Hyperbolic sine

Percentage Accurate: 54.5% → 100.0%

Time: 7.5s

Alternatives: 6

Speedup: 18.7×

• 49.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 54.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ \begin{array}{l} t_0 := e^{x\_m} - e^{-x\_m}\\ x\_s \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq 0.1:\\ \;\;\;\;\frac{x\_m \cdot \left(2 + \left(x\_m \cdot x\_m\right) \cdot \left(0.3333333333333333 + \left(x\_m \cdot x\_m\right) \cdot \left(0.016666666666666666 + \left(x\_m \cdot x\_m\right) \cdot 0.0003968253968253968\right)\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t\_0}{2}\\ \end{array} \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (let* ((t_0 (- (exp x_m) (exp (- x_m)))))
   (*
    x_s
    (if (<= t_0 0.1)
      (/
       (*
        x_m
        (+
         2.0
         (*
          (* x_m x_m)
          (+
           0.3333333333333333
           (*
            (* x_m x_m)
            (+ 0.016666666666666666 (* (* x_m x_m) 0.0003968253968253968)))))))
       2.0)
      (/ t_0 2.0)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double t_0 = exp(x_m) - exp(-x_m);
	double tmp;
	if (t_0 <= 0.1) {
		tmp = (x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0;
	} else {
		tmp = t_0 / 2.0;
	}
	return x_s * tmp;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: t_0
    real(8) :: tmp
    t_0 = exp(x_m) - exp(-x_m)
    if (t_0 <= 0.1d0) then
        tmp = (x_m * (2.0d0 + ((x_m * x_m) * (0.3333333333333333d0 + ((x_m * x_m) * (0.016666666666666666d0 + ((x_m * x_m) * 0.0003968253968253968d0))))))) / 2.0d0
    else
        tmp = t_0 / 2.0d0
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double t_0 = Math.exp(x_m) - Math.exp(-x_m);
	double tmp;
	if (t_0 <= 0.1) {
		tmp = (x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0;
	} else {
		tmp = t_0 / 2.0;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	t_0 = math.exp(x_m) - math.exp(-x_m)
	tmp = 0
	if t_0 <= 0.1:
		tmp = (x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0
	else:
		tmp = t_0 / 2.0
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	t_0 = Float64(exp(x_m) - exp(Float64(-x_m)))
	tmp = 0.0
	if (t_0 <= 0.1)
		tmp = Float64(Float64(x_m * Float64(2.0 + Float64(Float64(x_m * x_m) * Float64(0.3333333333333333 + Float64(Float64(x_m * x_m) * Float64(0.016666666666666666 + Float64(Float64(x_m * x_m) * 0.0003968253968253968))))))) / 2.0);
	else
		tmp = Float64(t_0 / 2.0);
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	t_0 = exp(x_m) - exp(-x_m);
	tmp = 0.0;
	if (t_0 <= 0.1)
		tmp = (x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0;
	else
		tmp = t_0 / 2.0;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := Block[{t$95$0 = N[(N[Exp[x$95$m], $MachinePrecision] - N[Exp[(-x$95$m)], $MachinePrecision]), $MachinePrecision]}, N[(x$95$s * If[LessEqual[t$95$0, 0.1], N[(N[(x$95$m * N[(2.0 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(0.3333333333333333 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(0.016666666666666666 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.0003968253968253968), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(t$95$0 / 2.0), $MachinePrecision]]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (exp.f64 x) (exp.f64 (neg.f64 x))) < 0.10000000000000001

   1. Initial program 40.9%

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

   3. Taylor expanded in x around 0 96.6%

      \[\leadsto \frac{\color{blue}{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + 0.0003968253968253968 \cdot {x}^{2}\right)\right)\right)}}{2} \]
   4. Step-by-step derivation

      1. *-commutative 96.6%

         \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{{x}^{2} \cdot 0.0003968253968253968}\right)\right)\right)}{2} \]

   5. Simplified 96.6%

      \[\leadsto \frac{\color{blue}{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + {x}^{2} \cdot 0.0003968253968253968\right)\right)\right)}}{2} \]
   6. Step-by-step derivation

      1. unpow2 96.6%

         \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

   7. Applied egg-rr 96.6%

      \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]
   8. Step-by-step derivation

      1. unpow2 96.6%

         \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

   9. Applied egg-rr 96.6%

      \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + \color{blue}{\left(x \cdot x\right)} \cdot \left(0.016666666666666666 + \left(x \cdot x\right) \cdot 0.0003968253968253968\right)\right)\right)}{2} \]
   10. Step-by-step derivation

