Logistic function from Lakshay Garg

Percentage Accurate: 54.3% → 99.6%

Time: 8.6s

Alternatives: 17

Speedup: 4.4×

Specification

Math

\[\begin{array}{l} \\ \frac{2}{1 + e^{-2 \cdot x}} - 1 \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 54.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{2}{1 + e^{-2 \cdot x}} - 1 \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 5 \cdot 10^{-5}:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{1 + e^{-2 \cdot x}} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 5e-5)
     (fma -0.3333333333333333 (* x (* x x)) x)
     (+ (/ 2.0 (+ 1.0 (exp (* -2.0 x)))) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 5e-5) {
		tmp = fma(-0.3333333333333333, (x * (x * x)), x);
	} else {
		tmp = (2.0 / (1.0 + exp((-2.0 * x)))) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 5e-5)
		tmp = fma(-0.3333333333333333, Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(1.0 + exp(Float64(-2.0 * x)))) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 5e-5], N[(-0.3333333333333333 * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(1.0 + N[Exp[N[(-2.0 * x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 5.00000000000000024e-5

   1. Initial program 7.7%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. distribute-lft-in N/A

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right)} \]

      2. *-rgt-identity N/A

         \[\leadsto \color{blue}{x} + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) + x} \]

      4. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left({x}^{2} \cdot \frac{-1}{3}\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{-1}{3}} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{3} \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \color{blue}{x \cdot {x}^{2}}, x\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      10. lower-*.f64 100.0

          \[\leadsto \mathsf{fma}\left(-0.3333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)} \]

   if 5.00000000000000024e-5 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing
3. Recombined 3 regimes into one program.

4. Final simplification 100.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 5 \cdot 10^{-5}:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{1 + e^{-2 \cdot x}} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(x \cdot x\right)\\ \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.05396825396825397, 0.13333333333333333\right), -0.3333333333333333\right), t\_0, x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(t\_0 \cdot t\_0\right) \cdot 64} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* x (* x x))))
   (if (<= (* -2.0 x) -500.0)
     (+ (/ 2.0 1.0) -1.0)
     (if (<= (* -2.0 x) 1.0)
       (fma
        (fma
         (* x x)
         (fma (* x x) -0.05396825396825397 0.13333333333333333)
         -0.3333333333333333)
        t_0
        x)
       (+ (/ 2.0 (* (* t_0 t_0) 64.0)) -1.0)))))

C

double code(double x, double y) {
	double t_0 = x * (x * x);
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(fma((x * x), fma((x * x), -0.05396825396825397, 0.13333333333333333), -0.3333333333333333), t_0, x);
	} else {
		tmp = (2.0 / ((t_0 * t_0) * 64.0)) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(x * Float64(x * x))
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(fma(Float64(x * x), fma(Float64(x * x), -0.05396825396825397, 0.13333333333333333), -0.3333333333333333), t_0, x);
	else
		tmp = Float64(Float64(2.0 / Float64(Float64(t_0 * t_0) * 64.0)) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(N[(N[(x * x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * -0.05396825396825397 + 0.13333333333333333), $MachinePrecision] + -0.3333333333333333), $MachinePrecision] * t$95$0 + x), $MachinePrecision], N[(N[(2.0 / N[(N[(t$95$0 * t$95$0), $MachinePrecision] * 64.0), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right)\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right) + 1\right)} \]

      2. distribute-rgt-in N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right)\right) \cdot x + 1 \cdot x} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right)\right)} + 1 \cdot x \]

      4. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right)} + 1 \cdot x \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right)} + 1 \cdot x \]

      6. *-lft-identity N/A

         \[\leadsto \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right) + \color{blue}{x} \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}, x \cdot {x}^{2}, x\right)} \]

