Logistic distribution

Percentage Accurate: 99.6% → 99.6%

Time: 5.3s

Alternatives: 10

Speedup: 1.1×

Specification

\[0 \leq s \land s \leq 1.0651631\]

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{\frac{-\left|x\right|}{s}}\\ t_1 := 1 + t\_0\\ \frac{t\_0}{\left(s \cdot t\_1\right) \cdot t\_1} \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (let* ((t_0 (exp (/ (- (fabs x)) s))) (t_1 (+ 1.0 t_0)))
   (/ t_0 (* (* s t_1) t_1))))

C

float code(float x, float s) {
	float t_0 = expf((-fabsf(x) / s));
	float t_1 = 1.0f + t_0;
	return t_0 / ((s * t_1) * t_1);
}

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(4) function code(x, s)
use fmin_fmax_functions
    real(4), intent (in) :: x
    real(4), intent (in) :: s
    real(4) :: t_0
    real(4) :: t_1
    t_0 = exp((-abs(x) / s))
    t_1 = 1.0e0 + t_0
    code = t_0 / ((s * t_1) * t_1)
end function

Julia

function code(x, s)
	t_0 = exp(Float32(Float32(-abs(x)) / s))
	t_1 = Float32(Float32(1.0) + t_0)
	return Float32(t_0 / Float32(Float32(s * t_1) * t_1))
end

MATLAB

function tmp = code(x, s)
	t_0 = exp((-abs(x) / s));
	t_1 = single(1.0) + t_0;
	tmp = t_0 / ((s * t_1) * t_1);
end

Initial Program: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := e^{\frac{-\left|x\right|}{s}}\\ t_1 := 1 + t\_0\\ \frac{t\_0}{\left(s \cdot t\_1\right) \cdot t\_1} \end{array} \end{array} \]

FPCore

(FPCore (x s)
 :precision binary32
 (let* ((t_0 (exp (/ (- (fabs x)) s))) (t_1 (+ 1.0 t_0)))
   (/ t_0 (* (* s t_1) t_1))))

C

float code(float x, float s) {
	float t_0 = expf((-fabsf(x) / s));
	float t_1 = 1.0f + t_0;
	return t_0 / ((s * t_1) * t_1);
}

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(4) function code(x, s)
use fmin_fmax_functions
    real(4), intent (in) :: x
    real(4), intent (in) :: s
    real(4) :: t_0
    real(4) :: t_1
    t_0 = exp((-abs(x) / s))
    t_1 = 1.0e0 + t_0
    code = t_0 / ((s * t_1) * t_1)
end function

Julia

function code(x, s)
	t_0 = exp(Float32(Float32(-abs(x)) / s))
	t_1 = Float32(Float32(1.0) + t_0)
	return Float32(t_0 / Float32(Float32(s * t_1) * t_1))
end

MATLAB

function tmp = code(x, s)
	t_0 = exp((-abs(x) / s));
	t_1 = single(1.0) + t_0;
	tmp = t_0 / ((s * t_1) * t_1);
end

Alternative 1: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} t_0 := e^{\frac{-\left|x\_m\right|}{s}}\\ \frac{t\_0}{\left(t\_0 \cdot s + s\right) \cdot \left(e^{\frac{x\_m}{-s}} + 1\right)} \end{array} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (let* ((t_0 (exp (/ (- (fabs x_m)) s))))
   (/ t_0 (* (+ (* t_0 s) s) (+ (exp (/ x_m (- s))) 1.0)))))

C

x_m = fabs(x);
float code(float x_m, float s) {
	float t_0 = expf((-fabsf(x_m) / s));
	return t_0 / (((t_0 * s) + s) * (expf((x_m / -s)) + 1.0f));
}

Fortran

x_m =     private
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(4) function code(x_m, s)
use fmin_fmax_functions
    real(4), intent (in) :: x_m
    real(4), intent (in) :: s
    real(4) :: t_0
    t_0 = exp((-abs(x_m) / s))
    code = t_0 / (((t_0 * s) + s) * (exp((x_m / -s)) + 1.0e0))
end function

Julia

x_m = abs(x)
function code(x_m, s)
	t_0 = exp(Float32(Float32(-abs(x_m)) / s))
	return Float32(t_0 / Float32(Float32(Float32(t_0 * s) + s) * Float32(exp(Float32(x_m / Float32(-s))) + Float32(1.0))))
end

MATLAB

x_m = abs(x);
function tmp = code(x_m, s)
	t_0 = exp((-abs(x_m) / s));
	tmp = t_0 / (((t_0 * s) + s) * (exp((x_m / -s)) + single(1.0)));
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. distribute-lft-in N/A

