Hyperbolic secant

Percentage Accurate: 100.0% → 100.0%

Time: 8.4s

Alternatives: 16

Speedup: 1.9×

Specification

Math

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

FPCore

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

C

double code(double x) {
	return 2.0 / (exp(x) + exp(-x));
}

Fortran

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

Java

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

Python

def code(x):
	return 2.0 / (math.exp(x) + math.exp(-x))

Julia

function code(x)
	return Float64(2.0 / Float64(exp(x) + exp(Float64(-x))))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (exp(x) + exp(-x));
end

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x) {
	return 2.0 / (exp(x) + exp(-x));
}

Fortran

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

Java

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

Python

def code(x):
	return 2.0 / (math.exp(x) + math.exp(-x))

Julia

function code(x)
	return Float64(2.0 / Float64(exp(x) + exp(Float64(-x))))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (exp(x) + exp(-x));
end

Wolfram

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

Alternative 1: 100.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\cosh x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 1.0 (cosh x)))

C

double code(double x) {
	return 1.0 / cosh(x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 1.0d0 / cosh(x)
end function

Java

public static double code(double x) {
	return 1.0 / Math.cosh(x);
}

Python

def code(x):
	return 1.0 / math.cosh(x)

Julia

function code(x)
	return Float64(1.0 / cosh(x))
end

MATLAB

function tmp = code(x)
	tmp = 1.0 / cosh(x);
end

Wolfram

code[x_] := N[(1.0 / N[Cosh[x], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{2}{e^{x} + e^{-x}} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. clear-num N/A

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

   3. lift-+.f64 N/A

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

   4. lift-exp.f64 N/A

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

   5. lift-exp.f64 N/A

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

   6. lift-neg.f64 N/A

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

   7. cosh-def N/A

      \[\leadsto \frac{1}{\color{blue}{\cosh x}} \]

   8. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\cosh x}} \]

   9. lower-cosh.f64 100.0

      \[\leadsto \frac{1}{\color{blue}{\cosh x}} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\frac{1}{\cosh x}} \]
5. Add Preprocessing

Alternative 2: 91.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(x \cdot x\right)\\ \mathbf{if}\;\frac{2}{e^{x} + e^{-x}} \leq 10^{-180}:\\ \;\;\;\;\frac{2}{\mathsf{fma}\left(x \cdot x, 0.002777777777777778, 0.08333333333333333\right) \cdot \left(x \cdot t\_0\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, t\_0, x\right), 2\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (* x (* x x))))
   (if (<= (/ 2.0 (+ (exp x) (exp (- x)))) 1e-180)
     (/
      2.0
      (* (fma (* x x) 0.002777777777777778 0.08333333333333333) (* x t_0)))
     (/ 2.0 (fma x (fma 0.08333333333333333 t_0 x) 2.0)))))

C

double code(double x) {
	double t_0 = x * (x * x);
	double tmp;
	if ((2.0 / (exp(x) + exp(-x))) <= 1e-180) {
		tmp = 2.0 / (fma((x * x), 0.002777777777777778, 0.08333333333333333) * (x * t_0));
	} else {
		tmp = 2.0 / fma(x, fma(0.08333333333333333, t_0, x), 2.0);
	}
	return tmp;
}

Julia

function code(x)
	t_0 = Float64(x * Float64(x * x))
	tmp = 0.0
	if (Float64(2.0 / Float64(exp(x) + exp(Float64(-x)))) <= 1e-180)
		tmp = Float64(2.0 / Float64(fma(Float64(x * x), 0.002777777777777778, 0.08333333333333333) * Float64(x * t_0)));
	else
		tmp = Float64(2.0 / fma(x, fma(0.08333333333333333, t_0, x), 2.0));
	end
	return tmp
end

Wolfram

code[x_] := Block[{t$95$0 = N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(2.0 / N[(N[Exp[x], $MachinePrecision] + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1e-180], N[(2.0 / N[(N[(N[(x * x), $MachinePrecision] * 0.002777777777777778 + 0.08333333333333333), $MachinePrecision] * N[(x * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 / N[(x * N[(0.08333333333333333 * t$95$0 + x), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 #s(literal 2 binary64) (+.f64 (exp.f64 x) (exp.f64 (neg.f64 x)))) < 1e-180

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right) + 2}} \]

      2. lower-fma.f64 N/A

         \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left({x}^{2}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)}} \]

      3. unpow2 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(\color{blue}{x \cdot x}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)} \]

      4. lower-*.f64 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(\color{blue}{x \cdot x}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)} \]

      5. +-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right) + 1}, 2\right)} \]

      6. lower-fma.f64 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right)}, 2\right)} \]

      7. unpow2 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right), 2\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right), 2\right)} \]