   5. Applied rewrites 99.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.05396825396825397, 0.13333333333333333\right), -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 99.9%

       \[\leadsto \frac{2}{\left(\left(x \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right) \cdot 64} - 1 \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.05396825396825397, 0.13333333333333333\right), -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(\left(x \cdot \left(x \cdot x\right)\right) \cdot \left(x \cdot \left(x \cdot x\right)\right)\right) \cdot 64} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.5% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.05396825396825397, 0.13333333333333333\right), -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x + x\right) \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma
      (fma
       (* x x)
       (fma (* x x) -0.05396825396825397 0.13333333333333333)
       -0.3333333333333333)
      (* x (* x x))
      x)
     (+ (/ 2.0 (* (+ x x) (* (* x x) (* x x)))) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(fma((x * x), fma((x * x), -0.05396825396825397, 0.13333333333333333), -0.3333333333333333), (x * (x * x)), x);
	} else {
		tmp = (2.0 / ((x + x) * ((x * x) * (x * x)))) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(fma(Float64(x * x), fma(Float64(x * x), -0.05396825396825397, 0.13333333333333333), -0.3333333333333333), Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(Float64(x + x) * Float64(Float64(x * x) * Float64(x * x)))) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(N[(N[(x * x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * -0.05396825396825397 + 0.13333333333333333), $MachinePrecision] + -0.3333333333333333), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(N[(x + x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right)\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right) + 1\right)} \]

      2. distribute-rgt-in N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right)\right) \cdot x + 1 \cdot x} \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right)\right)} + 1 \cdot x \]

      4. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right)} + 1 \cdot x \]

      5. *-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right)} + 1 \cdot x \]

      6. *-lft-identity N/A

         \[\leadsto \left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right) + \color{blue}{x} \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{2}{15} + \frac{-17}{315} \cdot {x}^{2}\right) - \frac{1}{3}, x \cdot {x}^{2}, x\right)} \]

   5. Applied rewrites 99.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.05396825396825397, 0.13333333333333333\right), -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 99.7%

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

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.05396825396825397, 0.13333333333333333\right), -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x + x\right) \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.5% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x + x\right) \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma
      (fma (* x x) 0.13333333333333333 -0.3333333333333333)
      (* x (* x x))
      x)
     (+ (/ 2.0 (* (+ x x) (* (* x x) (* x x)))) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(fma((x * x), 0.13333333333333333, -0.3333333333333333), (x * (x * x)), x);
	} else {
		tmp = (2.0 / ((x + x) * ((x * x) * (x * x)))) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(fma(Float64(x * x), 0.13333333333333333, -0.3333333333333333), Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(Float64(x + x) * Float64(Float64(x * x) * Float64(x * x)))) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(N[(N[(x * x), $MachinePrecision] * 0.13333333333333333 + -0.3333333333333333), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(N[(x + x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} \]
   4. Step-by-step derivation

      1. distribute-rgt-in N/A

         \[\leadsto \color{blue}{1 \cdot x + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x} \]

      2. *-lft-identity N/A

         \[\leadsto \color{blue}{x} + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x + x} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{2}{15} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right)}, x \cdot {x}^{2}, x\right) \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{2}{15}} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right), x \cdot {x}^{2}, x\right) \]

      10. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left({x}^{2} \cdot \frac{2}{15} + \color{blue}{\frac{-1}{3}}, x \cdot {x}^{2}, x\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{2}{15}, \frac{-1}{3}\right)}, x \cdot {x}^{2}, x\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      14. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), \color{blue}{x \cdot {x}^{2}}, x\right) \]

      15. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      16. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 99.7%

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

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x + x\right) \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.5% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot 16} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma
      (fma (* x x) 0.13333333333333333 -0.3333333333333333)
      (* x (* x x))
      x)
     (+ (/ 2.0 (* (* (* x x) (* x x)) 16.0)) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(fma((x * x), 0.13333333333333333, -0.3333333333333333), (x * (x * x)), x);
	} else {
		tmp = (2.0 / (((x * x) * (x * x)) * 16.0)) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(fma(Float64(x * x), 0.13333333333333333, -0.3333333333333333), Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(Float64(Float64(x * x) * Float64(x * x)) * 16.0)) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(N[(N[(x * x), $MachinePrecision] * 0.13333333333333333 + -0.3333333333333333), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(N[(N[(x * x), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision] * 16.0), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} \]
   4. Step-by-step derivation