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

   2. lower-fma.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   3. lower-*.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, \color{blue}{s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   4. distribute-frac-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   6. lower-exp.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot \color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   7. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   8. lower-neg.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   9. lower-/.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\color{blue}{\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   10. lower-fabs.f32 99.6

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\color{blue}{\left|x\right|}}{s}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

4. Applied rewrites 99.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\left|x\right|}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
5. Step-by-step derivation

   1. *-rgt-identity N/A

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

   2. +-commutative N/A

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

   3. lower-+.f32 N/A

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

   4. *-commutative N/A

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

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\color{blue}{-1 \cdot \frac{\left|x\right|}{s}}} \cdot s + s\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   6. lower-*.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(\color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}} \cdot s} + s\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   7. lower-exp.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(\color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}}} \cdot s + s\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   8. mul-1-neg N/A

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

   9. distribute-neg-frac2 N/A

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

   10. lower-/.f32 N/A

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

   11. lower-fabs.f32 N/A

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

   12. lower-neg.f32 99.6

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{\color{blue}{-s}}} \cdot s + s\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

6. Applied rewrites 99.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
7. Step-by-step derivation

   1. +-commutative N/A

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

   2. rem-sqrt-square-rev N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{\frac{\mathsf{neg}\left(\color{blue}{\sqrt{x \cdot x}}\right)}{s}} + 1\right)} \]

   3. sqrt-unprod N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{\frac{\mathsf{neg}\left(\color{blue}{\sqrt{x} \cdot \sqrt{x}}\right)}{s}} + 1\right)} \]

   4. rem-square-sqrt N/A

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

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{\frac{\color{blue}{-1 \cdot x}}{s}} + 1\right)} \]

   6. associate-*r/ N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{\color{blue}{-1 \cdot \frac{x}{s}}} + 1\right)} \]

   7. rem-square-sqrt N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{-1 \cdot \frac{\color{blue}{\sqrt{x} \cdot \sqrt{x}}}{s}} + 1\right)} \]

   8. sqrt-unprod N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{-1 \cdot \frac{\color{blue}{\sqrt{x \cdot x}}}{s}} + 1\right)} \]

   9. rem-sqrt-square-rev N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{-1 \cdot \frac{\color{blue}{\left|x\right|}}{s}} + 1\right)} \]

   10. lower-+.f32 N/A

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \color{blue}{\left(e^{-1 \cdot \frac{\left|x\right|}{s}} + 1\right)}} \]

   11. lower-exp.f32 N/A

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(\color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}}} + 1\right)} \]

   12. mul-1-neg N/A

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

   13. rem-sqrt-square-rev N/A

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{\mathsf{neg}\left(\frac{\color{blue}{\sqrt{x \cdot x}}}{s}\right)} + 1\right)} \]

   14. sqrt-unprod N/A

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{\mathsf{neg}\left(\frac{\color{blue}{\sqrt{x} \cdot \sqrt{x}}}{s}\right)} + 1\right)} \]

   15. rem-square-sqrt N/A

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

   16. distribute-frac-neg2 N/A

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

   17. lower-/.f32 N/A

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

   18. lower-neg.f32 95.7

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \left(e^{\frac{x}{\color{blue}{-s}}} + 1\right)} \]

8. Applied rewrites 95.7%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right) \cdot \color{blue}{\left(e^{\frac{x}{-s}} + 1\right)}} \]

9. Final simplification 95.7%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{-\left|x\right|}{s}} \cdot s + s\right) \cdot \left(e^{\frac{x}{-s}} + 1\right)} \]
10. Add Preprocessing

Alternative 2: 97.5% accurate, 0.7× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} t_0 := e^{\frac{-\left|x\_m\right|}{s}}\\ t_1 := x\_m \cdot \frac{x\_m}{s}\\ t_2 := 1 + t\_0\\ \mathbf{if}\;\frac{t\_0}{\left(s \cdot t\_2\right) \cdot t\_2} \leq 0.20000000298023224:\\ \;\;\;\;\frac{t\_0}{s}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(t\_1, 0.125, 0.25 \cdot 0\right) - \mathsf{fma}\left(0.0625, t\_1 \cdot 3, \frac{\left(0.25 \cdot 0\right) \cdot x\_m}{s}\right)}{s} + 0.25}{s}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (let* ((t_0 (exp (/ (- (fabs x_m)) s)))
        (t_1 (* x_m (/ x_m s)))
        (t_2 (+ 1.0 t_0)))
   (if (<= (/ t_0 (* (* s t_2) t_2)) 0.20000000298023224)
     (/ t_0 s)
     (/
      (+
       (/
        (-
         (fma t_1 0.125 (* 0.25 0.0))
         (fma 0.0625 (* t_1 3.0) (/ (* (* 0.25 0.0) x_m) s)))
        s)
       0.25)
      s))))

