Average Error: 32.9 → 7.9
Time: 14.6s
Precision: binary64
\[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]
↓
\[\begin{array}{l} t_0 := \frac{\mathsf{log1p}\left(x\right)}{n}\\ t_1 := \frac{\log x}{n}\\ \mathbf{if}\;x \leq 2.0394736518688587 \cdot 10^{-286}:\\ \;\;\;\;e^{t_0} - {x}^{\left(\frac{1}{n}\right)}\\ \mathbf{elif}\;x \leq 95875.6746204785:\\ \;\;\;\;\mathsf{fma}\left(0.5, \frac{{\left(\mathsf{log1p}\left(x\right)\right)}^{2}}{n \cdot n}, \mathsf{fma}\left(0.16666666666666666, {t_0}^{3}, t_0\right)\right) - \mathsf{fma}\left(0.16666666666666666, {t_1}^{3}, \mathsf{fma}\left(0.5, \frac{{\log x}^{2}}{n \cdot n}, t_1\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{t_1}}{x \cdot n}\\ \end{array} \]
(FPCore (x n)
 :precision binary64
 (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n))))
↓
(FPCore (x n)
 :precision binary64
 (let* ((t_0 (/ (log1p x) n)) (t_1 (/ (log x) n)))
   (if (<= x 2.0394736518688587e-286)
     (- (exp t_0) (pow x (/ 1.0 n)))
     (if (<= x 95875.6746204785)
       (-
        (fma
         0.5
         (/ (pow (log1p x) 2.0) (* n n))
         (fma 0.16666666666666666 (pow t_0 3.0) t_0))
        (fma
         0.16666666666666666
         (pow t_1 3.0)
         (fma 0.5 (/ (pow (log x) 2.0) (* n n)) t_1)))
       (/ (exp t_1) (* x n))))))
double code(double x, double n) {
	return pow((x + 1.0), (1.0 / n)) - pow(x, (1.0 / n));
}
↓
double code(double x, double n) {
	double t_0 = log1p(x) / n;
	double t_1 = log(x) / n;
	double tmp;
	if (x <= 2.0394736518688587e-286) {
		tmp = exp(t_0) - pow(x, (1.0 / n));
	} else if (x <= 95875.6746204785) {
		tmp = fma(0.5, (pow(log1p(x), 2.0) / (n * n)), fma(0.16666666666666666, pow(t_0, 3.0), t_0)) - fma(0.16666666666666666, pow(t_1, 3.0), fma(0.5, (pow(log(x), 2.0) / (n * n)), t_1));
	} else {
		tmp = exp(t_1) / (x * n);
	}
	return tmp;
}

function code(x, n)
	return Float64((Float64(x + 1.0) ^ Float64(1.0 / n)) - (x ^ Float64(1.0 / n)))
end
↓
function code(x, n)
	t_0 = Float64(log1p(x) / n)
	t_1 = Float64(log(x) / n)
	tmp = 0.0
	if (x <= 2.0394736518688587e-286)
		tmp = Float64(exp(t_0) - (x ^ Float64(1.0 / n)));
	elseif (x <= 95875.6746204785)
		tmp = Float64(fma(0.5, Float64((log1p(x) ^ 2.0) / Float64(n * n)), fma(0.16666666666666666, (t_0 ^ 3.0), t_0)) - fma(0.16666666666666666, (t_1 ^ 3.0), fma(0.5, Float64((log(x) ^ 2.0) / Float64(n * n)), t_1)));
	else
		tmp = Float64(exp(t_1) / Float64(x * n));
	end
	return tmp
end

code[x_, n_] := N[(N[Power[N[(x + 1.0), $MachinePrecision], N[(1.0 / n), $MachinePrecision]], $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]
↓
code[x_, n_] := Block[{t$95$0 = N[(N[Log[1 + x], $MachinePrecision] / n), $MachinePrecision]}, Block[{t$95$1 = N[(N[Log[x], $MachinePrecision] / n), $MachinePrecision]}, If[LessEqual[x, 2.0394736518688587e-286], N[(N[Exp[t$95$0], $MachinePrecision] - N[Power[x, N[(1.0 / n), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], If[LessEqual[x, 95875.6746204785], N[(N[(0.5 * N[(N[Power[N[Log[1 + x], $MachinePrecision], 2.0], $MachinePrecision] / N[(n * n), $MachinePrecision]), $MachinePrecision] + N[(0.16666666666666666 * N[Power[t$95$0, 3.0], $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision] - N[(0.16666666666666666 * N[Power[t$95$1, 3.0], $MachinePrecision] + N[(0.5 * N[(N[Power[N[Log[x], $MachinePrecision], 2.0], $MachinePrecision] / N[(n * n), $MachinePrecision]), $MachinePrecision] + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Exp[t$95$1], $MachinePrecision] / N[(x * n), $MachinePrecision]), $MachinePrecision]]]]]

