]], $MachinePrecision] + N[(N[Log[z], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a - 1/2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision]

Derivation

1. Initial program 99.6%

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

2. Taylor expanded in t around inf

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

   1. lower-*.f64 77.5%

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

4. Applied rewrites 77.5%

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

5. Taylor expanded in x around 0

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

   1. lower--.f64 N/A

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

   2. lower-+.f64 N/A

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

   3. lower-log.f64 N/A

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

   4. lower-+.f64 N/A

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

   5. lower-log.f64 N/A

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

   6. lower-*.f64 N/A

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

   7. lower-log.f64 N/A

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

   8. lower--.f64 69.3%

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

7. Applied rewrites 69.3%

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

Alternative 2: 98.6% accurate, 0.6× speedup

Math

\[\begin{array}{l} \mathbf{if}\;t \leq 190000:\\ \;\;\;\;\log z + \left(\log \left(\mathsf{min}\left(x, y\right) + \mathsf{max}\left(x, y\right)\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\log \left(\mathsf{max}\left(x, y\right)\right) + \left(\log z + a \cdot \log t\right)\right) - t\\ \end{array} \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (if (<= t 190000)
  (+
   (log z)
   (+ (log (+ (fmin x y) (fmax x y))) (* (log t) (- a 1/2))))
  (- (+ (log (fmax x y)) (+ (log z) (* a (log t)))) t)))

C

double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= 190000.0) {
		tmp = log(z) + (log((fmin(x, y) + fmax(x, y))) + (log(t) * (a - 0.5)));
	} else {
		tmp = (log(fmax(x, y)) + (log(z) + (a * log(t)))) - t;
	}
	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)
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) :: tmp
    if (t <= 190000.0d0) then
        tmp = log(z) + (log((fmin(x, y) + fmax(x, y))) + (log(t) * (a - 0.5d0)))
    else
        tmp = (log(fmax(x, y)) + (log(z) + (a * log(t)))) - t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double tmp;
	if (t <= 190000.0) {
		tmp = Math.log(z) + (Math.log((fmin(x, y) + fmax(x, y))) + (Math.log(t) * (a - 0.5)));
	} else {
		tmp = (Math.log(fmax(x, y)) + (Math.log(z) + (a * Math.log(t)))) - t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	tmp = 0
	if t <= 190000.0:
		tmp = math.log(z) + (math.log((fmin(x, y) + fmax(x, y))) + (math.log(t) * (a - 0.5)))
	else:
		tmp = (math.log(fmax(x, y)) + (math.log(z) + (a * math.log(t)))) - t
	return tmp

Julia

function code(x, y, z, t, a)
	tmp = 0.0
	if (t <= 190000.0)
		tmp = Float64(log(z) + Float64(log(Float64(fmin(x, y) + fmax(x, y))) + Float64(log(t) * Float64(a - 0.5))));
	else
		tmp = Float64(Float64(log(fmax(x, y)) + Float64(log(z) + Float64(a * log(t)))) - t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	tmp = 0.0;
	if (t <= 190000.0)
		tmp = log(z) + (log((min(x, y) + max(x, y))) + (log(t) * (a - 0.5)));
	else
		tmp = (log(max(x, y)) + (log(z) + (a * log(t)))) - t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := If[LessEqual[t, 190000], N[(N[Log[z], $MachinePrecision] + N[(N[Log[N[(N[Min[x, y], $MachinePrecision] + N[Max[x, y], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a - 1/2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[Log[N[Max[x, y], $MachinePrecision]], $MachinePrecision] + N[(N[Log[z], $MachinePrecision] + N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if t < 1.9e5

   1. Initial program 99.6%

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

   2. Taylor expanded in t around 0

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

      1. lower-+.f64 N/A

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

      2. lower-log.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-log.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower--.f64 63.4%

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

   4. Applied rewrites 63.4%

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

   if 1.9e5 < t

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-log.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower--.f64 69.3%

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

   7. Applied rewrites 69.3%

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

   8. Taylor expanded in a around inf

      \[\leadsto \left(\log y + \left(\log z + a \cdot \log t\right)\right) - t \]
   9. Step-by-step derivation