C

x_m = fabs(x);
float code(float x_m, float s) {
	float t_0 = expf((-fabsf(x_m) / s));
	float t_1 = x_m * (x_m / s);
	float t_2 = 1.0f + t_0;
	float tmp;
	if ((t_0 / ((s * t_2) * t_2)) <= 0.20000000298023224f) {
		tmp = t_0 / s;
	} else {
		tmp = (((fmaf(t_1, 0.125f, (0.25f * 0.0f)) - fmaf(0.0625f, (t_1 * 3.0f), (((0.25f * 0.0f) * x_m) / s))) / s) + 0.25f) / s;
	}
	return tmp;
}

Julia

x_m = abs(x)
function code(x_m, s)
	t_0 = exp(Float32(Float32(-abs(x_m)) / s))
	t_1 = Float32(x_m * Float32(x_m / s))
	t_2 = Float32(Float32(1.0) + t_0)
	tmp = Float32(0.0)
	if (Float32(t_0 / Float32(Float32(s * t_2) * t_2)) <= Float32(0.20000000298023224))
		tmp = Float32(t_0 / s);
	else
		tmp = Float32(Float32(Float32(Float32(fma(t_1, Float32(0.125), Float32(Float32(0.25) * Float32(0.0))) - fma(Float32(0.0625), Float32(t_1 * Float32(3.0)), Float32(Float32(Float32(Float32(0.25) * Float32(0.0)) * x_m) / s))) / s) + Float32(0.25)) / s);
	end
	return tmp
end

Derivation

1. Split input into 2 regimes

2. if (/.f32 (exp.f32 (/.f32 (neg.f32 (fabs.f32 x)) s)) (*.f32 (*.f32 s (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 (fabs.f32 x)) s)))) (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 (fabs.f32 x)) s))))) < 0.200000003

   1. Initial program 99.7%

      \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. distribute-lft-in N/A

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

      2. lower-fma.f32 N/A

         \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

      3. lower-*.f32 N/A

         \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, \color{blue}{s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

      4. distribute-frac-neg N/A

         \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

      6. lower-exp.f32 N/A

         \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot \color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

      7. mul-1-neg N/A

         \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

      8. lower-neg.f32 N/A

         \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

      9. lower-/.f32 N/A

         \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\color{blue}{\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

      10. lower-fabs.f32 99.7

          \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\color{blue}{\left|x\right|}}{s}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   4. Applied rewrites 99.7%

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\left|x\right|}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
   5. Step-by-step derivation

      1. distribute-lft-in N/A

         \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{\color{blue}{\left(s \cdot \left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)\right)} \cdot \left(1 + e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}\right)} \]

      2. distribute-frac-neg N/A

         \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{\left(s \cdot \left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)\right) \cdot \left(1 + e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right)} \]

      3. mul-1-neg N/A

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

      4. associate-*r* N/A

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

      5. mul-1-neg N/A

         \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{s \cdot \left(\left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right) \cdot \left(1 + e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right)\right)} \]

      6. unpow2 N/A

         \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{s \cdot \color{blue}{{\left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)}^{2}}} \]

      7. *-commutative N/A

         \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{\color{blue}{{\left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)}^{2} \cdot s}} \]

      8. associate-/r* N/A

         \[\leadsto \color{blue}{\frac{\frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{{\left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)}^{2}}}{s}} \]

   6. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{e^{\frac{\left|x\right|}{-s} - \mathsf{log1p}\left(e^{\frac{\left|x\right|}{-s}}\right) \cdot 2}}{s}} \]

   7. Taylor expanded in s around 0

      \[\leadsto \frac{e^{\color{blue}{-1 \cdot \frac{\left|x\right|}{s}}}}{s} \]
   8. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}{s} \]

      2. distribute-frac-neg2 N/A

         \[\leadsto \frac{e^{\frac{\left|x\right|}{\color{blue}{\mathsf{neg}\left(s\right)}}}}{s} \]

      3. lower-/.f32 N/A

         \[\leadsto \frac{e^{\frac{\left|x\right|}{\color{blue}{\mathsf{neg}\left(s\right)}}}}{s} \]

      4. lower-fabs.f32 N/A

         \[\leadsto \frac{e^{\frac{\left|x\right|}{\mathsf{neg}\left(\color{blue}{s}\right)}}}{s} \]

      5. lower-neg.f32 99.5

         \[\leadsto \frac{e^{\frac{\left|x\right|}{-s}}}{s} \]

   9. Applied rewrites 99.5%

      \[\leadsto \frac{e^{\color{blue}{\frac{\left|x\right|}{-s}}}}{s} \]

   if 0.200000003 < (/.f32 (exp.f32 (/.f32 (neg.f32 (fabs.f32 x)) s)) (*.f32 (*.f32 s (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 (fabs.f32 x)) s)))) (+.f32 #s(literal 1 binary32) (exp.f32 (/.f32 (neg.f32 (fabs.f32 x)) s)))))

