Quotient of sum of exps

Percentage Accurate: 99.0% → 99.3%

Time: 3.4s

Alternatives: 15

Speedup: 1.4×

• could not determine a ground truth

  1. a = 2.7979316671346766e+78
  2. b = -3.784861948768936e-9

Specification

Math

\[\begin{array}{l} \\ \frac{e^{a}}{e^{a} + e^{b}} \end{array} \]

FPCore

(FPCore (a b) :precision binary64 (/ (exp a) (+ (exp a) (exp b))))

C

double code(double a, double b) {
	return exp(a) / (exp(a) + exp(b));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = exp(a) / (exp(a) + exp(b))
end function

Java

public static double code(double a, double b) {
	return Math.exp(a) / (Math.exp(a) + Math.exp(b));
}

Python

def code(a, b):
	return math.exp(a) / (math.exp(a) + math.exp(b))

Julia

function code(a, b)
	return Float64(exp(a) / Float64(exp(a) + exp(b)))
end

MATLAB

function tmp = code(a, b)
	tmp = exp(a) / (exp(a) + exp(b));
end

Wolfram

code[a_, b_] := N[(N[Exp[a], $MachinePrecision] / N[(N[Exp[a], $MachinePrecision] + N[Exp[b], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{e^{a}}{e^{a} + e^{b}} \end{array} \]

FPCore

(FPCore (a b) :precision binary64 (/ (exp a) (+ (exp a) (exp b))))

C

double code(double a, double b) {
	return exp(a) / (exp(a) + exp(b));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = exp(a) / (exp(a) + exp(b))
end function

Java

public static double code(double a, double b) {
	return Math.exp(a) / (Math.exp(a) + Math.exp(b));
}

Python

def code(a, b):
	return math.exp(a) / (math.exp(a) + math.exp(b))

Julia

function code(a, b)
	return Float64(exp(a) / Float64(exp(a) + exp(b)))
end

MATLAB

function tmp = code(a, b)
	tmp = exp(a) / (exp(a) + exp(b));
end

Wolfram

code[a_, b_] := N[(N[Exp[a], $MachinePrecision] / N[(N[Exp[a], $MachinePrecision] + N[Exp[b], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 1\right)\\ \mathbf{if}\;a \leq -27000000000:\\ \;\;\;\;\frac{e^{a}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t\_0}{t\_0 + e^{b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (let* ((t_0 (fma (fma 0.5 a 1.0) a 1.0)))
   (if (<= a -27000000000.0) (/ (exp a) 2.0) (/ t_0 (+ t_0 (exp b))))))

C

double code(double a, double b) {
	double t_0 = fma(fma(0.5, a, 1.0), a, 1.0);
	double tmp;
	if (a <= -27000000000.0) {
		tmp = exp(a) / 2.0;
	} else {
		tmp = t_0 / (t_0 + exp(b));
	}
	return tmp;
}

Julia

function code(a, b)
	t_0 = fma(fma(0.5, a, 1.0), a, 1.0)
	tmp = 0.0
	if (a <= -27000000000.0)
		tmp = Float64(exp(a) / 2.0);
	else
		tmp = Float64(t_0 / Float64(t_0 + exp(b)));
	end
	return tmp
end

Wolfram

code[a_, b_] := Block[{t$95$0 = N[(N[(0.5 * a + 1.0), $MachinePrecision] * a + 1.0), $MachinePrecision]}, If[LessEqual[a, -27000000000.0], N[(N[Exp[a], $MachinePrecision] / 2.0), $MachinePrecision], N[(t$95$0 / N[(t$95$0 + N[Exp[b], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if a < -2.7e10

   1. Initial program 99.1%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 100.0

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 100.0%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2} \]
   6. Step-by-step derivation

   7. Applied rewrites 100.0%

      \[\leadsto \frac{e^{a}}{2} \]

   if -2.7e10 < a

   1. Initial program 99.0%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

         \[\leadsto \frac{\left(1 + \frac{1}{2} \cdot a\right) \cdot a + 1}{e^{a} + e^{b}} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(1 + \frac{1}{2} \cdot a, \color{blue}{a}, 1\right)}{e^{a} + e^{b}} \]

      4. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\frac{1}{2} \cdot a + 1, a, 1\right)}{e^{a} + e^{b}} \]

      5. lower-fma.f64 97.9

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 1\right)}{e^{a} + e^{b}} \]

   4. Applied rewrites 97.9%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 1\right)}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{2}, a, 1\right), a, 1\right)}{\color{blue}{\left(1 + a \cdot \left(1 + \frac{1}{2} \cdot a\right)\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{2}, a, 1\right), a, 1\right)}{\left(a \cdot \left(1 + \frac{1}{2} \cdot a\right) + \color{blue}{1}\right) + e^{b}} \]

      2. *-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{2}, a, 1\right), a, 1\right)}{\left(\left(1 + \frac{1}{2} \cdot a\right) \cdot a + 1\right) + e^{b}} \]

      3. lower-fma.f64 N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{2}, a, 1\right), a, 1\right)}{\mathsf{fma}\left(1 + \frac{1}{2} \cdot a, \color{blue}{a}, 1\right) + e^{b}} \]

      4. +-commutative N/A

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{2}, a, 1\right), a, 1\right)}{\mathsf{fma}\left(\frac{1}{2} \cdot a + 1, a, 1\right) + e^{b}} \]

      5. lower-fma.f64 99.1

         \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 1\right)}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 1\right) + e^{b}} \]

   7. Applied rewrites 99.1%

      \[\leadsto \frac{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 1\right)}{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 1\right)} + e^{b}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -27000000000:\\ \;\;\;\;\frac{e^{a}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{a - -1}{\left(a - -1\right) + e^{b}}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= a -27000000000.0)
   (/ (exp a) 2.0)
   (/ (- a -1.0) (+ (- a -1.0) (exp b)))))

C

double code(double a, double b) {
	double tmp;
	if (a <= -27000000000.0) {
		tmp = exp(a) / 2.0;
	} else {
		tmp = (a - -1.0) / ((a - -1.0) + exp(b));
	}
	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(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (a <= (-27000000000.0d0)) then
        tmp = exp(a) / 2.0d0
    else
        tmp = (a - (-1.0d0)) / ((a - (-1.0d0)) + exp(b))
    end if
    code = tmp
end function

Java

public static double code(double a, double b) {
	double tmp;
	if (a <= -27000000000.0) {
		tmp = Math.exp(a) / 2.0;
	} else {
		tmp = (a - -1.0) / ((a - -1.0) + Math.exp(b));
	}
	return tmp;
}

Python

def code(a, b):
	tmp = 0
	if a <= -27000000000.0:
		tmp = math.exp(a) / 2.0
	else:
		tmp = (a - -1.0) / ((a - -1.0) + math.exp(b))
	return tmp

Julia

function code(a, b)
	tmp = 0.0
	if (a <= -27000000000.0)
		tmp = Float64(exp(a) / 2.0);
	else
		tmp = Float64(Float64(a - -1.0) / Float64(Float64(a - -1.0) + exp(b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b)
	tmp = 0.0;
	if (a <= -27000000000.0)
		tmp = exp(a) / 2.0;
	else
		tmp = (a - -1.0) / ((a - -1.0) + exp(b));
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_] := If[LessEqual[a, -27000000000.0], N[(N[Exp[a], $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(a - -1.0), $MachinePrecision] / N[(N[(a - -1.0), $MachinePrecision] + N[Exp[b], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -2.7e10

   1. Initial program 99.1%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 100.0

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 100.0%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2} \]
   6. Step-by-step derivation