      9. +-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{360} \cdot {x}^{2} + \frac{1}{12}}, 1\right), 2\right)} \]

      10. *-commutative N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{360}} + \frac{1}{12}, 1\right), 2\right)} \]

      11. lower-fma.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{360}, \frac{1}{12}\right)}, 1\right), 2\right)} \]

      12. unpow2 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{360}, \frac{1}{12}\right), 1\right), 2\right)} \]

      13. lower-*.f64 88.3

          \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)} \]

   5. Applied rewrites 88.3%

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)}} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{2}{{x}^{6} \cdot \color{blue}{\left(\frac{1}{360} + \frac{1}{12} \cdot \frac{1}{{x}^{2}}\right)}} \]

   8. Applied rewrites 88.3%

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, 0.002777777777777778, 0.08333333333333333\right) \cdot \color{blue}{\left(x \cdot \left(x \cdot \left(x \cdot x\right)\right)\right)}} \]

   if 1e-180 < (/.f64 #s(literal 2 binary64) (+.f64 (exp.f64 x) (exp.f64 (neg.f64 x))))

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

      2. unpow2 N/A

         \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

      3. associate-*l* N/A

         \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

      4. *-commutative N/A

         \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

      6. +-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

      7. distribute-lft1-in N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

      8. *-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

      9. *-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

      10. associate-*r* N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

      11. *-commutative N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

      12. lower-fma.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

      13. lower-*.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

      14. unpow2 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

      15. lower-*.f64 99.4

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   5. Applied rewrites 99.4%

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 92.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(x \cdot x\right)\\ \mathbf{if}\;\frac{2}{e^{x} + e^{-x}} \leq 10^{-180}:\\ \;\;\;\;\frac{2}{0.002777777777777778 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot t\_0\right)\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, t\_0, x\right), 2\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (* x (* x x))))
   (if (<= (/ 2.0 (+ (exp x) (exp (- x)))) 1e-180)
     (/ 2.0 (* 0.002777777777777778 (* (* x x) (* x t_0))))
     (/ 2.0 (fma x (fma 0.08333333333333333 t_0 x) 2.0)))))

C

double code(double x) {
	double t_0 = x * (x * x);
	double tmp;
	if ((2.0 / (exp(x) + exp(-x))) <= 1e-180) {
		tmp = 2.0 / (0.002777777777777778 * ((x * x) * (x * t_0)));
	} else {
		tmp = 2.0 / fma(x, fma(0.08333333333333333, t_0, x), 2.0);
	}
	return tmp;
}

Julia

function code(x)
	t_0 = Float64(x * Float64(x * x))
	tmp = 0.0
	if (Float64(2.0 / Float64(exp(x) + exp(Float64(-x)))) <= 1e-180)
		tmp = Float64(2.0 / Float64(0.002777777777777778 * Float64(Float64(x * x) * Float64(x * t_0))));
	else
		tmp = Float64(2.0 / fma(x, fma(0.08333333333333333, t_0, x), 2.0));
	end
	return tmp
end

Wolfram

code[x_] := Block[{t$95$0 = N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[(2.0 / N[(N[Exp[x], $MachinePrecision] + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 1e-180], N[(2.0 / N[(0.002777777777777778 * N[(N[(x * x), $MachinePrecision] * N[(x * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(2.0 / N[(x * N[(0.08333333333333333 * t$95$0 + x), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (/.f64 #s(literal 2 binary64) (+.f64 (exp.f64 x) (exp.f64 (neg.f64 x)))) < 1e-180

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right) + 2}} \]

      2. lower-fma.f64 N/A

         \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left({x}^{2}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)}} \]

      3. unpow2 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(\color{blue}{x \cdot x}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)} \]

      4. lower-*.f64 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(\color{blue}{x \cdot x}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)} \]

      5. +-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right) + 1}, 2\right)} \]

      6. lower-fma.f64 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right)}, 2\right)} \]

      7. unpow2 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right), 2\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right), 2\right)} \]

      9. +-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{360} \cdot {x}^{2} + \frac{1}{12}}, 1\right), 2\right)} \]

      10. *-commutative N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{360}} + \frac{1}{12}, 1\right), 2\right)} \]

      11. lower-fma.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{360}, \frac{1}{12}\right)}, 1\right), 2\right)} \]

      12. unpow2 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{360}, \frac{1}{12}\right), 1\right), 2\right)} \]

      13. lower-*.f64 88.3

          \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)} \]

   5. Applied rewrites 88.3%

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)}} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{2}{\frac{1}{360} \cdot \color{blue}{{x}^{6}}} \]
   7. Step-by-step derivation