   1. Initial program 99.4%

      \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in s around inf

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

   5. Applied rewrites 81.4%

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

      1. rem-sqrt-square-rev N/A

         \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x \cdot x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

      2. sqrt-prod N/A

         \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x} \cdot \sqrt{x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

      3. lower-*.f32 N/A

         \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x} \cdot \sqrt{x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

      4. lower-sqrt.f32 N/A

         \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x}} \cdot \sqrt{x}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

      5. lower-sqrt.f32 40.1

         \[\leadsto \frac{e^{\frac{-\sqrt{x} \cdot \color{blue}{\sqrt{x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

   7. Applied rewrites 40.1%

      \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x} \cdot \sqrt{x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

   8. Taylor expanded in s around -inf

      \[\leadsto \color{blue}{-1 \cdot \frac{-1 \cdot \frac{\left(-1 \cdot \left(\frac{1}{4} \cdot x - \frac{1}{4} \cdot \left|x\right|\right) + \frac{1}{8} \cdot \frac{{x}^{2}}{s}\right) - \left(\frac{1}{16} \cdot \frac{2 \cdot {\left(\left|x\right|\right)}^{2} + {\left(\left|x\right|\right)}^{2}}{s} + \frac{\left|x\right| \cdot \left(\frac{1}{4} \cdot x - \frac{1}{4} \cdot \left|x\right|\right)}{s}\right)}{s} - \frac{1}{4}}{s}} \]

   10. Applied rewrites 90.5%

       \[\leadsto \color{blue}{-\frac{\left(-\frac{\mathsf{fma}\left(x \cdot \frac{x}{s}, 0.125, -0.25 \cdot \left(x - x\right)\right) - \mathsf{fma}\left(0.0625, \left(x \cdot \frac{x}{s}\right) \cdot 3, \frac{\left(0.25 \cdot \left(x - x\right)\right) \cdot x}{s}\right)}{s}\right) - 0.25}{s}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 96.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \leq 0.20000000298023224:\\ \;\;\;\;\frac{e^{\frac{-\left|x\right|}{s}}}{s}\\ \mathbf{else}:\\ \;\;\;\;\frac{\frac{\mathsf{fma}\left(x \cdot \frac{x}{s}, 0.125, 0.25 \cdot 0\right) - \mathsf{fma}\left(0.0625, \left(x \cdot \frac{x}{s}\right) \cdot 3, \frac{\left(0.25 \cdot 0\right) \cdot x}{s}\right)}{s} + 0.25}{s}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} t_0 := \frac{-\left|x\_m\right|}{s}\\ \frac{e^{t\_0 - \mathsf{log1p}\left(e^{t\_0}\right) \cdot 2}}{s} \end{array} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (let* ((t_0 (/ (- (fabs x_m)) s)))
   (/ (exp (- t_0 (* (log1p (exp t_0)) 2.0))) s)))

C

x_m = fabs(x);
float code(float x_m, float s) {
	float t_0 = -fabsf(x_m) / s;
	return expf((t_0 - (log1pf(expf(t_0)) * 2.0f))) / s;
}

Julia

x_m = abs(x)
function code(x_m, s)
	t_0 = Float32(Float32(-abs(x_m)) / s)
	return Float32(exp(Float32(t_0 - Float32(log1p(exp(t_0)) * Float32(2.0)))) / s)
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. distribute-lft-in N/A

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

   2. lower-fma.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   3. lower-*.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, \color{blue}{s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   4. distribute-frac-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   6. lower-exp.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot \color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   7. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   8. lower-neg.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   9. lower-/.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\color{blue}{\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   10. lower-fabs.f32 99.6

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\color{blue}{\left|x\right|}}{s}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

4. Applied rewrites 99.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\left|x\right|}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
5. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{\color{blue}{\left(s \cdot \left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)\right)} \cdot \left(1 + e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}\right)} \]

   2. distribute-frac-neg N/A

      \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{\left(s \cdot \left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)\right) \cdot \left(1 + e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right)} \]

   3. mul-1-neg N/A

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

   4. associate-*r* N/A

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

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{s \cdot \left(\left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right) \cdot \left(1 + e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right)\right)} \]

   6. unpow2 N/A

      \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{s \cdot \color{blue}{{\left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)}^{2}}} \]

   7. *-commutative N/A

      \[\leadsto \frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{\color{blue}{{\left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)}^{2} \cdot s}} \]

   8. associate-/r* N/A

      \[\leadsto \color{blue}{\frac{\frac{e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}{{\left(1 + e^{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}\right)}^{2}}}{s}} \]