      1. lower-*.f64 N/A

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

      2. lower-log.f64 58.8%

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

   10. Applied rewrites 58.8%

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

Alternative 3: 97.9% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := \log \left(\mathsf{max}\left(x, y\right)\right)\\ t_2 := \left(\left(\log \left(\mathsf{min}\left(x, y\right) + \mathsf{max}\left(x, y\right)\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t\\ t_3 := a \cdot \log t\\ \mathbf{if}\;t\_2 \leq -20000000000000000000:\\ \;\;\;\;-1 \cdot t + t\_3\\ \mathbf{elif}\;t\_2 \leq 2000:\\ \;\;\;\;\left(t\_1 + \left(\log z + \frac{-1}{2} \cdot \log t\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;\left(t\_1 + \left(\log z + t\_3\right)\right) - t\\ \end{array} \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (let* ((t_1 (log (fmax x y)))
       (t_2
        (+
         (- (+ (log (+ (fmin x y) (fmax x y))) (log z)) t)
         (* (- a 1/2) (log t))))
       (t_3 (* a (log t))))
  (if (<= t_2 -20000000000000000000)
    (+ (* -1 t) t_3)
    (if (<= t_2 2000)
      (- (+ t_1 (+ (log z) (* -1/2 (log t)))) t)
      (- (+ t_1 (+ (log z) t_3)) t)))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = log(fmax(x, y));
	double t_2 = ((log((fmin(x, y) + fmax(x, y))) + log(z)) - t) + ((a - 0.5) * log(t));
	double t_3 = a * log(t);
	double tmp;
	if (t_2 <= -2e+19) {
		tmp = (-1.0 * t) + t_3;
	} else if (t_2 <= 2000.0) {
		tmp = (t_1 + (log(z) + (-0.5 * log(t)))) - t;
	} else {
		tmp = (t_1 + (log(z) + t_3)) - t;
	}
	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)
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) :: t_1
    real(8) :: t_2
    real(8) :: t_3
    real(8) :: tmp
    t_1 = log(fmax(x, y))
    t_2 = ((log((fmin(x, y) + fmax(x, y))) + log(z)) - t) + ((a - 0.5d0) * log(t))
    t_3 = a * log(t)
    if (t_2 <= (-2d+19)) then
        tmp = ((-1.0d0) * t) + t_3
    else if (t_2 <= 2000.0d0) then
        tmp = (t_1 + (log(z) + ((-0.5d0) * log(t)))) - t
    else
        tmp = (t_1 + (log(z) + t_3)) - t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = Math.log(fmax(x, y));
	double t_2 = ((Math.log((fmin(x, y) + fmax(x, y))) + Math.log(z)) - t) + ((a - 0.5) * Math.log(t));
	double t_3 = a * Math.log(t);
	double tmp;
	if (t_2 <= -2e+19) {
		tmp = (-1.0 * t) + t_3;
	} else if (t_2 <= 2000.0) {
		tmp = (t_1 + (Math.log(z) + (-0.5 * Math.log(t)))) - t;
	} else {
		tmp = (t_1 + (Math.log(z) + t_3)) - t;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = math.log(fmax(x, y))
	t_2 = ((math.log((fmin(x, y) + fmax(x, y))) + math.log(z)) - t) + ((a - 0.5) * math.log(t))
	t_3 = a * math.log(t)
	tmp = 0
	if t_2 <= -2e+19:
		tmp = (-1.0 * t) + t_3
	elif t_2 <= 2000.0:
		tmp = (t_1 + (math.log(z) + (-0.5 * math.log(t)))) - t
	else:
		tmp = (t_1 + (math.log(z) + t_3)) - t
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = log(fmax(x, y))
	t_2 = Float64(Float64(Float64(log(Float64(fmin(x, y) + fmax(x, y))) + log(z)) - t) + Float64(Float64(a - 0.5) * log(t)))
	t_3 = Float64(a * log(t))
	tmp = 0.0
	if (t_2 <= -2e+19)
		tmp = Float64(Float64(-1.0 * t) + t_3);
	elseif (t_2 <= 2000.0)
		tmp = Float64(Float64(t_1 + Float64(log(z) + Float64(-0.5 * log(t)))) - t);
	else
		tmp = Float64(Float64(t_1 + Float64(log(z) + t_3)) - t);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = log(max(x, y));
	t_2 = ((log((min(x, y) + max(x, y))) + log(z)) - t) + ((a - 0.5) * log(t));
	t_3 = a * log(t);
	tmp = 0.0;
	if (t_2 <= -2e+19)
		tmp = (-1.0 * t) + t_3;
	elseif (t_2 <= 2000.0)
		tmp = (t_1 + (log(z) + (-0.5 * log(t)))) - t;
	else
		tmp = (t_1 + (log(z) + t_3)) - t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[Log[N[Max[x, y], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(N[(N[(N[Log[N[(N[Min[x, y], $MachinePrecision] + N[Max[x, y], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 1/2), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$3 = N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$2, -20000000000000000000], N[(N[(-1 * t), $MachinePrecision] + t$95$3), $MachinePrecision], If[LessEqual[t$95$2, 2000], N[(N[(t$95$1 + N[(N[Log[z], $MachinePrecision] + N[(-1/2 * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision], N[(N[(t$95$1 + N[(N[Log[z], $MachinePrecision] + t$95$3), $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < -2e19

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in a around inf

      \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto -1 \cdot t + a \cdot \color{blue}{\log t} \]

      2. lower-log.f64 75.0%

         \[\leadsto -1 \cdot t + a \cdot \log t \]

   7. Applied rewrites 75.0%

      \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]

   if -2e19 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < 2e3

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-log.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower--.f64 69.3%

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

   7. Applied rewrites 69.3%

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

   8. Taylor expanded in a around inf

      \[\leadsto \left(\log y + \left(\log z + a \cdot \log t\right)\right) - t \]
   9. Step-by-step derivation

      1. lower-*.f64 N/A

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

      2. lower-log.f64 58.8%

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

   10. Applied rewrites 58.8%

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

   11. Taylor expanded in a around 0

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

       1. lower-+.f64 N/A

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

       2. lower-log.f64 N/A

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

       3. lower-*.f64 N/A

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

       4. lower-log.f64 40.5%

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

   13. Applied rewrites 40.5%

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

   if 2e3 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t)))