       1. unpow2 96.6%

          \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

   11. Applied egg-rr 96.6%

       \[\leadsto \frac{x \cdot \left(2 + \color{blue}{\left(x \cdot x\right)} \cdot \left(0.3333333333333333 + \left(x \cdot x\right) \cdot \left(0.016666666666666666 + \left(x \cdot x\right) \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

   if 0.10000000000000001 < (-.f64 (exp.f64 x) (exp.f64 (neg.f64 x)))

   1. Initial program 100.0%

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

Alternative 2: 99.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;e^{x\_m} - e^{-x\_m} \leq 20:\\ \;\;\;\;\frac{x\_m \cdot \left(2 + \left(x\_m \cdot x\_m\right) \cdot \left(0.3333333333333333 + \left(x\_m \cdot x\_m\right) \cdot \left(0.016666666666666666 + \left(x\_m \cdot x\_m\right) \cdot 0.0003968253968253968\right)\right)\right)}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x\_m} - \left(1 - x\_m\right)}{2}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= (- (exp x_m) (exp (- x_m))) 20.0)
    (/
     (*
      x_m
      (+
       2.0
       (*
        (* x_m x_m)
        (+
         0.3333333333333333
         (*
          (* x_m x_m)
          (+ 0.016666666666666666 (* (* x_m x_m) 0.0003968253968253968)))))))
     2.0)
    (/ (- (exp x_m) (- 1.0 x_m)) 2.0))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if ((exp(x_m) - exp(-x_m)) <= 20.0) {
		tmp = (x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0;
	} else {
		tmp = (exp(x_m) - (1.0 - x_m)) / 2.0;
	}
	return x_s * tmp;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    real(8) :: tmp
    if ((exp(x_m) - exp(-x_m)) <= 20.0d0) then
        tmp = (x_m * (2.0d0 + ((x_m * x_m) * (0.3333333333333333d0 + ((x_m * x_m) * (0.016666666666666666d0 + ((x_m * x_m) * 0.0003968253968253968d0))))))) / 2.0d0
    else
        tmp = (exp(x_m) - (1.0d0 - x_m)) / 2.0d0
    end if
    code = x_s * tmp
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	double tmp;
	if ((Math.exp(x_m) - Math.exp(-x_m)) <= 20.0) {
		tmp = (x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0;
	} else {
		tmp = (Math.exp(x_m) - (1.0 - x_m)) / 2.0;
	}
	return x_s * tmp;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	tmp = 0
	if (math.exp(x_m) - math.exp(-x_m)) <= 20.0:
		tmp = (x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0
	else:
		tmp = (math.exp(x_m) - (1.0 - x_m)) / 2.0
	return x_s * tmp

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	tmp = 0.0
	if (Float64(exp(x_m) - exp(Float64(-x_m))) <= 20.0)
		tmp = Float64(Float64(x_m * Float64(2.0 + Float64(Float64(x_m * x_m) * Float64(0.3333333333333333 + Float64(Float64(x_m * x_m) * Float64(0.016666666666666666 + Float64(Float64(x_m * x_m) * 0.0003968253968253968))))))) / 2.0);
	else
		tmp = Float64(Float64(exp(x_m) - Float64(1.0 - x_m)) / 2.0);
	end
	return Float64(x_s * tmp)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp_2 = code(x_s, x_m)
	tmp = 0.0;
	if ((exp(x_m) - exp(-x_m)) <= 20.0)
		tmp = (x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0;
	else
		tmp = (exp(x_m) - (1.0 - x_m)) / 2.0;
	end
	tmp_2 = x_s * tmp;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * If[LessEqual[N[(N[Exp[x$95$m], $MachinePrecision] - N[Exp[(-x$95$m)], $MachinePrecision]), $MachinePrecision], 20.0], N[(N[(x$95$m * N[(2.0 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(0.3333333333333333 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(0.016666666666666666 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.0003968253968253968), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[(N[Exp[x$95$m], $MachinePrecision] - N[(1.0 - x$95$m), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (exp.f64 x) (exp.f64 (neg.f64 x))) < 20

   1. Initial program 40.9%

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

   3. Taylor expanded in x around 0 96.6%

      \[\leadsto \frac{\color{blue}{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + 0.0003968253968253968 \cdot {x}^{2}\right)\right)\right)}}{2} \]
   4. Step-by-step derivation

