Hyperbolic sine

Percentage Accurate: 53.6% → 99.8%

Time: 6.3s

Alternatives: 10

Speedup: 1.9×

• 48.8% 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: 53.6% 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: 99.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ \begin{array}{l} t_0 := e^{x\_m} - e^{-x\_m}\\ x\_s \cdot \begin{array}{l} \mathbf{if}\;t\_0 \leq 10^{-7}:\\ \;\;\;\;\frac{x\_m \cdot 2 + 0.3333333333333333 \cdot {x\_m}^{3}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{t\_0}{2}\\ \end{array} \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (let* ((t_0 (- (exp x_m) (exp (- x_m)))))
   (*
    x_s
    (if (<= t_0 1e-7)
      (/ (+ (* x_m 2.0) (* 0.3333333333333333 (pow x_m 3.0))) 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 <= 1e-7) {
		tmp = ((x_m * 2.0) + (0.3333333333333333 * pow(x_m, 3.0))) / 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 <= 1d-7) then
        tmp = ((x_m * 2.0d0) + (0.3333333333333333d0 * (x_m ** 3.0d0))) / 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 <= 1e-7) {
		tmp = ((x_m * 2.0) + (0.3333333333333333 * Math.pow(x_m, 3.0))) / 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 <= 1e-7:
		tmp = ((x_m * 2.0) + (0.3333333333333333 * math.pow(x_m, 3.0))) / 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 <= 1e-7)
		tmp = Float64(Float64(Float64(x_m * 2.0) + Float64(0.3333333333333333 * (x_m ^ 3.0))) / 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 <= 1e-7)
		tmp = ((x_m * 2.0) + (0.3333333333333333 * (x_m ^ 3.0))) / 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, 1e-7], N[(N[(N[(x$95$m * 2.0), $MachinePrecision] + N[(0.3333333333333333 * N[Power[x$95$m, 3.0], $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))) < 9.9999999999999995e-8

   1. Initial program 38.2%

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

   3. Taylor expanded in x around 0 88.1%

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

      1. distribute-rgt-in 88.2%

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

      2. fma-define 88.2%

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

      3. associate-*l* 88.2%

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

      4. pow-plus 88.2%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot \color{blue}{{x}^{\left(2 + 1\right)}}\right)}{2} \]

      5. metadata-eval 88.2%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{\color{blue}{3}}\right)}{2} \]

   5. Simplified 88.2%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{3}\right)}}{2} \]
   6. Step-by-step derivation

      1. fma-undefine 88.2%

         \[\leadsto \frac{\color{blue}{2 \cdot x + 0.3333333333333333 \cdot {x}^{3}}}{2} \]

      2. *-commutative 88.2%

         \[\leadsto \frac{\color{blue}{x \cdot 2} + 0.3333333333333333 \cdot {x}^{3}}{2} \]

   7. Applied egg-rr 88.2%

      \[\leadsto \frac{\color{blue}{x \cdot 2 + 0.3333333333333333 \cdot {x}^{3}}}{2} \]

   if 9.9999999999999995e-8 < (-.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. Final simplification 90.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;e^{x} - e^{-x} \leq 10^{-7}:\\ \;\;\;\;\frac{x \cdot 2 + 0.3333333333333333 \cdot {x}^{3}}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{x} - e^{-x}}{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 91.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 850000:\\ \;\;\;\;\frac{x\_m \cdot 2 + 0.3333333333333333 \cdot {x\_m}^{3}}{2}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{{x\_m}^{6} \cdot 0.027777777777777776}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 850000.0)
    (/ (+ (* x_m 2.0) (* 0.3333333333333333 (pow x_m 3.0))) 2.0)
    (sqrt (* (pow x_m 6.0) 0.027777777777777776)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 850000.0) {
		tmp = ((x_m * 2.0) + (0.3333333333333333 * pow(x_m, 3.0))) / 2.0;
	} else {
		tmp = sqrt((pow(x_m, 6.0) * 0.027777777777777776));
	}
	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 (x_m <= 850000.0d0) then
        tmp = ((x_m * 2.0d0) + (0.3333333333333333d0 * (x_m ** 3.0d0))) / 2.0d0
    else
        tmp = sqrt(((x_m ** 6.0d0) * 0.027777777777777776d0))
    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 (x_m <= 850000.0) {
		tmp = ((x_m * 2.0) + (0.3333333333333333 * Math.pow(x_m, 3.0))) / 2.0;
	} else {
		tmp = Math.sqrt((Math.pow(x_m, 6.0) * 0.027777777777777776));
	}
	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 x_m <= 850000.0:
		tmp = ((x_m * 2.0) + (0.3333333333333333 * math.pow(x_m, 3.0))) / 2.0
	else:
		tmp = math.sqrt((math.pow(x_m, 6.0) * 0.027777777777777776))
	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 (x_m <= 850000.0)
		tmp = Float64(Float64(Float64(x_m * 2.0) + Float64(0.3333333333333333 * (x_m ^ 3.0))) / 2.0);
	else
		tmp = sqrt(Float64((x_m ^ 6.0) * 0.027777777777777776));
	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 (x_m <= 850000.0)
		tmp = ((x_m * 2.0) + (0.3333333333333333 * (x_m ^ 3.0))) / 2.0;
	else
		tmp = sqrt(((x_m ^ 6.0) * 0.027777777777777776));
	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[x$95$m, 850000.0], N[(N[(N[(x$95$m * 2.0), $MachinePrecision] + N[(0.3333333333333333 * N[Power[x$95$m, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision], N[Sqrt[N[(N[Power[x$95$m, 6.0], $MachinePrecision] * 0.027777777777777776), $MachinePrecision]], $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 8.5e5