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-log.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower--.f64 69.3%

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

   7. Applied rewrites 69.3%

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

   8. Taylor expanded in a around inf

      \[\leadsto \left(\log y + \left(\log z + a \cdot \log t\right)\right) - t \]
   9. Step-by-step derivation

      1. lower-*.f64 N/A

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

      2. lower-log.f64 58.8%

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

   10. Applied rewrites 58.8%

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

Alternative 4: 97.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_1 := \left(-t\right) - \left(\frac{1}{2} - a\right) \cdot \log t\\ \mathbf{if}\;a \leq -4600000000:\\ \;\;\;\;t\_1\\ \mathbf{elif}\;a \leq 420000000:\\ \;\;\;\;\left(\log \left(\mathsf{max}\left(x, y\right)\right) + \left(\log z + \frac{-1}{2} \cdot \log t\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;t\_1\\ \end{array} \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (let* ((t_1 (- (- t) (* (- 1/2 a) (log t)))))
  (if (<= a -4600000000)
    t_1
    (if (<= a 420000000)
      (- (+ (log (fmax x y)) (+ (log z) (* -1/2 (log t)))) t)
      t_1))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = -t - ((0.5 - a) * log(t));
	double tmp;
	if (a <= -4600000000.0) {
		tmp = t_1;
	} else if (a <= 420000000.0) {
		tmp = (log(fmax(x, y)) + (log(z) + (-0.5 * log(t)))) - t;
	} 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)
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) :: t_1
    real(8) :: tmp
    t_1 = -t - ((0.5d0 - a) * log(t))
    if (a <= (-4600000000.0d0)) then
        tmp = t_1
    else if (a <= 420000000.0d0) then
        tmp = (log(fmax(x, y)) + (log(z) + ((-0.5d0) * log(t)))) - t
    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 t_1 = -t - ((0.5 - a) * Math.log(t));
	double tmp;
	if (a <= -4600000000.0) {
		tmp = t_1;
	} else if (a <= 420000000.0) {
		tmp = (Math.log(fmax(x, y)) + (Math.log(z) + (-0.5 * Math.log(t)))) - t;
	} else {
		tmp = t_1;
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = -t - ((0.5 - a) * math.log(t))
	tmp = 0
	if a <= -4600000000.0:
		tmp = t_1
	elif a <= 420000000.0:
		tmp = (math.log(fmax(x, y)) + (math.log(z) + (-0.5 * math.log(t)))) - t
	else:
		tmp = t_1
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(-t) - Float64(Float64(0.5 - a) * log(t)))
	tmp = 0.0
	if (a <= -4600000000.0)
		tmp = t_1;
	elseif (a <= 420000000.0)
		tmp = Float64(Float64(log(fmax(x, y)) + Float64(log(z) + Float64(-0.5 * log(t)))) - t);
	else
		tmp = t_1;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = -t - ((0.5 - a) * log(t));
	tmp = 0.0;
	if (a <= -4600000000.0)
		tmp = t_1;
	elseif (a <= 420000000.0)
		tmp = (log(max(x, y)) + (log(z) + (-0.5 * log(t)))) - t;
	else
		tmp = t_1;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[((-t) - N[(N[(1/2 - a), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[a, -4600000000], t$95$1, If[LessEqual[a, 420000000], N[(N[(N[Log[N[Max[x, y], $MachinePrecision]], $MachinePrecision] + N[(N[Log[z], $MachinePrecision] + N[(-1/2 * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision], t$95$1]]]

Derivation

1. Split input into 2 regimes

2. if a < -4.6e9 or 4.2e8 < a

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

         \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)} \]

      3. lift-*.f64 N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)} \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{t} - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      8. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(t\right)\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      9. lift-neg.f64 N/A

         \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      10. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift-*.f64, \left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)\right)\right) \]

      11. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(*-commutative, \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)\right)\right) \]

      12. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(distribute-lft-neg-out, \left(\left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right)\right)\right) \cdot \log t\right)\right) \]

      13. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(a - \frac{1}{2}\right)\right)\right)\right) \cdot \log t \]

      14. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(sub-negate-rev, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

      15. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower-*.f64, \left(\left(\frac{1}{2} - a\right) \cdot \log t\right)\right) \]

      16. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower--.f64, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

   6. Applied rewrites 77.5%

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

   if -4.6e9 < a < 4.2e8

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-log.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower--.f64 69.3%

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

   7. Applied rewrites 69.3%

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

   8. Taylor expanded in a around inf

      \[\leadsto \left(\log y + \left(\log z + a \cdot \log t\right)\right) - t \]
   9. Step-by-step derivation