{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)}
↓
\begin{array}{l}
t_0 := \frac{\mathsf{log1p}\left(x\right)}{n}\\
t_1 := \frac{\log x}{n}\\
\mathbf{if}\;x \leq 2.0394736518688587 \cdot 10^{-286}:\\
\;\;\;\;e^{t_0} - {x}^{\left(\frac{1}{n}\right)}\\

\mathbf{elif}\;x \leq 95875.6746204785:\\
\;\;\;\;\mathsf{fma}\left(0.5, \frac{{\left(\mathsf{log1p}\left(x\right)\right)}^{2}}{n \cdot n}, \mathsf{fma}\left(0.16666666666666666, {t_0}^{3}, t_0\right)\right) - \mathsf{fma}\left(0.16666666666666666, {t_1}^{3}, \mathsf{fma}\left(0.5, \frac{{\log x}^{2}}{n \cdot n}, t_1\right)\right)\\

\mathbf{else}:\\
\;\;\;\;\frac{e^{t_1}}{x \cdot n}\\


\end{array}

Error

Bits error versus x

Bits error versus n

Derivation

  1. Split input into 3 regimes

  2. if x < 2.0394736518688587e-286

    1. Initial program 40.0

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

    2. Taylor expanded in n around 0 40.0

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

    3. Simplified39.3

      \[\leadsto \color{blue}{e^{\frac{\mathsf{log1p}\left(x\right)}{n}} - {x}^{\left(\frac{1}{n}\right)}} \]

    if 2.0394736518688587e-286 < x < 95875.674620478501

    1. Initial program 47.3

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

    2. Taylor expanded in n around inf 13.3

      \[\leadsto \color{blue}{\left(0.5 \cdot \frac{{\log \left(1 + x\right)}^{2}}{{n}^{2}} + \left(0.16666666666666666 \cdot \frac{{\log \left(1 + x\right)}^{3}}{{n}^{3}} + \frac{\log \left(1 + x\right)}{n}\right)\right) - \left(0.16666666666666666 \cdot \frac{{\log x}^{3}}{{n}^{3}} + \left(0.5 \cdot \frac{{\log x}^{2}}{{n}^{2}} + \frac{\log x}{n}\right)\right)} \]

    3. Simplified13.3

      \[\leadsto \color{blue}{\mathsf{fma}\left(0.5, \frac{{\left(\mathsf{log1p}\left(x\right)\right)}^{2}}{n \cdot n}, \mathsf{fma}\left(0.16666666666666666, {\left(\frac{\mathsf{log1p}\left(x\right)}{n}\right)}^{3}, \frac{\mathsf{log1p}\left(x\right)}{n}\right)\right) - \mathsf{fma}\left(0.16666666666666666, {\left(\frac{\log x}{n}\right)}^{3}, \mathsf{fma}\left(0.5, \frac{{\log x}^{2}}{n \cdot n}, \frac{\log x}{n}\right)\right)} \]

    if 95875.674620478501 < x

    1. Initial program 21.1

      \[{\left(x + 1\right)}^{\left(\frac{1}{n}\right)} - {x}^{\left(\frac{1}{n}\right)} \]

    2. Taylor expanded in x around -inf 64.0

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

    3. Simplified1.3

      \[\leadsto \color{blue}{\frac{e^{\frac{0 + \log x}{n}}}{x \cdot n}} \]
  3. Recombined 3 regimes into one program.

  4. Final simplification7.9

    \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2.0394736518688587 \cdot 10^{-286}:\\ \;\;\;\;e^{\frac{\mathsf{log1p}\left(x\right)}{n}} - {x}^{\left(\frac{1}{n}\right)}\\ \mathbf{elif}\;x \leq 95875.6746204785:\\ \;\;\;\;\mathsf{fma}\left(0.5, \frac{{\left(\mathsf{log1p}\left(x\right)\right)}^{2}}{n \cdot n}, \mathsf{fma}\left(0.16666666666666666, {\left(\frac{\mathsf{log1p}\left(x\right)}{n}\right)}^{3}, \frac{\mathsf{log1p}\left(x\right)}{n}\right)\right) - \mathsf{fma}\left(0.16666666666666666, {\left(\frac{\log x}{n}\right)}^{3}, \mathsf{fma}\left(0.5, \frac{{\log x}^{2}}{n \cdot n}, \frac{\log x}{n}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{\frac{\log x}{n}}}{x \cdot n}\\ \end{array} \]

Reproduce

herbie shell --seed 2022131 
(FPCore (x n)
  :name "2nthrt (problem 3.4.6)"
  :precision binary64
  (- (pow (+ x 1.0) (/ 1.0 n)) (pow x (/ 1.0 n))))