   1. Initial program 39.1%

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

   3. Taylor expanded in x around 0 86.9%

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

      1. distribute-rgt-in 87.0%

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

      2. fma-define 87.0%

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

      3. associate-*l* 87.0%

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

      4. pow-plus 87.0%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot \color{blue}{{x}^{\left(2 + 1\right)}}\right)}{2} \]

      5. metadata-eval 87.0%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{\color{blue}{3}}\right)}{2} \]

   5. Simplified 87.0%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{3}\right)}}{2} \]
   6. Step-by-step derivation

      1. fma-undefine 87.0%

         \[\leadsto \frac{\color{blue}{2 \cdot x + 0.3333333333333333 \cdot {x}^{3}}}{2} \]

      2. *-commutative 87.0%

         \[\leadsto \frac{\color{blue}{x \cdot 2} + 0.3333333333333333 \cdot {x}^{3}}{2} \]

   7. Applied egg-rr 87.0%

      \[\leadsto \frac{\color{blue}{x \cdot 2 + 0.3333333333333333 \cdot {x}^{3}}}{2} \]

   if 8.5e5 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 65.4%

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

      1. distribute-rgt-in 65.4%

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

      2. fma-define 65.4%

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

      3. associate-*l* 65.4%

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

      4. pow-plus 65.4%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot \color{blue}{{x}^{\left(2 + 1\right)}}\right)}{2} \]

      5. metadata-eval 65.4%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{\color{blue}{3}}\right)}{2} \]

   5. Simplified 65.4%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{3}\right)}}{2} \]

   6. Taylor expanded in x around inf 65.4%

      \[\leadsto \frac{\color{blue}{0.3333333333333333 \cdot {x}^{3}}}{2} \]
   7. Step-by-step derivation

      1. add-sqr-sqrt 65.4%

         \[\leadsto \color{blue}{\sqrt{\frac{0.3333333333333333 \cdot {x}^{3}}{2}} \cdot \sqrt{\frac{0.3333333333333333 \cdot {x}^{3}}{2}}} \]

      2. sqrt-unprod 83.2%

         \[\leadsto \color{blue}{\sqrt{\frac{0.3333333333333333 \cdot {x}^{3}}{2} \cdot \frac{0.3333333333333333 \cdot {x}^{3}}{2}}} \]

      3. div-inv 83.2%

         \[\leadsto \sqrt{\color{blue}{\left(\left(0.3333333333333333 \cdot {x}^{3}\right) \cdot \frac{1}{2}\right)} \cdot \frac{0.3333333333333333 \cdot {x}^{3}}{2}} \]

      4. div-inv 83.2%

         \[\leadsto \sqrt{\left(\left(0.3333333333333333 \cdot {x}^{3}\right) \cdot \frac{1}{2}\right) \cdot \color{blue}{\left(\left(0.3333333333333333 \cdot {x}^{3}\right) \cdot \frac{1}{2}\right)}} \]

      5. swap-sqr 83.2%

         \[\leadsto \sqrt{\color{blue}{\left(\left(0.3333333333333333 \cdot {x}^{3}\right) \cdot \left(0.3333333333333333 \cdot {x}^{3}\right)\right) \cdot \left(\frac{1}{2} \cdot \frac{1}{2}\right)}} \]