   7. Applied rewrites 100.0%

      \[\leadsto \frac{e^{a}}{2} \]

   if -2.7e10 < a

   1. Initial program 99.0%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{a + \color{blue}{1}}{e^{a} + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}}{e^{a} + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1 \cdot 1}{e^{a} + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{e^{a} + e^{b}} \]

      6. lower--.f64 97.6

         \[\leadsto \frac{a - \color{blue}{-1}}{e^{a} + e^{b}} \]

   4. Applied rewrites 97.6%

      \[\leadsto \frac{\color{blue}{a - -1}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{a - -1}{\color{blue}{\left(1 + a\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{a - -1}{\left(a + \color{blue}{1}\right) + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}\right) + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1 \cdot 1\right) + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1\right) + e^{b}} \]

      6. lower--.f64 98.8

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{-1}\right) + e^{b}} \]

   7. Applied rewrites 98.8%

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

Alternative 3: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{e^{a}}{e^{a} + e^{b}} \end{array} \]

FPCore

(FPCore (a b) :precision binary64 (/ (exp a) (+ (exp a) (exp b))))

C

double code(double a, double b) {
	return exp(a) / (exp(a) + exp(b));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = exp(a) / (exp(a) + exp(b))
end function

Java

public static double code(double a, double b) {
	return Math.exp(a) / (Math.exp(a) + Math.exp(b));
}

Python

def code(a, b):
	return math.exp(a) / (math.exp(a) + math.exp(b))

Julia

function code(a, b)
	return Float64(exp(a) / Float64(exp(a) + exp(b)))
end

MATLAB

function tmp = code(a, b)
	tmp = exp(a) / (exp(a) + exp(b));
end

Wolfram

code[a_, b_] := N[(N[Exp[a], $MachinePrecision] / N[(N[Exp[a], $MachinePrecision] + N[Exp[b], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.0%

   \[\frac{e^{a}}{e^{a} + e^{b}} \]
2. Add Preprocessing

Alternative 4: 98.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -27000000000:\\ \;\;\;\;\frac{e^{a}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{e^{b} - -1}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= a -27000000000.0) (/ (exp a) 2.0) (/ 1.0 (- (exp b) -1.0))))

C

double code(double a, double b) {
	double tmp;
	if (a <= -27000000000.0) {
		tmp = exp(a) / 2.0;
	} else {
		tmp = 1.0 / (exp(b) - -1.0);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (a <= (-27000000000.0d0)) then
        tmp = exp(a) / 2.0d0
    else
        tmp = 1.0d0 / (exp(b) - (-1.0d0))
    end if
    code = tmp
end function

Java

public static double code(double a, double b) {
	double tmp;
	if (a <= -27000000000.0) {
		tmp = Math.exp(a) / 2.0;
	} else {
		tmp = 1.0 / (Math.exp(b) - -1.0);
	}
	return tmp;
}

Python

def code(a, b):
	tmp = 0
	if a <= -27000000000.0:
		tmp = math.exp(a) / 2.0
	else:
		tmp = 1.0 / (math.exp(b) - -1.0)
	return tmp

Julia

function code(a, b)
	tmp = 0.0
	if (a <= -27000000000.0)
		tmp = Float64(exp(a) / 2.0);
	else
		tmp = Float64(1.0 / Float64(exp(b) - -1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b)
	tmp = 0.0;
	if (a <= -27000000000.0)
		tmp = exp(a) / 2.0;
	else
		tmp = 1.0 / (exp(b) - -1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_] := If[LessEqual[a, -27000000000.0], N[(N[Exp[a], $MachinePrecision] / 2.0), $MachinePrecision], N[(1.0 / N[(N[Exp[b], $MachinePrecision] - -1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -2.7e10

   1. Initial program 99.1%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 100.0

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 100.0%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2} \]
   6. Step-by-step derivation

   7. Applied rewrites 100.0%

      \[\leadsto \frac{e^{a}}{2} \]

   if -2.7e10 < a

   1. Initial program 99.0%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1} \]

      7. lower--.f64 N/A

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

      8. lift-exp.f64 98.1

         \[\leadsto \frac{1}{e^{b} - -1} \]

   4. Applied rewrites 98.1%

      \[\leadsto \color{blue}{\frac{1}{e^{b} - -1}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 74.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq 1.75 \cdot 10^{+58}:\\ \;\;\;\;\frac{e^{a}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{a}{a + \mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), b, 1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= b 1.75e+58) (/ (exp a) 2.0) (/ a (+ a (fma (fma 0.5 b 1.0) b 1.0)))))

C

double code(double a, double b) {
	double tmp;
	if (b <= 1.75e+58) {
		tmp = exp(a) / 2.0;
	} else {
		tmp = a / (a + fma(fma(0.5, b, 1.0), b, 1.0));
	}
	return tmp;
}

Julia

function code(a, b)
	tmp = 0.0
	if (b <= 1.75e+58)
		tmp = Float64(exp(a) / 2.0);
	else
		tmp = Float64(a / Float64(a + fma(fma(0.5, b, 1.0), b, 1.0)));
	end
	return tmp
end

Wolfram

code[a_, b_] := If[LessEqual[b, 1.75e+58], N[(N[Exp[a], $MachinePrecision] / 2.0), $MachinePrecision], N[(a / N[(a + N[(N[(0.5 * b + 1.0), $MachinePrecision] * b + 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < 1.7499999999999999e58

   1. Initial program 98.9%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 74.4

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 74.4%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2} \]
   6. Step-by-step derivation

   7. Applied rewrites 73.2%

      \[\leadsto \frac{e^{a}}{2} \]

   if 1.7499999999999999e58 < b

   1. Initial program 99.4%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{a + \color{blue}{1}}{e^{a} + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}}{e^{a} + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1 \cdot 1}{e^{a} + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{e^{a} + e^{b}} \]

      6. lower--.f64 100.0

         \[\leadsto \frac{a - \color{blue}{-1}}{e^{a} + e^{b}} \]

   4. Applied rewrites 100.0%

      \[\leadsto \frac{\color{blue}{a - -1}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{a - -1}{\color{blue}{\left(1 + a\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{a - -1}{\left(a + \color{blue}{1}\right) + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}\right) + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1 \cdot 1\right) + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1\right) + e^{b}} \]

      6. lower--.f64 100.0

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{-1}\right) + e^{b}} \]

   7. Applied rewrites 100.0%

      \[\leadsto \frac{a - -1}{\color{blue}{\left(a - -1\right)} + e^{b}} \]

   8. Taylor expanded in b around 0

      \[\leadsto \frac{a - -1}{\left(a - -1\right) + \color{blue}{1}} \]
   9. Step-by-step derivation

   10. Applied rewrites 3.1%

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

   11. Taylor expanded in a around inf

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]
   12. Step-by-step derivation

   13. Applied rewrites 4.7%

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]

   14. Taylor expanded in a around inf

       \[\leadsto \frac{a}{a + 1} \]
   15. Step-by-step derivation

   16. Applied rewrites 4.6%

       \[\leadsto \frac{a}{a + 1} \]

   17. Taylor expanded in b around 0

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

       1. +-commutative N/A

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

       2. *-commutative N/A

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

       3. +-commutative N/A

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

       4. lower-fma.f64 N/A

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

       5. lift-fma.f64 79.2

          \[\leadsto \frac{a}{a + \mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), b, 1\right)} \]