      1. *-commutative 96.6%

         \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{{x}^{2} \cdot 0.0003968253968253968}\right)\right)\right)}{2} \]

   5. Simplified 96.6%

      \[\leadsto \frac{\color{blue}{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + {x}^{2} \cdot 0.0003968253968253968\right)\right)\right)}}{2} \]
   6. Step-by-step derivation

      1. unpow2 96.6%

         \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

   7. Applied egg-rr 96.6%

      \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]
   8. Step-by-step derivation

      1. unpow2 96.6%

         \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

   9. Applied egg-rr 96.6%

      \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + \color{blue}{\left(x \cdot x\right)} \cdot \left(0.016666666666666666 + \left(x \cdot x\right) \cdot 0.0003968253968253968\right)\right)\right)}{2} \]
   10. Step-by-step derivation

       1. unpow2 96.6%

          \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

   11. Applied egg-rr 96.6%

       \[\leadsto \frac{x \cdot \left(2 + \color{blue}{\left(x \cdot x\right)} \cdot \left(0.3333333333333333 + \left(x \cdot x\right) \cdot \left(0.016666666666666666 + \left(x \cdot x\right) \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

   if 20 < (-.f64 (exp.f64 x) (exp.f64 (neg.f64 x)))

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 100.0%

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

      1. mul-1-neg 100.0%

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

      2. unsub-neg 100.0%

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

   5. Simplified 100.0%

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

Alternative 3: 93.3% accurate, 9.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \frac{x\_m \cdot \left(2 + \left(x\_m \cdot x\_m\right) \cdot \left(0.3333333333333333 + \left(x\_m \cdot x\_m\right) \cdot \left(0.016666666666666666 + \left(x\_m \cdot x\_m\right) \cdot 0.0003968253968253968\right)\right)\right)}{2} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (/
   (*
    x_m
    (+
     2.0
     (*
      (* x_m x_m)
      (+
       0.3333333333333333
       (*
        (* x_m x_m)
        (+ 0.016666666666666666 (* (* x_m x_m) 0.0003968253968253968)))))))
   2.0)))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * ((x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0);
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * ((x_m * (2.0d0 + ((x_m * x_m) * (0.3333333333333333d0 + ((x_m * x_m) * (0.016666666666666666d0 + ((x_m * x_m) * 0.0003968253968253968d0))))))) / 2.0d0)
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	return x_s * ((x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0);
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * ((x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0)

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * Float64(Float64(x_m * Float64(2.0 + Float64(Float64(x_m * x_m) * Float64(0.3333333333333333 + Float64(Float64(x_m * x_m) * Float64(0.016666666666666666 + Float64(Float64(x_m * x_m) * 0.0003968253968253968))))))) / 2.0))
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * ((x_m * (2.0 + ((x_m * x_m) * (0.3333333333333333 + ((x_m * x_m) * (0.016666666666666666 + ((x_m * x_m) * 0.0003968253968253968))))))) / 2.0);
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * N[(N[(x$95$m * N[(2.0 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(0.3333333333333333 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * N[(0.016666666666666666 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.0003968253968253968), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 56.3%

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

3. Taylor expanded in x around 0 93.1%

   \[\leadsto \frac{\color{blue}{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + 0.0003968253968253968 \cdot {x}^{2}\right)\right)\right)}}{2} \]
4. Step-by-step derivation

   1. *-commutative 93.1%

      \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{{x}^{2} \cdot 0.0003968253968253968}\right)\right)\right)}{2} \]

5. Simplified 93.1%

   \[\leadsto \frac{\color{blue}{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + {x}^{2} \cdot 0.0003968253968253968\right)\right)\right)}}{2} \]
6. Step-by-step derivation

   1. unpow2 93.1%

      \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

7. Applied egg-rr 93.1%

   \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]
8. Step-by-step derivation

   1. unpow2 93.1%

      \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

9. Applied egg-rr 93.1%

   \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + \color{blue}{\left(x \cdot x\right)} \cdot \left(0.016666666666666666 + \left(x \cdot x\right) \cdot 0.0003968253968253968\right)\right)\right)}{2} \]
10. Step-by-step derivation