      6. *-commutative 83.2%

         \[\leadsto \sqrt{\left(\color{blue}{\left({x}^{3} \cdot 0.3333333333333333\right)} \cdot \left(0.3333333333333333 \cdot {x}^{3}\right)\right) \cdot \left(\frac{1}{2} \cdot \frac{1}{2}\right)} \]

      7. *-commutative 83.2%

         \[\leadsto \sqrt{\left(\left({x}^{3} \cdot 0.3333333333333333\right) \cdot \color{blue}{\left({x}^{3} \cdot 0.3333333333333333\right)}\right) \cdot \left(\frac{1}{2} \cdot \frac{1}{2}\right)} \]

      8. swap-sqr 83.2%

         \[\leadsto \sqrt{\color{blue}{\left(\left({x}^{3} \cdot {x}^{3}\right) \cdot \left(0.3333333333333333 \cdot 0.3333333333333333\right)\right)} \cdot \left(\frac{1}{2} \cdot \frac{1}{2}\right)} \]

      9. pow-prod-up 83.2%

         \[\leadsto \sqrt{\left(\color{blue}{{x}^{\left(3 + 3\right)}} \cdot \left(0.3333333333333333 \cdot 0.3333333333333333\right)\right) \cdot \left(\frac{1}{2} \cdot \frac{1}{2}\right)} \]

      10. metadata-eval 83.2%

          \[\leadsto \sqrt{\left({x}^{\color{blue}{6}} \cdot \left(0.3333333333333333 \cdot 0.3333333333333333\right)\right) \cdot \left(\frac{1}{2} \cdot \frac{1}{2}\right)} \]

      11. metadata-eval 83.2%

          \[\leadsto \sqrt{\left({x}^{6} \cdot \color{blue}{0.1111111111111111}\right) \cdot \left(\frac{1}{2} \cdot \frac{1}{2}\right)} \]

      12. metadata-eval 83.2%

          \[\leadsto \sqrt{\left({x}^{6} \cdot 0.1111111111111111\right) \cdot \left(\color{blue}{0.5} \cdot \frac{1}{2}\right)} \]

      13. metadata-eval 83.2%

          \[\leadsto \sqrt{\left({x}^{6} \cdot 0.1111111111111111\right) \cdot \left(0.5 \cdot \color{blue}{0.5}\right)} \]

      14. metadata-eval 83.2%

          \[\leadsto \sqrt{\left({x}^{6} \cdot 0.1111111111111111\right) \cdot \color{blue}{0.25}} \]

   8. Applied egg-rr 83.2%

      \[\leadsto \color{blue}{\sqrt{\left({x}^{6} \cdot 0.1111111111111111\right) \cdot 0.25}} \]
   9. Step-by-step derivation

      1. associate-*l* 83.2%

         \[\leadsto \sqrt{\color{blue}{{x}^{6} \cdot \left(0.1111111111111111 \cdot 0.25\right)}} \]

      2. metadata-eval 83.2%

         \[\leadsto \sqrt{{x}^{6} \cdot \color{blue}{0.027777777777777776}} \]

   10. Simplified 83.2%

       \[\leadsto \color{blue}{\sqrt{{x}^{6} \cdot 0.027777777777777776}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 86.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 850000:\\ \;\;\;\;\frac{x \cdot 2 + 0.3333333333333333 \cdot {x}^{3}}{2}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{{x}^{6} \cdot 0.027777777777777776}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 84.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \frac{x\_m \cdot 2 + 0.3333333333333333 \cdot {x\_m}^{3}}{2} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (* x_s (/ (+ (* x_m 2.0) (* 0.3333333333333333 (pow x_m 3.0))) 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) + (0.3333333333333333 * pow(x_m, 3.0))) / 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) + (0.3333333333333333d0 * (x_m ** 3.0d0))) / 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) + (0.3333333333333333 * Math.pow(x_m, 3.0))) / 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) + (0.3333333333333333 * math.pow(x_m, 3.0))) / 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(Float64(x_m * 2.0) + Float64(0.3333333333333333 * (x_m ^ 3.0))) / 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) + (0.3333333333333333 * (x_m ^ 3.0))) / 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[(N[(x$95$m * 2.0), $MachinePrecision] + N[(0.3333333333333333 * N[Power[x$95$m, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.7%