      1. lower-*.f64 N/A

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

      2. lower-log.f64 58.8%

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

   10. Applied rewrites 58.8%

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

   11. Taylor expanded in a around 0

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

       1. lower-+.f64 N/A

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

       2. lower-log.f64 N/A

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

       3. lower-*.f64 N/A

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

       4. lower-log.f64 40.5%

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

   13. Applied rewrites 40.5%

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

Alternative 5: 94.1% accurate, 0.3× speedup

Math

\[\begin{array}{l} t_1 := \log \left(\mathsf{min}\left(x, y\right) + \mathsf{max}\left(x, y\right)\right) + \log z\\ \mathbf{if}\;t\_1 \leq -750:\\ \;\;\;\;-1 \cdot t + a \cdot \log t\\ \mathbf{elif}\;t\_1 \leq 705:\\ \;\;\;\;\left(\log \left(\mathsf{max}\left(x, y\right) \cdot z\right) + \log t \cdot \left(a - \frac{1}{2}\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;\left(-t\right) - \left(\frac{1}{2} - a\right) \cdot \log t\\ \end{array} \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (let* ((t_1 (+ (log (+ (fmin x y) (fmax x y))) (log z))))
  (if (<= t_1 -750)
    (+ (* -1 t) (* a (log t)))
    (if (<= t_1 705)
      (- (+ (log (* (fmax x y) z)) (* (log t) (- a 1/2))) t)
      (- (- t) (* (- 1/2 a) (log t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = log((fmin(x, y) + fmax(x, y))) + log(z);
	double tmp;
	if (t_1 <= -750.0) {
		tmp = (-1.0 * t) + (a * log(t));
	} else if (t_1 <= 705.0) {
		tmp = (log((fmax(x, y) * z)) + (log(t) * (a - 0.5))) - t;
	} else {
		tmp = -t - ((0.5 - a) * log(t));
	}
	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)
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) :: t_1
    real(8) :: tmp
    t_1 = log((fmin(x, y) + fmax(x, y))) + log(z)
    if (t_1 <= (-750.0d0)) then
        tmp = ((-1.0d0) * t) + (a * log(t))
    else if (t_1 <= 705.0d0) then
        tmp = (log((fmax(x, y) * z)) + (log(t) * (a - 0.5d0))) - t
    else
        tmp = -t - ((0.5d0 - a) * log(t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = Math.log((fmin(x, y) + fmax(x, y))) + Math.log(z);
	double tmp;
	if (t_1 <= -750.0) {
		tmp = (-1.0 * t) + (a * Math.log(t));
	} else if (t_1 <= 705.0) {
		tmp = (Math.log((fmax(x, y) * z)) + (Math.log(t) * (a - 0.5))) - t;
	} else {
		tmp = -t - ((0.5 - a) * Math.log(t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = math.log((fmin(x, y) + fmax(x, y))) + math.log(z)
	tmp = 0
	if t_1 <= -750.0:
		tmp = (-1.0 * t) + (a * math.log(t))
	elif t_1 <= 705.0:
		tmp = (math.log((fmax(x, y) * z)) + (math.log(t) * (a - 0.5))) - t
	else:
		tmp = -t - ((0.5 - a) * math.log(t))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(log(Float64(fmin(x, y) + fmax(x, y))) + log(z))
	tmp = 0.0
	if (t_1 <= -750.0)
		tmp = Float64(Float64(-1.0 * t) + Float64(a * log(t)));
	elseif (t_1 <= 705.0)
		tmp = Float64(Float64(log(Float64(fmax(x, y) * z)) + Float64(log(t) * Float64(a - 0.5))) - t);
	else
		tmp = Float64(Float64(-t) - Float64(Float64(0.5 - a) * log(t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = log((min(x, y) + max(x, y))) + log(z);
	tmp = 0.0;
	if (t_1 <= -750.0)
		tmp = (-1.0 * t) + (a * log(t));
	elseif (t_1 <= 705.0)
		tmp = (log((max(x, y) * z)) + (log(t) * (a - 0.5))) - t;
	else
		tmp = -t - ((0.5 - a) * log(t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[Log[N[(N[Min[x, y], $MachinePrecision] + N[Max[x, y], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -750], N[(N[(-1 * t), $MachinePrecision] + N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 705], N[(N[(N[Log[N[(N[Max[x, y], $MachinePrecision] * z), $MachinePrecision]], $MachinePrecision] + N[(N[Log[t], $MachinePrecision] * N[(a - 1/2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision], N[((-t) - N[(N[(1/2 - a), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) < -750

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in a around inf

      \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto -1 \cdot t + a \cdot \color{blue}{\log t} \]

      2. lower-log.f64 75.0%

         \[\leadsto -1 \cdot t + a \cdot \log t \]

   7. Applied rewrites 75.0%

      \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]

   if -750 < (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) < 705

   1. Initial program 99.6%

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

      1. lift-+.f64 N/A

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

      2. lift--.f64 N/A

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

      3. sub-flip N/A

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

      4. +-commutative N/A

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

      5. add-flip N/A

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

      6. associate-+l- N/A

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

      7. lower--.f64 N/A

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

      8. lower-neg.f64 N/A

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

      9. lower--.f64 N/A

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

   3. Applied rewrites 76.9%

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

   4. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. lower-*.f64 N/A

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

      6. lower-log.f64 N/A

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

      7. lower--.f64 53.6%

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

   6. Applied rewrites 53.6%

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

   if 705 < (+.f64 (log.f64 (+.f64 x y)) (log.f64 z))

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

         \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)} \]

      3. lift-*.f64 N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)} \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{t} - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      8. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(t\right)\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      9. lift-neg.f64 N/A

         \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      10. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift-*.f64, \left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)\right)\right) \]

