NMSE Section 6.1 mentioned, A

Percentage Accurate: 73.1% → 99.5%

Time: 17.2s

Alternatives: 19

Speedup: 2.0×

• 38.7% of points produce a very large (infinite) output. You may want to add a precondition.

• could not determine a ground truth

  1. x = -1.99999999999999997e77
  2. eps = -2e-231

Specification

Math

\[\begin{array}{l} \\ \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (/
  (-
   (* (+ 1.0 (/ 1.0 eps)) (exp (- (* (- 1.0 eps) x))))
   (* (- (/ 1.0 eps) 1.0) (exp (- (* (+ 1.0 eps) x)))))
  2.0))

C

double code(double x, double eps) {
	return (((1.0 + (1.0 / eps)) * exp(-((1.0 - eps) * x))) - (((1.0 / eps) - 1.0) * exp(-((1.0 + eps) * x)))) / 2.0;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = (((1.0d0 + (1.0d0 / eps)) * exp(-((1.0d0 - eps) * x))) - (((1.0d0 / eps) - 1.0d0) * exp(-((1.0d0 + eps) * x)))) / 2.0d0
end function

Java

public static double code(double x, double eps) {
	return (((1.0 + (1.0 / eps)) * Math.exp(-((1.0 - eps) * x))) - (((1.0 / eps) - 1.0) * Math.exp(-((1.0 + eps) * x)))) / 2.0;
}

Python

def code(x, eps):
	return (((1.0 + (1.0 / eps)) * math.exp(-((1.0 - eps) * x))) - (((1.0 / eps) - 1.0) * math.exp(-((1.0 + eps) * x)))) / 2.0

Julia

function code(x, eps)
	return Float64(Float64(Float64(Float64(1.0 + Float64(1.0 / eps)) * exp(Float64(-Float64(Float64(1.0 - eps) * x)))) - Float64(Float64(Float64(1.0 / eps) - 1.0) * exp(Float64(-Float64(Float64(1.0 + eps) * x))))) / 2.0)
end

MATLAB

function tmp = code(x, eps)
	tmp = (((1.0 + (1.0 / eps)) * exp(-((1.0 - eps) * x))) - (((1.0 / eps) - 1.0) * exp(-((1.0 + eps) * x)))) / 2.0;
end

Wolfram

code[x_, eps_] := N[(N[(N[(N[(1.0 + N[(1.0 / eps), $MachinePrecision]), $MachinePrecision] * N[Exp[(-N[(N[(1.0 - eps), $MachinePrecision] * x), $MachinePrecision])], $MachinePrecision]), $MachinePrecision] - N[(N[(N[(1.0 / eps), $MachinePrecision] - 1.0), $MachinePrecision] * N[Exp[(-N[(N[(1.0 + eps), $MachinePrecision] * x), $MachinePrecision])], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]

Initial Program: 73.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (/
  (-
   (* (+ 1.0 (/ 1.0 eps)) (exp (- (* (- 1.0 eps) x))))
   (* (- (/ 1.0 eps) 1.0) (exp (- (* (+ 1.0 eps) x)))))
  2.0))

C

double code(double x, double eps) {
	return (((1.0 + (1.0 / eps)) * exp(-((1.0 - eps) * x))) - (((1.0 / eps) - 1.0) * exp(-((1.0 + eps) * x)))) / 2.0;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = (((1.0d0 + (1.0d0 / eps)) * exp(-((1.0d0 - eps) * x))) - (((1.0d0 / eps) - 1.0d0) * exp(-((1.0d0 + eps) * x)))) / 2.0d0
end function

Java

public static double code(double x, double eps) {
	return (((1.0 + (1.0 / eps)) * Math.exp(-((1.0 - eps) * x))) - (((1.0 / eps) - 1.0) * Math.exp(-((1.0 + eps) * x)))) / 2.0;
}

Python

def code(x, eps):
	return (((1.0 + (1.0 / eps)) * math.exp(-((1.0 - eps) * x))) - (((1.0 / eps) - 1.0) * math.exp(-((1.0 + eps) * x)))) / 2.0

Julia

function code(x, eps)
	return Float64(Float64(Float64(Float64(1.0 + Float64(1.0 / eps)) * exp(Float64(-Float64(Float64(1.0 - eps) * x)))) - Float64(Float64(Float64(1.0 / eps) - 1.0) * exp(Float64(-Float64(Float64(1.0 + eps) * x))))) / 2.0)
end

MATLAB

function tmp = code(x, eps)
	tmp = (((1.0 + (1.0 / eps)) * exp(-((1.0 - eps) * x))) - (((1.0 / eps) - 1.0) * exp(-((1.0 + eps) * x)))) / 2.0;
end

Wolfram

code[x_, eps_] := N[(N[(N[(N[(1.0 + N[(1.0 / eps), $MachinePrecision]), $MachinePrecision] * N[Exp[(-N[(N[(1.0 - eps), $MachinePrecision] * x), $MachinePrecision])], $MachinePrecision]), $MachinePrecision] - N[(N[(N[(1.0 / eps), $MachinePrecision] - 1.0), $MachinePrecision] * N[Exp[(-N[(N[(1.0 + eps), $MachinePrecision] * x), $MachinePrecision])], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]

Alternative 1: 99.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} t_0 := \left(x + 1\right) \cdot e^{-x}\\ \mathbf{if}\;eps_m \leq 4 \cdot 10^{-64}:\\ \;\;\;\;\frac{t_0 + t_0}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + eps_m\right)} + e^{x \cdot \left(-eps_m\right)}}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (let* ((t_0 (* (+ x 1.0) (exp (- x)))))
   (if (<= eps_m 4e-64)
     (/ (+ t_0 t_0) 2.0)
     (/ (+ (exp (* x (+ -1.0 eps_m))) (exp (* x (- eps_m)))) 2.0))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double t_0 = (x + 1.0) * exp(-x);
	double tmp;
	if (eps_m <= 4e-64) {
		tmp = (t_0 + t_0) / 2.0;
	} else {
		tmp = (exp((x * (-1.0 + eps_m))) + exp((x * -eps_m))) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (x + 1.0d0) * exp(-x)
    if (eps_m <= 4d-64) then
        tmp = (t_0 + t_0) / 2.0d0
    else
        tmp = (exp((x * ((-1.0d0) + eps_m))) + exp((x * -eps_m))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double t_0 = (x + 1.0) * Math.exp(-x);
	double tmp;
	if (eps_m <= 4e-64) {
		tmp = (t_0 + t_0) / 2.0;
	} else {
		tmp = (Math.exp((x * (-1.0 + eps_m))) + Math.exp((x * -eps_m))) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	t_0 = (x + 1.0) * math.exp(-x)
	tmp = 0
	if eps_m <= 4e-64:
		tmp = (t_0 + t_0) / 2.0
	else:
		tmp = (math.exp((x * (-1.0 + eps_m))) + math.exp((x * -eps_m))) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	t_0 = Float64(Float64(x + 1.0) * exp(Float64(-x)))
	tmp = 0.0
	if (eps_m <= 4e-64)
		tmp = Float64(Float64(t_0 + t_0) / 2.0);
	else
		tmp = Float64(Float64(exp(Float64(x * Float64(-1.0 + eps_m))) + exp(Float64(x * Float64(-eps_m)))) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	t_0 = (x + 1.0) * exp(-x);
	tmp = 0.0;
	if (eps_m <= 4e-64)
		tmp = (t_0 + t_0) / 2.0;
	else
		tmp = (exp((x * (-1.0 + eps_m))) + exp((x * -eps_m))) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := Block[{t$95$0 = N[(N[(x + 1.0), $MachinePrecision] * N[Exp[(-x)], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[eps$95$m, 4e-64], N[(N[(t$95$0 + t$95$0), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[Exp[N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[Exp[N[(x * (-eps$95$m)), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if eps < 3.99999999999999986e-64

   1. Initial program 55.1%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 55.1%

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

   5. Taylor expanded in eps around 0 76.1%

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

   7. Simplified 76.6%

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

   if 3.99999999999999986e-64 < eps

   1. Initial program 94.1%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 94.1%

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

   5. Taylor expanded in eps around inf 99.9%

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

   6. Taylor expanded in eps around inf 99.9%

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

      1. *-commutative 99.9%

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

   8. Simplified 99.9%

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

   9. Taylor expanded in x around inf 99.9%

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

       1. associate-*r* 99.9%

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

       2. sub-neg 99.9%

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

       3. neg-mul-1 99.9%

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

       4. associate-*r* 99.9%

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

       5. associate-*r* 99.9%

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

       6. neg-mul-1 99.9%

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

       7. neg-mul-1 99.9%

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

       8. sub-neg 99.9%

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

       9. mul-1-neg 99.9%

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

       10. associate-*r* 99.9%

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

       11. neg-mul-1 99.9%

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

   11. Simplified 99.9%

       \[\leadsto \frac{\color{blue}{e^{\left(-x\right) \cdot \left(1 - \varepsilon\right)} - \left(-e^{\left(-\varepsilon\right) \cdot x}\right)}}{2} \]
3. Recombined 2 regimes into one program.

4. Final simplification 84.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\varepsilon \leq 4 \cdot 10^{-64}:\\ \;\;\;\;\frac{\left(x + 1\right) \cdot e^{-x} + \left(x + 1\right) \cdot e^{-x}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + \varepsilon\right)} + e^{x \cdot \left(-\varepsilon\right)}}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 98.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \frac{{\left(e^{-1}\right)}^{\left(x \cdot \left(1 - eps_m\right)\right)} + e^{x \cdot \left(-1 - eps_m\right)}}{2} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (/ (+ (pow (exp -1.0) (* x (- 1.0 eps_m))) (exp (* x (- -1.0 eps_m)))) 2.0))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	return (pow(exp(-1.0), (x * (1.0 - eps_m))) + exp((x * (-1.0 - eps_m)))) / 2.0;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    code = ((exp((-1.0d0)) ** (x * (1.0d0 - eps_m))) + exp((x * ((-1.0d0) - eps_m)))) / 2.0d0
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	return (Math.pow(Math.exp(-1.0), (x * (1.0 - eps_m))) + Math.exp((x * (-1.0 - eps_m)))) / 2.0;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	return (math.pow(math.exp(-1.0), (x * (1.0 - eps_m))) + math.exp((x * (-1.0 - eps_m)))) / 2.0

Julia

eps_m = abs(eps)
function code(x, eps_m)
	return Float64(Float64((exp(-1.0) ^ Float64(x * Float64(1.0 - eps_m))) + exp(Float64(x * Float64(-1.0 - eps_m)))) / 2.0)
end

MATLAB

eps_m = abs(eps);
function tmp = code(x, eps_m)
	tmp = ((exp(-1.0) ^ (x * (1.0 - eps_m))) + exp((x * (-1.0 - eps_m)))) / 2.0;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := N[(N[(N[Power[N[Exp[-1.0], $MachinePrecision], N[(x * N[(1.0 - eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[Exp[N[(x * N[(-1.0 - eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]

Derivation

1. Initial program 67.8%

   \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
2. Step-by-step derivation