      1. distribute-rgt-in N/A

         \[\leadsto \color{blue}{1 \cdot x + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x} \]

      2. *-lft-identity N/A

         \[\leadsto \color{blue}{x} + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x + x} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{2}{15} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right)}, x \cdot {x}^{2}, x\right) \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{2}{15}} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right), x \cdot {x}^{2}, x\right) \]

      10. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left({x}^{2} \cdot \frac{2}{15} + \color{blue}{\frac{-1}{3}}, x \cdot {x}^{2}, x\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{2}{15}, \frac{-1}{3}\right)}, x \cdot {x}^{2}, x\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      14. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), \color{blue}{x \cdot {x}^{2}}, x\right) \]

      15. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      16. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 99.5%

       \[\leadsto \frac{2}{\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot 16} - 1 \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right) \cdot 16} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 99.5% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(x \cdot x\right)\\ \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), t\_0, x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{t\_0 \cdot \left(x + x\right)} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* x (* x x))))
   (if (<= (* -2.0 x) -500.0)
     (+ (/ 2.0 1.0) -1.0)
     (if (<= (* -2.0 x) 1.0)
       (fma (fma (* x x) 0.13333333333333333 -0.3333333333333333) t_0 x)
       (+ (/ 2.0 (* t_0 (+ x x))) -1.0)))))

C

double code(double x, double y) {
	double t_0 = x * (x * x);
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(fma((x * x), 0.13333333333333333, -0.3333333333333333), t_0, x);
	} else {
		tmp = (2.0 / (t_0 * (x + x))) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(x * Float64(x * x))
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(fma(Float64(x * x), 0.13333333333333333, -0.3333333333333333), t_0, x);
	else
		tmp = Float64(Float64(2.0 / Float64(t_0 * Float64(x + x))) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(N[(N[(x * x), $MachinePrecision] * 0.13333333333333333 + -0.3333333333333333), $MachinePrecision] * t$95$0 + x), $MachinePrecision], N[(N[(2.0 / N[(t$95$0 * N[(x + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} \]
   4. Step-by-step derivation

      1. distribute-rgt-in N/A

         \[\leadsto \color{blue}{1 \cdot x + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x} \]

      2. *-lft-identity N/A

         \[\leadsto \color{blue}{x} + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x + x} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{2}{15} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right)}, x \cdot {x}^{2}, x\right) \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{2}{15}} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right), x \cdot {x}^{2}, x\right) \]

      10. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left({x}^{2} \cdot \frac{2}{15} + \color{blue}{\frac{-1}{3}}, x \cdot {x}^{2}, x\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{2}{15}, \frac{-1}{3}\right)}, x \cdot {x}^{2}, x\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      14. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), \color{blue}{x \cdot {x}^{2}}, x\right) \]

      15. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      16. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 99.5%

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

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x \cdot \left(x \cdot x\right)\right) \cdot \left(x + x\right)} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 99.5% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(x \cdot x\right)\\ \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), t\_0, x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{t\_0 \cdot -64} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* x (* x x))))
   (if (<= (* -2.0 x) -500.0)
     (+ (/ 2.0 1.0) -1.0)
     (if (<= (* -2.0 x) 1.0)
       (fma (fma (* x x) 0.13333333333333333 -0.3333333333333333) t_0 x)
       (+ (/ 2.0 (* t_0 -64.0)) -1.0)))))

C

double code(double x, double y) {
	double t_0 = x * (x * x);
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(fma((x * x), 0.13333333333333333, -0.3333333333333333), t_0, x);
	} else {
		tmp = (2.0 / (t_0 * -64.0)) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	t_0 = Float64(x * Float64(x * x))
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(fma(Float64(x * x), 0.13333333333333333, -0.3333333333333333), t_0, x);
	else
		tmp = Float64(Float64(2.0 / Float64(t_0 * -64.0)) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(N[(N[(x * x), $MachinePrecision] * 0.13333333333333333 + -0.3333333333333333), $MachinePrecision] * t$95$0 + x), $MachinePrecision], N[(N[(2.0 / N[(t$95$0 * -64.0), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} \]
   4. Step-by-step derivation