   19. Applied rewrites 79.2%

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

Alternative 6: 73.4% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq 5 \cdot 10^{+134}:\\ \;\;\;\;\frac{e^{a}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(b \cdot b\right) \cdot 0.5}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= b 5e+134) (/ (exp a) 2.0) (/ 1.0 (* (* b b) 0.5))))

C

double code(double a, double b) {
	double tmp;
	if (b <= 5e+134) {
		tmp = exp(a) / 2.0;
	} else {
		tmp = 1.0 / ((b * b) * 0.5);
	}
	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(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (b <= 5d+134) then
        tmp = exp(a) / 2.0d0
    else
        tmp = 1.0d0 / ((b * b) * 0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b) {
	double tmp;
	if (b <= 5e+134) {
		tmp = Math.exp(a) / 2.0;
	} else {
		tmp = 1.0 / ((b * b) * 0.5);
	}
	return tmp;
}

Python

def code(a, b):
	tmp = 0
	if b <= 5e+134:
		tmp = math.exp(a) / 2.0
	else:
		tmp = 1.0 / ((b * b) * 0.5)
	return tmp

Julia

function code(a, b)
	tmp = 0.0
	if (b <= 5e+134)
		tmp = Float64(exp(a) / 2.0);
	else
		tmp = Float64(1.0 / Float64(Float64(b * b) * 0.5));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b)
	tmp = 0.0;
	if (b <= 5e+134)
		tmp = exp(a) / 2.0;
	else
		tmp = 1.0 / ((b * b) * 0.5);
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_] := If[LessEqual[b, 5e+134], N[(N[Exp[a], $MachinePrecision] / 2.0), $MachinePrecision], N[(1.0 / N[(N[(b * b), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < 4.99999999999999981e134

   1. Initial program 98.9%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 71.5

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 71.5%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2} \]
   6. Step-by-step derivation

   7. Applied rewrites 70.4%

      \[\leadsto \frac{e^{a}}{2} \]

   if 4.99999999999999981e134 < b

   1. Initial program 99.6%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1} \]

      7. lower--.f64 N/A

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

      8. lift-exp.f64 100.0

         \[\leadsto \frac{1}{e^{b} - -1} \]

   4. Applied rewrites 100.0%

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

   5. Taylor expanded in b around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(1 + \frac{1}{2} \cdot b, b, 2\right)} \]

      4. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot b + 1, b, 2\right)} \]

      5. lower-fma.f64 89.7

         \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), b, 2\right)} \]

   7. Applied rewrites 89.7%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), \color{blue}{b}, 2\right)} \]

   8. Taylor expanded in b around inf

      \[\leadsto \frac{1}{\frac{1}{2} \cdot {b}^{\color{blue}{2}}} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{{b}^{2} \cdot \frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{{b}^{2} \cdot \frac{1}{2}} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot \frac{1}{2}} \]

      4. lift-*.f64 89.8

         \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot 0.5} \]

   10. Applied rewrites 89.8%

       \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot 0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 63.4% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq 3.3 \cdot 10^{+60}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 2\right)}\\ \mathbf{elif}\;b \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;\frac{a}{a + \left(1 + b\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(b \cdot b\right) \cdot 0.5}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= b 3.3e+60)
   (/ 1.0 (fma (fma 0.5 a 1.0) a 2.0))
   (if (<= b 1.35e+154) (/ a (+ a (+ 1.0 b))) (/ 1.0 (* (* b b) 0.5)))))

C

double code(double a, double b) {
	double tmp;
	if (b <= 3.3e+60) {
		tmp = 1.0 / fma(fma(0.5, a, 1.0), a, 2.0);
	} else if (b <= 1.35e+154) {
		tmp = a / (a + (1.0 + b));
	} else {
		tmp = 1.0 / ((b * b) * 0.5);
	}
	return tmp;
}

Julia

function code(a, b)
	tmp = 0.0
	if (b <= 3.3e+60)
		tmp = Float64(1.0 / fma(fma(0.5, a, 1.0), a, 2.0));
	elseif (b <= 1.35e+154)
		tmp = Float64(a / Float64(a + Float64(1.0 + b)));
	else
		tmp = Float64(1.0 / Float64(Float64(b * b) * 0.5));
	end
	return tmp
end

Wolfram

code[a_, b_] := If[LessEqual[b, 3.3e+60], N[(1.0 / N[(N[(0.5 * a + 1.0), $MachinePrecision] * a + 2.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.35e+154], N[(a / N[(a + N[(1.0 + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(b * b), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b < 3.2999999999999998e60

   1. Initial program 98.9%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 74.2

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 74.2%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{a \cdot \left(1 + \frac{1}{2} \cdot a\right) + 2} \]

      2. *-commutative N/A

         \[\leadsto \frac{e^{a}}{\left(1 + \frac{1}{2} \cdot a\right) \cdot a + 2} \]

      3. +-commutative N/A

         \[\leadsto \frac{e^{a}}{\left(\frac{1}{2} \cdot a + 1\right) \cdot a + 2} \]

      4. lower-fma.f64 N/A

         \[\leadsto \frac{e^{a}}{\mathsf{fma}\left(\frac{1}{2} \cdot a + 1, a, 2\right)} \]

      5. lift-fma.f64 73.7

         \[\leadsto \frac{e^{a}}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 2\right)} \]

   7. Applied rewrites 73.7%

      \[\leadsto \frac{e^{a}}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), \color{blue}{a}, 2\right)} \]

   8. Taylor expanded in a around 0

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

   10. Applied rewrites 61.2%

       \[\leadsto \frac{\color{blue}{1}}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, a, 1\right), a, 2\right)} \]

   if 3.2999999999999998e60 < b < 1.35000000000000003e154

   1. Initial program 99.2%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{a + \color{blue}{1}}{e^{a} + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}}{e^{a} + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1 \cdot 1}{e^{a} + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{e^{a} + e^{b}} \]

      6. lower--.f64 100.0

         \[\leadsto \frac{a - \color{blue}{-1}}{e^{a} + e^{b}} \]

   4. Applied rewrites 100.0%

      \[\leadsto \frac{\color{blue}{a - -1}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{a - -1}{\color{blue}{\left(1 + a\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{a - -1}{\left(a + \color{blue}{1}\right) + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}\right) + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1 \cdot 1\right) + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1\right) + e^{b}} \]

      6. lower--.f64 100.0

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{-1}\right) + e^{b}} \]

   7. Applied rewrites 100.0%

      \[\leadsto \frac{a - -1}{\color{blue}{\left(a - -1\right)} + e^{b}} \]

   8. Taylor expanded in b around 0

      \[\leadsto \frac{a - -1}{\left(a - -1\right) + \color{blue}{1}} \]
   9. Step-by-step derivation

   10. Applied rewrites 3.1%

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

   11. Taylor expanded in a around inf

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]
   12. Step-by-step derivation

   13. Applied rewrites 4.6%

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]

   14. Taylor expanded in a around inf

       \[\leadsto \frac{a}{a + 1} \]
   15. Step-by-step derivation

   16. Applied rewrites 4.6%

       \[\leadsto \frac{a}{a + 1} \]

   17. Taylor expanded in b around 0

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

       1. lower-+.f64 23.6

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

   19. Applied rewrites 23.6%

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

   if 1.35000000000000003e154 < b

   1. Initial program 99.5%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1} \]

      7. lower--.f64 N/A

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

      8. lift-exp.f64 100.0

         \[\leadsto \frac{1}{e^{b} - -1} \]

   4. Applied rewrites 100.0%

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

   5. Taylor expanded in b around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(1 + \frac{1}{2} \cdot b, b, 2\right)} \]

      4. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot b + 1, b, 2\right)} \]