   8. Applied rewrites 88.3%

      \[\leadsto \frac{2}{0.002777777777777778 \cdot \color{blue}{\left(\left(x \cdot x\right) \cdot \left(x \cdot \left(x \cdot \left(x \cdot x\right)\right)\right)\right)}} \]

   if 1e-180 < (/.f64 #s(literal 2 binary64) (+.f64 (exp.f64 x) (exp.f64 (neg.f64 x))))

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

      2. unpow2 N/A

         \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

      3. associate-*l* N/A

         \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

      4. *-commutative N/A

         \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

      6. +-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

      7. distribute-lft1-in N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

      8. *-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

      9. *-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

      10. associate-*r* N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

      11. *-commutative N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

      12. lower-fma.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

      13. lower-*.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

      14. unpow2 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

      15. lower-*.f64 99.4

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   5. Applied rewrites 99.4%

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 76.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{x} + e^{-x} \leq 5:\\ \;\;\;\;\mathsf{fma}\left(-0.5, x \cdot x, 1\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (+ (exp x) (exp (- x))) 5.0) (fma -0.5 (* x x) 1.0) (/ 2.0 (* x x))))

C

double code(double x) {
	double tmp;
	if ((exp(x) + exp(-x)) <= 5.0) {
		tmp = fma(-0.5, (x * x), 1.0);
	} else {
		tmp = 2.0 / (x * x);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(exp(x) + exp(Float64(-x))) <= 5.0)
		tmp = fma(-0.5, Float64(x * x), 1.0);
	else
		tmp = Float64(2.0 / Float64(x * x));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(N[Exp[x], $MachinePrecision] + N[Exp[(-x)], $MachinePrecision]), $MachinePrecision], 5.0], N[(-0.5 * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision], N[(2.0 / N[(x * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (+.f64 (exp.f64 x) (exp.f64 (neg.f64 x))) < 5

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

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

      1. +-commutative N/A

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

      2. lower-fma.f64 N/A

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

      3. unpow2 N/A

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

      4. lower-*.f64 99.3

         \[\leadsto \mathsf{fma}\left(-0.5, \color{blue}{x \cdot x}, 1\right) \]

   5. Applied rewrites 99.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5, x \cdot x, 1\right)} \]

   if 5 < (+.f64 (exp.f64 x) (exp.f64 (neg.f64 x)))

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2}}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. unpow2 N/A

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

      3. lower-fma.f64 52.0

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

   5. Applied rewrites 52.0%

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

   6. Taylor expanded in x around inf

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

   8. Applied rewrites 52.0%

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

Alternative 5: 95.9% accurate, 2.2× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x \cdot \left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right), \mathsf{fma}\left(x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.0005787037037037037, -0.006944444444444444\right), -0.08333333333333333\right), \frac{-1}{x}\right), 2\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/
  2.0
  (fma
   (*
    x
    (*
     (fma (* x x) (* x 0.08333333333333333) x)
     (* x (fma x (* x 0.08333333333333333) -1.0))))
   (fma
    x
    (fma
     (* x x)
     (fma (* x x) -0.0005787037037037037 -0.006944444444444444)
     -0.08333333333333333)
    (/ -1.0 x))
   2.0)))

C

double code(double x) {
	return 2.0 / fma((x * (fma((x * x), (x * 0.08333333333333333), x) * (x * fma(x, (x * 0.08333333333333333), -1.0)))), fma(x, fma((x * x), fma((x * x), -0.0005787037037037037, -0.006944444444444444), -0.08333333333333333), (-1.0 / x)), 2.0);
}

Julia

function code(x)
	return Float64(2.0 / fma(Float64(x * Float64(fma(Float64(x * x), Float64(x * 0.08333333333333333), x) * Float64(x * fma(x, Float64(x * 0.08333333333333333), -1.0)))), fma(x, fma(Float64(x * x), fma(Float64(x * x), -0.0005787037037037037, -0.006944444444444444), -0.08333333333333333), Float64(-1.0 / x)), 2.0))
end

Wolfram

code[x_] := N[(2.0 / N[(N[(x * N[(N[(N[(x * x), $MachinePrecision] * N[(x * 0.08333333333333333), $MachinePrecision] + x), $MachinePrecision] * N[(x * N[(x * N[(x * 0.08333333333333333), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(x * N[(N[(x * x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * -0.0005787037037037037 + -0.006944444444444444), $MachinePrecision] + -0.08333333333333333), $MachinePrecision] + N[(-1.0 / x), $MachinePrecision]), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

   3. associate-*l* N/A

      \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

   4. *-commutative N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

   6. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

   7. distribute-lft1-in N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

   8. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

   9. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

   10. associate-*r* N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

   11. *-commutative N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

   12. lower-fma.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

   13. lower-*.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

   14. unpow2 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   15. lower-*.f64 85.8