3. Simplified 67.8%

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

5. Taylor expanded in eps around inf 99.2%

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

   1. exp-prod 99.2%

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

7. Applied egg-rr 99.2%

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

8. Final simplification 99.2%

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

Alternative 3: 94.2% accurate, 1.1× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} t_0 := e^{x \cdot \left(-1 + eps_m\right)}\\ t_1 := e^{x \cdot \left(-eps_m\right)}\\ \mathbf{if}\;x \leq 1.8:\\ \;\;\;\;\frac{t_1 + e^{x \cdot eps_m}}{2}\\ \mathbf{elif}\;x \leq 8.2 \cdot 10^{+192}:\\ \;\;\;\;\frac{t_0 \cdot \left(1 + \frac{1}{eps_m}\right) - \left(\left(x \cdot eps_m + \left(-1 + x\right)\right) - x\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_0 + t_1}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (let* ((t_0 (exp (* x (+ -1.0 eps_m)))) (t_1 (exp (* x (- eps_m)))))
   (if (<= x 1.8)
     (/ (+ t_1 (exp (* x eps_m))) 2.0)
     (if (<= x 8.2e+192)
       (/
        (- (* t_0 (+ 1.0 (/ 1.0 eps_m))) (- (+ (* x eps_m) (+ -1.0 x)) x))
        2.0)
       (/ (+ t_0 t_1) 2.0)))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double t_0 = exp((x * (-1.0 + eps_m)));
	double t_1 = exp((x * -eps_m));
	double tmp;
	if (x <= 1.8) {
		tmp = (t_1 + exp((x * eps_m))) / 2.0;
	} else if (x <= 8.2e+192) {
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	} else {
		tmp = (t_0 + t_1) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = exp((x * ((-1.0d0) + eps_m)))
    t_1 = exp((x * -eps_m))
    if (x <= 1.8d0) then
        tmp = (t_1 + exp((x * eps_m))) / 2.0d0
    else if (x <= 8.2d+192) then
        tmp = ((t_0 * (1.0d0 + (1.0d0 / eps_m))) - (((x * eps_m) + ((-1.0d0) + x)) - x)) / 2.0d0
    else
        tmp = (t_0 + t_1) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double t_0 = Math.exp((x * (-1.0 + eps_m)));
	double t_1 = Math.exp((x * -eps_m));
	double tmp;
	if (x <= 1.8) {
		tmp = (t_1 + Math.exp((x * eps_m))) / 2.0;
	} else if (x <= 8.2e+192) {
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	} else {
		tmp = (t_0 + t_1) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	t_0 = math.exp((x * (-1.0 + eps_m)))
	t_1 = math.exp((x * -eps_m))
	tmp = 0
	if x <= 1.8:
		tmp = (t_1 + math.exp((x * eps_m))) / 2.0
	elif x <= 8.2e+192:
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0
	else:
		tmp = (t_0 + t_1) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	t_0 = exp(Float64(x * Float64(-1.0 + eps_m)))
	t_1 = exp(Float64(x * Float64(-eps_m)))
	tmp = 0.0
	if (x <= 1.8)
		tmp = Float64(Float64(t_1 + exp(Float64(x * eps_m))) / 2.0);
	elseif (x <= 8.2e+192)
		tmp = Float64(Float64(Float64(t_0 * Float64(1.0 + Float64(1.0 / eps_m))) - Float64(Float64(Float64(x * eps_m) + Float64(-1.0 + x)) - x)) / 2.0);
	else
		tmp = Float64(Float64(t_0 + t_1) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	t_0 = exp((x * (-1.0 + eps_m)));
	t_1 = exp((x * -eps_m));
	tmp = 0.0;
	if (x <= 1.8)
		tmp = (t_1 + exp((x * eps_m))) / 2.0;
	elseif (x <= 8.2e+192)
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	else
		tmp = (t_0 + t_1) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := Block[{t$95$0 = N[Exp[N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Exp[N[(x * (-eps$95$m)), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[x, 1.8], N[(N[(t$95$1 + N[Exp[N[(x * eps$95$m), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 8.2e+192], N[(N[(N[(t$95$0 * N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * eps$95$m), $MachinePrecision] + N[(-1.0 + x), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(t$95$0 + t$95$1), $MachinePrecision] / 2.0), $MachinePrecision]]]]]

Derivation

1. Split input into 3 regimes

2. if x < 1.80000000000000004

   1. Initial program 56.3%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 56.3%

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

   5. Taylor expanded in eps around inf 98.8%

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

   6. Taylor expanded in eps around inf 98.9%

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

      1. *-commutative 98.9%

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

   8. Simplified 98.9%

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

   9. Taylor expanded in x around inf 98.9%

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

       1. associate-*r* 98.9%

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

       2. sub-neg 98.9%

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

       3. neg-mul-1 98.9%

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

       4. associate-*r* 98.9%

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

       5. associate-*r* 98.9%

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

       6. neg-mul-1 98.9%

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

       7. neg-mul-1 98.9%

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

       8. sub-neg 98.9%

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

       9. mul-1-neg 98.9%

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

       10. associate-*r* 98.9%

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

       11. neg-mul-1 98.9%

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

   11. Simplified 98.9%

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

   12. Taylor expanded in eps around inf 99.2%

       \[\leadsto \frac{e^{\color{blue}{\varepsilon \cdot x}} - \left(-e^{\left(-\varepsilon\right) \cdot x}\right)}{2} \]

   if 1.80000000000000004 < x < 8.20000000000000006e192

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 38.5%

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

   6. Taylor expanded in eps around inf 91.8%

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

      1. +-commutative 91.8%

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

      2. neg-mul-1 91.8%

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

      3. unsub-neg 91.8%

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

      4. +-commutative 91.8%

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

      5. mul-1-neg 91.8%

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

      6. unsub-neg 91.8%

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

      7. *-commutative 91.8%

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

      8. neg-mul-1 91.8%

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

      9. unsub-neg 91.8%

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

   8. Simplified 91.8%

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

   if 8.20000000000000006e192 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in eps around inf 100.0%

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

   6. Taylor expanded in eps around inf 85.9%

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

      1. *-commutative 85.9%

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

   8. Simplified 85.9%

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

   9. Taylor expanded in x around inf 85.9%

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

       1. associate-*r* 85.9%

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

       2. sub-neg 85.9%

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

       3. neg-mul-1 85.9%

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

       4. associate-*r* 85.9%

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

       5. associate-*r* 85.9%

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

       6. neg-mul-1 85.9%

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

       7. neg-mul-1 85.9%

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

       8. sub-neg 85.9%

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

       9. mul-1-neg 85.9%

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

       10. associate-*r* 85.9%

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

       11. neg-mul-1 85.9%

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

   11. Simplified 85.9%

       \[\leadsto \frac{\color{blue}{e^{\left(-x\right) \cdot \left(1 - \varepsilon\right)} - \left(-e^{\left(-\varepsilon\right) \cdot x}\right)}}{2} \]
3. Recombined 3 regimes into one program.

4. Final simplification 96.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.8:\\ \;\;\;\;\frac{e^{x \cdot \left(-\varepsilon\right)} + e^{x \cdot \varepsilon}}{2}\\ \mathbf{elif}\;x \leq 8.2 \cdot 10^{+192}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + \varepsilon\right)} \cdot \left(1 + \frac{1}{\varepsilon}\right) - \left(\left(x \cdot \varepsilon + \left(-1 + x\right)\right) - x\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + \varepsilon\right)} + e^{x \cdot \left(-\varepsilon\right)}}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} \mathbf{if}\;eps_m \leq 4 \cdot 10^{-64}:\\ \;\;\;\;\frac{\left(x + 1\right) \cdot e^{-x} + \frac{x + 1}{e^{x}}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + eps_m\right)} + e^{x \cdot \left(-eps_m\right)}}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (if (<= eps_m 4e-64)
   (/ (+ (* (+ x 1.0) (exp (- x))) (/ (+ x 1.0) (exp x))) 2.0)
   (/ (+ (exp (* x (+ -1.0 eps_m))) (exp (* x (- eps_m)))) 2.0)))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double tmp;
	if (eps_m <= 4e-64) {
		tmp = (((x + 1.0) * exp(-x)) + ((x + 1.0) / exp(x))) / 2.0;
	} else {
		tmp = (exp((x * (-1.0 + eps_m))) + exp((x * -eps_m))) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: tmp
    if (eps_m <= 4d-64) then
        tmp = (((x + 1.0d0) * exp(-x)) + ((x + 1.0d0) / exp(x))) / 2.0d0
    else
        tmp = (exp((x * ((-1.0d0) + eps_m))) + exp((x * -eps_m))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double tmp;
	if (eps_m <= 4e-64) {
		tmp = (((x + 1.0) * Math.exp(-x)) + ((x + 1.0) / Math.exp(x))) / 2.0;
	} else {
		tmp = (Math.exp((x * (-1.0 + eps_m))) + Math.exp((x * -eps_m))) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	tmp = 0
	if eps_m <= 4e-64:
		tmp = (((x + 1.0) * math.exp(-x)) + ((x + 1.0) / math.exp(x))) / 2.0
	else:
		tmp = (math.exp((x * (-1.0 + eps_m))) + math.exp((x * -eps_m))) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	tmp = 0.0
	if (eps_m <= 4e-64)
		tmp = Float64(Float64(Float64(Float64(x + 1.0) * exp(Float64(-x))) + Float64(Float64(x + 1.0) / exp(x))) / 2.0);
	else
		tmp = Float64(Float64(exp(Float64(x * Float64(-1.0 + eps_m))) + exp(Float64(x * Float64(-eps_m)))) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	tmp = 0.0;
	if (eps_m <= 4e-64)
		tmp = (((x + 1.0) * exp(-x)) + ((x + 1.0) / exp(x))) / 2.0;
	else
		tmp = (exp((x * (-1.0 + eps_m))) + exp((x * -eps_m))) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := If[LessEqual[eps$95$m, 4e-64], N[(N[(N[(N[(x + 1.0), $MachinePrecision] * N[Exp[(-x)], $MachinePrecision]), $MachinePrecision] + N[(N[(x + 1.0), $MachinePrecision] / N[Exp[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[Exp[N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[Exp[N[(x * (-eps$95$m)), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if eps < 3.99999999999999986e-64

   1. Initial program 55.1%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 55.1%

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

   5. Taylor expanded in eps around 0 76.1%

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

   7. Simplified 76.6%

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

      1. exp-neg 76.6%

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

      2. un-div-inv 76.6%

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

   9. Applied egg-rr 76.6%

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

   if 3.99999999999999986e-64 < eps

   1. Initial program 94.1%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 94.1%

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

   5. Taylor expanded in eps around inf 99.9%

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

   6. Taylor expanded in eps around inf 99.9%

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

      1. *-commutative 99.9%

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

   8. Simplified 99.9%

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

   9. Taylor expanded in x around inf 99.9%

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

       1. associate-*r* 99.9%

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

       2. sub-neg 99.9%

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

       3. neg-mul-1 99.9%

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

       4. associate-*r* 99.9%

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

       5. associate-*r* 99.9%

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

       6. neg-mul-1 99.9%

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

       7. neg-mul-1 99.9%

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

       8. sub-neg 99.9%

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

       9. mul-1-neg 99.9%

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

       10. associate-*r* 99.9%

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

       11. neg-mul-1 99.9%

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

   11. Simplified 99.9%

       \[\leadsto \frac{\color{blue}{e^{\left(-x\right) \cdot \left(1 - \varepsilon\right)} - \left(-e^{\left(-\varepsilon\right) \cdot x}\right)}}{2} \]
3. Recombined 2 regimes into one program.