      5. lower-fma.f64 99.9

         \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), b, 2\right)} \]

   7. Applied rewrites 99.9%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), \color{blue}{b}, 2\right)} \]

   8. Taylor expanded in b around inf

      \[\leadsto \frac{1}{\frac{1}{2} \cdot {b}^{\color{blue}{2}}} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{{b}^{2} \cdot \frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{{b}^{2} \cdot \frac{1}{2}} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot \frac{1}{2}} \]

      4. lift-*.f64 100.0

         \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot 0.5} \]

   10. Applied rewrites 100.0%

       \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot 0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 56.6% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -27000000000:\\ \;\;\;\;\left(\left(b \cdot b\right) \cdot b\right) \cdot 0.020833333333333332\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), b, 2\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= a -27000000000.0)
   (* (* (* b b) b) 0.020833333333333332)
   (/ 1.0 (fma (fma 0.5 b 1.0) b 2.0))))

C

double code(double a, double b) {
	double tmp;
	if (a <= -27000000000.0) {
		tmp = ((b * b) * b) * 0.020833333333333332;
	} else {
		tmp = 1.0 / fma(fma(0.5, b, 1.0), b, 2.0);
	}
	return tmp;
}

Julia

function code(a, b)
	tmp = 0.0
	if (a <= -27000000000.0)
		tmp = Float64(Float64(Float64(b * b) * b) * 0.020833333333333332);
	else
		tmp = Float64(1.0 / fma(fma(0.5, b, 1.0), b, 2.0));
	end
	return tmp
end

Wolfram

code[a_, b_] := If[LessEqual[a, -27000000000.0], N[(N[(N[(b * b), $MachinePrecision] * b), $MachinePrecision] * 0.020833333333333332), $MachinePrecision], N[(1.0 / N[(N[(0.5 * b + 1.0), $MachinePrecision] * b + 2.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -2.7e10

   1. Initial program 99.1%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1} \]

      7. lower--.f64 N/A

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

      8. lift-exp.f64 35.3

         \[\leadsto \frac{1}{e^{b} - -1} \]

   4. Applied rewrites 35.3%

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

   5. Taylor expanded in b around 0

      \[\leadsto \frac{1}{2} + \color{blue}{b \cdot \left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto b \cdot \left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}\right) + \frac{1}{2} \]

      2. *-commutative N/A

         \[\leadsto \left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}\right) \cdot b + \frac{1}{2} \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      4. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left({b}^{2} \cdot \frac{1}{48} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left({b}^{2} \cdot \frac{1}{48} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      7. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\left(b \cdot b\right) \cdot \frac{1}{48} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      8. lower-*.f64 2.7

         \[\leadsto \mathsf{fma}\left(\left(b \cdot b\right) \cdot 0.020833333333333332 - 0.25, b, 0.5\right) \]

   7. Applied rewrites 2.7%

      \[\leadsto \mathsf{fma}\left(\left(b \cdot b\right) \cdot 0.020833333333333332 - 0.25, \color{blue}{b}, 0.5\right) \]

   8. Taylor expanded in b around inf

      \[\leadsto \frac{1}{48} \cdot {b}^{\color{blue}{3}} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto {b}^{3} \cdot \frac{1}{48} \]

      2. lower-*.f64 N/A

         \[\leadsto {b}^{3} \cdot \frac{1}{48} \]

      3. unpow3 N/A

         \[\leadsto \left(\left(b \cdot b\right) \cdot b\right) \cdot \frac{1}{48} \]

      4. pow2 N/A

         \[\leadsto \left({b}^{2} \cdot b\right) \cdot \frac{1}{48} \]

      5. lower-*.f64 N/A

         \[\leadsto \left({b}^{2} \cdot b\right) \cdot \frac{1}{48} \]

      6. pow2 N/A

         \[\leadsto \left(\left(b \cdot b\right) \cdot b\right) \cdot \frac{1}{48} \]

      7. lift-*.f64 44.5

         \[\leadsto \left(\left(b \cdot b\right) \cdot b\right) \cdot 0.020833333333333332 \]

   10. Applied rewrites 44.5%

       \[\leadsto \left(\left(b \cdot b\right) \cdot b\right) \cdot 0.020833333333333332 \]

   if -2.7e10 < a

   1. Initial program 99.0%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1} \]

      7. lower--.f64 N/A

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

      8. lift-exp.f64 98.1

         \[\leadsto \frac{1}{e^{b} - -1} \]

   4. Applied rewrites 98.1%

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

   5. Taylor expanded in b around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(1 + \frac{1}{2} \cdot b, b, 2\right)} \]

      4. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot b + 1, b, 2\right)} \]

      5. lower-fma.f64 61.1

         \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), b, 2\right)} \]

   7. Applied rewrites 61.1%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), \color{blue}{b}, 2\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 54.6% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq 320:\\ \;\;\;\;\frac{a - -1}{\left(a - -1\right) + 1}\\ \mathbf{elif}\;b \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;\frac{a}{a + \left(1 + b\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(b \cdot b\right) \cdot 0.5}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= b 320.0)
   (/ (- a -1.0) (+ (- a -1.0) 1.0))
   (if (<= b 1.35e+154) (/ a (+ a (+ 1.0 b))) (/ 1.0 (* (* b b) 0.5)))))

C

double code(double a, double b) {
	double tmp;
	if (b <= 320.0) {
		tmp = (a - -1.0) / ((a - -1.0) + 1.0);
	} else if (b <= 1.35e+154) {
		tmp = a / (a + (1.0 + b));
	} else {
		tmp = 1.0 / ((b * b) * 0.5);
	}
	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(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (b <= 320.0d0) then
        tmp = (a - (-1.0d0)) / ((a - (-1.0d0)) + 1.0d0)
    else if (b <= 1.35d+154) then
        tmp = a / (a + (1.0d0 + b))
    else
        tmp = 1.0d0 / ((b * b) * 0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b) {
	double tmp;
	if (b <= 320.0) {
		tmp = (a - -1.0) / ((a - -1.0) + 1.0);
	} else if (b <= 1.35e+154) {
		tmp = a / (a + (1.0 + b));
	} else {
		tmp = 1.0 / ((b * b) * 0.5);
	}
	return tmp;
}

Python

def code(a, b):
	tmp = 0
	if b <= 320.0:
		tmp = (a - -1.0) / ((a - -1.0) + 1.0)
	elif b <= 1.35e+154:
		tmp = a / (a + (1.0 + b))
	else:
		tmp = 1.0 / ((b * b) * 0.5)
	return tmp

Julia

function code(a, b)
	tmp = 0.0
	if (b <= 320.0)
		tmp = Float64(Float64(a - -1.0) / Float64(Float64(a - -1.0) + 1.0));
	elseif (b <= 1.35e+154)
		tmp = Float64(a / Float64(a + Float64(1.0 + b)));
	else
		tmp = Float64(1.0 / Float64(Float64(b * b) * 0.5));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b)
	tmp = 0.0;
	if (b <= 320.0)
		tmp = (a - -1.0) / ((a - -1.0) + 1.0);
	elseif (b <= 1.35e+154)
		tmp = a / (a + (1.0 + b));
	else
		tmp = 1.0 / ((b * b) * 0.5);
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_] := If[LessEqual[b, 320.0], N[(N[(a - -1.0), $MachinePrecision] / N[(N[(a - -1.0), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.35e+154], N[(a / N[(a + N[(1.0 + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(b * b), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b < 320

   1. Initial program 98.8%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{a + \color{blue}{1}}{e^{a} + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}}{e^{a} + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1 \cdot 1}{e^{a} + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{e^{a} + e^{b}} \]