      11. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(*-commutative, \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)\right)\right) \]

      12. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(distribute-lft-neg-out, \left(\left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right)\right)\right) \cdot \log t\right)\right) \]

      13. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(a - \frac{1}{2}\right)\right)\right)\right) \cdot \log t \]

      14. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(sub-negate-rev, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

      15. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower-*.f64, \left(\left(\frac{1}{2} - a\right) \cdot \log t\right)\right) \]

      16. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower--.f64, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

   6. Applied rewrites 77.5%

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

Alternative 6: 91.0% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := \left(\left(\log \left(\mathsf{min}\left(x, y\right) + \mathsf{max}\left(x, y\right)\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t\\ \mathbf{if}\;t\_1 \leq -20000000000000000000:\\ \;\;\;\;-1 \cdot t + a \cdot \log t\\ \mathbf{elif}\;t\_1 \leq 700:\\ \;\;\;\;\log \left(\left({t}^{\left(a - \frac{1}{2}\right)} \cdot z\right) \cdot \mathsf{max}\left(x, y\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;\left(-t\right) - \left(\frac{1}{2} - a\right) \cdot \log t\\ \end{array} \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (let* ((t_1
        (+
         (- (+ (log (+ (fmin x y) (fmax x y))) (log z)) t)
         (* (- a 1/2) (log t)))))
  (if (<= t_1 -20000000000000000000)
    (+ (* -1 t) (* a (log t)))
    (if (<= t_1 700)
      (- (log (* (* (pow t (- a 1/2)) z) (fmax x y))) t)
      (- (- t) (* (- 1/2 a) (log t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = ((log((fmin(x, y) + fmax(x, y))) + log(z)) - t) + ((a - 0.5) * log(t));
	double tmp;
	if (t_1 <= -2e+19) {
		tmp = (-1.0 * t) + (a * log(t));
	} else if (t_1 <= 700.0) {
		tmp = log(((pow(t, (a - 0.5)) * z) * fmax(x, y))) - t;
	} else {
		tmp = -t - ((0.5 - a) * log(t));
	}
	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)
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) :: t_1
    real(8) :: tmp
    t_1 = ((log((fmin(x, y) + fmax(x, y))) + log(z)) - t) + ((a - 0.5d0) * log(t))
    if (t_1 <= (-2d+19)) then
        tmp = ((-1.0d0) * t) + (a * log(t))
    else if (t_1 <= 700.0d0) then
        tmp = log((((t ** (a - 0.5d0)) * z) * fmax(x, y))) - t
    else
        tmp = -t - ((0.5d0 - a) * log(t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = ((Math.log((fmin(x, y) + fmax(x, y))) + Math.log(z)) - t) + ((a - 0.5) * Math.log(t));
	double tmp;
	if (t_1 <= -2e+19) {
		tmp = (-1.0 * t) + (a * Math.log(t));
	} else if (t_1 <= 700.0) {
		tmp = Math.log(((Math.pow(t, (a - 0.5)) * z) * fmax(x, y))) - t;
	} else {
		tmp = -t - ((0.5 - a) * Math.log(t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = ((math.log((fmin(x, y) + fmax(x, y))) + math.log(z)) - t) + ((a - 0.5) * math.log(t))
	tmp = 0
	if t_1 <= -2e+19:
		tmp = (-1.0 * t) + (a * math.log(t))
	elif t_1 <= 700.0:
		tmp = math.log(((math.pow(t, (a - 0.5)) * z) * fmax(x, y))) - t
	else:
		tmp = -t - ((0.5 - a) * math.log(t))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(Float64(log(Float64(fmin(x, y) + fmax(x, y))) + log(z)) - t) + Float64(Float64(a - 0.5) * log(t)))
	tmp = 0.0
	if (t_1 <= -2e+19)
		tmp = Float64(Float64(-1.0 * t) + Float64(a * log(t)));
	elseif (t_1 <= 700.0)
		tmp = Float64(log(Float64(Float64((t ^ Float64(a - 0.5)) * z) * fmax(x, y))) - t);
	else
		tmp = Float64(Float64(-t) - Float64(Float64(0.5 - a) * log(t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = ((log((min(x, y) + max(x, y))) + log(z)) - t) + ((a - 0.5) * log(t));
	tmp = 0.0;
	if (t_1 <= -2e+19)
		tmp = (-1.0 * t) + (a * log(t));
	elseif (t_1 <= 700.0)
		tmp = log((((t ^ (a - 0.5)) * z) * max(x, y))) - t;
	else
		tmp = -t - ((0.5 - a) * log(t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(N[Log[N[(N[Min[x, y], $MachinePrecision] + N[Max[x, y], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 1/2), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -20000000000000000000], N[(N[(-1 * t), $MachinePrecision] + N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 700], N[(N[Log[N[(N[(N[Power[t, N[(a - 1/2), $MachinePrecision]], $MachinePrecision] * z), $MachinePrecision] * N[Max[x, y], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - t), $MachinePrecision], N[((-t) - N[(N[(1/2 - a), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < -2e19

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in a around inf

      \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto -1 \cdot t + a \cdot \color{blue}{\log t} \]