4. Final simplification 84.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\varepsilon \leq 4 \cdot 10^{-64}:\\ \;\;\;\;\frac{\left(x + 1\right) \cdot e^{-x} + \frac{x + 1}{e^{x}}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + \varepsilon\right)} + e^{x \cdot \left(-\varepsilon\right)}}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \frac{e^{x \cdot \left(-1 - eps_m\right)} + e^{x \cdot \left(-1 + eps_m\right)}}{2} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (/ (+ (exp (* x (- -1.0 eps_m))) (exp (* x (+ -1.0 eps_m)))) 2.0))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	return (exp((x * (-1.0 - eps_m))) + exp((x * (-1.0 + eps_m)))) / 2.0;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    code = (exp((x * ((-1.0d0) - eps_m))) + exp((x * ((-1.0d0) + eps_m)))) / 2.0d0
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	return (Math.exp((x * (-1.0 - eps_m))) + Math.exp((x * (-1.0 + eps_m)))) / 2.0;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	return (math.exp((x * (-1.0 - eps_m))) + math.exp((x * (-1.0 + eps_m)))) / 2.0

Julia

eps_m = abs(eps)
function code(x, eps_m)
	return Float64(Float64(exp(Float64(x * Float64(-1.0 - eps_m))) + exp(Float64(x * Float64(-1.0 + eps_m)))) / 2.0)
end

MATLAB

eps_m = abs(eps);
function tmp = code(x, eps_m)
	tmp = (exp((x * (-1.0 - eps_m))) + exp((x * (-1.0 + eps_m)))) / 2.0;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := N[(N[(N[Exp[N[(x * N[(-1.0 - eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] + N[Exp[N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]

Derivation

1. Initial program 67.8%

   \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
2. Step-by-step derivation

3. Simplified 67.8%

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

5. Taylor expanded in eps around inf 99.2%

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

6. Final simplification 99.2%

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

Alternative 6: 90.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} \mathbf{if}\;x \leq 1.8:\\ \;\;\;\;\frac{e^{x \cdot \left(-eps_m\right)} + e^{x \cdot eps_m}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + eps_m\right)} \cdot \left(1 + \frac{1}{eps_m}\right) - \left(\left(x \cdot eps_m + \left(-1 + x\right)\right) - x\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (if (<= x 1.8)
   (/ (+ (exp (* x (- eps_m))) (exp (* x eps_m))) 2.0)
   (/
    (-
     (* (exp (* x (+ -1.0 eps_m))) (+ 1.0 (/ 1.0 eps_m)))
     (- (+ (* x eps_m) (+ -1.0 x)) x))
    2.0)))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double tmp;
	if (x <= 1.8) {
		tmp = (exp((x * -eps_m)) + exp((x * eps_m))) / 2.0;
	} else {
		tmp = ((exp((x * (-1.0 + eps_m))) * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: tmp
    if (x <= 1.8d0) then
        tmp = (exp((x * -eps_m)) + exp((x * eps_m))) / 2.0d0
    else
        tmp = ((exp((x * ((-1.0d0) + eps_m))) * (1.0d0 + (1.0d0 / eps_m))) - (((x * eps_m) + ((-1.0d0) + x)) - x)) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double tmp;
	if (x <= 1.8) {
		tmp = (Math.exp((x * -eps_m)) + Math.exp((x * eps_m))) / 2.0;
	} else {
		tmp = ((Math.exp((x * (-1.0 + eps_m))) * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	tmp = 0
	if x <= 1.8:
		tmp = (math.exp((x * -eps_m)) + math.exp((x * eps_m))) / 2.0
	else:
		tmp = ((math.exp((x * (-1.0 + eps_m))) * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	tmp = 0.0
	if (x <= 1.8)
		tmp = Float64(Float64(exp(Float64(x * Float64(-eps_m))) + exp(Float64(x * eps_m))) / 2.0);
	else
		tmp = Float64(Float64(Float64(exp(Float64(x * Float64(-1.0 + eps_m))) * Float64(1.0 + Float64(1.0 / eps_m))) - Float64(Float64(Float64(x * eps_m) + Float64(-1.0 + x)) - x)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	tmp = 0.0;
	if (x <= 1.8)
		tmp = (exp((x * -eps_m)) + exp((x * eps_m))) / 2.0;
	else
		tmp = ((exp((x * (-1.0 + eps_m))) * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := If[LessEqual[x, 1.8], N[(N[(N[Exp[N[(x * (-eps$95$m)), $MachinePrecision]], $MachinePrecision] + N[Exp[N[(x * eps$95$m), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[(N[Exp[N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * eps$95$m), $MachinePrecision] + N[(-1.0 + x), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.80000000000000004

   1. Initial program 56.3%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 56.3%

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

   5. Taylor expanded in eps around inf 98.8%

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

   6. Taylor expanded in eps around inf 98.9%

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

      1. *-commutative 98.9%

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

   8. Simplified 98.9%

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

   9. Taylor expanded in x around inf 98.9%

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

       1. associate-*r* 98.9%

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

       2. sub-neg 98.9%

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

       3. neg-mul-1 98.9%

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

       4. associate-*r* 98.9%

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

       5. associate-*r* 98.9%

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

       6. neg-mul-1 98.9%

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

       7. neg-mul-1 98.9%

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

       8. sub-neg 98.9%

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

       9. mul-1-neg 98.9%

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

       10. associate-*r* 98.9%

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

       11. neg-mul-1 98.9%

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

   11. Simplified 98.9%

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

   12. Taylor expanded in eps around inf 99.2%

       \[\leadsto \frac{e^{\color{blue}{\varepsilon \cdot x}} - \left(-e^{\left(-\varepsilon\right) \cdot x}\right)}{2} \]

   if 1.80000000000000004 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 34.4%

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

   6. Taylor expanded in eps around inf 85.7%

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

      1. +-commutative 85.7%

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

      2. neg-mul-1 85.7%

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

      3. unsub-neg 85.7%

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

      4. +-commutative 85.7%

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

      5. mul-1-neg 85.7%

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

      6. unsub-neg 85.7%

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

      7. *-commutative 85.7%

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

      8. neg-mul-1 85.7%

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

      9. unsub-neg 85.7%

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

   8. Simplified 85.7%

      \[\leadsto \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{\left(1 - \varepsilon\right) \cdot \left(-x\right)} - \color{blue}{\left(\left(x \cdot \varepsilon - \left(1 - x\right)\right) - x\right)}}{2} \]
3. Recombined 2 regimes into one program.

4. Final simplification 95.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.8:\\ \;\;\;\;\frac{e^{x \cdot \left(-\varepsilon\right)} + e^{x \cdot \varepsilon}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + \varepsilon\right)} \cdot \left(1 + \frac{1}{\varepsilon}\right) - \left(\left(x \cdot \varepsilon + \left(-1 + x\right)\right) - x\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 90.2% accurate, 1.8× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} t_0 := e^{x \cdot \left(-1 + eps_m\right)}\\ \mathbf{if}\;x \leq -1 \cdot 10^{-279}:\\ \;\;\;\;\frac{e^{x \cdot \left(1 - eps_m\right)} - -1}{2}\\ \mathbf{elif}\;x \leq 1.8:\\ \;\;\;\;\frac{\left(t_0 - x \cdot eps_m\right) + \left(x + \left(1 - x\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_0 \cdot \left(1 + \frac{1}{eps_m}\right) - \left(\left(x \cdot eps_m + \left(-1 + x\right)\right) - x\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (let* ((t_0 (exp (* x (+ -1.0 eps_m)))))
   (if (<= x -1e-279)
     (/ (- (exp (* x (- 1.0 eps_m))) -1.0) 2.0)
     (if (<= x 1.8)
       (/ (+ (- t_0 (* x eps_m)) (+ x (- 1.0 x))) 2.0)
       (/
        (- (* t_0 (+ 1.0 (/ 1.0 eps_m))) (- (+ (* x eps_m) (+ -1.0 x)) x))
        2.0)))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double t_0 = exp((x * (-1.0 + eps_m)));
	double tmp;
	if (x <= -1e-279) {
		tmp = (exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	} else if (x <= 1.8) {
		tmp = ((t_0 - (x * eps_m)) + (x + (1.0 - x))) / 2.0;
	} else {
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: t_0
    real(8) :: tmp
    t_0 = exp((x * ((-1.0d0) + eps_m)))
    if (x <= (-1d-279)) then
        tmp = (exp((x * (1.0d0 - eps_m))) - (-1.0d0)) / 2.0d0
    else if (x <= 1.8d0) then
        tmp = ((t_0 - (x * eps_m)) + (x + (1.0d0 - x))) / 2.0d0
    else
        tmp = ((t_0 * (1.0d0 + (1.0d0 / eps_m))) - (((x * eps_m) + ((-1.0d0) + x)) - x)) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double t_0 = Math.exp((x * (-1.0 + eps_m)));
	double tmp;
	if (x <= -1e-279) {
		tmp = (Math.exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	} else if (x <= 1.8) {
		tmp = ((t_0 - (x * eps_m)) + (x + (1.0 - x))) / 2.0;
	} else {
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	t_0 = math.exp((x * (-1.0 + eps_m)))
	tmp = 0
	if x <= -1e-279:
		tmp = (math.exp((x * (1.0 - eps_m))) - -1.0) / 2.0
	elif x <= 1.8:
		tmp = ((t_0 - (x * eps_m)) + (x + (1.0 - x))) / 2.0
	else:
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	t_0 = exp(Float64(x * Float64(-1.0 + eps_m)))
	tmp = 0.0
	if (x <= -1e-279)
		tmp = Float64(Float64(exp(Float64(x * Float64(1.0 - eps_m))) - -1.0) / 2.0);
	elseif (x <= 1.8)
		tmp = Float64(Float64(Float64(t_0 - Float64(x * eps_m)) + Float64(x + Float64(1.0 - x))) / 2.0);
	else
		tmp = Float64(Float64(Float64(t_0 * Float64(1.0 + Float64(1.0 / eps_m))) - Float64(Float64(Float64(x * eps_m) + Float64(-1.0 + x)) - x)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	t_0 = exp((x * (-1.0 + eps_m)));
	tmp = 0.0;
	if (x <= -1e-279)
		tmp = (exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	elseif (x <= 1.8)
		tmp = ((t_0 - (x * eps_m)) + (x + (1.0 - x))) / 2.0;
	else
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) - (((x * eps_m) + (-1.0 + x)) - x)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := Block[{t$95$0 = N[Exp[N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[x, -1e-279], N[(N[(N[Exp[N[(x * N[(1.0 - eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - -1.0), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 1.8], N[(N[(N[(t$95$0 - N[(x * eps$95$m), $MachinePrecision]), $MachinePrecision] + N[(x + N[(1.0 - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[(t$95$0 * N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(N[(x * eps$95$m), $MachinePrecision] + N[(-1.0 + x), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -1.00000000000000006e-279

   1. Initial program 59.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 59.8%

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

   5. Taylor expanded in eps around inf 99.0%

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

      1. exp-prod 99.0%

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

   7. Applied egg-rr 99.0%

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

   8. Taylor expanded in x around 0 71.0%

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

      1. pow-exp 71.0%

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

      2. add-sqr-sqrt 63.3%

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

      3. sqrt-unprod 98.8%

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

      4. mul-1-neg 98.8%

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

      5. mul-1-neg 98.8%

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

      6. sqr-neg 98.8%

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

      7. sqrt-unprod 35.5%

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

      8. add-sqr-sqrt 83.0%

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

   10. Applied egg-rr 83.0%

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

   if -1.00000000000000006e-279 < x < 1.80000000000000004

   1. Initial program 52.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 52.0%

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

   5. Taylor expanded in x around 0 40.7%

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

   6. Taylor expanded in eps around inf 87.4%

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

      1. +-commutative 87.4%

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

      2. associate-*r* 87.4%

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

      3. sub-neg 87.4%

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

      4. neg-mul-1 87.4%

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

      5. associate-*r* 87.4%

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

      6. neg-mul-1 87.4%

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

      7. distribute-rgt-neg-in 87.4%

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

      8. associate-*r* 87.4%

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

      9. neg-mul-1 87.4%

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

      10. sub-neg 87.4%

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

      11. neg-mul-1 87.4%

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

      12. distribute-lft-out 87.4%

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

      13. neg-mul-1 87.4%

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

   8. Simplified 87.4%

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

   if 1.80000000000000004 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 34.4%

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

   6. Taylor expanded in eps around inf 85.7%

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

      1. +-commutative 85.7%

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

      2. neg-mul-1 85.7%

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

      3. unsub-neg 85.7%

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

      4. +-commutative 85.7%

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

      5. mul-1-neg 85.7%

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

      6. unsub-neg 85.7%

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

      7. *-commutative 85.7%

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

      8. neg-mul-1 85.7%

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

      9. unsub-neg 85.7%

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

   8. Simplified 85.7%

      \[\leadsto \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{\left(1 - \varepsilon\right) \cdot \left(-x\right)} - \color{blue}{\left(\left(x \cdot \varepsilon - \left(1 - x\right)\right) - x\right)}}{2} \]
3. Recombined 3 regimes into one program.