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

5. Applied rewrites 85.8%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]
6. Step-by-step derivation

7. Applied rewrites 56.2%

   \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right) \cdot x, \color{blue}{\frac{1}{x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)}}, 2\right)} \]

8. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot \frac{1}{12}, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot \frac{1}{12}, -1\right)\right)\right) \cdot x, \frac{{x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{-1}{1728} \cdot {x}^{2} - \frac{1}{144}\right) - \frac{1}{12}\right) - 1}{\color{blue}{x}}, 2\right)} \]
9. Step-by-step derivation

10. Applied rewrites 96.8%

    \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right) \cdot x, \mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.0005787037037037037, -0.006944444444444444\right), -0.08333333333333333\right)}, \frac{-1}{x}\right), 2\right)} \]

11. Final simplification 96.8%

    \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot \left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right), \mathsf{fma}\left(x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, -0.0005787037037037037, -0.006944444444444444\right), -0.08333333333333333\right), \frac{-1}{x}\right), 2\right)} \]
12. Add Preprocessing

Alternative 6: 95.1% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x \cdot \left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right), \mathsf{fma}\left(x, \mathsf{fma}\left(x \cdot x, -0.006944444444444444, -0.08333333333333333\right), \frac{-1}{x}\right), 2\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/
  2.0
  (fma
   (*
    x
    (*
     (fma (* x x) (* x 0.08333333333333333) x)
     (* x (fma x (* x 0.08333333333333333) -1.0))))
   (fma x (fma (* x x) -0.006944444444444444 -0.08333333333333333) (/ -1.0 x))
   2.0)))

C

double code(double x) {
	return 2.0 / fma((x * (fma((x * x), (x * 0.08333333333333333), x) * (x * fma(x, (x * 0.08333333333333333), -1.0)))), fma(x, fma((x * x), -0.006944444444444444, -0.08333333333333333), (-1.0 / x)), 2.0);
}

Julia

function code(x)
	return Float64(2.0 / fma(Float64(x * Float64(fma(Float64(x * x), Float64(x * 0.08333333333333333), x) * Float64(x * fma(x, Float64(x * 0.08333333333333333), -1.0)))), fma(x, fma(Float64(x * x), -0.006944444444444444, -0.08333333333333333), Float64(-1.0 / x)), 2.0))
end

Wolfram

code[x_] := N[(2.0 / N[(N[(x * N[(N[(N[(x * x), $MachinePrecision] * N[(x * 0.08333333333333333), $MachinePrecision] + x), $MachinePrecision] * N[(x * N[(x * N[(x * 0.08333333333333333), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(x * N[(N[(x * x), $MachinePrecision] * -0.006944444444444444 + -0.08333333333333333), $MachinePrecision] + N[(-1.0 / x), $MachinePrecision]), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

   3. associate-*l* N/A

      \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

   4. *-commutative N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

   6. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

   7. distribute-lft1-in N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

   8. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

   9. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

   10. associate-*r* N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

   11. *-commutative N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

   12. lower-fma.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

   13. lower-*.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

   14. unpow2 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   15. lower-*.f64 85.8

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

5. Applied rewrites 85.8%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]
6. Step-by-step derivation

7. Applied rewrites 56.2%

   \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right) \cdot x, \color{blue}{\frac{1}{x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)}}, 2\right)} \]

8. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot \frac{1}{12}, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot \frac{1}{12}, -1\right)\right)\right) \cdot x, \frac{{x}^{2} \cdot \left(\frac{-1}{144} \cdot {x}^{2} - \frac{1}{12}\right) - 1}{\color{blue}{x}}, 2\right)} \]
9. Step-by-step derivation

10. Applied rewrites 96.4%

    \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right) \cdot x, \mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(x \cdot x, -0.006944444444444444, -0.08333333333333333\right)}, \frac{-1}{x}\right), 2\right)} \]

11. Final simplification 96.4%

    \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot \left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right), \mathsf{fma}\left(x, \mathsf{fma}\left(x \cdot x, -0.006944444444444444, -0.08333333333333333\right), \frac{-1}{x}\right), 2\right)} \]
12. Add Preprocessing

Alternative 7: 94.0% accurate, 2.8× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x \cdot \left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right), \mathsf{fma}\left(x, -0.08333333333333333, \frac{-1}{x}\right), 2\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/
  2.0
  (fma
   (*
    x
    (*
     (fma (* x x) (* x 0.08333333333333333) x)
     (* x (fma x (* x 0.08333333333333333) -1.0))))
   (fma x -0.08333333333333333 (/ -1.0 x))
   2.0)))