      2. lower-log.f64 75.0%

         \[\leadsto -1 \cdot t + a \cdot \log t \]

   7. Applied rewrites 75.0%

      \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]

   if -2e19 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < 700

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-log.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower--.f64 69.3%

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

   7. Applied rewrites 69.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. *-commutative N/A

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

      6. lift--.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. +-commutative N/A

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

      9. lift--.f64 N/A

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

      10. log-pow-rev N/A

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

      11. lift-pow.f64 N/A

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

      12. lift-log.f64 N/A

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

      13. sum-log N/A

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

      14. lift-log.f64 N/A

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

      15. sum-log N/A

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

      16. lower-log.f64 N/A

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

      17. lower-*.f64 N/A

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

      18. lower-*.f64 24.8%

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

   9. Applied rewrites 24.8%

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

   if 700 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t)))

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

         \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)} \]

      3. lift-*.f64 N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)} \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{t} - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      8. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(t\right)\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      9. lift-neg.f64 N/A

         \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      10. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift-*.f64, \left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)\right)\right) \]

      11. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(*-commutative, \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)\right)\right) \]

      12. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(distribute-lft-neg-out, \left(\left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right)\right)\right) \cdot \log t\right)\right) \]

      13. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(a - \frac{1}{2}\right)\right)\right)\right) \cdot \log t \]

      14. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(sub-negate-rev, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

      15. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower-*.f64, \left(\left(\frac{1}{2} - a\right) \cdot \log t\right)\right) \]

      16. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower--.f64, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

   6. Applied rewrites 77.5%

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

Alternative 7: 90.7% accurate, 0.2× speedup

Math

\[\begin{array}{l} t_1 := \left(\left(\log \left(\mathsf{min}\left(x, y\right) + \mathsf{max}\left(x, y\right)\right) + \log z\right) - t\right) + \left(a - \frac{1}{2}\right) \cdot \log t\\ \mathbf{if}\;t\_1 \leq -20000000000000000000:\\ \;\;\;\;-1 \cdot t + a \cdot \log t\\ \mathbf{elif}\;t\_1 \leq 700:\\ \;\;\;\;\log \left(\left({t}^{\frac{-1}{2}} \cdot z\right) \cdot \mathsf{max}\left(x, y\right)\right) - t\\ \mathbf{else}:\\ \;\;\;\;\left(-t\right) - \left(\frac{1}{2} - a\right) \cdot \log t\\ \end{array} \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (let* ((t_1
        (+
         (- (+ (log (+ (fmin x y) (fmax x y))) (log z)) t)
         (* (- a 1/2) (log t)))))
  (if (<= t_1 -20000000000000000000)
    (+ (* -1 t) (* a (log t)))
    (if (<= t_1 700)
      (- (log (* (* (pow t -1/2) z) (fmax x y))) t)
      (- (- t) (* (- 1/2 a) (log t)))))))

C

double code(double x, double y, double z, double t, double a) {
	double t_1 = ((log((fmin(x, y) + fmax(x, y))) + log(z)) - t) + ((a - 0.5) * log(t));
	double tmp;
	if (t_1 <= -2e+19) {
		tmp = (-1.0 * t) + (a * log(t));
	} else if (t_1 <= 700.0) {
		tmp = log(((pow(t, -0.5) * z) * fmax(x, y))) - t;
	} else {
		tmp = -t - ((0.5 - a) * log(t));
	}
	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)
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) :: t_1
    real(8) :: tmp
    t_1 = ((log((fmin(x, y) + fmax(x, y))) + log(z)) - t) + ((a - 0.5d0) * log(t))
    if (t_1 <= (-2d+19)) then
        tmp = ((-1.0d0) * t) + (a * log(t))
    else if (t_1 <= 700.0d0) then
        tmp = log((((t ** (-0.5d0)) * z) * fmax(x, y))) - t
    else
        tmp = -t - ((0.5d0 - a) * log(t))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t, double a) {
	double t_1 = ((Math.log((fmin(x, y) + fmax(x, y))) + Math.log(z)) - t) + ((a - 0.5) * Math.log(t));
	double tmp;
	if (t_1 <= -2e+19) {
		tmp = (-1.0 * t) + (a * Math.log(t));
	} else if (t_1 <= 700.0) {
		tmp = Math.log(((Math.pow(t, -0.5) * z) * fmax(x, y))) - t;
	} else {
		tmp = -t - ((0.5 - a) * Math.log(t));
	}
	return tmp;
}

Python

def code(x, y, z, t, a):
	t_1 = ((math.log((fmin(x, y) + fmax(x, y))) + math.log(z)) - t) + ((a - 0.5) * math.log(t))
	tmp = 0
	if t_1 <= -2e+19:
		tmp = (-1.0 * t) + (a * math.log(t))
	elif t_1 <= 700.0:
		tmp = math.log(((math.pow(t, -0.5) * z) * fmax(x, y))) - t
	else:
		tmp = -t - ((0.5 - a) * math.log(t))
	return tmp