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

3. Taylor expanded in x around 0 82.2%

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

   1. distribute-rgt-in 82.2%

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

   2. fma-define 82.2%

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

   3. associate-*l* 82.2%

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

   4. pow-plus 82.2%

      \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot \color{blue}{{x}^{\left(2 + 1\right)}}\right)}{2} \]

   5. metadata-eval 82.2%

      \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{\color{blue}{3}}\right)}{2} \]

5. Simplified 82.2%

   \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{3}\right)}}{2} \]
6. Step-by-step derivation

   1. fma-undefine 82.2%

      \[\leadsto \frac{\color{blue}{2 \cdot x + 0.3333333333333333 \cdot {x}^{3}}}{2} \]

   2. *-commutative 82.2%

      \[\leadsto \frac{\color{blue}{x \cdot 2} + 0.3333333333333333 \cdot {x}^{3}}{2} \]

7. Applied egg-rr 82.2%

   \[\leadsto \frac{\color{blue}{x \cdot 2 + 0.3333333333333333 \cdot {x}^{3}}}{2} \]

8. Final simplification 82.2%

   \[\leadsto \frac{x \cdot 2 + 0.3333333333333333 \cdot {x}^{3}}{2} \]
9. Add Preprocessing

Alternative 4: 83.7% accurate, 1.9× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 2.5:\\ \;\;\;\;\frac{x\_m \cdot 2}{2}\\ \mathbf{else}:\\ \;\;\;\;{x\_m}^{3} \cdot 0.16666666666666666\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 2.5) (/ (* x_m 2.0) 2.0) (* (pow x_m 3.0) 0.16666666666666666))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 2.5) {
		tmp = (x_m * 2.0) / 2.0;
	} else {
		tmp = pow(x_m, 3.0) * 0.16666666666666666;
	}
	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 (x_m <= 2.5d0) then
        tmp = (x_m * 2.0d0) / 2.0d0
    else
        tmp = (x_m ** 3.0d0) * 0.16666666666666666d0
    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 (x_m <= 2.5) {
		tmp = (x_m * 2.0) / 2.0;
	} else {
		tmp = Math.pow(x_m, 3.0) * 0.16666666666666666;
	}
	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 x_m <= 2.5:
		tmp = (x_m * 2.0) / 2.0
	else:
		tmp = math.pow(x_m, 3.0) * 0.16666666666666666
	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 (x_m <= 2.5)
		tmp = Float64(Float64(x_m * 2.0) / 2.0);
	else
		tmp = Float64((x_m ^ 3.0) * 0.16666666666666666);
	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 (x_m <= 2.5)
		tmp = (x_m * 2.0) / 2.0;
	else
		tmp = (x_m ^ 3.0) * 0.16666666666666666;
	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[x$95$m, 2.5], N[(N[(x$95$m * 2.0), $MachinePrecision] / 2.0), $MachinePrecision], N[(N[Power[x$95$m, 3.0], $MachinePrecision] * 0.16666666666666666), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 2.5

   1. Initial program 38.2%

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

   3. Taylor expanded in x around 0 68.6%

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

   if 2.5 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 62.6%

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

      1. distribute-rgt-in 62.6%

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

      2. fma-define 62.6%

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

      3. associate-*l* 62.6%

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

      4. pow-plus 62.6%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot \color{blue}{{x}^{\left(2 + 1\right)}}\right)}{2} \]

      5. metadata-eval 62.6%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{\color{blue}{3}}\right)}{2} \]

   5. Simplified 62.6%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{3}\right)}}{2} \]

   6. Taylor expanded in x around inf 62.6%

      \[\leadsto \frac{\color{blue}{0.3333333333333333 \cdot {x}^{3}}}{2} \]
   7. Step-by-step derivation

      1. *-commutative 62.6%

         \[\leadsto \frac{\color{blue}{{x}^{3} \cdot 0.3333333333333333}}{2} \]

      2. associate-/l* 62.6%

         \[\leadsto \color{blue}{{x}^{3} \cdot \frac{0.3333333333333333}{2}} \]

      3. metadata-eval 62.6%

         \[\leadsto {x}^{3} \cdot \color{blue}{0.16666666666666666} \]

   8. Applied egg-rr 62.6%

      \[\leadsto \color{blue}{{x}^{3} \cdot 0.16666666666666666} \]
3. Recombined 2 regimes into one program.