      6. lower--.f64 73.4

         \[\leadsto \frac{a - \color{blue}{-1}}{e^{a} + e^{b}} \]

   4. Applied rewrites 73.4%

      \[\leadsto \frac{\color{blue}{a - -1}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{a - -1}{\color{blue}{\left(1 + a\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{a - -1}{\left(a + \color{blue}{1}\right) + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}\right) + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1 \cdot 1\right) + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1\right) + e^{b}} \]

      6. lower--.f64 74.8

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{-1}\right) + e^{b}} \]

   7. Applied rewrites 74.8%

      \[\leadsto \frac{a - -1}{\color{blue}{\left(a - -1\right)} + e^{b}} \]

   8. Taylor expanded in b around 0

      \[\leadsto \frac{a - -1}{\left(a - -1\right) + \color{blue}{1}} \]
   9. Step-by-step derivation

   10. Applied rewrites 52.5%

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

   if 320 < b < 1.35000000000000003e154

   1. Initial program 99.3%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{a + \color{blue}{1}}{e^{a} + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}}{e^{a} + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1 \cdot 1}{e^{a} + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{e^{a} + e^{b}} \]

      6. lower--.f64 99.8

         \[\leadsto \frac{a - \color{blue}{-1}}{e^{a} + e^{b}} \]

   4. Applied rewrites 99.8%

      \[\leadsto \frac{\color{blue}{a - -1}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{a - -1}{\color{blue}{\left(1 + a\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{a - -1}{\left(a + \color{blue}{1}\right) + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}\right) + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1 \cdot 1\right) + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1\right) + e^{b}} \]

      6. lower--.f64 99.8

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{-1}\right) + e^{b}} \]

   7. Applied rewrites 99.8%

      \[\leadsto \frac{a - -1}{\color{blue}{\left(a - -1\right)} + e^{b}} \]

   8. Taylor expanded in b around 0

      \[\leadsto \frac{a - -1}{\left(a - -1\right) + \color{blue}{1}} \]
   9. Step-by-step derivation

   10. Applied rewrites 3.1%

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

   11. Taylor expanded in a around inf

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]
   12. Step-by-step derivation

   13. Applied rewrites 4.7%

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]

   14. Taylor expanded in a around inf

       \[\leadsto \frac{a}{a + 1} \]
   15. Step-by-step derivation

   16. Applied rewrites 4.6%

       \[\leadsto \frac{a}{a + 1} \]

   17. Taylor expanded in b around 0

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

       1. lower-+.f64 19.0

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

   19. Applied rewrites 19.0%

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

   if 1.35000000000000003e154 < b

   1. Initial program 99.5%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1} \]

      7. lower--.f64 N/A

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

      8. lift-exp.f64 100.0

         \[\leadsto \frac{1}{e^{b} - -1} \]

   4. Applied rewrites 100.0%

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

   5. Taylor expanded in b around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(1 + \frac{1}{2} \cdot b, b, 2\right)} \]

      4. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot b + 1, b, 2\right)} \]

      5. lower-fma.f64 99.9

         \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), b, 2\right)} \]

   7. Applied rewrites 99.9%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), \color{blue}{b}, 2\right)} \]

   8. Taylor expanded in b around inf

      \[\leadsto \frac{1}{\frac{1}{2} \cdot {b}^{\color{blue}{2}}} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{{b}^{2} \cdot \frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{{b}^{2} \cdot \frac{1}{2}} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot \frac{1}{2}} \]

      4. lift-*.f64 100.0

         \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot 0.5} \]

   10. Applied rewrites 100.0%

       \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot 0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 54.6% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq 320:\\ \;\;\;\;\frac{1 + a}{2 + a}\\ \mathbf{elif}\;b \leq 1.35 \cdot 10^{+154}:\\ \;\;\;\;\frac{a}{a + \left(1 + b\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{\left(b \cdot b\right) \cdot 0.5}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= b 320.0)
   (/ (+ 1.0 a) (+ 2.0 a))
   (if (<= b 1.35e+154) (/ a (+ a (+ 1.0 b))) (/ 1.0 (* (* b b) 0.5)))))

C

double code(double a, double b) {
	double tmp;
	if (b <= 320.0) {
		tmp = (1.0 + a) / (2.0 + a);
	} else if (b <= 1.35e+154) {
		tmp = a / (a + (1.0 + b));
	} else {
		tmp = 1.0 / ((b * b) * 0.5);
	}
	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(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (b <= 320.0d0) then
        tmp = (1.0d0 + a) / (2.0d0 + a)
    else if (b <= 1.35d+154) then
        tmp = a / (a + (1.0d0 + b))
    else
        tmp = 1.0d0 / ((b * b) * 0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b) {
	double tmp;
	if (b <= 320.0) {
		tmp = (1.0 + a) / (2.0 + a);
	} else if (b <= 1.35e+154) {
		tmp = a / (a + (1.0 + b));
	} else {
		tmp = 1.0 / ((b * b) * 0.5);
	}
	return tmp;
}

Python

def code(a, b):
	tmp = 0
	if b <= 320.0:
		tmp = (1.0 + a) / (2.0 + a)
	elif b <= 1.35e+154:
		tmp = a / (a + (1.0 + b))
	else:
		tmp = 1.0 / ((b * b) * 0.5)
	return tmp

Julia

function code(a, b)
	tmp = 0.0
	if (b <= 320.0)
		tmp = Float64(Float64(1.0 + a) / Float64(2.0 + a));
	elseif (b <= 1.35e+154)
		tmp = Float64(a / Float64(a + Float64(1.0 + b)));
	else
		tmp = Float64(1.0 / Float64(Float64(b * b) * 0.5));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b)
	tmp = 0.0;
	if (b <= 320.0)
		tmp = (1.0 + a) / (2.0 + a);
	elseif (b <= 1.35e+154)
		tmp = a / (a + (1.0 + b));
	else
		tmp = 1.0 / ((b * b) * 0.5);
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_] := If[LessEqual[b, 320.0], N[(N[(1.0 + a), $MachinePrecision] / N[(2.0 + a), $MachinePrecision]), $MachinePrecision], If[LessEqual[b, 1.35e+154], N[(a / N[(a + N[(1.0 + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(1.0 / N[(N[(b * b), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b < 320

   1. Initial program 98.8%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 77.2

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 77.2%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]
   6. Step-by-step derivation

      1. lower-+.f64 76.4

         \[\leadsto \frac{e^{a}}{2 + a} \]

   7. Applied rewrites 76.4%

      \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]

   8. Taylor expanded in a around 0

      \[\leadsto \frac{\color{blue}{1 + a}}{2 + a} \]
   9. Step-by-step derivation

      1. lower-+.f64 52.5

         \[\leadsto \frac{1 + \color{blue}{a}}{2 + a} \]

   10. Applied rewrites 52.5%

       \[\leadsto \frac{\color{blue}{1 + a}}{2 + a} \]

   if 320 < b < 1.35000000000000003e154

   1. Initial program 99.3%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{a + \color{blue}{1}}{e^{a} + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}}{e^{a} + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1 \cdot 1}{e^{a} + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{e^{a} + e^{b}} \]

      6. lower--.f64 99.8

         \[\leadsto \frac{a - \color{blue}{-1}}{e^{a} + e^{b}} \]

   4. Applied rewrites 99.8%

      \[\leadsto \frac{\color{blue}{a - -1}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{a - -1}{\color{blue}{\left(1 + a\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{a - -1}{\left(a + \color{blue}{1}\right) + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}\right) + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1 \cdot 1\right) + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1\right) + e^{b}} \]