4. Final simplification 85.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1 \cdot 10^{-279}:\\ \;\;\;\;\frac{e^{x \cdot \left(1 - \varepsilon\right)} - -1}{2}\\ \mathbf{elif}\;x \leq 1.8:\\ \;\;\;\;\frac{\left(e^{x \cdot \left(-1 + \varepsilon\right)} - x \cdot \varepsilon\right) + \left(x + \left(1 - x\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \left(-1 + \varepsilon\right)} \cdot \left(1 + \frac{1}{\varepsilon}\right) - \left(\left(x \cdot \varepsilon + \left(-1 + x\right)\right) - x\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 91.1% accurate, 1.9× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} t_0 := e^{x \cdot eps_m}\\ \mathbf{if}\;x \leq -8.2 \cdot 10^{-280}:\\ \;\;\;\;\frac{e^{x \cdot \left(1 - eps_m\right)} - -1}{2}\\ \mathbf{elif}\;x \leq 1.8:\\ \;\;\;\;\frac{1 + t_0}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_0 \cdot \left(1 + \frac{1}{eps_m}\right) + \left(\frac{-1}{eps_m} - -1\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (let* ((t_0 (exp (* x eps_m))))
   (if (<= x -8.2e-280)
     (/ (- (exp (* x (- 1.0 eps_m))) -1.0) 2.0)
     (if (<= x 1.8)
       (/ (+ 1.0 t_0) 2.0)
       (/ (+ (* t_0 (+ 1.0 (/ 1.0 eps_m))) (- (/ -1.0 eps_m) -1.0)) 2.0)))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double t_0 = exp((x * eps_m));
	double tmp;
	if (x <= -8.2e-280) {
		tmp = (exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	} else if (x <= 1.8) {
		tmp = (1.0 + t_0) / 2.0;
	} else {
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: t_0
    real(8) :: tmp
    t_0 = exp((x * eps_m))
    if (x <= (-8.2d-280)) then
        tmp = (exp((x * (1.0d0 - eps_m))) - (-1.0d0)) / 2.0d0
    else if (x <= 1.8d0) then
        tmp = (1.0d0 + t_0) / 2.0d0
    else
        tmp = ((t_0 * (1.0d0 + (1.0d0 / eps_m))) + (((-1.0d0) / eps_m) - (-1.0d0))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double t_0 = Math.exp((x * eps_m));
	double tmp;
	if (x <= -8.2e-280) {
		tmp = (Math.exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	} else if (x <= 1.8) {
		tmp = (1.0 + t_0) / 2.0;
	} else {
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	t_0 = math.exp((x * eps_m))
	tmp = 0
	if x <= -8.2e-280:
		tmp = (math.exp((x * (1.0 - eps_m))) - -1.0) / 2.0
	elif x <= 1.8:
		tmp = (1.0 + t_0) / 2.0
	else:
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	t_0 = exp(Float64(x * eps_m))
	tmp = 0.0
	if (x <= -8.2e-280)
		tmp = Float64(Float64(exp(Float64(x * Float64(1.0 - eps_m))) - -1.0) / 2.0);
	elseif (x <= 1.8)
		tmp = Float64(Float64(1.0 + t_0) / 2.0);
	else
		tmp = Float64(Float64(Float64(t_0 * Float64(1.0 + Float64(1.0 / eps_m))) + Float64(Float64(-1.0 / eps_m) - -1.0)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	t_0 = exp((x * eps_m));
	tmp = 0.0;
	if (x <= -8.2e-280)
		tmp = (exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	elseif (x <= 1.8)
		tmp = (1.0 + t_0) / 2.0;
	else
		tmp = ((t_0 * (1.0 + (1.0 / eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := Block[{t$95$0 = N[Exp[N[(x * eps$95$m), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[x, -8.2e-280], N[(N[(N[Exp[N[(x * N[(1.0 - eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - -1.0), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 1.8], N[(N[(1.0 + t$95$0), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[(t$95$0 * N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(-1.0 / eps$95$m), $MachinePrecision] - -1.0), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < -8.2000000000000003e-280

   1. Initial program 59.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 59.8%

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

   5. Taylor expanded in eps around inf 99.0%

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

      1. exp-prod 99.0%

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

   7. Applied egg-rr 99.0%

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

   8. Taylor expanded in x around 0 71.0%

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

      1. pow-exp 71.0%

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

      2. add-sqr-sqrt 63.3%

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

      3. sqrt-unprod 98.8%

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

      4. mul-1-neg 98.8%

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

      5. mul-1-neg 98.8%

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

      6. sqr-neg 98.8%

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

      7. sqrt-unprod 35.5%

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

      8. add-sqr-sqrt 83.0%

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

   10. Applied egg-rr 83.0%

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

   if -8.2000000000000003e-280 < x < 1.80000000000000004

   1. Initial program 52.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 52.0%

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

   5. Taylor expanded in x around 0 40.7%

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

   6. Taylor expanded in eps around inf 40.7%

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

   7. Taylor expanded in x around 0 39.7%

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

   8. Taylor expanded in eps around inf 87.1%

      \[\leadsto \frac{\color{blue}{1 + e^{\varepsilon \cdot x}}}{2} \]

   if 1.80000000000000004 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 34.4%

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

   6. Taylor expanded in eps around inf 34.3%

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

   7. Taylor expanded in x around 0 44.6%

      \[\leadsto \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{\varepsilon \cdot x} - \color{blue}{\left(\frac{1}{\varepsilon} - 1\right)}}{2} \]
3. Recombined 3 regimes into one program.

4. Final simplification 74.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -8.2 \cdot 10^{-280}:\\ \;\;\;\;\frac{e^{x \cdot \left(1 - \varepsilon\right)} - -1}{2}\\ \mathbf{elif}\;x \leq 1.8:\\ \;\;\;\;\frac{1 + e^{x \cdot \varepsilon}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \varepsilon} \cdot \left(1 + \frac{1}{\varepsilon}\right) + \left(\frac{-1}{\varepsilon} - -1\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 91.1% accurate, 1.9× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{-279}:\\ \;\;\;\;\frac{e^{x \cdot \left(1 - eps_m\right)} - -1}{2}\\ \mathbf{elif}\;x \leq 1.8:\\ \;\;\;\;\frac{\left(e^{x \cdot \left(-1 + eps_m\right)} - x \cdot eps_m\right) + \left(x + \left(1 - x\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot eps_m} \cdot \left(1 + \frac{1}{eps_m}\right) + \left(\frac{-1}{eps_m} - -1\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (if (<= x -2e-279)
   (/ (- (exp (* x (- 1.0 eps_m))) -1.0) 2.0)
   (if (<= x 1.8)
     (/ (+ (- (exp (* x (+ -1.0 eps_m))) (* x eps_m)) (+ x (- 1.0 x))) 2.0)
     (/
      (+ (* (exp (* x eps_m)) (+ 1.0 (/ 1.0 eps_m))) (- (/ -1.0 eps_m) -1.0))
      2.0))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double tmp;
	if (x <= -2e-279) {
		tmp = (exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	} else if (x <= 1.8) {
		tmp = ((exp((x * (-1.0 + eps_m))) - (x * eps_m)) + (x + (1.0 - x))) / 2.0;
	} else {
		tmp = ((exp((x * eps_m)) * (1.0 + (1.0 / eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: tmp
    if (x <= (-2d-279)) then
        tmp = (exp((x * (1.0d0 - eps_m))) - (-1.0d0)) / 2.0d0
    else if (x <= 1.8d0) then
        tmp = ((exp((x * ((-1.0d0) + eps_m))) - (x * eps_m)) + (x + (1.0d0 - x))) / 2.0d0
    else
        tmp = ((exp((x * eps_m)) * (1.0d0 + (1.0d0 / eps_m))) + (((-1.0d0) / eps_m) - (-1.0d0))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double tmp;
	if (x <= -2e-279) {
		tmp = (Math.exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	} else if (x <= 1.8) {
		tmp = ((Math.exp((x * (-1.0 + eps_m))) - (x * eps_m)) + (x + (1.0 - x))) / 2.0;
	} else {
		tmp = ((Math.exp((x * eps_m)) * (1.0 + (1.0 / eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	tmp = 0
	if x <= -2e-279:
		tmp = (math.exp((x * (1.0 - eps_m))) - -1.0) / 2.0
	elif x <= 1.8:
		tmp = ((math.exp((x * (-1.0 + eps_m))) - (x * eps_m)) + (x + (1.0 - x))) / 2.0
	else:
		tmp = ((math.exp((x * eps_m)) * (1.0 + (1.0 / eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	tmp = 0.0
	if (x <= -2e-279)
		tmp = Float64(Float64(exp(Float64(x * Float64(1.0 - eps_m))) - -1.0) / 2.0);
	elseif (x <= 1.8)
		tmp = Float64(Float64(Float64(exp(Float64(x * Float64(-1.0 + eps_m))) - Float64(x * eps_m)) + Float64(x + Float64(1.0 - x))) / 2.0);
	else
		tmp = Float64(Float64(Float64(exp(Float64(x * eps_m)) * Float64(1.0 + Float64(1.0 / eps_m))) + Float64(Float64(-1.0 / eps_m) - -1.0)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	tmp = 0.0;
	if (x <= -2e-279)
		tmp = (exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	elseif (x <= 1.8)
		tmp = ((exp((x * (-1.0 + eps_m))) - (x * eps_m)) + (x + (1.0 - x))) / 2.0;
	else
		tmp = ((exp((x * eps_m)) * (1.0 + (1.0 / eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := If[LessEqual[x, -2e-279], N[(N[(N[Exp[N[(x * N[(1.0 - eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - -1.0), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 1.8], N[(N[(N[(N[Exp[N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - N[(x * eps$95$m), $MachinePrecision]), $MachinePrecision] + N[(x + N[(1.0 - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[(N[Exp[N[(x * eps$95$m), $MachinePrecision]], $MachinePrecision] * N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(-1.0 / eps$95$m), $MachinePrecision] - -1.0), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -2.00000000000000011e-279

   1. Initial program 59.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 59.8%

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

   5. Taylor expanded in eps around inf 99.0%

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

      1. exp-prod 99.0%

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

   7. Applied egg-rr 99.0%

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

   8. Taylor expanded in x around 0 71.0%

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

      1. pow-exp 71.0%

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

      2. add-sqr-sqrt 63.3%

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

      3. sqrt-unprod 98.8%

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

      4. mul-1-neg 98.8%

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

      5. mul-1-neg 98.8%

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

      6. sqr-neg 98.8%

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

      7. sqrt-unprod 35.5%

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

      8. add-sqr-sqrt 83.0%

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

   10. Applied egg-rr 83.0%

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

   if -2.00000000000000011e-279 < x < 1.80000000000000004

   1. Initial program 52.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 52.0%

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

   5. Taylor expanded in x around 0 40.7%

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

   6. Taylor expanded in eps around inf 87.4%

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

      1. +-commutative 87.4%

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

      2. associate-*r* 87.4%

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

      3. sub-neg 87.4%

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

      4. neg-mul-1 87.4%

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

      5. associate-*r* 87.4%

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

      6. neg-mul-1 87.4%

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

      7. distribute-rgt-neg-in 87.4%

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

      8. associate-*r* 87.4%

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

      9. neg-mul-1 87.4%

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

      10. sub-neg 87.4%

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

      11. neg-mul-1 87.4%

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

      12. distribute-lft-out 87.4%

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

      13. neg-mul-1 87.4%

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

   8. Simplified 87.4%

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

   if 1.80000000000000004 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 34.4%

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

   6. Taylor expanded in eps around inf 34.3%

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

   7. Taylor expanded in x around 0 44.6%

      \[\leadsto \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{\varepsilon \cdot x} - \color{blue}{\left(\frac{1}{\varepsilon} - 1\right)}}{2} \]
3. Recombined 3 regimes into one program.