      1. distribute-rgt-in N/A

         \[\leadsto \color{blue}{1 \cdot x + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x} \]

      2. *-lft-identity N/A

         \[\leadsto \color{blue}{x} + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x + x} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{2}{15} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right)}, x \cdot {x}^{2}, x\right) \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{2}{15}} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right), x \cdot {x}^{2}, x\right) \]

      10. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left({x}^{2} \cdot \frac{2}{15} + \color{blue}{\frac{-1}{3}}, x \cdot {x}^{2}, x\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{2}{15}, \frac{-1}{3}\right)}, x \cdot {x}^{2}, x\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      14. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), \color{blue}{x \cdot {x}^{2}}, x\right) \]

      15. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      16. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 99.4%

       \[\leadsto \frac{2}{\left(x \cdot \left(x \cdot x\right)\right) \cdot -64} - 1 \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x \cdot \left(x \cdot x\right)\right) \cdot -64} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 99.5% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x \cdot x\right) \cdot \left(x + x\right)} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma
      (fma (* x x) 0.13333333333333333 -0.3333333333333333)
      (* x (* x x))
      x)
     (+ (/ 2.0 (* (* x x) (+ x x))) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(fma((x * x), 0.13333333333333333, -0.3333333333333333), (x * (x * x)), x);
	} else {
		tmp = (2.0 / ((x * x) * (x + x))) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(fma(Float64(x * x), 0.13333333333333333, -0.3333333333333333), Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(Float64(x * x) * Float64(x + x))) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(N[(N[(x * x), $MachinePrecision] * 0.13333333333333333 + -0.3333333333333333), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(N[(x * x), $MachinePrecision] * N[(x + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} \]
   4. Step-by-step derivation

      1. distribute-rgt-in N/A

         \[\leadsto \color{blue}{1 \cdot x + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x} \]

      2. *-lft-identity N/A

         \[\leadsto \color{blue}{x} + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x + x} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{2}{15} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right)}, x \cdot {x}^{2}, x\right) \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{2}{15}} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right), x \cdot {x}^{2}, x\right) \]

      10. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left({x}^{2} \cdot \frac{2}{15} + \color{blue}{\frac{-1}{3}}, x \cdot {x}^{2}, x\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{2}{15}, \frac{-1}{3}\right)}, x \cdot {x}^{2}, x\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      14. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), \color{blue}{x \cdot {x}^{2}}, x\right) \]

      15. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      16. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 99.3%

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

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x \cdot x\right) \cdot \left(x + x\right)} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 99.4% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x \cdot x\right) \cdot 16} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma
      (fma (* x x) 0.13333333333333333 -0.3333333333333333)
      (* x (* x x))
      x)
     (+ (/ 2.0 (* (* x x) 16.0)) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(fma((x * x), 0.13333333333333333, -0.3333333333333333), (x * (x * x)), x);
	} else {
		tmp = (2.0 / ((x * x) * 16.0)) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(fma(Float64(x * x), 0.13333333333333333, -0.3333333333333333), Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(Float64(x * x) * 16.0)) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(N[(N[(x * x), $MachinePrecision] * 0.13333333333333333 + -0.3333333333333333), $MachinePrecision] * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(N[(x * x), $MachinePrecision] * 16.0), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} \]
   4. Step-by-step derivation

      1. distribute-rgt-in N/A

         \[\leadsto \color{blue}{1 \cdot x + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x} \]

      2. *-lft-identity N/A

         \[\leadsto \color{blue}{x} + \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right) \cdot x + x} \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right)} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}\right) \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{2}{15} \cdot {x}^{2} - \frac{1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{2}{15} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right)}, x \cdot {x}^{2}, x\right) \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{2}{15}} + \left(\mathsf{neg}\left(\frac{1}{3}\right)\right), x \cdot {x}^{2}, x\right) \]