      6. lower--.f64 99.8

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{-1}\right) + e^{b}} \]

   7. Applied rewrites 99.8%

      \[\leadsto \frac{a - -1}{\color{blue}{\left(a - -1\right)} + e^{b}} \]

   8. Taylor expanded in b around 0

      \[\leadsto \frac{a - -1}{\left(a - -1\right) + \color{blue}{1}} \]
   9. Step-by-step derivation

   10. Applied rewrites 3.1%

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

   11. Taylor expanded in a around inf

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]
   12. Step-by-step derivation

   13. Applied rewrites 4.7%

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]

   14. Taylor expanded in a around inf

       \[\leadsto \frac{a}{a + 1} \]
   15. Step-by-step derivation

   16. Applied rewrites 4.6%

       \[\leadsto \frac{a}{a + 1} \]

   17. Taylor expanded in b around 0

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

       1. lower-+.f64 19.0

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

   19. Applied rewrites 19.0%

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

   if 1.35000000000000003e154 < b

   1. Initial program 99.5%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1} \]

      7. lower--.f64 N/A

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

      8. lift-exp.f64 100.0

         \[\leadsto \frac{1}{e^{b} - -1} \]

   4. Applied rewrites 100.0%

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

   5. Taylor expanded in b around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(1 + \frac{1}{2} \cdot b, b, 2\right)} \]

      4. +-commutative N/A

         \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{1}{2} \cdot b + 1, b, 2\right)} \]

      5. lower-fma.f64 99.9

         \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), b, 2\right)} \]

   7. Applied rewrites 99.9%

      \[\leadsto \frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, b, 1\right), \color{blue}{b}, 2\right)} \]

   8. Taylor expanded in b around inf

      \[\leadsto \frac{1}{\frac{1}{2} \cdot {b}^{\color{blue}{2}}} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{{b}^{2} \cdot \frac{1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{{b}^{2} \cdot \frac{1}{2}} \]

      3. pow2 N/A

         \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot \frac{1}{2}} \]

      4. lift-*.f64 100.0

         \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot 0.5} \]

   10. Applied rewrites 100.0%

       \[\leadsto \frac{1}{\left(b \cdot b\right) \cdot 0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 11: 50.3% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \leq -27000000000:\\ \;\;\;\;\left(\left(b \cdot b\right) \cdot b\right) \cdot 0.020833333333333332\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + a}{2 + a}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= a -27000000000.0)
   (* (* (* b b) b) 0.020833333333333332)
   (/ (+ 1.0 a) (+ 2.0 a))))

C

double code(double a, double b) {
	double tmp;
	if (a <= -27000000000.0) {
		tmp = ((b * b) * b) * 0.020833333333333332;
	} else {
		tmp = (1.0 + a) / (2.0 + a);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (a <= (-27000000000.0d0)) then
        tmp = ((b * b) * b) * 0.020833333333333332d0
    else
        tmp = (1.0d0 + a) / (2.0d0 + a)
    end if
    code = tmp
end function

Java

public static double code(double a, double b) {
	double tmp;
	if (a <= -27000000000.0) {
		tmp = ((b * b) * b) * 0.020833333333333332;
	} else {
		tmp = (1.0 + a) / (2.0 + a);
	}
	return tmp;
}

Python

def code(a, b):
	tmp = 0
	if a <= -27000000000.0:
		tmp = ((b * b) * b) * 0.020833333333333332
	else:
		tmp = (1.0 + a) / (2.0 + a)
	return tmp

Julia

function code(a, b)
	tmp = 0.0
	if (a <= -27000000000.0)
		tmp = Float64(Float64(Float64(b * b) * b) * 0.020833333333333332);
	else
		tmp = Float64(Float64(1.0 + a) / Float64(2.0 + a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b)
	tmp = 0.0;
	if (a <= -27000000000.0)
		tmp = ((b * b) * b) * 0.020833333333333332;
	else
		tmp = (1.0 + a) / (2.0 + a);
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_] := If[LessEqual[a, -27000000000.0], N[(N[(N[(b * b), $MachinePrecision] * b), $MachinePrecision] * 0.020833333333333332), $MachinePrecision], N[(N[(1.0 + a), $MachinePrecision] / N[(2.0 + a), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if a < -2.7e10

   1. Initial program 99.1%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. lower-/.f64 N/A

         \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

      3. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

      6. metadata-eval N/A

         \[\leadsto \frac{1}{e^{b} - -1} \]

      7. lower--.f64 N/A

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

      8. lift-exp.f64 35.3

         \[\leadsto \frac{1}{e^{b} - -1} \]

   4. Applied rewrites 35.3%

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

   5. Taylor expanded in b around 0

      \[\leadsto \frac{1}{2} + \color{blue}{b \cdot \left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto b \cdot \left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}\right) + \frac{1}{2} \]

      2. *-commutative N/A

         \[\leadsto \left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}\right) \cdot b + \frac{1}{2} \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      4. lower--.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{48} \cdot {b}^{2} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left({b}^{2} \cdot \frac{1}{48} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      6. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left({b}^{2} \cdot \frac{1}{48} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      7. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\left(b \cdot b\right) \cdot \frac{1}{48} - \frac{1}{4}, b, \frac{1}{2}\right) \]

      8. lower-*.f64 2.7

         \[\leadsto \mathsf{fma}\left(\left(b \cdot b\right) \cdot 0.020833333333333332 - 0.25, b, 0.5\right) \]

   7. Applied rewrites 2.7%

      \[\leadsto \mathsf{fma}\left(\left(b \cdot b\right) \cdot 0.020833333333333332 - 0.25, \color{blue}{b}, 0.5\right) \]

   8. Taylor expanded in b around inf

      \[\leadsto \frac{1}{48} \cdot {b}^{\color{blue}{3}} \]
   9. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto {b}^{3} \cdot \frac{1}{48} \]

      2. lower-*.f64 N/A

         \[\leadsto {b}^{3} \cdot \frac{1}{48} \]

      3. unpow3 N/A

         \[\leadsto \left(\left(b \cdot b\right) \cdot b\right) \cdot \frac{1}{48} \]

      4. pow2 N/A

         \[\leadsto \left({b}^{2} \cdot b\right) \cdot \frac{1}{48} \]

      5. lower-*.f64 N/A

         \[\leadsto \left({b}^{2} \cdot b\right) \cdot \frac{1}{48} \]

      6. pow2 N/A

         \[\leadsto \left(\left(b \cdot b\right) \cdot b\right) \cdot \frac{1}{48} \]

      7. lift-*.f64 44.5

         \[\leadsto \left(\left(b \cdot b\right) \cdot b\right) \cdot 0.020833333333333332 \]

   10. Applied rewrites 44.5%

       \[\leadsto \left(\left(b \cdot b\right) \cdot b\right) \cdot 0.020833333333333332 \]

   if -2.7e10 < a

   1. Initial program 99.0%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 53.4

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 53.4%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]
   6. Step-by-step derivation

      1. lower-+.f64 52.6

         \[\leadsto \frac{e^{a}}{2 + a} \]

   7. Applied rewrites 52.6%

      \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]

   8. Taylor expanded in a around 0

      \[\leadsto \frac{\color{blue}{1 + a}}{2 + a} \]
   9. Step-by-step derivation

      1. lower-+.f64 52.4

         \[\leadsto \frac{1 + \color{blue}{a}}{2 + a} \]

