Average Error: 32.2 → 0.3
Time: 4.5s
Precision: binary64
\[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]
↓
\[\begin{array}{l} t_0 := \log \left(\sqrt{{\left(\mathsf{hypot}\left(re, im\right)\right)}^{\left({\log 10}^{-0.5}\right)}}\right)\\ \frac{1}{\sqrt{\log 10}} \cdot \left(t_0 + t_0\right) \end{array} \]
(FPCore (re im)
 :precision binary64
 (/ (log (sqrt (+ (* re re) (* im im)))) (log 10.0)))
↓
(FPCore (re im)
 :precision binary64
 (let* ((t_0 (log (sqrt (pow (hypot re im) (pow (log 10.0) -0.5))))))
   (* (/ 1.0 (sqrt (log 10.0))) (+ t_0 t_0))))
double code(double re, double im) {
	return log(sqrt(((re * re) + (im * im)))) / log(10.0);
}
↓
double code(double re, double im) {
	double t_0 = log(sqrt(pow(hypot(re, im), pow(log(10.0), -0.5))));
	return (1.0 / sqrt(log(10.0))) * (t_0 + t_0);
}

public static double code(double re, double im) {
	return Math.log(Math.sqrt(((re * re) + (im * im)))) / Math.log(10.0);
}
↓
public static double code(double re, double im) {
	double t_0 = Math.log(Math.sqrt(Math.pow(Math.hypot(re, im), Math.pow(Math.log(10.0), -0.5))));
	return (1.0 / Math.sqrt(Math.log(10.0))) * (t_0 + t_0);
}

def code(re, im):
	return math.log(math.sqrt(((re * re) + (im * im)))) / math.log(10.0)
↓
def code(re, im):
	t_0 = math.log(math.sqrt(math.pow(math.hypot(re, im), math.pow(math.log(10.0), -0.5))))
	return (1.0 / math.sqrt(math.log(10.0))) * (t_0 + t_0)

function code(re, im)
	return Float64(log(sqrt(Float64(Float64(re * re) + Float64(im * im)))) / log(10.0))
end
↓
function code(re, im)
	t_0 = log(sqrt((hypot(re, im) ^ (log(10.0) ^ -0.5))))
	return Float64(Float64(1.0 / sqrt(log(10.0))) * Float64(t_0 + t_0))
end

function tmp = code(re, im)
	tmp = log(sqrt(((re * re) + (im * im)))) / log(10.0);
end
↓
function tmp = code(re, im)
	t_0 = log(sqrt((hypot(re, im) ^ (log(10.0) ^ -0.5))));
	tmp = (1.0 / sqrt(log(10.0))) * (t_0 + t_0);
end

code[re_, im_] := N[(N[Log[N[Sqrt[N[(N[(re * re), $MachinePrecision] + N[(im * im), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] / N[Log[10.0], $MachinePrecision]), $MachinePrecision]
↓
code[re_, im_] := Block[{t$95$0 = N[Log[N[Sqrt[N[Power[N[Sqrt[re ^ 2 + im ^ 2], $MachinePrecision], N[Power[N[Log[10.0], $MachinePrecision], -0.5], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]], $MachinePrecision]}, N[(N[(1.0 / N[Sqrt[N[Log[10.0], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(t$95$0 + t$95$0), $MachinePrecision]), $MachinePrecision]]

\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10}
↓
\begin{array}{l}
t_0 := \log \left(\sqrt{{\left(\mathsf{hypot}\left(re, im\right)\right)}^{\left({\log 10}^{-0.5}\right)}}\right)\\
\frac{1}{\sqrt{\log 10}} \cdot \left(t_0 + t_0\right)
\end{array}

Error

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Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 32.2

    \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]

  2. Simplified0.6

    \[\leadsto \color{blue}{\frac{\log \left(\mathsf{hypot}\left(re, im\right)\right)}{\log 10}} \]

  3. Applied egg-rr0.5

    \[\leadsto \color{blue}{\frac{1}{\sqrt{\log 10}} \cdot \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right)}{\sqrt{\log 10}}} \]

  4. Applied egg-rr0.3

    \[\leadsto \frac{1}{\sqrt{\log 10}} \cdot \color{blue}{\left(\log \left(\sqrt{{\left(\mathsf{hypot}\left(re, im\right)\right)}^{\left({\log 10}^{-0.5}\right)}}\right) + \log \left(\sqrt{{\left(\mathsf{hypot}\left(re, im\right)\right)}^{\left({\log 10}^{-0.5}\right)}}\right)\right)} \]

  5. Final simplification0.3

    \[\leadsto \frac{1}{\sqrt{\log 10}} \cdot \left(\log \left(\sqrt{{\left(\mathsf{hypot}\left(re, im\right)\right)}^{\left({\log 10}^{-0.5}\right)}}\right) + \log \left(\sqrt{{\left(\mathsf{hypot}\left(re, im\right)\right)}^{\left({\log 10}^{-0.5}\right)}}\right)\right) \]

Reproduce

herbie shell --seed 2022192 
(FPCore (re im)
  :name "math.log10 on complex, real part"
  :precision binary64
  (/ (log (sqrt (+ (* re re) (* im im)))) (log 10.0)))