4. Final simplification 74.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{-279}:\\ \;\;\;\;\frac{e^{x \cdot \left(1 - \varepsilon\right)} - -1}{2}\\ \mathbf{elif}\;x \leq 1.8:\\ \;\;\;\;\frac{\left(e^{x \cdot \left(-1 + \varepsilon\right)} - x \cdot \varepsilon\right) + \left(x + \left(1 - x\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x \cdot \varepsilon} \cdot \left(1 + \frac{1}{\varepsilon}\right) + \left(\frac{-1}{\varepsilon} - -1\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 76.5% accurate, 2.0× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} t_0 := x \cdot \left(-1 + eps_m\right)\\ \mathbf{if}\;x \leq -1 \cdot 10^{-279}:\\ \;\;\;\;\frac{1 + e^{-x}}{2}\\ \mathbf{elif}\;x \leq 1.1 \cdot 10^{+55}:\\ \;\;\;\;\frac{1 + e^{x \cdot eps_m}}{2}\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+146}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{eps_m}\right) \cdot \left(1 + t_0\right) - \left(-1 + \frac{1}{eps_m}\right) \cdot \left(1 - x \cdot \left(1 + eps_m\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + e^{t_0}}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (let* ((t_0 (* x (+ -1.0 eps_m))))
   (if (<= x -1e-279)
     (/ (+ 1.0 (exp (- x))) 2.0)
     (if (<= x 1.1e+55)
       (/ (+ 1.0 (exp (* x eps_m))) 2.0)
       (if (<= x 1.35e+146)
         (/
          (-
           (* (+ 1.0 (/ 1.0 eps_m)) (+ 1.0 t_0))
           (* (+ -1.0 (/ 1.0 eps_m)) (- 1.0 (* x (+ 1.0 eps_m)))))
          2.0)
         (/ (+ 1.0 (exp t_0)) 2.0))))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double t_0 = x * (-1.0 + eps_m);
	double tmp;
	if (x <= -1e-279) {
		tmp = (1.0 + exp(-x)) / 2.0;
	} else if (x <= 1.1e+55) {
		tmp = (1.0 + exp((x * eps_m))) / 2.0;
	} else if (x <= 1.35e+146) {
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + t_0)) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	} else {
		tmp = (1.0 + exp(t_0)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x * ((-1.0d0) + eps_m)
    if (x <= (-1d-279)) then
        tmp = (1.0d0 + exp(-x)) / 2.0d0
    else if (x <= 1.1d+55) then
        tmp = (1.0d0 + exp((x * eps_m))) / 2.0d0
    else if (x <= 1.35d+146) then
        tmp = (((1.0d0 + (1.0d0 / eps_m)) * (1.0d0 + t_0)) - (((-1.0d0) + (1.0d0 / eps_m)) * (1.0d0 - (x * (1.0d0 + eps_m))))) / 2.0d0
    else
        tmp = (1.0d0 + exp(t_0)) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double t_0 = x * (-1.0 + eps_m);
	double tmp;
	if (x <= -1e-279) {
		tmp = (1.0 + Math.exp(-x)) / 2.0;
	} else if (x <= 1.1e+55) {
		tmp = (1.0 + Math.exp((x * eps_m))) / 2.0;
	} else if (x <= 1.35e+146) {
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + t_0)) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	} else {
		tmp = (1.0 + Math.exp(t_0)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	t_0 = x * (-1.0 + eps_m)
	tmp = 0
	if x <= -1e-279:
		tmp = (1.0 + math.exp(-x)) / 2.0
	elif x <= 1.1e+55:
		tmp = (1.0 + math.exp((x * eps_m))) / 2.0
	elif x <= 1.35e+146:
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + t_0)) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0
	else:
		tmp = (1.0 + math.exp(t_0)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	t_0 = Float64(x * Float64(-1.0 + eps_m))
	tmp = 0.0
	if (x <= -1e-279)
		tmp = Float64(Float64(1.0 + exp(Float64(-x))) / 2.0);
	elseif (x <= 1.1e+55)
		tmp = Float64(Float64(1.0 + exp(Float64(x * eps_m))) / 2.0);
	elseif (x <= 1.35e+146)
		tmp = Float64(Float64(Float64(Float64(1.0 + Float64(1.0 / eps_m)) * Float64(1.0 + t_0)) - Float64(Float64(-1.0 + Float64(1.0 / eps_m)) * Float64(1.0 - Float64(x * Float64(1.0 + eps_m))))) / 2.0);
	else
		tmp = Float64(Float64(1.0 + exp(t_0)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	t_0 = x * (-1.0 + eps_m);
	tmp = 0.0;
	if (x <= -1e-279)
		tmp = (1.0 + exp(-x)) / 2.0;
	elseif (x <= 1.1e+55)
		tmp = (1.0 + exp((x * eps_m))) / 2.0;
	elseif (x <= 1.35e+146)
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + t_0)) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	else
		tmp = (1.0 + exp(t_0)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := Block[{t$95$0 = N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -1e-279], N[(N[(1.0 + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 1.1e+55], N[(N[(1.0 + N[Exp[N[(x * eps$95$m), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 1.35e+146], N[(N[(N[(N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision] * N[(1.0 + t$95$0), $MachinePrecision]), $MachinePrecision] - N[(N[(-1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(x * N[(1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(1.0 + N[Exp[t$95$0], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -1.00000000000000006e-279

   1. Initial program 59.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 59.8%

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

   5. Taylor expanded in eps around inf 99.0%

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

   6. Taylor expanded in x around 0 71.0%

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

   7. Taylor expanded in eps around 0 82.3%

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

      1. neg-mul-1 82.3%

         \[\leadsto \frac{1 + e^{\color{blue}{-x}}}{2} \]

   9. Simplified 82.3%

      \[\leadsto \frac{\color{blue}{1 + e^{-x}}}{2} \]

   if -1.00000000000000006e-279 < x < 1.10000000000000005e55

   1. Initial program 58.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 58.8%

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

   5. Taylor expanded in x around 0 42.3%

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

   6. Taylor expanded in eps around inf 42.3%

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

   7. Taylor expanded in x around 0 43.3%

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

   8. Taylor expanded in eps around inf 81.1%

      \[\leadsto \frac{\color{blue}{1 + e^{\varepsilon \cdot x}}}{2} \]

   if 1.10000000000000005e55 < x < 1.34999999999999994e146

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 32.9%

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

   6. Taylor expanded in x around 0 63.5%

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

   if 1.34999999999999994e146 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in eps around inf 100.0%

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

   6. Taylor expanded in x around 0 23.1%

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

   7. Taylor expanded in x around inf 23.1%

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

      1. associate-*r* 23.1%

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

      2. sub-neg 23.1%

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

      3. neg-mul-1 23.1%

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

      4. associate-*r* 23.1%

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

      5. associate-*r* 23.1%

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

      6. neg-mul-1 23.1%

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

      7. neg-mul-1 23.1%

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

      8. sub-neg 23.1%

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

   9. Simplified 23.1%

      \[\leadsto \frac{\color{blue}{1 + e^{\left(-x\right) \cdot \left(1 - \varepsilon\right)}}}{2} \]
3. Recombined 4 regimes into one program.

4. Final simplification 72.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1 \cdot 10^{-279}:\\ \;\;\;\;\frac{1 + e^{-x}}{2}\\ \mathbf{elif}\;x \leq 1.1 \cdot 10^{+55}:\\ \;\;\;\;\frac{1 + e^{x \cdot \varepsilon}}{2}\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+146}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \left(1 + x \cdot \left(-1 + \varepsilon\right)\right) - \left(-1 + \frac{1}{\varepsilon}\right) \cdot \left(1 - x \cdot \left(1 + \varepsilon\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + e^{x \cdot \left(-1 + \varepsilon\right)}}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 83.3% accurate, 2.0× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} t_0 := x \cdot \left(-1 + eps_m\right)\\ \mathbf{if}\;x \leq -2 \cdot 10^{-279}:\\ \;\;\;\;\frac{e^{x \cdot \left(1 - eps_m\right)} - -1}{2}\\ \mathbf{elif}\;x \leq 1.15 \cdot 10^{+55}:\\ \;\;\;\;\frac{1 + e^{x \cdot eps_m}}{2}\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+146}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{eps_m}\right) \cdot \left(1 + t_0\right) - \left(-1 + \frac{1}{eps_m}\right) \cdot \left(1 - x \cdot \left(1 + eps_m\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + e^{t_0}}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (let* ((t_0 (* x (+ -1.0 eps_m))))
   (if (<= x -2e-279)
     (/ (- (exp (* x (- 1.0 eps_m))) -1.0) 2.0)
     (if (<= x 1.15e+55)
       (/ (+ 1.0 (exp (* x eps_m))) 2.0)
       (if (<= x 1.35e+146)
         (/
          (-
           (* (+ 1.0 (/ 1.0 eps_m)) (+ 1.0 t_0))
           (* (+ -1.0 (/ 1.0 eps_m)) (- 1.0 (* x (+ 1.0 eps_m)))))
          2.0)
         (/ (+ 1.0 (exp t_0)) 2.0))))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double t_0 = x * (-1.0 + eps_m);
	double tmp;
	if (x <= -2e-279) {
		tmp = (exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	} else if (x <= 1.15e+55) {
		tmp = (1.0 + exp((x * eps_m))) / 2.0;
	} else if (x <= 1.35e+146) {
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + t_0)) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	} else {
		tmp = (1.0 + exp(t_0)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x * ((-1.0d0) + eps_m)
    if (x <= (-2d-279)) then
        tmp = (exp((x * (1.0d0 - eps_m))) - (-1.0d0)) / 2.0d0
    else if (x <= 1.15d+55) then
        tmp = (1.0d0 + exp((x * eps_m))) / 2.0d0
    else if (x <= 1.35d+146) then
        tmp = (((1.0d0 + (1.0d0 / eps_m)) * (1.0d0 + t_0)) - (((-1.0d0) + (1.0d0 / eps_m)) * (1.0d0 - (x * (1.0d0 + eps_m))))) / 2.0d0
    else
        tmp = (1.0d0 + exp(t_0)) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double t_0 = x * (-1.0 + eps_m);
	double tmp;
	if (x <= -2e-279) {
		tmp = (Math.exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	} else if (x <= 1.15e+55) {
		tmp = (1.0 + Math.exp((x * eps_m))) / 2.0;
	} else if (x <= 1.35e+146) {
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + t_0)) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	} else {
		tmp = (1.0 + Math.exp(t_0)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	t_0 = x * (-1.0 + eps_m)
	tmp = 0
	if x <= -2e-279:
		tmp = (math.exp((x * (1.0 - eps_m))) - -1.0) / 2.0
	elif x <= 1.15e+55:
		tmp = (1.0 + math.exp((x * eps_m))) / 2.0
	elif x <= 1.35e+146:
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + t_0)) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0
	else:
		tmp = (1.0 + math.exp(t_0)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	t_0 = Float64(x * Float64(-1.0 + eps_m))
	tmp = 0.0
	if (x <= -2e-279)
		tmp = Float64(Float64(exp(Float64(x * Float64(1.0 - eps_m))) - -1.0) / 2.0);
	elseif (x <= 1.15e+55)
		tmp = Float64(Float64(1.0 + exp(Float64(x * eps_m))) / 2.0);
	elseif (x <= 1.35e+146)
		tmp = Float64(Float64(Float64(Float64(1.0 + Float64(1.0 / eps_m)) * Float64(1.0 + t_0)) - Float64(Float64(-1.0 + Float64(1.0 / eps_m)) * Float64(1.0 - Float64(x * Float64(1.0 + eps_m))))) / 2.0);
	else
		tmp = Float64(Float64(1.0 + exp(t_0)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	t_0 = x * (-1.0 + eps_m);
	tmp = 0.0;
	if (x <= -2e-279)
		tmp = (exp((x * (1.0 - eps_m))) - -1.0) / 2.0;
	elseif (x <= 1.15e+55)
		tmp = (1.0 + exp((x * eps_m))) / 2.0;
	elseif (x <= 1.35e+146)
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + t_0)) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	else
		tmp = (1.0 + exp(t_0)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := Block[{t$95$0 = N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2e-279], N[(N[(N[Exp[N[(x * N[(1.0 - eps$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] - -1.0), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 1.15e+55], N[(N[(1.0 + N[Exp[N[(x * eps$95$m), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 1.35e+146], N[(N[(N[(N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision] * N[(1.0 + t$95$0), $MachinePrecision]), $MachinePrecision] - N[(N[(-1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(x * N[(1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(1.0 + N[Exp[t$95$0], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -2.00000000000000011e-279

   1. Initial program 59.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 59.8%

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

   5. Taylor expanded in eps around inf 99.0%

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

      1. exp-prod 99.0%

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

   7. Applied egg-rr 99.0%

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

   8. Taylor expanded in x around 0 71.0%

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

      1. pow-exp 71.0%

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

      2. add-sqr-sqrt 63.3%

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

      3. sqrt-unprod 98.8%

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

      4. mul-1-neg 98.8%

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

      5. mul-1-neg 98.8%

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

      6. sqr-neg 98.8%

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

      7. sqrt-unprod 35.5%

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

      8. add-sqr-sqrt 83.0%

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

   10. Applied egg-rr 83.0%

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

   if -2.00000000000000011e-279 < x < 1.14999999999999994e55

   1. Initial program 58.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 58.8%

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

   5. Taylor expanded in x around 0 42.3%

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

   6. Taylor expanded in eps around inf 42.3%

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

   7. Taylor expanded in x around 0 43.3%

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

   8. Taylor expanded in eps around inf 81.1%

      \[\leadsto \frac{\color{blue}{1 + e^{\varepsilon \cdot x}}}{2} \]

   if 1.14999999999999994e55 < x < 1.34999999999999994e146

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 32.9%

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

   6. Taylor expanded in x around 0 63.5%

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

   if 1.34999999999999994e146 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in eps around inf 100.0%

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

   6. Taylor expanded in x around 0 23.1%

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

   7. Taylor expanded in x around inf 23.1%

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

      1. associate-*r* 23.1%

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

      2. sub-neg 23.1%

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

      3. neg-mul-1 23.1%

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

      4. associate-*r* 23.1%

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

      5. associate-*r* 23.1%

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

      6. neg-mul-1 23.1%

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

      7. neg-mul-1 23.1%

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

      8. sub-neg 23.1%

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

   9. Simplified 23.1%

      \[\leadsto \frac{\color{blue}{1 + e^{\left(-x\right) \cdot \left(1 - \varepsilon\right)}}}{2} \]
3. Recombined 4 regimes into one program.