C

double code(double x) {
	return 2.0 / fma((x * (fma((x * x), (x * 0.08333333333333333), x) * (x * fma(x, (x * 0.08333333333333333), -1.0)))), fma(x, -0.08333333333333333, (-1.0 / x)), 2.0);
}

Julia

function code(x)
	return Float64(2.0 / fma(Float64(x * Float64(fma(Float64(x * x), Float64(x * 0.08333333333333333), x) * Float64(x * fma(x, Float64(x * 0.08333333333333333), -1.0)))), fma(x, -0.08333333333333333, Float64(-1.0 / x)), 2.0))
end

Wolfram

code[x_] := N[(2.0 / N[(N[(x * N[(N[(N[(x * x), $MachinePrecision] * N[(x * 0.08333333333333333), $MachinePrecision] + x), $MachinePrecision] * N[(x * N[(x * N[(x * 0.08333333333333333), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(x * -0.08333333333333333 + N[(-1.0 / x), $MachinePrecision]), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

   3. associate-*l* N/A

      \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

   4. *-commutative N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

   6. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

   7. distribute-lft1-in N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

   8. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

   9. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

   10. associate-*r* N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

   11. *-commutative N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

   12. lower-fma.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

   13. lower-*.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

   14. unpow2 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   15. lower-*.f64 85.8

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

5. Applied rewrites 85.8%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]
6. Step-by-step derivation

7. Applied rewrites 56.2%

   \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right) \cdot x, \color{blue}{\frac{1}{x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)}}, 2\right)} \]

8. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot \frac{1}{12}, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot \frac{1}{12}, -1\right)\right)\right) \cdot x, \frac{\frac{-1}{12} \cdot {x}^{2} - 1}{\color{blue}{x}}, 2\right)} \]
9. Step-by-step derivation

10. Applied rewrites 95.3%

    \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right) \cdot x, \mathsf{fma}\left(x, \color{blue}{-0.08333333333333333}, \frac{-1}{x}\right), 2\right)} \]

11. Final simplification 95.3%

    \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot \left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right), \mathsf{fma}\left(x, -0.08333333333333333, \frac{-1}{x}\right), 2\right)} \]
12. Add Preprocessing

Alternative 8: 93.0% accurate, 3.1× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x \cdot \left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right), \frac{-1}{x}, 2\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/
  2.0
  (fma
   (*
    x
    (*
     (fma (* x x) (* x 0.08333333333333333) x)
     (* x (fma x (* x 0.08333333333333333) -1.0))))
   (/ -1.0 x)
   2.0)))

C

double code(double x) {
	return 2.0 / fma((x * (fma((x * x), (x * 0.08333333333333333), x) * (x * fma(x, (x * 0.08333333333333333), -1.0)))), (-1.0 / x), 2.0);
}

Julia

function code(x)
	return Float64(2.0 / fma(Float64(x * Float64(fma(Float64(x * x), Float64(x * 0.08333333333333333), x) * Float64(x * fma(x, Float64(x * 0.08333333333333333), -1.0)))), Float64(-1.0 / x), 2.0))
end

Wolfram

code[x_] := N[(2.0 / N[(N[(x * N[(N[(N[(x * x), $MachinePrecision] * N[(x * 0.08333333333333333), $MachinePrecision] + x), $MachinePrecision] * N[(x * N[(x * N[(x * 0.08333333333333333), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(-1.0 / x), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

   3. associate-*l* N/A

      \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

   4. *-commutative N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

   6. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

   7. distribute-lft1-in N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

   8. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

   9. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

   10. associate-*r* N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

   11. *-commutative N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

   12. lower-fma.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

   13. lower-*.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

   14. unpow2 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   15. lower-*.f64 85.8

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

5. Applied rewrites 85.8%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]
6. Step-by-step derivation

7. Applied rewrites 56.2%

   \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right) \cdot x, \color{blue}{\frac{1}{x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)}}, 2\right)} \]

8. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot \frac{1}{12}, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot \frac{1}{12}, -1\right)\right)\right) \cdot x, \frac{-1}{\color{blue}{x}}, 2\right)} \]
9. Step-by-step derivation

10. Applied rewrites 93.8%

    \[\leadsto \frac{2}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right) \cdot x, \frac{-1}{\color{blue}{x}}, 2\right)} \]

11. Final simplification 93.8%

    \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot \left(\mathsf{fma}\left(x \cdot x, x \cdot 0.08333333333333333, x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -1\right)\right)\right), \frac{-1}{x}, 2\right)} \]
12. Add Preprocessing

Alternative 9: 92.0% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/
  2.0
  (fma
   (* x x)
   (fma (* x x) (fma (* x x) 0.002777777777777778 0.08333333333333333) 1.0)
   2.0)))