      10. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left({x}^{2} \cdot \frac{2}{15} + \color{blue}{\frac{-1}{3}}, x \cdot {x}^{2}, x\right) \]

      11. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{2}{15}, \frac{-1}{3}\right)}, x \cdot {x}^{2}, x\right) \]

      12. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      13. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{2}{15}, \frac{-1}{3}\right), x \cdot {x}^{2}, x\right) \]

      14. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), \color{blue}{x \cdot {x}^{2}}, x\right) \]

      15. unpow2 N/A

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{2}{15}, \frac{-1}{3}\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      16. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 98.9%

       \[\leadsto \frac{2}{\left(x \cdot x\right) \cdot 16} - 1 \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.13333333333333333, -0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x \cdot x\right) \cdot 16} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 99.3% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x \cdot x\right) \cdot 16} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma -0.3333333333333333 (* x (* x x)) x)
     (+ (/ 2.0 (* (* x x) 16.0)) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(-0.3333333333333333, (x * (x * x)), x);
	} else {
		tmp = (2.0 / ((x * x) * 16.0)) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(-0.3333333333333333, Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(Float64(x * x) * 16.0)) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(-0.3333333333333333 * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(N[(x * x), $MachinePrecision] * 16.0), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. distribute-lft-in N/A

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right)} \]

      2. *-rgt-identity N/A

         \[\leadsto \color{blue}{x} + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) + x} \]

      4. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left({x}^{2} \cdot \frac{-1}{3}\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{-1}{3}} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{3} \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \color{blue}{x \cdot {x}^{2}}, x\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      10. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(-0.3333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 98.9%

       \[\leadsto \frac{2}{\left(x \cdot x\right) \cdot 16} - 1 \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\left(x \cdot x\right) \cdot 16} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 99.2% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x \cdot \left(x + x\right)} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma -0.3333333333333333 (* x (* x x)) x)
     (+ (/ 2.0 (* x (+ x x))) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(-0.3333333333333333, (x * (x * x)), x);
	} else {
		tmp = (2.0 / (x * (x + x))) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(-0.3333333333333333, Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(x * Float64(x + x))) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(-0.3333333333333333 * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(x * N[(x + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. distribute-lft-in N/A

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right)} \]

      2. *-rgt-identity N/A

         \[\leadsto \color{blue}{x} + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) + x} \]

      4. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left({x}^{2} \cdot \frac{-1}{3}\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{-1}{3}} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{3} \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \color{blue}{x \cdot {x}^{2}}, x\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      10. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(-0.3333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 98.8%

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

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x \cdot \left(x + x\right)} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 12: 99.0% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x \cdot 8} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma -0.3333333333333333 (* x (* x x)) x)
     (+ (/ 2.0 (* x 8.0)) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(-0.3333333333333333, (x * (x * x)), x);
	} else {
		tmp = (2.0 / (x * 8.0)) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(-0.3333333333333333, Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(x * 8.0)) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(-0.3333333333333333 * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(x * 8.0), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. distribute-lft-in N/A

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right)} \]

      2. *-rgt-identity N/A

         \[\leadsto \color{blue}{x} + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) + x} \]

      4. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left({x}^{2} \cdot \frac{-1}{3}\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{-1}{3}} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{3} \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \color{blue}{x \cdot {x}^{2}}, x\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      10. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(-0.3333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 97.6%

       \[\leadsto \frac{2}{x \cdot 8} - 1 \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x \cdot 8} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 99.0% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x + x} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (if (<= (* -2.0 x) 1.0)
     (fma -0.3333333333333333 (* x (* x x)) x)
     (+ (/ 2.0 (+ x x)) -1.0))))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else if ((-2.0 * x) <= 1.0) {
		tmp = fma(-0.3333333333333333, (x * (x * x)), x);
	} else {
		tmp = (2.0 / (x + x)) + -1.0;
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	elseif (Float64(-2.0 * x) <= 1.0)
		tmp = fma(-0.3333333333333333, Float64(x * Float64(x * x)), x);
	else
		tmp = Float64(Float64(2.0 / Float64(x + x)) + -1.0);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], If[LessEqual[N[(-2.0 * x), $MachinePrecision], 1.0], N[(-0.3333333333333333 * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision], N[(N[(2.0 / N[(x + x), $MachinePrecision]), $MachinePrecision] + -1.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x) < 1