6. Applied rewrites 99.6%

   \[\leadsto \color{blue}{\frac{e^{\frac{\left|x\right|}{-s} - \mathsf{log1p}\left(e^{\frac{\left|x\right|}{-s}}\right) \cdot 2}}{s}} \]

7. Final simplification 99.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s} - \mathsf{log1p}\left(e^{\frac{-\left|x\right|}{s}}\right) \cdot 2}}{s} \]
8. Add Preprocessing

Alternative 4: 99.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} t_0 := e^{\frac{x\_m}{-s}}\\ \frac{t\_0}{{\left(t\_0 + 1\right)}^{2} \cdot s} \end{array} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (let* ((t_0 (exp (/ x_m (- s))))) (/ t_0 (* (pow (+ t_0 1.0) 2.0) s))))

C

x_m = fabs(x);
float code(float x_m, float s) {
	float t_0 = expf((x_m / -s));
	return t_0 / (powf((t_0 + 1.0f), 2.0f) * s);
}

Fortran

x_m =     private
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(4) function code(x_m, s)
use fmin_fmax_functions
    real(4), intent (in) :: x_m
    real(4), intent (in) :: s
    real(4) :: t_0
    t_0 = exp((x_m / -s))
    code = t_0 / (((t_0 + 1.0e0) ** 2.0e0) * s)
end function

Julia

x_m = abs(x)
function code(x_m, s)
	t_0 = exp(Float32(x_m / Float32(-s)))
	return Float32(t_0 / Float32((Float32(t_0 + Float32(1.0)) ^ Float32(2.0)) * s))
end

MATLAB

x_m = abs(x);
function tmp = code(x_m, s)
	t_0 = exp((x_m / -s));
	tmp = t_0 / (((t_0 + single(1.0)) ^ single(2.0)) * s);
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. distribute-lft-in N/A

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

   2. lower-fma.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   3. lower-*.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, \color{blue}{s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   4. distribute-frac-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   6. lower-exp.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot \color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   7. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   8. lower-neg.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   9. lower-/.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\color{blue}{\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   10. lower-fabs.f32 99.6

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\color{blue}{\left|x\right|}}{s}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

4. Applied rewrites 99.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\left|x\right|}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
5. Step-by-step derivation

   1. *-rgt-identity N/A

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

   2. +-commutative N/A

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

   3. lower-+.f32 N/A

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

   4. *-commutative N/A

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

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\color{blue}{-1 \cdot \frac{\left|x\right|}{s}}} \cdot s + s\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   6. lower-*.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(\color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}} \cdot s} + s\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   7. lower-exp.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(\color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}}} \cdot s + s\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   8. mul-1-neg N/A

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

   9. distribute-neg-frac2 N/A

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

   10. lower-/.f32 N/A

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

   11. lower-fabs.f32 N/A

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

   12. lower-neg.f32 99.6

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(e^{\frac{\left|x\right|}{\color{blue}{-s}}} \cdot s + s\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

6. Applied rewrites 99.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\left(e^{\frac{\left|x\right|}{-s}} \cdot s + s\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

8. Applied rewrites 63.7%

   \[\leadsto \color{blue}{\frac{e^{\frac{x}{-s}}}{{\left(e^{\frac{x}{-s}} + 1\right)}^{2} \cdot s}} \]
9. Add Preprocessing

Alternative 5: 96.7% accurate, 1.2× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} t_0 := e^{\frac{-\left|x\_m\right|}{s}}\\ \frac{t\_0}{\left(s \cdot \left(1 + t\_0\right)\right) \cdot \left(1 + \mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x\_m \cdot x\_m}{s}, -0.5, \left|x\_m\right|\right)}{s}, -1, 1\right)\right)} \end{array} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (let* ((t_0 (exp (/ (- (fabs x_m)) s))))
   (/
    t_0
    (*
     (* s (+ 1.0 t_0))
     (+ 1.0 (fma (/ (fma (/ (* x_m x_m) s) -0.5 (fabs x_m)) s) -1.0 1.0))))))

C

x_m = fabs(x);
float code(float x_m, float s) {
	float t_0 = expf((-fabsf(x_m) / s));
	return t_0 / ((s * (1.0f + t_0)) * (1.0f + fmaf((fmaf(((x_m * x_m) / s), -0.5f, fabsf(x_m)) / s), -1.0f, 1.0f)));
}

Julia

x_m = abs(x)
function code(x_m, s)
	t_0 = exp(Float32(Float32(-abs(x_m)) / s))
	return Float32(t_0 / Float32(Float32(s * Float32(Float32(1.0) + t_0)) * Float32(Float32(1.0) + fma(Float32(fma(Float32(Float32(x_m * x_m) / s), Float32(-0.5), abs(x_m)) / s), Float32(-1.0), Float32(1.0)))))
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing

3. Taylor expanded in s around -inf

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

   1. +-commutative N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + \left(-1 \cdot \frac{\left|x\right| + \frac{-1}{2} \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s}}{s} + \color{blue}{1}\right)\right)} \]

   2. *-commutative N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + \left(\frac{\left|x\right| + \frac{-1}{2} \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s}}{s} \cdot -1 + 1\right)\right)} \]

   3. lower-fma.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + \mathsf{fma}\left(\frac{\left|x\right| + \frac{-1}{2} \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s}}{s}, \color{blue}{-1}, 1\right)\right)} \]

   4. lower-/.f32 N/A

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

   5. +-commutative N/A

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

   6. *-commutative N/A

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

   7. lower-fma.f32 N/A

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

   8. lower-/.f32 N/A

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

   9. unpow2 N/A

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

   10. sqr-abs-rev N/A

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

   11. lower-*.f32 N/A

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

   12. lower-fabs.f32 95.6

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + \mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x \cdot x}{s}, -0.5, \left|x\right|\right)}{s}, -1, 1\right)\right)} \]

5. Applied rewrites 95.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x \cdot x}{s}, -0.5, \left|x\right|\right)}{s}, -1, 1\right)}\right)} \]
6. Add Preprocessing

Alternative 6: 96.7% accurate, 1.3× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} t_0 := e^{\frac{-\left|x\_m\right|}{s}}\\ \frac{t\_0}{\left(s \cdot \left(1 + t\_0\right)\right) \cdot \mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x\_m \cdot x\_m}{s}, -0.5, \left|x\_m\right|\right)}{s}, -1, 2\right)} \end{array} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (let* ((t_0 (exp (/ (- (fabs x_m)) s))))
   (/
    t_0
    (*
     (* s (+ 1.0 t_0))
     (fma (/ (fma (/ (* x_m x_m) s) -0.5 (fabs x_m)) s) -1.0 2.0)))))

C

x_m = fabs(x);
float code(float x_m, float s) {
	float t_0 = expf((-fabsf(x_m) / s));
	return t_0 / ((s * (1.0f + t_0)) * fmaf((fmaf(((x_m * x_m) / s), -0.5f, fabsf(x_m)) / s), -1.0f, 2.0f));
}

Julia

x_m = abs(x)
function code(x_m, s)
	t_0 = exp(Float32(Float32(-abs(x_m)) / s))
	return Float32(t_0 / Float32(Float32(s * Float32(Float32(1.0) + t_0)) * fma(Float32(fma(Float32(Float32(x_m * x_m) / s), Float32(-0.5), abs(x_m)) / s), Float32(-1.0), Float32(2.0))))
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing

3. Taylor expanded in s around -inf

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

   1. +-commutative N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(-1 \cdot \frac{\left|x\right| + \frac{-1}{2} \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s}}{s} + \color{blue}{2}\right)} \]

   2. *-commutative N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(\frac{\left|x\right| + \frac{-1}{2} \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s}}{s} \cdot -1 + 2\right)} \]

   3. lower-fma.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \mathsf{fma}\left(\frac{\left|x\right| + \frac{-1}{2} \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s}}{s}, \color{blue}{-1}, 2\right)} \]

   4. lower-/.f32 N/A

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

   5. +-commutative N/A

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

   6. *-commutative N/A

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

   7. lower-fma.f32 N/A

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

   8. lower-/.f32 N/A

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

   9. unpow2 N/A

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

   10. sqr-abs-rev N/A

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

   11. lower-*.f32 N/A

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

   12. lower-fabs.f32 95.6

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x \cdot x}{s}, -0.5, \left|x\right|\right)}{s}, -1, 2\right)} \]

5. Applied rewrites 95.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(\frac{x \cdot x}{s}, -0.5, \left|x\right|\right)}{s}, -1, 2\right)}} \]
6. Add Preprocessing

Alternative 7: 96.4% accurate, 2.1× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \frac{e^{\frac{-\left|x\_m\right|}{s}}}{\left(\frac{\mathsf{fma}\left(-4, \left|x\_m\right|, 3 \cdot \frac{x\_m \cdot x\_m}{s}\right)}{s} + 4\right) \cdot s} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (/
  (exp (/ (- (fabs x_m)) s))
  (* (+ (/ (fma -4.0 (fabs x_m) (* 3.0 (/ (* x_m x_m) s))) s) 4.0) s)))

C

x_m = fabs(x);
float code(float x_m, float s) {
	return expf((-fabsf(x_m) / s)) / (((fmaf(-4.0f, fabsf(x_m), (3.0f * ((x_m * x_m) / s))) / s) + 4.0f) * s);
}