C

double code(double x) {
	return 2.0 / fma((x * x), fma((x * x), fma((x * x), 0.002777777777777778, 0.08333333333333333), 1.0), 2.0);
}

Julia

function code(x)
	return Float64(2.0 / fma(Float64(x * x), fma(Float64(x * x), fma(Float64(x * x), 0.002777777777777778, 0.08333333333333333), 1.0), 2.0))
end

Wolfram

code[x_] := N[(2.0 / N[(N[(x * x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * 0.002777777777777778 + 0.08333333333333333), $MachinePrecision] + 1.0), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right) + 2}} \]

   2. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left({x}^{2}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)}} \]

   3. unpow2 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(\color{blue}{x \cdot x}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)} \]

   4. lower-*.f64 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(\color{blue}{x \cdot x}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)} \]

   5. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right) + 1}, 2\right)} \]

   6. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right)}, 2\right)} \]

   7. unpow2 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right), 2\right)} \]

   8. lower-*.f64 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right), 2\right)} \]

   9. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{360} \cdot {x}^{2} + \frac{1}{12}}, 1\right), 2\right)} \]

   10. *-commutative N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{360}} + \frac{1}{12}, 1\right), 2\right)} \]

   11. lower-fma.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{360}, \frac{1}{12}\right)}, 1\right), 2\right)} \]

   12. unpow2 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{360}, \frac{1}{12}\right), 1\right), 2\right)} \]

   13. lower-*.f64 93.1

       \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)} \]

5. Applied rewrites 93.1%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)}} \]
6. Add Preprocessing

Alternative 10: 91.6% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x \cdot x, x \cdot \left(x \cdot \left(\left(x \cdot x\right) \cdot 0.002777777777777778\right)\right), 2\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/ 2.0 (fma (* x x) (* x (* x (* (* x x) 0.002777777777777778))) 2.0)))

C

double code(double x) {
	return 2.0 / fma((x * x), (x * (x * ((x * x) * 0.002777777777777778))), 2.0);
}

Julia

function code(x)
	return Float64(2.0 / fma(Float64(x * x), Float64(x * Float64(x * Float64(Float64(x * x) * 0.002777777777777778))), 2.0))
end

Wolfram

code[x_] := N[(2.0 / N[(N[(x * x), $MachinePrecision] * N[(x * N[(x * N[(N[(x * x), $MachinePrecision] * 0.002777777777777778), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right) + 2}} \]

   2. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left({x}^{2}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)}} \]

   3. unpow2 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(\color{blue}{x \cdot x}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)} \]

   4. lower-*.f64 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(\color{blue}{x \cdot x}, 1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right), 2\right)} \]

   5. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right) + 1}, 2\right)} \]

   6. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right)}, 2\right)} \]

   7. unpow2 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right), 2\right)} \]

   8. lower-*.f64 N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{12} + \frac{1}{360} \cdot {x}^{2}, 1\right), 2\right)} \]

   9. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{360} \cdot {x}^{2} + \frac{1}{12}}, 1\right), 2\right)} \]

   10. *-commutative N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{360}} + \frac{1}{12}, 1\right), 2\right)} \]

   11. lower-fma.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{360}, \frac{1}{12}\right)}, 1\right), 2\right)} \]

   12. unpow2 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{360}, \frac{1}{12}\right), 1\right), 2\right)} \]

   13. lower-*.f64 93.1

       \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)} \]

5. Applied rewrites 93.1%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 0.002777777777777778, 0.08333333333333333\right), 1\right), 2\right)}} \]

6. Taylor expanded in x around inf

   \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, \frac{1}{360} \cdot \color{blue}{{x}^{4}}, 2\right)} \]
7. Step-by-step derivation

8. Applied rewrites 92.7%

   \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot x, x \cdot \color{blue}{\left(x \cdot \left(\left(x \cdot x\right) \cdot 0.002777777777777778\right)\right)}, 2\right)} \]
9. Add Preprocessing

Alternative 11: 88.1% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x \cdot \left(x \cdot \left(x \cdot x\right)\right), 0.08333333333333333, \mathsf{fma}\left(x, x, 2\right)\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/ 2.0 (fma (* x (* x (* x x))) 0.08333333333333333 (fma x x 2.0))))

C

double code(double x) {
	return 2.0 / fma((x * (x * (x * x))), 0.08333333333333333, fma(x, x, 2.0));
}

Julia

function code(x)
	return Float64(2.0 / fma(Float64(x * Float64(x * Float64(x * x))), 0.08333333333333333, fma(x, x, 2.0)))
end

Wolfram

code[x_] := N[(2.0 / N[(N[(x * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.08333333333333333 + N[(x * x + 2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