4. Final simplification 67.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2.5:\\ \;\;\;\;\frac{x \cdot 2}{2}\\ \mathbf{else}:\\ \;\;\;\;{x}^{3} \cdot 0.16666666666666666\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 83.7% accurate, 1.9× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot \begin{array}{l} \mathbf{if}\;x\_m \leq 2.5:\\ \;\;\;\;\frac{x\_m \cdot 2}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.16666666666666666}{{x\_m}^{-3}}\\ \end{array} \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (*
  x_s
  (if (<= x_m 2.5)
    (/ (* x_m 2.0) 2.0)
    (/ 0.16666666666666666 (pow x_m -3.0)))))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	double tmp;
	if (x_m <= 2.5) {
		tmp = (x_m * 2.0) / 2.0;
	} else {
		tmp = 0.16666666666666666 / pow(x_m, -3.0);
	}
	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 (x_m <= 2.5d0) then
        tmp = (x_m * 2.0d0) / 2.0d0
    else
        tmp = 0.16666666666666666d0 / (x_m ** (-3.0d0))
    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 (x_m <= 2.5) {
		tmp = (x_m * 2.0) / 2.0;
	} else {
		tmp = 0.16666666666666666 / Math.pow(x_m, -3.0);
	}
	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 x_m <= 2.5:
		tmp = (x_m * 2.0) / 2.0
	else:
		tmp = 0.16666666666666666 / math.pow(x_m, -3.0)
	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 (x_m <= 2.5)
		tmp = Float64(Float64(x_m * 2.0) / 2.0);
	else
		tmp = Float64(0.16666666666666666 / (x_m ^ -3.0));
	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 (x_m <= 2.5)
		tmp = (x_m * 2.0) / 2.0;
	else
		tmp = 0.16666666666666666 / (x_m ^ -3.0);
	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[x$95$m, 2.5], N[(N[(x$95$m * 2.0), $MachinePrecision] / 2.0), $MachinePrecision], N[(0.16666666666666666 / N[Power[x$95$m, -3.0], $MachinePrecision]), $MachinePrecision]]), $MachinePrecision]

Derivation

1. Split input into 2 regimes

2. if x < 2.5

   1. Initial program 38.2%

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

   3. Taylor expanded in x around 0 68.6%

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

   if 2.5 < x

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0 62.6%

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

      1. distribute-rgt-in 62.6%

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

      2. fma-define 62.6%

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

      3. associate-*l* 62.6%

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

      4. pow-plus 62.6%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot \color{blue}{{x}^{\left(2 + 1\right)}}\right)}{2} \]

      5. metadata-eval 62.6%

         \[\leadsto \frac{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{\color{blue}{3}}\right)}{2} \]

   5. Simplified 62.6%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(2, x, 0.3333333333333333 \cdot {x}^{3}\right)}}{2} \]

   6. Taylor expanded in x around inf 62.6%

      \[\leadsto \frac{\color{blue}{0.3333333333333333 \cdot {x}^{3}}}{2} \]
   7. Step-by-step derivation

      1. *-commutative 62.6%

         \[\leadsto \frac{\color{blue}{{x}^{3} \cdot 0.3333333333333333}}{2} \]

      2. associate-/l* 62.6%

         \[\leadsto \color{blue}{{x}^{3} \cdot \frac{0.3333333333333333}{2}} \]

      3. metadata-eval 62.6%

         \[\leadsto {x}^{3} \cdot \color{blue}{0.16666666666666666} \]

   8. Applied egg-rr 62.6%

      \[\leadsto \color{blue}{{x}^{3} \cdot 0.16666666666666666} \]
   9. Step-by-step derivation

      1. metadata-eval 62.6%

         \[\leadsto {x}^{3} \cdot \color{blue}{\frac{1}{6}} \]

      2. div-inv 62.6%

         \[\leadsto \color{blue}{\frac{{x}^{3}}{6}} \]

      3. clear-num 62.6%

         \[\leadsto \color{blue}{\frac{1}{\frac{6}{{x}^{3}}}} \]

      4. div-inv 62.6%

         \[\leadsto \frac{1}{\color{blue}{6 \cdot \frac{1}{{x}^{3}}}} \]

      5. associate-/r* 62.6%

         \[\leadsto \color{blue}{\frac{\frac{1}{6}}{\frac{1}{{x}^{3}}}} \]

      6. metadata-eval 62.6%

         \[\leadsto \frac{\color{blue}{0.16666666666666666}}{\frac{1}{{x}^{3}}} \]