    1. unpow2 93.1%

       \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

11. Applied egg-rr 93.1%

    \[\leadsto \frac{x \cdot \left(2 + \color{blue}{\left(x \cdot x\right)} \cdot \left(0.3333333333333333 + \left(x \cdot x\right) \cdot \left(0.016666666666666666 + \left(x \cdot x\right) \cdot 0.0003968253968253968\right)\right)\right)}{2} \]
12. Add Preprocessing

Alternative 4: 84.3% accurate, 18.7× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \frac{x\_m \cdot \left(2 + \left(x\_m \cdot x\_m\right) \cdot 0.3333333333333333\right)}{2} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m)
 :precision binary64
 (* x_s (/ (* x_m (+ 2.0 (* (* x_m x_m) 0.3333333333333333))) 2.0)))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * ((x_m * (2.0 + ((x_m * x_m) * 0.3333333333333333))) / 2.0);
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * ((x_m * (2.0d0 + ((x_m * x_m) * 0.3333333333333333d0))) / 2.0d0)
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	return x_s * ((x_m * (2.0 + ((x_m * x_m) * 0.3333333333333333))) / 2.0);
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * ((x_m * (2.0 + ((x_m * x_m) * 0.3333333333333333))) / 2.0)

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * Float64(Float64(x_m * Float64(2.0 + Float64(Float64(x_m * x_m) * 0.3333333333333333))) / 2.0))
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * ((x_m * (2.0 + ((x_m * x_m) * 0.3333333333333333))) / 2.0);
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * N[(N[(x$95$m * N[(2.0 + N[(N[(x$95$m * x$95$m), $MachinePrecision] * 0.3333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 56.3%

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

3. Taylor expanded in x around 0 84.2%

   \[\leadsto \frac{\color{blue}{x \cdot \left(2 + 0.3333333333333333 \cdot {x}^{2}\right)}}{2} \]
4. Step-by-step derivation

   1. unpow2 93.1%

      \[\leadsto \frac{x \cdot \left(2 + {x}^{2} \cdot \left(0.3333333333333333 + {x}^{2} \cdot \left(0.016666666666666666 + \color{blue}{\left(x \cdot x\right)} \cdot 0.0003968253968253968\right)\right)\right)}{2} \]

5. Applied egg-rr 84.2%

   \[\leadsto \frac{x \cdot \left(2 + 0.3333333333333333 \cdot \color{blue}{\left(x \cdot x\right)}\right)}{2} \]

6. Final simplification 84.2%

   \[\leadsto \frac{x \cdot \left(2 + \left(x \cdot x\right) \cdot 0.3333333333333333\right)}{2} \]
7. Add Preprocessing

Alternative 5: 52.1% accurate, 206.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot x\_m \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m) :precision binary64 (* x_s x_m))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * x_m;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * x_m
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	return x_s * x_m;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * x_m

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * x_m)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * x_m;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * x$95$m), $MachinePrecision]

Derivation

1. Initial program 56.3%

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

3. Taylor expanded in x around 0 84.2%

   \[\leadsto \frac{\color{blue}{x \cdot \left(2 + 0.3333333333333333 \cdot {x}^{2}\right)}}{2} \]

4. Taylor expanded in x around 0 50.0%

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

Alternative 6: 4.3% accurate, 206.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot 13 \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 #s(literal 1 binary64) x)
(FPCore (x_s x_m) :precision binary64 (* x_s 13.0))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * 13.0;
}

Fortran

x\_m = abs(x)
x\_s = copysign(1.0d0, x)
real(8) function code(x_s, x_m)
    real(8), intent (in) :: x_s
    real(8), intent (in) :: x_m
    code = x_s * 13.0d0
end function

Java

x\_m = Math.abs(x);
x\_s = Math.copySign(1.0, x);
public static double code(double x_s, double x_m) {
	return x_s * 13.0;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * 13.0

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * 13.0)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * 13.0;
end

Wolfram

x\_m = N[Abs[x], $MachinePrecision]
x\_s = N[With[{TMP1 = Abs[1.0], TMP2 = Sign[x]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision]
code[x$95$s_, x$95$m_] := N[(x$95$s * 13.0), $MachinePrecision]

Derivation

1. Initial program 56.3%

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

4. Applied egg-rr 28.3%

   \[\leadsto \frac{\color{blue}{27} - e^{-x}}{2} \]

5. Taylor expanded in x around 0 2.7%

   \[\leadsto \color{blue}{13} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024191 
(FPCore (x)
  :name "Hyperbolic sine"
  :precision binary64
  (/ (- (exp x) (exp (- x))) 2.0))