4. Final simplification 72.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{-279}:\\ \;\;\;\;\frac{e^{x \cdot \left(1 - \varepsilon\right)} - -1}{2}\\ \mathbf{elif}\;x \leq 1.15 \cdot 10^{+55}:\\ \;\;\;\;\frac{1 + e^{x \cdot \varepsilon}}{2}\\ \mathbf{elif}\;x \leq 1.35 \cdot 10^{+146}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \left(1 + x \cdot \left(-1 + \varepsilon\right)\right) - \left(-1 + \frac{1}{\varepsilon}\right) \cdot \left(1 - x \cdot \left(1 + \varepsilon\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{1 + e^{x \cdot \left(-1 + \varepsilon\right)}}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 76.5% accurate, 2.0× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{-280}:\\ \;\;\;\;\frac{1 + e^{-x}}{2}\\ \mathbf{elif}\;x \leq 1.15 \cdot 10^{+55} \lor \neg \left(x \leq 1.45 \cdot 10^{+146}\right):\\ \;\;\;\;\frac{1 + e^{x \cdot eps_m}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{eps_m}\right) \cdot \left(1 + x \cdot \left(-1 + eps_m\right)\right) - \left(-1 + \frac{1}{eps_m}\right) \cdot \left(1 - x \cdot \left(1 + eps_m\right)\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (if (<= x -9e-280)
   (/ (+ 1.0 (exp (- x))) 2.0)
   (if (or (<= x 1.15e+55) (not (<= x 1.45e+146)))
     (/ (+ 1.0 (exp (* x eps_m))) 2.0)
     (/
      (-
       (* (+ 1.0 (/ 1.0 eps_m)) (+ 1.0 (* x (+ -1.0 eps_m))))
       (* (+ -1.0 (/ 1.0 eps_m)) (- 1.0 (* x (+ 1.0 eps_m)))))
      2.0))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double tmp;
	if (x <= -9e-280) {
		tmp = (1.0 + exp(-x)) / 2.0;
	} else if ((x <= 1.15e+55) || !(x <= 1.45e+146)) {
		tmp = (1.0 + exp((x * eps_m))) / 2.0;
	} else {
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + (x * (-1.0 + eps_m)))) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: tmp
    if (x <= (-9d-280)) then
        tmp = (1.0d0 + exp(-x)) / 2.0d0
    else if ((x <= 1.15d+55) .or. (.not. (x <= 1.45d+146))) then
        tmp = (1.0d0 + exp((x * eps_m))) / 2.0d0
    else
        tmp = (((1.0d0 + (1.0d0 / eps_m)) * (1.0d0 + (x * ((-1.0d0) + eps_m)))) - (((-1.0d0) + (1.0d0 / eps_m)) * (1.0d0 - (x * (1.0d0 + eps_m))))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double tmp;
	if (x <= -9e-280) {
		tmp = (1.0 + Math.exp(-x)) / 2.0;
	} else if ((x <= 1.15e+55) || !(x <= 1.45e+146)) {
		tmp = (1.0 + Math.exp((x * eps_m))) / 2.0;
	} else {
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + (x * (-1.0 + eps_m)))) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	tmp = 0
	if x <= -9e-280:
		tmp = (1.0 + math.exp(-x)) / 2.0
	elif (x <= 1.15e+55) or not (x <= 1.45e+146):
		tmp = (1.0 + math.exp((x * eps_m))) / 2.0
	else:
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + (x * (-1.0 + eps_m)))) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	tmp = 0.0
	if (x <= -9e-280)
		tmp = Float64(Float64(1.0 + exp(Float64(-x))) / 2.0);
	elseif ((x <= 1.15e+55) || !(x <= 1.45e+146))
		tmp = Float64(Float64(1.0 + exp(Float64(x * eps_m))) / 2.0);
	else
		tmp = Float64(Float64(Float64(Float64(1.0 + Float64(1.0 / eps_m)) * Float64(1.0 + Float64(x * Float64(-1.0 + eps_m)))) - Float64(Float64(-1.0 + Float64(1.0 / eps_m)) * Float64(1.0 - Float64(x * Float64(1.0 + eps_m))))) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	tmp = 0.0;
	if (x <= -9e-280)
		tmp = (1.0 + exp(-x)) / 2.0;
	elseif ((x <= 1.15e+55) || ~((x <= 1.45e+146)))
		tmp = (1.0 + exp((x * eps_m))) / 2.0;
	else
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + (x * (-1.0 + eps_m)))) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := If[LessEqual[x, -9e-280], N[(N[(1.0 + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], If[Or[LessEqual[x, 1.15e+55], N[Not[LessEqual[x, 1.45e+146]], $MachinePrecision]], N[(N[(1.0 + N[Exp[N[(x * eps$95$m), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[(N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision] * N[(1.0 + N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(-1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(x * N[(1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -8.9999999999999991e-280

   1. Initial program 59.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 59.8%

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

   5. Taylor expanded in eps around inf 99.0%

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

   6. Taylor expanded in x around 0 71.0%

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

   7. Taylor expanded in eps around 0 82.3%

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

      1. neg-mul-1 82.3%

         \[\leadsto \frac{1 + e^{\color{blue}{-x}}}{2} \]

   9. Simplified 82.3%

      \[\leadsto \frac{\color{blue}{1 + e^{-x}}}{2} \]

   if -8.9999999999999991e-280 < x < 1.14999999999999994e55 or 1.4499999999999999e146 < x

   1. Initial program 69.4%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 69.4%

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

   5. Taylor expanded in x around 0 38.6%

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

   6. Taylor expanded in eps around inf 38.6%

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

   7. Taylor expanded in x around 0 40.9%

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

   8. Taylor expanded in eps around inf 66.2%

      \[\leadsto \frac{\color{blue}{1 + e^{\varepsilon \cdot x}}}{2} \]

   if 1.14999999999999994e55 < x < 1.4499999999999999e146

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 32.9%

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

   6. Taylor expanded in x around 0 63.5%

      \[\leadsto \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \color{blue}{\left(1 + -1 \cdot \left(x \cdot \left(1 - \varepsilon\right)\right)\right)} - \left(\frac{1}{\varepsilon} + -1\right) \cdot \left(1 + -1 \cdot \left(x \cdot \left(1 + \varepsilon\right)\right)\right)}{2} \]
3. Recombined 3 regimes into one program.

4. Final simplification 72.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -9 \cdot 10^{-280}:\\ \;\;\;\;\frac{1 + e^{-x}}{2}\\ \mathbf{elif}\;x \leq 1.15 \cdot 10^{+55} \lor \neg \left(x \leq 1.45 \cdot 10^{+146}\right):\\ \;\;\;\;\frac{1 + e^{x \cdot \varepsilon}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \left(1 + x \cdot \left(-1 + \varepsilon\right)\right) - \left(-1 + \frac{1}{\varepsilon}\right) \cdot \left(1 - x \cdot \left(1 + \varepsilon\right)\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 66.7% accurate, 2.1× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} t_0 := 1 + \frac{1}{eps_m}\\ \mathbf{if}\;x \leq 2.4 \cdot 10^{+40}:\\ \;\;\;\;\frac{1 + e^{-x}}{2}\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{+156}:\\ \;\;\;\;\frac{t_0 \cdot \left(1 + x \cdot \left(-1 + eps_m\right)\right) - \left(-1 + \frac{1}{eps_m}\right) \cdot \left(1 - x \cdot \left(1 + eps_m\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_0 \cdot \left(1 + x \cdot eps_m\right) + \left(\frac{-1}{eps_m} - -1\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (let* ((t_0 (+ 1.0 (/ 1.0 eps_m))))
   (if (<= x 2.4e+40)
     (/ (+ 1.0 (exp (- x))) 2.0)
     (if (<= x 2.3e+156)
       (/
        (-
         (* t_0 (+ 1.0 (* x (+ -1.0 eps_m))))
         (* (+ -1.0 (/ 1.0 eps_m)) (- 1.0 (* x (+ 1.0 eps_m)))))
        2.0)
       (/ (+ (* t_0 (+ 1.0 (* x eps_m))) (- (/ -1.0 eps_m) -1.0)) 2.0)))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double t_0 = 1.0 + (1.0 / eps_m);
	double tmp;
	if (x <= 2.4e+40) {
		tmp = (1.0 + exp(-x)) / 2.0;
	} else if (x <= 2.3e+156) {
		tmp = ((t_0 * (1.0 + (x * (-1.0 + eps_m)))) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	} else {
		tmp = ((t_0 * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 + (1.0d0 / eps_m)
    if (x <= 2.4d+40) then
        tmp = (1.0d0 + exp(-x)) / 2.0d0
    else if (x <= 2.3d+156) then
        tmp = ((t_0 * (1.0d0 + (x * ((-1.0d0) + eps_m)))) - (((-1.0d0) + (1.0d0 / eps_m)) * (1.0d0 - (x * (1.0d0 + eps_m))))) / 2.0d0
    else
        tmp = ((t_0 * (1.0d0 + (x * eps_m))) + (((-1.0d0) / eps_m) - (-1.0d0))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double t_0 = 1.0 + (1.0 / eps_m);
	double tmp;
	if (x <= 2.4e+40) {
		tmp = (1.0 + Math.exp(-x)) / 2.0;
	} else if (x <= 2.3e+156) {
		tmp = ((t_0 * (1.0 + (x * (-1.0 + eps_m)))) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	} else {
		tmp = ((t_0 * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	t_0 = 1.0 + (1.0 / eps_m)
	tmp = 0
	if x <= 2.4e+40:
		tmp = (1.0 + math.exp(-x)) / 2.0
	elif x <= 2.3e+156:
		tmp = ((t_0 * (1.0 + (x * (-1.0 + eps_m)))) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0
	else:
		tmp = ((t_0 * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	t_0 = Float64(1.0 + Float64(1.0 / eps_m))
	tmp = 0.0
	if (x <= 2.4e+40)
		tmp = Float64(Float64(1.0 + exp(Float64(-x))) / 2.0);
	elseif (x <= 2.3e+156)
		tmp = Float64(Float64(Float64(t_0 * Float64(1.0 + Float64(x * Float64(-1.0 + eps_m)))) - Float64(Float64(-1.0 + Float64(1.0 / eps_m)) * Float64(1.0 - Float64(x * Float64(1.0 + eps_m))))) / 2.0);
	else
		tmp = Float64(Float64(Float64(t_0 * Float64(1.0 + Float64(x * eps_m))) + Float64(Float64(-1.0 / eps_m) - -1.0)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	t_0 = 1.0 + (1.0 / eps_m);
	tmp = 0.0;
	if (x <= 2.4e+40)
		tmp = (1.0 + exp(-x)) / 2.0;
	elseif (x <= 2.3e+156)
		tmp = ((t_0 * (1.0 + (x * (-1.0 + eps_m)))) - ((-1.0 + (1.0 / eps_m)) * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	else
		tmp = ((t_0 * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := Block[{t$95$0 = N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, 2.4e+40], N[(N[(1.0 + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 2.3e+156], N[(N[(N[(t$95$0 * N[(1.0 + N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(N[(-1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(x * N[(1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[(t$95$0 * N[(1.0 + N[(x * eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(-1.0 / eps$95$m), $MachinePrecision] - -1.0), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if x < 2.4e40

   1. Initial program 57.7%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 57.7%

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

   5. Taylor expanded in eps around inf 98.9%

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

   6. Taylor expanded in x around 0 77.6%

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

   7. Taylor expanded in eps around 0 79.0%

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

      1. neg-mul-1 79.0%

         \[\leadsto \frac{1 + e^{\color{blue}{-x}}}{2} \]

   9. Simplified 79.0%

      \[\leadsto \frac{\color{blue}{1 + e^{-x}}}{2} \]

   if 2.4e40 < x < 2.2999999999999999e156

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 34.9%

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

   6. Taylor expanded in x around 0 57.1%

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

   if 2.2999999999999999e156 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 27.2%

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

   6. Taylor expanded in eps around inf 27.2%

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

   7. Taylor expanded in x around 0 33.8%

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

   8. Taylor expanded in eps around 0 24.1%

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

      1. *-commutative 24.1%

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

   10. Simplified 24.1%

       \[\leadsto \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \color{blue}{\left(1 + x \cdot \varepsilon\right)} - \left(\frac{1}{\varepsilon} - 1\right)}{2} \]
3. Recombined 3 regimes into one program.