Julia

function code(x, y, z, t, a)
	t_1 = Float64(Float64(Float64(log(Float64(fmin(x, y) + fmax(x, y))) + log(z)) - t) + Float64(Float64(a - 0.5) * log(t)))
	tmp = 0.0
	if (t_1 <= -2e+19)
		tmp = Float64(Float64(-1.0 * t) + Float64(a * log(t)));
	elseif (t_1 <= 700.0)
		tmp = Float64(log(Float64(Float64((t ^ -0.5) * z) * fmax(x, y))) - t);
	else
		tmp = Float64(Float64(-t) - Float64(Float64(0.5 - a) * log(t)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t, a)
	t_1 = ((log((min(x, y) + max(x, y))) + log(z)) - t) + ((a - 0.5) * log(t));
	tmp = 0.0;
	if (t_1 <= -2e+19)
		tmp = (-1.0 * t) + (a * log(t));
	elseif (t_1 <= 700.0)
		tmp = log((((t ^ -0.5) * z) * max(x, y))) - t;
	else
		tmp = -t - ((0.5 - a) * log(t));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_, a_] := Block[{t$95$1 = N[(N[(N[(N[Log[N[(N[Min[x, y], $MachinePrecision] + N[Max[x, y], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[Log[z], $MachinePrecision]), $MachinePrecision] - t), $MachinePrecision] + N[(N[(a - 1/2), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$1, -20000000000000000000], N[(N[(-1 * t), $MachinePrecision] + N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$1, 700], N[(N[Log[N[(N[(N[Power[t, -1/2], $MachinePrecision] * z), $MachinePrecision] * N[Max[x, y], $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - t), $MachinePrecision], N[((-t) - N[(N[(1/2 - a), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < -2e19

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in a around inf

      \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]
   6. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto -1 \cdot t + a \cdot \color{blue}{\log t} \]

      2. lower-log.f64 75.0%

         \[\leadsto -1 \cdot t + a \cdot \log t \]

   7. Applied rewrites 75.0%

      \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]

   if -2e19 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t))) < 700

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

   5. Taylor expanded in x around 0

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

      1. lower--.f64 N/A

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

      2. lower-+.f64 N/A

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

      3. lower-log.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. lower-log.f64 N/A

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

      6. lower-*.f64 N/A

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

      7. lower-log.f64 N/A

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

      8. lower--.f64 69.3%

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

   7. Applied rewrites 69.3%

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

      1. lift-+.f64 N/A

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

      2. +-commutative N/A

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

      3. lift-+.f64 N/A

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

      4. lift-*.f64 N/A

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

      5. *-commutative N/A

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

      6. lift--.f64 N/A

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

      7. lift-log.f64 N/A

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

      8. +-commutative N/A

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

      9. lift--.f64 N/A

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

      10. log-pow-rev N/A

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

      11. lift-pow.f64 N/A

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

      12. lift-log.f64 N/A

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

      13. sum-log N/A

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

      14. lift-log.f64 N/A

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

      15. sum-log N/A

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

      16. lower-log.f64 N/A

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

      17. lower-*.f64 N/A

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

      18. lower-*.f64 24.8%

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

   9. Applied rewrites 24.8%

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

   10. Taylor expanded in a around 0

       \[\leadsto \log \left(\left({t}^{\frac{-1}{2}} \cdot z\right) \cdot y\right) - t \]
   11. Step-by-step derivation

   12. Applied rewrites 29.3%

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

   if 700 < (+.f64 (-.f64 (+.f64 (log.f64 (+.f64 x y)) (log.f64 z)) t) (*.f64 (-.f64 a #s(literal 1/2 binary64)) (log.f64 t)))

   1. Initial program 99.6%

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

   2. Taylor expanded in t around inf

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

      1. lower-*.f64 77.5%

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

   4. Applied rewrites 77.5%

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

      1. lift-+.f64 N/A

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

      2. add-flip N/A

         \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)} \]

      3. lift-*.f64 N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t}\right)\right) \]

      4. *-commutative N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

      5. lift-*.f64 N/A

         \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

      6. lower--.f64 N/A

         \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)} \]

      7. lift-*.f64 N/A

         \[\leadsto -1 \cdot \color{blue}{t} - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      8. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(t\right)\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      9. lift-neg.f64 N/A

         \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

      10. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift-*.f64, \left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)\right)\right) \]

      11. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(*-commutative, \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)\right)\right) \]

      12. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(distribute-lft-neg-out, \left(\left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right)\right)\right) \cdot \log t\right)\right) \]

      13. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(a - \frac{1}{2}\right)\right)\right)\right) \cdot \log t \]

      14. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(sub-negate-rev, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

      15. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower-*.f64, \left(\left(\frac{1}{2} - a\right) \cdot \log t\right)\right) \]

      16. lift-neg.f64 N/A

          \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower--.f64, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

   6. Applied rewrites 77.5%

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

Alternative 8: 77.5% accurate, 2.8× speedup

Math

\[\left(-t\right) - \left(\frac{1}{2} - a\right) \cdot \log t \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (- (- t) (* (- 1/2 a) (log t))))

C

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

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)
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
    code = -t - ((0.5d0 - a) * log(t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[((-t) - N[(N[(1/2 - a), $MachinePrecision] * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

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

2. Taylor expanded in t around inf

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

   1. lower-*.f64 77.5%

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

4. Applied rewrites 77.5%

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

   1. lift-+.f64 N/A

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

   2. add-flip N/A

      \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)} \]