   1. Initial program 8.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. distribute-lft-in N/A

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right)} \]

      2. *-rgt-identity N/A

         \[\leadsto \color{blue}{x} + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) + x} \]

      4. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left({x}^{2} \cdot \frac{-1}{3}\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{-1}{3}} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{3} \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \color{blue}{x \cdot {x}^{2}}, x\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      10. lower-*.f64 99.5

          \[\leadsto \mathsf{fma}\left(-0.3333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)} \]

   if 1 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 98.8

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

   5. Applied rewrites 98.8%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 98.8%

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

   10. Applied rewrites 97.3%

       \[\leadsto \frac{2}{x + x} - 1 \]
3. Recombined 3 regimes into one program.

4. Final simplification 99.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{elif}\;-2 \cdot x \leq 1:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x + x} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 75.2% accurate, 4.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= (* -2.0 x) -500.0)
   (+ (/ 2.0 1.0) -1.0)
   (fma -0.3333333333333333 (* x (* x x)) x)))

C

double code(double x, double y) {
	double tmp;
	if ((-2.0 * x) <= -500.0) {
		tmp = (2.0 / 1.0) + -1.0;
	} else {
		tmp = fma(-0.3333333333333333, (x * (x * x)), x);
	}
	return tmp;
}

Julia

function code(x, y)
	tmp = 0.0
	if (Float64(-2.0 * x) <= -500.0)
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	else
		tmp = fma(-0.3333333333333333, Float64(x * Float64(x * x)), x);
	end
	return tmp
end

Wolfram

code[x_, y_] := If[LessEqual[N[(-2.0 * x), $MachinePrecision], -500.0], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision], N[(-0.3333333333333333 * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal -2 binary64) x) < -500

   1. Initial program 100.0%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 1.6

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

   5. Applied rewrites 1.6%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 1.6%

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

   10. Applied rewrites 100.0%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 100.0%

       \[\leadsto \frac{2}{1} - 1 \]

   if -500 < (*.f64 #s(literal -2 binary64) x)

   1. Initial program 39.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. distribute-lft-in N/A

         \[\leadsto \color{blue}{x \cdot 1 + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right)} \]

      2. *-rgt-identity N/A

         \[\leadsto \color{blue}{x} + x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) \]

      3. +-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(\frac{-1}{3} \cdot {x}^{2}\right) + x} \]

      4. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left({x}^{2} \cdot \frac{-1}{3}\right)} + x \]

      5. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{-1}{3}} + x \]

      6. *-commutative N/A

         \[\leadsto \color{blue}{\frac{-1}{3} \cdot \left(x \cdot {x}^{2}\right)} + x \]

      7. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{3}, x \cdot {x}^{2}, x\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, \color{blue}{x \cdot {x}^{2}}, x\right) \]

      9. unpow2 N/A

         \[\leadsto \mathsf{fma}\left(\frac{-1}{3}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

      10. lower-*.f64 66.1

          \[\leadsto \mathsf{fma}\left(-0.3333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]

   5. Applied rewrites 66.1%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 73.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;-2 \cdot x \leq -500:\\ \;\;\;\;\frac{2}{1} + -1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 15: 30.6% accurate, 5.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.1 \cdot 10^{-154}:\\ \;\;\;\;\left(x + 1\right) + -1\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{1} + -1\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= x 1.1e-154) (+ (+ x 1.0) -1.0) (+ (/ 2.0 1.0) -1.0)))