4. Final simplification 69.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2.4 \cdot 10^{+40}:\\ \;\;\;\;\frac{1 + e^{-x}}{2}\\ \mathbf{elif}\;x \leq 2.3 \cdot 10^{+156}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \left(1 + x \cdot \left(-1 + \varepsilon\right)\right) - \left(-1 + \frac{1}{\varepsilon}\right) \cdot \left(1 - x \cdot \left(1 + \varepsilon\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \left(1 + x \cdot \varepsilon\right) + \left(\frac{-1}{\varepsilon} - -1\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 60.0% accurate, 6.5× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} t_0 := 1 + \frac{1}{eps_m}\\ t_1 := -1 + \frac{1}{eps_m}\\ \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(\left(1 + eps_m\right) \cdot t_1\right)}{2}\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{+40}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{+156}:\\ \;\;\;\;\frac{t_0 \cdot \left(1 + x \cdot \left(-1 + eps_m\right)\right) - t_1 \cdot \left(1 - x \cdot \left(1 + eps_m\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t_0 \cdot \left(1 + x \cdot eps_m\right) + \left(\frac{-1}{eps_m} - -1\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (let* ((t_0 (+ 1.0 (/ 1.0 eps_m))) (t_1 (+ -1.0 (/ 1.0 eps_m))))
   (if (<= x -7.6e-19)
     (/ (* x (* (+ 1.0 eps_m) t_1)) 2.0)
     (if (<= x 2.4e+40)
       1.0
       (if (<= x 2.6e+156)
         (/
          (-
           (* t_0 (+ 1.0 (* x (+ -1.0 eps_m))))
           (* t_1 (- 1.0 (* x (+ 1.0 eps_m)))))
          2.0)
         (/ (+ (* t_0 (+ 1.0 (* x eps_m))) (- (/ -1.0 eps_m) -1.0)) 2.0))))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double t_0 = 1.0 + (1.0 / eps_m);
	double t_1 = -1.0 + (1.0 / eps_m);
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * ((1.0 + eps_m) * t_1)) / 2.0;
	} else if (x <= 2.4e+40) {
		tmp = 1.0;
	} else if (x <= 2.6e+156) {
		tmp = ((t_0 * (1.0 + (x * (-1.0 + eps_m)))) - (t_1 * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	} else {
		tmp = ((t_0 * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = 1.0d0 + (1.0d0 / eps_m)
    t_1 = (-1.0d0) + (1.0d0 / eps_m)
    if (x <= (-7.6d-19)) then
        tmp = (x * ((1.0d0 + eps_m) * t_1)) / 2.0d0
    else if (x <= 2.4d+40) then
        tmp = 1.0d0
    else if (x <= 2.6d+156) then
        tmp = ((t_0 * (1.0d0 + (x * ((-1.0d0) + eps_m)))) - (t_1 * (1.0d0 - (x * (1.0d0 + eps_m))))) / 2.0d0
    else
        tmp = ((t_0 * (1.0d0 + (x * eps_m))) + (((-1.0d0) / eps_m) - (-1.0d0))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double t_0 = 1.0 + (1.0 / eps_m);
	double t_1 = -1.0 + (1.0 / eps_m);
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * ((1.0 + eps_m) * t_1)) / 2.0;
	} else if (x <= 2.4e+40) {
		tmp = 1.0;
	} else if (x <= 2.6e+156) {
		tmp = ((t_0 * (1.0 + (x * (-1.0 + eps_m)))) - (t_1 * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	} else {
		tmp = ((t_0 * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	t_0 = 1.0 + (1.0 / eps_m)
	t_1 = -1.0 + (1.0 / eps_m)
	tmp = 0
	if x <= -7.6e-19:
		tmp = (x * ((1.0 + eps_m) * t_1)) / 2.0
	elif x <= 2.4e+40:
		tmp = 1.0
	elif x <= 2.6e+156:
		tmp = ((t_0 * (1.0 + (x * (-1.0 + eps_m)))) - (t_1 * (1.0 - (x * (1.0 + eps_m))))) / 2.0
	else:
		tmp = ((t_0 * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	t_0 = Float64(1.0 + Float64(1.0 / eps_m))
	t_1 = Float64(-1.0 + Float64(1.0 / eps_m))
	tmp = 0.0
	if (x <= -7.6e-19)
		tmp = Float64(Float64(x * Float64(Float64(1.0 + eps_m) * t_1)) / 2.0);
	elseif (x <= 2.4e+40)
		tmp = 1.0;
	elseif (x <= 2.6e+156)
		tmp = Float64(Float64(Float64(t_0 * Float64(1.0 + Float64(x * Float64(-1.0 + eps_m)))) - Float64(t_1 * Float64(1.0 - Float64(x * Float64(1.0 + eps_m))))) / 2.0);
	else
		tmp = Float64(Float64(Float64(t_0 * Float64(1.0 + Float64(x * eps_m))) + Float64(Float64(-1.0 / eps_m) - -1.0)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	t_0 = 1.0 + (1.0 / eps_m);
	t_1 = -1.0 + (1.0 / eps_m);
	tmp = 0.0;
	if (x <= -7.6e-19)
		tmp = (x * ((1.0 + eps_m) * t_1)) / 2.0;
	elseif (x <= 2.4e+40)
		tmp = 1.0;
	elseif (x <= 2.6e+156)
		tmp = ((t_0 * (1.0 + (x * (-1.0 + eps_m)))) - (t_1 * (1.0 - (x * (1.0 + eps_m))))) / 2.0;
	else
		tmp = ((t_0 * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := Block[{t$95$0 = N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(-1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -7.6e-19], N[(N[(x * N[(N[(1.0 + eps$95$m), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 2.4e+40], 1.0, If[LessEqual[x, 2.6e+156], N[(N[(N[(t$95$0 * N[(1.0 + N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(t$95$1 * N[(1.0 - N[(x * N[(1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[(t$95$0 * N[(1.0 + N[(x * eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(-1.0 / eps$95$m), $MachinePrecision] - -1.0), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]]]]

Derivation

1. Split input into 4 regimes

2. if x < -7.6e-19

   1. Initial program 96.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 96.8%

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

   5. Taylor expanded in x around 0 65.9%

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

   6. Taylor expanded in x around inf 43.3%

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

   if -7.6e-19 < x < 2.4e40

   1. Initial program 50.3%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 50.3%

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

   5. Taylor expanded in x around 0 76.7%

      \[\leadsto \frac{\color{blue}{2}}{2} \]

   if 2.4e40 < x < 2.60000000000000019e156

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 34.9%

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

   6. Taylor expanded in x around 0 57.1%

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

   if 2.60000000000000019e156 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 27.2%

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

   6. Taylor expanded in eps around inf 27.2%

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

   7. Taylor expanded in x around 0 33.8%

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

   8. Taylor expanded in eps around 0 24.1%

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

      1. *-commutative 24.1%

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

   10. Simplified 24.1%

       \[\leadsto \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \color{blue}{\left(1 + x \cdot \varepsilon\right)} - \left(\frac{1}{\varepsilon} - 1\right)}{2} \]
3. Recombined 4 regimes into one program.

4. Final simplification 64.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(\left(1 + \varepsilon\right) \cdot \left(-1 + \frac{1}{\varepsilon}\right)\right)}{2}\\ \mathbf{elif}\;x \leq 2.4 \cdot 10^{+40}:\\ \;\;\;\;1\\ \mathbf{elif}\;x \leq 2.6 \cdot 10^{+156}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \left(1 + x \cdot \left(-1 + \varepsilon\right)\right) - \left(-1 + \frac{1}{\varepsilon}\right) \cdot \left(1 - x \cdot \left(1 + \varepsilon\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \left(1 + x \cdot \varepsilon\right) + \left(\frac{-1}{\varepsilon} - -1\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 63.6% accurate, 9.8× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(\left(1 + eps_m\right) \cdot \left(-1 + \frac{1}{eps_m}\right)\right)}{2}\\ \mathbf{elif}\;x \leq 1.8:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{eps_m}\right) \cdot \left(1 + x \cdot eps_m\right) + \left(\frac{-1}{eps_m} - -1\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (if (<= x -7.6e-19)
   (/ (* x (* (+ 1.0 eps_m) (+ -1.0 (/ 1.0 eps_m)))) 2.0)
   (if (<= x 1.8)
     1.0
     (/
      (+ (* (+ 1.0 (/ 1.0 eps_m)) (+ 1.0 (* x eps_m))) (- (/ -1.0 eps_m) -1.0))
      2.0))))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * ((1.0 + eps_m) * (-1.0 + (1.0 / eps_m)))) / 2.0;
	} else if (x <= 1.8) {
		tmp = 1.0;
	} else {
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: tmp
    if (x <= (-7.6d-19)) then
        tmp = (x * ((1.0d0 + eps_m) * ((-1.0d0) + (1.0d0 / eps_m)))) / 2.0d0
    else if (x <= 1.8d0) then
        tmp = 1.0d0
    else
        tmp = (((1.0d0 + (1.0d0 / eps_m)) * (1.0d0 + (x * eps_m))) + (((-1.0d0) / eps_m) - (-1.0d0))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * ((1.0 + eps_m) * (-1.0 + (1.0 / eps_m)))) / 2.0;
	} else if (x <= 1.8) {
		tmp = 1.0;
	} else {
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	tmp = 0
	if x <= -7.6e-19:
		tmp = (x * ((1.0 + eps_m) * (-1.0 + (1.0 / eps_m)))) / 2.0
	elif x <= 1.8:
		tmp = 1.0
	else:
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	tmp = 0.0
	if (x <= -7.6e-19)
		tmp = Float64(Float64(x * Float64(Float64(1.0 + eps_m) * Float64(-1.0 + Float64(1.0 / eps_m)))) / 2.0);
	elseif (x <= 1.8)
		tmp = 1.0;
	else
		tmp = Float64(Float64(Float64(Float64(1.0 + Float64(1.0 / eps_m)) * Float64(1.0 + Float64(x * eps_m))) + Float64(Float64(-1.0 / eps_m) - -1.0)) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	tmp = 0.0;
	if (x <= -7.6e-19)
		tmp = (x * ((1.0 + eps_m) * (-1.0 + (1.0 / eps_m)))) / 2.0;
	elseif (x <= 1.8)
		tmp = 1.0;
	else
		tmp = (((1.0 + (1.0 / eps_m)) * (1.0 + (x * eps_m))) + ((-1.0 / eps_m) - -1.0)) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := If[LessEqual[x, -7.6e-19], N[(N[(x * N[(N[(1.0 + eps$95$m), $MachinePrecision] * N[(-1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], If[LessEqual[x, 1.8], 1.0, N[(N[(N[(N[(1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision] * N[(1.0 + N[(x * eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(N[(-1.0 / eps$95$m), $MachinePrecision] - -1.0), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x < -7.6e-19

   1. Initial program 96.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 96.8%

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

   5. Taylor expanded in x around 0 65.9%

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

   6. Taylor expanded in x around inf 43.3%

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

   if -7.6e-19 < x < 1.80000000000000004

   1. Initial program 48.4%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 48.4%

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

   5. Taylor expanded in x around 0 79.5%

      \[\leadsto \frac{\color{blue}{2}}{2} \]

   if 1.80000000000000004 < x

   1. Initial program 100.0%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 100.0%

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

   5. Taylor expanded in x around 0 34.4%

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

   6. Taylor expanded in eps around inf 34.3%

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

   7. Taylor expanded in x around 0 44.6%

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

   8. Taylor expanded in eps around 0 24.3%

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

      1. *-commutative 24.3%

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

   10. Simplified 24.3%

       \[\leadsto \frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \color{blue}{\left(1 + x \cdot \varepsilon\right)} - \left(\frac{1}{\varepsilon} - 1\right)}{2} \]
3. Recombined 3 regimes into one program.