   10. Applied rewrites 52.4%

       \[\leadsto \frac{\color{blue}{1 + a}}{2 + a} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 48.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{e^{a}}{e^{a} + e^{b}} \leq 0:\\ \;\;\;\;\frac{a}{a + \left(1 + b\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + a}{2 + a}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= (/ (exp a) (+ (exp a) (exp b))) 0.0)
   (/ a (+ a (+ 1.0 b)))
   (/ (+ 1.0 a) (+ 2.0 a))))

C

double code(double a, double b) {
	double tmp;
	if ((exp(a) / (exp(a) + exp(b))) <= 0.0) {
		tmp = a / (a + (1.0 + b));
	} else {
		tmp = (1.0 + a) / (2.0 + a);
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if ((exp(a) / (exp(a) + exp(b))) <= 0.0d0) then
        tmp = a / (a + (1.0d0 + b))
    else
        tmp = (1.0d0 + a) / (2.0d0 + a)
    end if
    code = tmp
end function

Java

public static double code(double a, double b) {
	double tmp;
	if ((Math.exp(a) / (Math.exp(a) + Math.exp(b))) <= 0.0) {
		tmp = a / (a + (1.0 + b));
	} else {
		tmp = (1.0 + a) / (2.0 + a);
	}
	return tmp;
}

Python

def code(a, b):
	tmp = 0
	if (math.exp(a) / (math.exp(a) + math.exp(b))) <= 0.0:
		tmp = a / (a + (1.0 + b))
	else:
		tmp = (1.0 + a) / (2.0 + a)
	return tmp

Julia

function code(a, b)
	tmp = 0.0
	if (Float64(exp(a) / Float64(exp(a) + exp(b))) <= 0.0)
		tmp = Float64(a / Float64(a + Float64(1.0 + b)));
	else
		tmp = Float64(Float64(1.0 + a) / Float64(2.0 + a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b)
	tmp = 0.0;
	if ((exp(a) / (exp(a) + exp(b))) <= 0.0)
		tmp = a / (a + (1.0 + b));
	else
		tmp = (1.0 + a) / (2.0 + a);
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_] := If[LessEqual[N[(N[Exp[a], $MachinePrecision] / N[(N[Exp[a], $MachinePrecision] + N[Exp[b], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], N[(a / N[(a + N[(1.0 + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(1.0 + a), $MachinePrecision] / N[(2.0 + a), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 (exp.f64 a) (+.f64 (exp.f64 a) (exp.f64 b))) < 0.0

   1. Initial program 100.0%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{a + \color{blue}{1}}{e^{a} + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}}{e^{a} + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1 \cdot 1}{e^{a} + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{e^{a} + e^{b}} \]

      6. lower--.f64 60.7

         \[\leadsto \frac{a - \color{blue}{-1}}{e^{a} + e^{b}} \]

   4. Applied rewrites 60.7%

      \[\leadsto \frac{\color{blue}{a - -1}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{a - -1}{\color{blue}{\left(1 + a\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{a - -1}{\left(a + \color{blue}{1}\right) + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}\right) + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1 \cdot 1\right) + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1\right) + e^{b}} \]

      6. lower--.f64 61.1

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{-1}\right) + e^{b}} \]

   7. Applied rewrites 61.1%

      \[\leadsto \frac{a - -1}{\color{blue}{\left(a - -1\right)} + e^{b}} \]

   8. Taylor expanded in b around 0

      \[\leadsto \frac{a - -1}{\left(a - -1\right) + \color{blue}{1}} \]
   9. Step-by-step derivation

   10. Applied rewrites 3.1%

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

   11. Taylor expanded in a around inf

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]
   12. Step-by-step derivation

   13. Applied rewrites 4.1%

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]

   14. Taylor expanded in a around inf

       \[\leadsto \frac{a}{a + 1} \]
   15. Step-by-step derivation

   16. Applied rewrites 4.0%

       \[\leadsto \frac{a}{a + 1} \]

   17. Taylor expanded in b around 0

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

       1. lower-+.f64 22.9

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

   19. Applied rewrites 22.9%

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

   if 0.0 < (/.f64 (exp.f64 a) (+.f64 (exp.f64 a) (exp.f64 b)))

   1. Initial program 98.2%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 69.4

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 69.4%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]
   6. Step-by-step derivation

      1. lower-+.f64 68.4

         \[\leadsto \frac{e^{a}}{2 + a} \]

   7. Applied rewrites 68.4%

      \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]

   8. Taylor expanded in a around 0

      \[\leadsto \frac{\color{blue}{1 + a}}{2 + a} \]
   9. Step-by-step derivation

      1. lower-+.f64 68.6

         \[\leadsto \frac{1 + \color{blue}{a}}{2 + a} \]

   10. Applied rewrites 68.6%

       \[\leadsto \frac{\color{blue}{1 + a}}{2 + a} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 47.8% accurate, 2.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b \leq 420:\\ \;\;\;\;\frac{1}{2 + a}\\ \mathbf{else}:\\ \;\;\;\;\frac{a}{a + \left(1 + b\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (if (<= b 420.0) (/ 1.0 (+ 2.0 a)) (/ a (+ a (+ 1.0 b)))))

C

double code(double a, double b) {
	double tmp;
	if (b <= 420.0) {
		tmp = 1.0 / (2.0 + a);
	} else {
		tmp = a / (a + (1.0 + b));
	}
	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(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8) :: tmp
    if (b <= 420.0d0) then
        tmp = 1.0d0 / (2.0d0 + a)
    else
        tmp = a / (a + (1.0d0 + b))
    end if
    code = tmp
end function

Java

public static double code(double a, double b) {
	double tmp;
	if (b <= 420.0) {
		tmp = 1.0 / (2.0 + a);
	} else {
		tmp = a / (a + (1.0 + b));
	}
	return tmp;
}

Python

def code(a, b):
	tmp = 0
	if b <= 420.0:
		tmp = 1.0 / (2.0 + a)
	else:
		tmp = a / (a + (1.0 + b))
	return tmp

Julia

function code(a, b)
	tmp = 0.0
	if (b <= 420.0)
		tmp = Float64(1.0 / Float64(2.0 + a));
	else
		tmp = Float64(a / Float64(a + Float64(1.0 + b)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b)
	tmp = 0.0;
	if (b <= 420.0)
		tmp = 1.0 / (2.0 + a);
	else
		tmp = a / (a + (1.0 + b));
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_] := If[LessEqual[b, 420.0], N[(1.0 / N[(2.0 + a), $MachinePrecision]), $MachinePrecision], N[(a / N[(a + N[(1.0 + b), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b < 420

   1. Initial program 98.8%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in b around 0

      \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
   3. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

      2. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

      5. metadata-eval N/A

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

      6. lower--.f64 N/A

         \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

      7. lift-exp.f64 77.2

         \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   4. Applied rewrites 77.2%

      \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]
   6. Step-by-step derivation

      1. lower-+.f64 76.4

         \[\leadsto \frac{e^{a}}{2 + a} \]

   7. Applied rewrites 76.4%

      \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]

   8. Taylor expanded in a around 0

      \[\leadsto \frac{\color{blue}{1}}{2 + a} \]
   9. Step-by-step derivation

   10. Applied rewrites 52.2%

       \[\leadsto \frac{\color{blue}{1}}{2 + a} \]

   if 420 < b

   1. Initial program 99.4%

      \[\frac{e^{a}}{e^{a} + e^{b}} \]

   2. Taylor expanded in a around 0

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

      1. +-commutative N/A

         \[\leadsto \frac{a + \color{blue}{1}}{e^{a} + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}}{e^{a} + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1 \cdot 1}{e^{a} + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{e^{a} + e^{b}} \]