Julia

x_m = abs(x)
function code(x_m, s)
	return Float32(exp(Float32(Float32(-abs(x_m)) / s)) / Float32(Float32(Float32(fma(Float32(-4.0), abs(x_m), Float32(Float32(3.0) * Float32(Float32(x_m * x_m) / s))) / s) + Float32(4.0)) * s))
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing

3. Taylor expanded in s around -inf

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{-1 \cdot \left(s \cdot \left(-1 \cdot \frac{-4 \cdot \left|x\right| + \left(2 \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s} + \frac{{\left(\left|x\right|\right)}^{2}}{s}\right)}{s} - 4\right)\right)}} \]
4. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{neg}\left(s \cdot \left(-1 \cdot \frac{-4 \cdot \left|x\right| + \left(2 \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s} + \frac{{\left(\left|x\right|\right)}^{2}}{s}\right)}{s} - 4\right)\right)} \]

   2. lower-neg.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{-s \cdot \left(-1 \cdot \frac{-4 \cdot \left|x\right| + \left(2 \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s} + \frac{{\left(\left|x\right|\right)}^{2}}{s}\right)}{s} - 4\right)} \]

   3. *-commutative N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{-\left(-1 \cdot \frac{-4 \cdot \left|x\right| + \left(2 \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s} + \frac{{\left(\left|x\right|\right)}^{2}}{s}\right)}{s} - 4\right) \cdot s} \]

   4. lower-*.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{-\left(-1 \cdot \frac{-4 \cdot \left|x\right| + \left(2 \cdot \frac{{\left(\left|x\right|\right)}^{2}}{s} + \frac{{\left(\left|x\right|\right)}^{2}}{s}\right)}{s} - 4\right) \cdot s} \]

5. Applied rewrites 95.3%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{-\left(\left(-\frac{\mathsf{fma}\left(-4, \left|x\right|, 3 \cdot \frac{x \cdot x}{s}\right)}{s}\right) - 4\right) \cdot s}} \]

6. Final simplification 95.3%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\left(\frac{\mathsf{fma}\left(-4, \left|x\right|, 3 \cdot \frac{x \cdot x}{s}\right)}{s} + 4\right) \cdot s} \]
7. Add Preprocessing

Alternative 8: 64.8% accurate, 7.8× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \frac{\mathsf{fma}\left(-0.25, \frac{x\_m}{s}, 0.25\right) - -0.25 \cdot \frac{x\_m}{s}}{s} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (/ (- (fma -0.25 (/ x_m s) 0.25) (* -0.25 (/ x_m s))) s))

C

x_m = fabs(x);
float code(float x_m, float s) {
	return (fmaf(-0.25f, (x_m / s), 0.25f) - (-0.25f * (x_m / s))) / s;
}

Julia

x_m = abs(x)
function code(x_m, s)
	return Float32(Float32(fma(Float32(-0.25), Float32(x_m / s), Float32(0.25)) - Float32(Float32(-0.25) * Float32(x_m / s))) / s)
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing

3. Taylor expanded in s around inf

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

5. Applied rewrites 93.7%

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

   1. rem-sqrt-square-rev N/A

      \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x \cdot x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

   2. sqrt-prod N/A

      \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x} \cdot \sqrt{x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

   3. lower-*.f32 N/A

      \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x} \cdot \sqrt{x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

   4. lower-sqrt.f32 N/A

      \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x}} \cdot \sqrt{x}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

   5. lower-sqrt.f32 45.5

      \[\leadsto \frac{e^{\frac{-\sqrt{x} \cdot \color{blue}{\sqrt{x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

7. Applied rewrites 45.5%

   \[\leadsto \frac{e^{\frac{-\color{blue}{\sqrt{x} \cdot \sqrt{x}}}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot 2} \]

8. Taylor expanded in s around inf

   \[\leadsto \color{blue}{\frac{\left(\frac{1}{4} + \frac{-1}{4} \cdot \frac{x}{s}\right) - \frac{-1}{4} \cdot \frac{\left|x\right|}{s}}{s}} \]
9. Step-by-step derivation

10. Applied rewrites 63.7%

    \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(-0.25, \frac{x}{s}, 0.25\right) - -0.25 \cdot \frac{x}{s}}{s}} \]
11. Add Preprocessing

Alternative 9: 27.2% accurate, 8.3× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \frac{\mathsf{fma}\left(\frac{x\_m}{s} \cdot \frac{x\_m}{s}, -0.0625, 0.25\right)}{s} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s)
 :precision binary32
 (/ (fma (* (/ x_m s) (/ x_m s)) -0.0625 0.25) s))

C

x_m = fabs(x);
float code(float x_m, float s) {
	return fmaf(((x_m / s) * (x_m / s)), -0.0625f, 0.25f) / s;
}