   3. associate-*l* N/A

      \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

   4. *-commutative N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

   6. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

   7. distribute-lft1-in N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

   8. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

   9. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

   10. associate-*r* N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

   11. *-commutative N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

   12. lower-fma.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

   13. lower-*.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

   14. unpow2 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   15. lower-*.f64 85.8

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

5. Applied rewrites 85.8%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]
6. Step-by-step derivation

7. Applied rewrites 86.2%

   \[\leadsto \frac{2}{\mathsf{fma}\left(x \cdot \left(x \cdot \left(x \cdot x\right)\right), \color{blue}{0.08333333333333333}, \mathsf{fma}\left(x, x, 2\right)\right)} \]
8. Add Preprocessing

Alternative 12: 82.0% accurate, 5.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 3.7:\\ \;\;\;\;\frac{2}{\mathsf{fma}\left(x, x, 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{0.08333333333333333 \cdot \left(x \cdot \left(x \cdot \left(x \cdot x\right)\right)\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 3.7)
   (/ 2.0 (fma x x 2.0))
   (/ 2.0 (* 0.08333333333333333 (* x (* x (* x x)))))))

C

double code(double x) {
	double tmp;
	if (x <= 3.7) {
		tmp = 2.0 / fma(x, x, 2.0);
	} else {
		tmp = 2.0 / (0.08333333333333333 * (x * (x * (x * x))));
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (x <= 3.7)
		tmp = Float64(2.0 / fma(x, x, 2.0));
	else
		tmp = Float64(2.0 / Float64(0.08333333333333333 * Float64(x * Float64(x * Float64(x * x)))));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[x, 3.7], N[(2.0 / N[(x * x + 2.0), $MachinePrecision]), $MachinePrecision], N[(2.0 / N[(0.08333333333333333 * N[(x * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 3.7000000000000002

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2}}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. unpow2 N/A

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

      3. lower-fma.f64 81.1

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

   5. Applied rewrites 81.1%

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

   if 3.7000000000000002 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

      2. unpow2 N/A

         \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

      3. associate-*l* N/A

         \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

      4. *-commutative N/A

         \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

      6. +-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

      7. distribute-lft1-in N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

      8. *-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

      9. *-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

      10. associate-*r* N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

      11. *-commutative N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

      12. lower-fma.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

      13. lower-*.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

      14. unpow2 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

      15. lower-*.f64 73.9

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   5. Applied rewrites 73.9%

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{2}{\frac{1}{12} \cdot \color{blue}{{x}^{4}}} \]
   7. Step-by-step derivation

   8. Applied rewrites 73.9%

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(x \cdot \left(\left(x \cdot x\right) \cdot 0.08333333333333333\right)\right)}} \]
   9. Step-by-step derivation

   10. Applied rewrites 73.9%

       \[\leadsto \frac{2}{\left(x \cdot \left(x \cdot \left(x \cdot x\right)\right)\right) \cdot 0.08333333333333333} \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 3.7:\\ \;\;\;\;\frac{2}{\mathsf{fma}\left(x, x, 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{0.08333333333333333 \cdot \left(x \cdot \left(x \cdot \left(x \cdot x\right)\right)\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 13: 81.9% accurate, 5.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 3.7:\\ \;\;\;\;\frac{2}{\mathsf{fma}\left(x, x, 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x \cdot \left(x \cdot \left(x \cdot \left(x \cdot 0.08333333333333333\right)\right)\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 3.7)
   (/ 2.0 (fma x x 2.0))
   (/ 2.0 (* x (* x (* x (* x 0.08333333333333333)))))))

C

double code(double x) {
	double tmp;
	if (x <= 3.7) {
		tmp = 2.0 / fma(x, x, 2.0);
	} else {
		tmp = 2.0 / (x * (x * (x * (x * 0.08333333333333333))));
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (x <= 3.7)
		tmp = Float64(2.0 / fma(x, x, 2.0));
	else
		tmp = Float64(2.0 / Float64(x * Float64(x * Float64(x * Float64(x * 0.08333333333333333)))));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[x, 3.7], N[(2.0 / N[(x * x + 2.0), $MachinePrecision]), $MachinePrecision], N[(2.0 / N[(x * N[(x * N[(x * N[(x * 0.08333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 3.7000000000000002

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2}}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

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

      2. unpow2 N/A

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

      3. lower-fma.f64 81.1

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

   5. Applied rewrites 81.1%

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

   if 3.7000000000000002 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

      \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

      2. unpow2 N/A

         \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

      3. associate-*l* N/A

         \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

      4. *-commutative N/A

         \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

      6. +-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

      7. distribute-lft1-in N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

      8. *-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

      9. *-commutative N/A

         \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

      10. associate-*r* N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

      11. *-commutative N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

      12. lower-fma.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

      13. lower-*.f64 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

      14. unpow2 N/A

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

      15. lower-*.f64 73.9

          \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   5. Applied rewrites 73.9%