   3. lift-*.f64 N/A

      \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\left(a - \frac{1}{2}\right) \cdot \log t}\right)\right) \]

   4. *-commutative N/A

      \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

   5. lift-*.f64 N/A

      \[\leadsto -1 \cdot t - \left(\mathsf{neg}\left(\color{blue}{\log t \cdot \left(a - \frac{1}{2}\right)}\right)\right) \]

   6. lower--.f64 N/A

      \[\leadsto \color{blue}{-1 \cdot t - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)} \]

   7. lift-*.f64 N/A

      \[\leadsto -1 \cdot \color{blue}{t} - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

   8. mul-1-neg N/A

      \[\leadsto \left(\mathsf{neg}\left(t\right)\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

   9. lift-neg.f64 N/A

      \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right) \]

   10. lift-neg.f64 N/A

       \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift-*.f64, \left(\log t \cdot \left(a - \frac{1}{2}\right)\right)\right)\right)\right) \]

   11. lift-neg.f64 N/A

       \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(*-commutative, \left(\left(a - \frac{1}{2}\right) \cdot \log t\right)\right)\right)\right) \]

   12. lift-neg.f64 N/A

       \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(distribute-lft-neg-out, \left(\left(\mathsf{neg}\left(\left(a - \frac{1}{2}\right)\right)\right) \cdot \log t\right)\right) \]

   13. lift-neg.f64 N/A

       \[\leadsto \left(-t\right) - \left(\mathsf{neg}\left(\mathsf{Rewrite=>}\left(lift--.f64, \left(a - \frac{1}{2}\right)\right)\right)\right) \cdot \log t \]

   14. lift-neg.f64 N/A

       \[\leadsto \left(-t\right) - \mathsf{Rewrite<=}\left(sub-negate-rev, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

   15. lift-neg.f64 N/A

       \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower-*.f64, \left(\left(\frac{1}{2} - a\right) \cdot \log t\right)\right) \]

   16. lift-neg.f64 N/A

       \[\leadsto \left(-t\right) - \mathsf{Rewrite=>}\left(lower--.f64, \left(\frac{1}{2} - a\right)\right) \cdot \log t \]

6. Applied rewrites 77.5%

   \[\leadsto \color{blue}{\left(-t\right) - \left(\frac{1}{2} - a\right) \cdot \log t} \]
7. Add Preprocessing

Alternative 9: 75.0% accurate, 2.8× speedup

Math

\[-1 \cdot t + a \cdot \log t \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (+ (* -1 t) (* a (log t))))

C

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

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)
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
    code = ((-1.0d0) * t) + (a * log(t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[(N[(-1 * t), $MachinePrecision] + N[(a * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

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

2. Taylor expanded in t around inf

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

   1. lower-*.f64 77.5%

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

4. Applied rewrites 77.5%

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

5. Taylor expanded in a around inf

   \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]
6. Step-by-step derivation

   1. lower-*.f64 N/A

      \[\leadsto -1 \cdot t + a \cdot \color{blue}{\log t} \]

   2. lower-log.f64 75.0%

      \[\leadsto -1 \cdot t + a \cdot \log t \]

7. Applied rewrites 75.0%

   \[\leadsto -1 \cdot t + \color{blue}{a \cdot \log t} \]
8. Add Preprocessing

Alternative 10: 40.0% accurate, 2.9× speedup

Math

\[\left(-t\right) + \frac{-1}{2} \cdot \log t \]

FPCore

(FPCore (x y z t a)
  :precision binary64
  (+ (- t) (* -1/2 (log t))))

C

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

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)
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
    code = -t + ((-0.5d0) * log(t))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_, t_, a_] := N[((-t) + N[(-1/2 * N[Log[t], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.6%

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

2. Taylor expanded in t around inf

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

   1. lower-*.f64 77.5%

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

4. Applied rewrites 77.5%

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

5. Taylor expanded in a around 0

   \[\leadsto -1 \cdot t + \color{blue}{\frac{-1}{2} \cdot \log t} \]
6. Step-by-step derivation

   1. lower-*.f64 N/A

      \[\leadsto -1 \cdot t + \frac{-1}{2} \cdot \color{blue}{\log t} \]

   2. lower-log.f64 40.0%

      \[\leadsto -1 \cdot t + \frac{-1}{2} \cdot \log t \]

7. Applied rewrites 40.0%

   \[\leadsto -1 \cdot t + \color{blue}{\frac{-1}{2} \cdot \log t} \]
8. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto -1 \cdot \color{blue}{t} + \frac{-1}{2} \cdot \log t \]

   2. mul-1-neg N/A

      \[\leadsto \left(\mathsf{neg}\left(t\right)\right) + \frac{-1}{2} \cdot \log t \]

   3. lower-neg.f64 40.0%

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

9. Applied rewrites 40.0%

   \[\leadsto \left(-t\right) + \frac{-1}{2} \cdot \log t \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2025271 -o generate:evaluate
(FPCore (x y z t a)
  :name "Numeric.SpecFunctions:logGammaL from math-functions-0.1.5.2"
  :precision binary64
  (+ (- (+ (log (+ x y)) (log z)) t) (* (- a 1/2) (log t))))