Julia

x_m = abs(x)
function code(x_m, s)
	return Float32(fma(Float32(Float32(x_m / s) * Float32(x_m / s)), Float32(-0.0625), Float32(0.25)) / s)
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. distribute-lft-in N/A

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

   2. lower-fma.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   3. lower-*.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, \color{blue}{s \cdot e^{\frac{\mathsf{neg}\left(\left|x\right|\right)}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   4. distribute-frac-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   5. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   6. lower-exp.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot \color{blue}{e^{-1 \cdot \frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   7. mul-1-neg N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{\mathsf{neg}\left(\frac{\left|x\right|}{s}\right)}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   8. lower-neg.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{\color{blue}{-\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   9. lower-/.f32 N/A

      \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\color{blue}{\frac{\left|x\right|}{s}}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

   10. lower-fabs.f32 99.6

       \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\color{blue}{\left|x\right|}}{s}}\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

4. Applied rewrites 99.6%

   \[\leadsto \frac{e^{\frac{-\left|x\right|}{s}}}{\color{blue}{\mathsf{fma}\left(s, 1, s \cdot e^{-\frac{\left|x\right|}{s}}\right)} \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]

5. Taylor expanded in s around inf

   \[\leadsto \color{blue}{\frac{\left(\frac{1}{4} + \frac{1}{8} \cdot \frac{{\left(\left|x\right|\right)}^{2}}{{s}^{2}}\right) - \frac{1}{16} \cdot \frac{2 \cdot {\left(\left|x\right|\right)}^{2} + {\left(\left|x\right|\right)}^{2}}{{s}^{2}}}{s}} \]

7. Applied rewrites 25.8%

   \[\leadsto \color{blue}{\frac{0.25 + \frac{\mathsf{fma}\left(x \cdot x, 0.125, -0.0625 \cdot \left(3 \cdot \left(x \cdot x\right)\right)\right)}{s \cdot s}}{s}} \]

9. Applied rewrites 26.6%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\frac{x \cdot x}{s \cdot s}, -0.0625, 0.25\right)}{s}} \]
10. Step-by-step derivation

    1. times-frac N/A

       \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{s} \cdot \frac{x}{s}, \frac{-1}{16}, \frac{1}{4}\right)}{s} \]

    2. lower-*.f32 N/A

       \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{s} \cdot \frac{x}{s}, \frac{-1}{16}, \frac{1}{4}\right)}{s} \]

    3. lower-/.f32 N/A

       \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{s} \cdot \frac{x}{s}, \frac{-1}{16}, \frac{1}{4}\right)}{s} \]

    4. lower-/.f32 29.2

       \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{s} \cdot \frac{x}{s}, -0.0625, 0.25\right)}{s} \]

11. Applied rewrites 29.2%

    \[\leadsto \frac{\mathsf{fma}\left(\frac{x}{s} \cdot \frac{x}{s}, -0.0625, 0.25\right)}{s} \]
12. Add Preprocessing

Alternative 10: 27.5% accurate, 31.1× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \frac{0.25}{s} \end{array} \]

FPCore

x_m = (fabs.f32 x)
(FPCore (x_m s) :precision binary32 (/ 0.25 s))

C

x_m = fabs(x);
float code(float x_m, float s) {
	return 0.25f / s;
}

Fortran

x_m =     private
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(4) function code(x_m, s)
use fmin_fmax_functions
    real(4), intent (in) :: x_m
    real(4), intent (in) :: s
    code = 0.25e0 / s
end function

Julia

x_m = abs(x)
function code(x_m, s)
	return Float32(Float32(0.25) / s)
end

MATLAB

x_m = abs(x);
function tmp = code(x_m, s)
	tmp = single(0.25) / s;
end

Derivation

1. Initial program 99.6%

   \[\frac{e^{\frac{-\left|x\right|}{s}}}{\left(s \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)\right) \cdot \left(1 + e^{\frac{-\left|x\right|}{s}}\right)} \]
2. Add Preprocessing

3. Taylor expanded in s around inf

   \[\leadsto \color{blue}{\frac{\frac{1}{4}}{s}} \]
4. Step-by-step derivation

   1. lower-/.f32 29.0

      \[\leadsto \frac{0.25}{\color{blue}{s}} \]

5. Applied rewrites 29.0%

   \[\leadsto \color{blue}{\frac{0.25}{s}} \]

6. Final simplification 29.0%

   \[\leadsto \frac{0.25}{s} \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2025044 
(FPCore (x s)
  :name "Logistic distribution"
  :precision binary32
  :pre (and (<= 0.0 s) (<= s 1.0651631))
  (/ (exp (/ (- (fabs x)) s)) (* (* s (+ 1.0 (exp (/ (- (fabs x)) s)))) (+ 1.0 (exp (/ (- (fabs x)) s))))))