C

double code(double x, double y) {
	double tmp;
	if (x <= 1.1e-154) {
		tmp = (x + 1.0) + -1.0;
	} else {
		tmp = (2.0 / 1.0) + -1.0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= 1.1d-154) then
        tmp = (x + 1.0d0) + (-1.0d0)
    else
        tmp = (2.0d0 / 1.0d0) + (-1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= 1.1e-154) {
		tmp = (x + 1.0) + -1.0;
	} else {
		tmp = (2.0 / 1.0) + -1.0;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= 1.1e-154:
		tmp = (x + 1.0) + -1.0
	else:
		tmp = (2.0 / 1.0) + -1.0
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 1.1e-154)
		tmp = Float64(Float64(x + 1.0) + -1.0);
	else
		tmp = Float64(Float64(2.0 / 1.0) + -1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= 1.1e-154)
		tmp = (x + 1.0) + -1.0;
	else
		tmp = (2.0 / 1.0) + -1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, 1.1e-154], N[(N[(x + 1.0), $MachinePrecision] + -1.0), $MachinePrecision], N[(N[(2.0 / 1.0), $MachinePrecision] + -1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.10000000000000004e-154

   1. Initial program 44.1%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-+.f64 7.4

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

   5. Applied rewrites 7.4%

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

   if 1.10000000000000004e-154 < x

   1. Initial program 72.4%

      \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. sub-neg N/A

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

      4. metadata-eval N/A

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

      5. +-commutative N/A

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

      6. lower-+.f64 N/A

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

      7. count-2 N/A

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

      8. lower-+.f64 2.7

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

   5. Applied rewrites 2.7%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 2.2%

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

   10. Applied rewrites 72.6%

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

   11. Taylor expanded in x around 0

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

   13. Applied rewrites 72.6%

       \[\leadsto \frac{2}{1} - 1 \]
3. Recombined 2 regimes into one program.

4. Final simplification 28.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.1 \cdot 10^{-154}:\\ \;\;\;\;\left(x + 1\right) + -1\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{1} + -1\\ \end{array} \]
5. Add Preprocessing

Alternative 16: 6.5% accurate, 17.6× speedup

Math

\[\begin{array}{l} \\ \left(x + 1\right) + -1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ (+ x 1.0) -1.0))

C

double code(double x, double y) {
	return (x + 1.0) + -1.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (x + 1.0d0) + (-1.0d0)
end function

Java

public static double code(double x, double y) {
	return (x + 1.0) + -1.0;
}

Python

def code(x, y):
	return (x + 1.0) + -1.0

Julia

function code(x, y)
	return Float64(Float64(x + 1.0) + -1.0)
end

MATLAB

function tmp = code(x, y)
	tmp = (x + 1.0) + -1.0;
end

Wolfram

code[x_, y_] := N[(N[(x + 1.0), $MachinePrecision] + -1.0), $MachinePrecision]

Derivation

1. Initial program 53.1%

   \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. lower-+.f64 6.7

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

5. Applied rewrites 6.7%

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

6. Final simplification 6.7%

   \[\leadsto \left(x + 1\right) + -1 \]
7. Add Preprocessing

Alternative 17: 4.3% accurate, 30.8× speedup

Math

\[\begin{array}{l} \\ 1 + -1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (+ 1.0 -1.0))

C

double code(double x, double y) {
	return 1.0 + -1.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 + (-1.0d0)
end function

Java

public static double code(double x, double y) {
	return 1.0 + -1.0;
}

Python

def code(x, y):
	return 1.0 + -1.0

Julia

function code(x, y)
	return Float64(1.0 + -1.0)
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 + -1.0;
end

Wolfram

code[x_, y_] := N[(1.0 + -1.0), $MachinePrecision]

Derivation

1. Initial program 53.1%

   \[\frac{2}{1 + e^{-2 \cdot x}} - 1 \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Applied rewrites 4.4%

   \[\leadsto \color{blue}{1} - 1 \]

6. Final simplification 4.4%

   \[\leadsto 1 + -1 \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024233 
(FPCore (x y)
  :name "Logistic function from Lakshay Garg"
  :precision binary64
  (- (/ 2.0 (+ 1.0 (exp (* -2.0 x)))) 1.0))