      7. pow-flip 62.6%

         \[\leadsto \frac{0.16666666666666666}{\color{blue}{{x}^{\left(-3\right)}}} \]

      8. metadata-eval 62.6%

         \[\leadsto \frac{0.16666666666666666}{{x}^{\color{blue}{-3}}} \]

   10. Applied egg-rr 62.6%

       \[\leadsto \color{blue}{\frac{0.16666666666666666}{{x}^{-3}}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 67.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2.5:\\ \;\;\;\;\frac{x \cdot 2}{2}\\ \mathbf{else}:\\ \;\;\;\;\frac{0.16666666666666666}{{x}^{-3}}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 84.0% accurate, 1.9× speedup

Math

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

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m)
 :precision binary64
 (* x_s (/ (* x_m (fma (* x_m 0.3333333333333333) x_m 2.0)) 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 * fma((x_m * 0.3333333333333333), x_m, 2.0)) / 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 * fma(Float64(x_m * 0.3333333333333333), x_m, 2.0)) / 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[(N[(x$95$m * 0.3333333333333333), $MachinePrecision] * x$95$m + 2.0), $MachinePrecision]), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.7%

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

3. Taylor expanded in x around 0 82.2%

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

   1. +-commutative 82.2%

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

   2. unpow2 82.2%

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

   3. associate-*r* 82.2%

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

   4. fma-define 82.2%

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

5. Applied egg-rr 82.2%

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

6. Final simplification 82.2%

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

Alternative 7: 52.9% accurate, 41.2× speedup

Math

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

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m) :precision binary64 (* x_s (/ (* x_m 2.0) 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) / 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) / 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) / 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) / 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 * 2.0) / 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) / 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 * 2.0), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.7%

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

3. Taylor expanded in x around 0 53.8%

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

4. Final simplification 53.8%

   \[\leadsto \frac{x \cdot 2}{2} \]
5. Add Preprocessing

Alternative 8: 3.5% accurate, 206.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot 0 \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m) :precision binary64 (* x_s 0.0))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * 0.0;
}

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 * 0.0d0
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 * 0.0;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * 0.0

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * 0.0)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * 0.0;
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 * 0.0), $MachinePrecision]

Derivation

1. Initial program 52.7%

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

4. Applied egg-rr 3.7%

   \[\leadsto \frac{\color{blue}{0}}{2} \]

5. Final simplification 3.7%

   \[\leadsto 0 \]
6. Add Preprocessing

Alternative 9: 4.3% accurate, 206.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot 0.25 \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m) :precision binary64 (* x_s 0.25))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * 0.25;
}

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 * 0.25d0
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 * 0.25;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * 0.25

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * 0.25)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * 0.25;
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 * 0.25), $MachinePrecision]

Derivation

1. Initial program 52.7%

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

4. Applied egg-rr 2.8%

   \[\leadsto \frac{\color{blue}{0.5}}{2} \]

5. Final simplification 2.8%

   \[\leadsto 0.25 \]
6. Add Preprocessing

Alternative 10: 4.3% accurate, 206.0× speedup

Math

\[\begin{array}{l} x\_m = \left|x\right| \\ x\_s = \mathsf{copysign}\left(1, x\right) \\ x\_s \cdot 4 \end{array} \]

FPCore

x\_m = (fabs.f64 x)
x\_s = (copysign.f64 1 x)
(FPCore (x_s x_m) :precision binary64 (* x_s 4.0))

C

x\_m = fabs(x);
x\_s = copysign(1.0, x);
double code(double x_s, double x_m) {
	return x_s * 4.0;
}

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 * 4.0d0
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 * 4.0;
}

Python

x\_m = math.fabs(x)
x\_s = math.copysign(1.0, x)
def code(x_s, x_m):
	return x_s * 4.0

Julia

x\_m = abs(x)
x\_s = copysign(1.0, x)
function code(x_s, x_m)
	return Float64(x_s * 4.0)
end

MATLAB

x\_m = abs(x);
x\_s = sign(x) * abs(1.0);
function tmp = code(x_s, x_m)
	tmp = x_s * 4.0;
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 * 4.0), $MachinePrecision]

Derivation

1. Initial program 52.7%

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

4. Applied egg-rr 2.8%

   \[\leadsto \frac{\color{blue}{8}}{2} \]

5. Final simplification 2.8%

   \[\leadsto 4 \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024086 
(FPCore (x)
  :name "Hyperbolic sine"
  :precision binary64
  (/ (- (exp x) (exp (- x))) 2.0))