      6. lower--.f64 99.9

         \[\leadsto \frac{a - \color{blue}{-1}}{e^{a} + e^{b}} \]

   4. Applied rewrites 99.9%

      \[\leadsto \frac{\color{blue}{a - -1}}{e^{a} + e^{b}} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{a - -1}{\color{blue}{\left(1 + a\right)} + e^{b}} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{a - -1}{\left(a + \color{blue}{1}\right) + e^{b}} \]

      2. metadata-eval N/A

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

      3. fp-cancel-sign-sub-inv N/A

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}\right) + e^{b}} \]

      4. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1 \cdot 1\right) + e^{b}} \]

      5. metadata-eval N/A

         \[\leadsto \frac{a - -1}{\left(a - -1\right) + e^{b}} \]

      6. lower--.f64 99.9

         \[\leadsto \frac{a - -1}{\left(a - \color{blue}{-1}\right) + e^{b}} \]

   7. Applied rewrites 99.9%

      \[\leadsto \frac{a - -1}{\color{blue}{\left(a - -1\right)} + e^{b}} \]

   8. Taylor expanded in b around 0

      \[\leadsto \frac{a - -1}{\left(a - -1\right) + \color{blue}{1}} \]
   9. Step-by-step derivation

   10. Applied rewrites 3.1%

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

   11. Taylor expanded in a around inf

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]
   12. Step-by-step derivation

   13. Applied rewrites 4.7%

       \[\leadsto \frac{a}{\left(a - -1\right) + 1} \]

   14. Taylor expanded in a around inf

       \[\leadsto \frac{a}{a + 1} \]
   15. Step-by-step derivation

   16. Applied rewrites 4.7%

       \[\leadsto \frac{a}{a + 1} \]

   17. Taylor expanded in b around 0

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

       1. lower-+.f64 36.0

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

   19. Applied rewrites 36.0%

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

Alternative 14: 39.1% accurate, 5.3× speedup

Math

\[\begin{array}{l} \\ \frac{1}{2 + a} \end{array} \]

FPCore

(FPCore (a b) :precision binary64 (/ 1.0 (+ 2.0 a)))

C

double code(double a, double b) {
	return 1.0 / (2.0 + a);
}

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(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = 1.0d0 / (2.0d0 + a)
end function

Java

public static double code(double a, double b) {
	return 1.0 / (2.0 + a);
}

Python

def code(a, b):
	return 1.0 / (2.0 + a)

Julia

function code(a, b)
	return Float64(1.0 / Float64(2.0 + a))
end

MATLAB

function tmp = code(a, b)
	tmp = 1.0 / (2.0 + a);
end

Wolfram

code[a_, b_] := N[(1.0 / N[(2.0 + a), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.0%

   \[\frac{e^{a}}{e^{a} + e^{b}} \]

2. Taylor expanded in b around 0

   \[\leadsto \frac{e^{a}}{\color{blue}{1 + e^{a}}} \]
3. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{e^{a}}{e^{a} + \color{blue}{1}} \]

   2. metadata-eval N/A

      \[\leadsto \frac{e^{a}}{e^{a} + 1 \cdot \color{blue}{1}} \]

   3. fp-cancel-sign-sub-inv N/A

      \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

   4. metadata-eval N/A

      \[\leadsto \frac{e^{a}}{e^{a} - -1 \cdot 1} \]

   5. metadata-eval N/A

      \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

   6. lower--.f64 N/A

      \[\leadsto \frac{e^{a}}{e^{a} - \color{blue}{-1}} \]

   7. lift-exp.f64 66.0

      \[\leadsto \frac{e^{a}}{e^{a} - -1} \]

4. Applied rewrites 66.0%

   \[\leadsto \frac{e^{a}}{\color{blue}{e^{a} - -1}} \]

5. Taylor expanded in a around 0

   \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]
6. Step-by-step derivation

   1. lower-+.f64 65.4

      \[\leadsto \frac{e^{a}}{2 + a} \]

7. Applied rewrites 65.4%

   \[\leadsto \frac{e^{a}}{2 + \color{blue}{a}} \]

8. Taylor expanded in a around 0

   \[\leadsto \frac{\color{blue}{1}}{2 + a} \]
9. Step-by-step derivation

10. Applied rewrites 39.1%

    \[\leadsto \frac{\color{blue}{1}}{2 + a} \]
11. Add Preprocessing

Alternative 15: 38.5% accurate, 37.5× speedup

Math

\[\begin{array}{l} \\ 0.5 \end{array} \]

FPCore

(FPCore (a b) :precision binary64 0.5)

C

double code(double a, double b) {
	return 0.5;
}

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(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = 0.5d0
end function

Java

public static double code(double a, double b) {
	return 0.5;
}

Python

def code(a, b):
	return 0.5

Julia

function code(a, b)
	return 0.5
end

MATLAB

function tmp = code(a, b)
	tmp = 0.5;
end

Wolfram

code[a_, b_] := 0.5

Derivation

1. Initial program 99.0%

   \[\frac{e^{a}}{e^{a} + e^{b}} \]

2. Taylor expanded in a around 0

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

   1. lower-/.f64 N/A

      \[\leadsto \frac{1}{\color{blue}{1 + e^{b}}} \]

   2. +-commutative N/A

      \[\leadsto \frac{1}{e^{b} + \color{blue}{1}} \]

   3. metadata-eval N/A

      \[\leadsto \frac{1}{e^{b} + 1 \cdot \color{blue}{1}} \]

   4. fp-cancel-sign-sub-inv N/A

      \[\leadsto \frac{1}{e^{b} - \color{blue}{\left(\mathsf{neg}\left(1\right)\right) \cdot 1}} \]

   5. metadata-eval N/A

      \[\leadsto \frac{1}{e^{b} - -1 \cdot 1} \]

   6. metadata-eval N/A

      \[\leadsto \frac{1}{e^{b} - -1} \]

   7. lower--.f64 N/A

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

   8. lift-exp.f64 81.2

      \[\leadsto \frac{1}{e^{b} - -1} \]

4. Applied rewrites 81.2%

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

5. Taylor expanded in b around 0

   \[\leadsto \frac{1}{2} \]
6. Step-by-step derivation

7. Applied rewrites 38.5%

   \[\leadsto 0.5 \]
8. Add Preprocessing

Developer Target 1: 100.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \frac{1}{1 + e^{b - a}} \end{array} \]

FPCore

(FPCore (a b) :precision binary64 (/ 1.0 (+ 1.0 (exp (- b a)))))

C

double code(double a, double b) {
	return 1.0 / (1.0 + exp((b - a)));
}

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(a, b)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = 1.0d0 / (1.0d0 + exp((b - a)))
end function

Java

public static double code(double a, double b) {
	return 1.0 / (1.0 + Math.exp((b - a)));
}

Python

def code(a, b):
	return 1.0 / (1.0 + math.exp((b - a)))

Julia

function code(a, b)
	return Float64(1.0 / Float64(1.0 + exp(Float64(b - a))))
end

MATLAB

function tmp = code(a, b)
	tmp = 1.0 / (1.0 + exp((b - a)));
end

Wolfram

code[a_, b_] := N[(1.0 / N[(1.0 + N[Exp[N[(b - a), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2025123 
(FPCore (a b)
  :name "Quotient of sum of exps"
  :precision binary64

  :alt
  (! :herbie-platform c (/ 1 (+ 1 (exp (- b a)))))

  (/ (exp a) (+ (exp a) (exp b))))