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]

   6. Taylor expanded in x around inf

      \[\leadsto \frac{2}{\frac{1}{12} \cdot \color{blue}{{x}^{4}}} \]
   7. Step-by-step derivation

   8. Applied rewrites 73.9%

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(x \cdot \left(\left(x \cdot x\right) \cdot 0.08333333333333333\right)\right)}} \]
   9. Step-by-step derivation

   10. Applied rewrites 73.9%

       \[\leadsto \frac{2}{x \cdot \left(x \cdot \left(\left(x \cdot 0.08333333333333333\right) \cdot x\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 3.7:\\ \;\;\;\;\frac{2}{\mathsf{fma}\left(x, x, 2\right)}\\ \mathbf{else}:\\ \;\;\;\;\frac{2}{x \cdot \left(x \cdot \left(x \cdot \left(x \cdot 0.08333333333333333\right)\right)\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 14: 88.0% accurate, 6.4× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (/ 2.0 (fma x (fma 0.08333333333333333 (* x (* x x)) x) 2.0)))

C

double code(double x) {
	return 2.0 / fma(x, fma(0.08333333333333333, (x * (x * x)), x), 2.0);
}

Julia

function code(x)
	return Float64(2.0 / fma(x, fma(0.08333333333333333, Float64(x * Float64(x * x)), x), 2.0))
end

Wolfram

code[x_] := N[(2.0 / N[(x * N[(0.08333333333333333 * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision] + 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{2}{\color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{\color{blue}{\left(x \cdot x\right)} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + 2} \]

   3. associate-*l* N/A

      \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)\right)} + 2} \]

   4. *-commutative N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x\right)} + 2} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \cdot x, 2\right)}} \]

   6. +-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \cdot x, 2\right)} \]

   7. distribute-lft1-in N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(\frac{1}{12} \cdot {x}^{2}\right) \cdot x + x}, 2\right)} \]

   8. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{x \cdot \left(\frac{1}{12} \cdot {x}^{2}\right)} + x, 2\right)} \]

   9. *-commutative N/A

      \[\leadsto \frac{2}{\mathsf{fma}\left(x, x \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + x, 2\right)} \]

   10. associate-*r* N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\left(x \cdot {x}^{2}\right) \cdot \frac{1}{12}} + x, 2\right)} \]

   11. *-commutative N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\frac{1}{12} \cdot \left(x \cdot {x}^{2}\right)} + x, 2\right)} \]

   12. lower-fma.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\frac{1}{12}, x \cdot {x}^{2}, x\right)}, 2\right)} \]

   13. lower-*.f64 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, \color{blue}{x \cdot {x}^{2}}, x\right), 2\right)} \]

   14. unpow2 N/A

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(\frac{1}{12}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

   15. lower-*.f64 85.8

       \[\leadsto \frac{2}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right), 2\right)} \]

5. Applied rewrites 85.8%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(0.08333333333333333, x \cdot \left(x \cdot x\right), x\right), 2\right)}} \]
6. Add Preprocessing

Alternative 15: 76.4% accurate, 12.1× speedup

Math

\[\begin{array}{l} \\ \frac{2}{\mathsf{fma}\left(x, x, 2\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (fma x x 2.0)))

C

double code(double x) {
	return 2.0 / fma(x, x, 2.0);
}

Julia

function code(x)
	return Float64(2.0 / fma(x, x, 2.0))
end

Wolfram

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

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

   \[\leadsto \frac{2}{\color{blue}{2 + {x}^{2}}} \]
4. Step-by-step derivation

   1. +-commutative N/A

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

   2. unpow2 N/A

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

   3. lower-fma.f64 72.4

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

5. Applied rewrites 72.4%

   \[\leadsto \frac{2}{\color{blue}{\mathsf{fma}\left(x, x, 2\right)}} \]
6. Add Preprocessing

Alternative 16: 51.4% accurate, 217.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 1.0)

C

double code(double x) {
	return 1.0;
}

Fortran

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

Java

public static double code(double x) {
	return 1.0;
}

Python

def code(x):
	return 1.0

Julia

function code(x)
	return 1.0
end

MATLAB

function tmp = code(x)
	tmp = 1.0;
end

Wolfram

code[x_] := 1.0

Derivation

1. Initial program 100.0%

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

3. Taylor expanded in x around 0

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

5. Applied rewrites 44.1%

   \[\leadsto \color{blue}{1} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024228 
(FPCore (x)
  :name "Hyperbolic secant"
  :precision binary64
  (/ 2.0 (+ (exp x) (exp (- x)))))