4. Final simplification 60.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(\left(1 + \varepsilon\right) \cdot \left(-1 + \frac{1}{\varepsilon}\right)\right)}{2}\\ \mathbf{elif}\;x \leq 1.8:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot \left(1 + x \cdot \varepsilon\right) + \left(\frac{-1}{\varepsilon} - -1\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 16: 56.9% accurate, 15.1× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(\left(1 + eps_m\right) \cdot \left(-1 + \frac{1}{eps_m}\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{2 + x \cdot \left(-1 + eps_m\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (if (<= x -7.6e-19)
   (/ (* x (* (+ 1.0 eps_m) (+ -1.0 (/ 1.0 eps_m)))) 2.0)
   (/ (+ 2.0 (* x (+ -1.0 eps_m))) 2.0)))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * ((1.0 + eps_m) * (-1.0 + (1.0 / eps_m)))) / 2.0;
	} else {
		tmp = (2.0 + (x * (-1.0 + eps_m))) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: tmp
    if (x <= (-7.6d-19)) then
        tmp = (x * ((1.0d0 + eps_m) * ((-1.0d0) + (1.0d0 / eps_m)))) / 2.0d0
    else
        tmp = (2.0d0 + (x * ((-1.0d0) + eps_m))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * ((1.0 + eps_m) * (-1.0 + (1.0 / eps_m)))) / 2.0;
	} else {
		tmp = (2.0 + (x * (-1.0 + eps_m))) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	tmp = 0
	if x <= -7.6e-19:
		tmp = (x * ((1.0 + eps_m) * (-1.0 + (1.0 / eps_m)))) / 2.0
	else:
		tmp = (2.0 + (x * (-1.0 + eps_m))) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	tmp = 0.0
	if (x <= -7.6e-19)
		tmp = Float64(Float64(x * Float64(Float64(1.0 + eps_m) * Float64(-1.0 + Float64(1.0 / eps_m)))) / 2.0);
	else
		tmp = Float64(Float64(2.0 + Float64(x * Float64(-1.0 + eps_m))) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	tmp = 0.0;
	if (x <= -7.6e-19)
		tmp = (x * ((1.0 + eps_m) * (-1.0 + (1.0 / eps_m)))) / 2.0;
	else
		tmp = (2.0 + (x * (-1.0 + eps_m))) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := If[LessEqual[x, -7.6e-19], N[(N[(x * N[(N[(1.0 + eps$95$m), $MachinePrecision] * N[(-1.0 + N[(1.0 / eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(2.0 + N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -7.6e-19

   1. Initial program 96.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 96.8%

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

   5. Taylor expanded in x around 0 65.9%

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

   6. Taylor expanded in x around inf 43.3%

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

   if -7.6e-19 < x

   1. Initial program 63.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 63.8%

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

   5. Taylor expanded in eps around inf 99.5%

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

      1. exp-prod 99.5%

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

   7. Applied egg-rr 99.5%

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

   8. Taylor expanded in x around 0 69.0%

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

   9. Taylor expanded in x around 0 57.0%

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

       1. mul-1-neg 57.0%

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

       2. unsub-neg 57.0%

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

   11. Simplified 57.0%

       \[\leadsto \frac{\color{blue}{2 - x \cdot \left(1 - \varepsilon\right)}}{2} \]
3. Recombined 2 regimes into one program.

4. Final simplification 55.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(\left(1 + \varepsilon\right) \cdot \left(-1 + \frac{1}{\varepsilon}\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{2 + x \cdot \left(-1 + \varepsilon\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 17: 56.9% accurate, 20.5× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(-eps_m\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{2 + x \cdot \left(-1 + eps_m\right)}{2}\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (if (<= x -7.6e-19)
   (/ (* x (- eps_m)) 2.0)
   (/ (+ 2.0 (* x (+ -1.0 eps_m))) 2.0)))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * -eps_m) / 2.0;
	} else {
		tmp = (2.0 + (x * (-1.0 + eps_m))) / 2.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: tmp
    if (x <= (-7.6d-19)) then
        tmp = (x * -eps_m) / 2.0d0
    else
        tmp = (2.0d0 + (x * ((-1.0d0) + eps_m))) / 2.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * -eps_m) / 2.0;
	} else {
		tmp = (2.0 + (x * (-1.0 + eps_m))) / 2.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	tmp = 0
	if x <= -7.6e-19:
		tmp = (x * -eps_m) / 2.0
	else:
		tmp = (2.0 + (x * (-1.0 + eps_m))) / 2.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	tmp = 0.0
	if (x <= -7.6e-19)
		tmp = Float64(Float64(x * Float64(-eps_m)) / 2.0);
	else
		tmp = Float64(Float64(2.0 + Float64(x * Float64(-1.0 + eps_m))) / 2.0);
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	tmp = 0.0;
	if (x <= -7.6e-19)
		tmp = (x * -eps_m) / 2.0;
	else
		tmp = (2.0 + (x * (-1.0 + eps_m))) / 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := If[LessEqual[x, -7.6e-19], N[(N[(x * (-eps$95$m)), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(2.0 + N[(x * N[(-1.0 + eps$95$m), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -7.6e-19

   1. Initial program 96.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 96.8%

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

   5. Taylor expanded in x around 0 65.9%

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

   6. Taylor expanded in x around inf 43.3%

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

   7. Taylor expanded in eps around inf 43.4%

      \[\leadsto \frac{\color{blue}{-1 \cdot \left(\varepsilon \cdot x\right)}}{2} \]
   8. Step-by-step derivation

      1. associate-*r* 43.4%

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

      2. neg-mul-1 43.4%

         \[\leadsto \frac{\color{blue}{\left(-\varepsilon\right)} \cdot x}{2} \]

   9. Simplified 43.4%

      \[\leadsto \frac{\color{blue}{\left(-\varepsilon\right) \cdot x}}{2} \]

   if -7.6e-19 < x

   1. Initial program 63.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 63.8%

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

   5. Taylor expanded in eps around inf 99.5%

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

      1. exp-prod 99.5%

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

   7. Applied egg-rr 99.5%

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

   8. Taylor expanded in x around 0 69.0%

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

   9. Taylor expanded in x around 0 57.0%

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

       1. mul-1-neg 57.0%

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

       2. unsub-neg 57.0%

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

   11. Simplified 57.0%

       \[\leadsto \frac{\color{blue}{2 - x \cdot \left(1 - \varepsilon\right)}}{2} \]
3. Recombined 2 regimes into one program.

4. Final simplification 55.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(-\varepsilon\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{2 + x \cdot \left(-1 + \varepsilon\right)}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 18: 50.6% accurate, 28.2× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(-eps_m\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m)
 :precision binary64
 (if (<= x -7.6e-19) (/ (* x (- eps_m)) 2.0) 1.0))

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * -eps_m) / 2.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    real(8) :: tmp
    if (x <= (-7.6d-19)) then
        tmp = (x * -eps_m) / 2.0d0
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	double tmp;
	if (x <= -7.6e-19) {
		tmp = (x * -eps_m) / 2.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	tmp = 0
	if x <= -7.6e-19:
		tmp = (x * -eps_m) / 2.0
	else:
		tmp = 1.0
	return tmp

Julia

eps_m = abs(eps)
function code(x, eps_m)
	tmp = 0.0
	if (x <= -7.6e-19)
		tmp = Float64(Float64(x * Float64(-eps_m)) / 2.0);
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

eps_m = abs(eps);
function tmp_2 = code(x, eps_m)
	tmp = 0.0;
	if (x <= -7.6e-19)
		tmp = (x * -eps_m) / 2.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := If[LessEqual[x, -7.6e-19], N[(N[(x * (-eps$95$m)), $MachinePrecision] / 2.0), $MachinePrecision], 1.0]

Derivation

1. Split input into 2 regimes

2. if x < -7.6e-19

   1. Initial program 96.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 96.8%

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

   5. Taylor expanded in x around 0 65.9%

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

   6. Taylor expanded in x around inf 43.3%

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

   7. Taylor expanded in eps around inf 43.4%

      \[\leadsto \frac{\color{blue}{-1 \cdot \left(\varepsilon \cdot x\right)}}{2} \]
   8. Step-by-step derivation

      1. associate-*r* 43.4%

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

      2. neg-mul-1 43.4%

         \[\leadsto \frac{\color{blue}{\left(-\varepsilon\right)} \cdot x}{2} \]

   9. Simplified 43.4%

      \[\leadsto \frac{\color{blue}{\left(-\varepsilon\right) \cdot x}}{2} \]

   if -7.6e-19 < x

   1. Initial program 63.8%

      \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
   2. Step-by-step derivation

   3. Simplified 63.8%

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

   5. Taylor expanded in x around 0 56.7%

      \[\leadsto \frac{\color{blue}{2}}{2} \]
3. Recombined 2 regimes into one program.

4. Final simplification 55.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -7.6 \cdot 10^{-19}:\\ \;\;\;\;\frac{x \cdot \left(-\varepsilon\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 19: 43.6% accurate, 227.0× speedup

Math

\[\begin{array}{l} eps_m = \left|\varepsilon\right| \\ 1 \end{array} \]

FPCore

eps_m = (fabs.f64 eps)
(FPCore (x eps_m) :precision binary64 1.0)

C

eps_m = fabs(eps);
double code(double x, double eps_m) {
	return 1.0;
}

Fortran

eps_m = abs(eps)
real(8) function code(x, eps_m)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps_m
    code = 1.0d0
end function

Java

eps_m = Math.abs(eps);
public static double code(double x, double eps_m) {
	return 1.0;
}

Python

eps_m = math.fabs(eps)
def code(x, eps_m):
	return 1.0

Julia

eps_m = abs(eps)
function code(x, eps_m)
	return 1.0
end

MATLAB

eps_m = abs(eps);
function tmp = code(x, eps_m)
	tmp = 1.0;
end

Wolfram

eps_m = N[Abs[eps], $MachinePrecision]
code[x_, eps$95$m_] := 1.0

Derivation

1. Initial program 67.8%

   \[\frac{\left(1 + \frac{1}{\varepsilon}\right) \cdot e^{-\left(1 - \varepsilon\right) \cdot x} - \left(\frac{1}{\varepsilon} - 1\right) \cdot e^{-\left(1 + \varepsilon\right) \cdot x}}{2} \]
2. Step-by-step derivation

3. Simplified 67.8%

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

5. Taylor expanded in x around 0 50.2%

   \[\leadsto \frac{\color{blue}{2}}{2} \]

6. Final simplification 50.2%

   \[\leadsto 1 \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024019 
(FPCore (x eps)
  :name "NMSE Section 6.1 mentioned, A"
  :precision binary64
  (/ (- (* (+ 1.0 (/ 1.0 eps)) (exp (- (* (- 1.0 eps) x)))) (* (- (/ 1.0 eps) 1.0) (exp (- (* (+ 1.0 eps) x))))) 2.0))