math.log10 on complex, real part

Percentage Accurate: 51.1% → 99.1%

Time: 10.1s

Alternatives: 7

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (re im)
 :precision binary64
 (/ (log (sqrt (+ (* re re) (* im im)))) (log 10.0)))

C

double code(double re, double im) {
	return log(sqrt(((re * re) + (im * im)))) / log(10.0);
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = log(sqrt(((re * re) + (im * im)))) / log(10.0d0)
end function

Java

public static double code(double re, double im) {
	return Math.log(Math.sqrt(((re * re) + (im * im)))) / Math.log(10.0);
}

Python

def code(re, im):
	return math.log(math.sqrt(((re * re) + (im * im)))) / math.log(10.0)

Julia

function code(re, im)
	return Float64(log(sqrt(Float64(Float64(re * re) + Float64(im * im)))) / log(10.0))
end

MATLAB

function tmp = code(re, im)
	tmp = log(sqrt(((re * re) + (im * im)))) / log(10.0);
end

Wolfram

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]

Initial Program: 51.1% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (re im)
 :precision binary64
 (/ (log (sqrt (+ (* re re) (* im im)))) (log 10.0)))

C

double code(double re, double im) {
	return log(sqrt(((re * re) + (im * im)))) / log(10.0);
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = log(sqrt(((re * re) + (im * im)))) / log(10.0d0)
end function

Java

public static double code(double re, double im) {
	return Math.log(Math.sqrt(((re * re) + (im * im)))) / Math.log(10.0);
}

Python

def code(re, im):
	return math.log(math.sqrt(((re * re) + (im * im)))) / math.log(10.0)

Julia

function code(re, im)
	return Float64(log(sqrt(Float64(Float64(re * re) + Float64(im * im)))) / log(10.0))
end

MATLAB

function tmp = code(re, im)
	tmp = log(sqrt(((re * re) + (im * im)))) / log(10.0);
end

Wolfram

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]

Alternative 1: 99.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \frac{{\log 10}^{-0.5}}{\frac{\sqrt{\log 10}}{\log \left(\mathsf{hypot}\left(im, re\right)\right)}} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (/ (pow (log 10.0) -0.5) (/ (sqrt (log 10.0)) (log (hypot im re)))))

C

double code(double re, double im) {
	return pow(log(10.0), -0.5) / (sqrt(log(10.0)) / log(hypot(im, re)));
}

Java

public static double code(double re, double im) {
	return Math.pow(Math.log(10.0), -0.5) / (Math.sqrt(Math.log(10.0)) / Math.log(Math.hypot(im, re)));
}

Python

def code(re, im):
	return math.pow(math.log(10.0), -0.5) / (math.sqrt(math.log(10.0)) / math.log(math.hypot(im, re)))

Julia

function code(re, im)
	return Float64((log(10.0) ^ -0.5) / Float64(sqrt(log(10.0)) / log(hypot(im, re))))
end

MATLAB

function tmp = code(re, im)
	tmp = (log(10.0) ^ -0.5) / (sqrt(log(10.0)) / log(hypot(im, re)));
end

Wolfram

code[re_, im_] := N[(N[Power[N[Log[10.0], $MachinePrecision], -0.5], $MachinePrecision] / N[(N[Sqrt[N[Log[10.0], $MachinePrecision]], $MachinePrecision] / N[Log[N[Sqrt[im ^ 2 + re ^ 2], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.0%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]
2. Step-by-step derivation

   1. +-commutative 52.0%

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

   2. +-commutative 52.0%

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

   3. sqr-neg 52.0%

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

   4. sqr-neg 52.0%

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

   5. sqr-neg 52.0%

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

   6. sqr-neg 52.0%

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

   7. hypot-define 99.0%

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

3. Simplified 99.0%

   \[\leadsto \color{blue}{\frac{\log \left(\mathsf{hypot}\left(re, im\right)\right)}{\log 10}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. clear-num 99.1%

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

   2. inv-pow 99.1%

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

   3. add-sqr-sqrt 99.1%

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

   4. associate-/l* 98.7%

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

   5. unpow-prod-down 99.0%

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

   6. inv-pow 99.0%

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

   7. pow1/2 99.0%

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

   8. pow-flip 99.0%

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

   9. metadata-eval 99.0%

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

6. Applied egg-rr 99.0%

   \[\leadsto \color{blue}{{\log 10}^{-0.5} \cdot {\left(\frac{\sqrt{\log 10}}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1}} \]
7. Step-by-step derivation

   1. unpow-1 99.0%

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

   2. associate-*r/ 99.1%

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

   3. *-rgt-identity 99.1%

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

   4. hypot-undefine 52.0%

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

   5. unpow2 52.0%

      \[\leadsto \frac{{\log 10}^{-0.5}}{\frac{\sqrt{\log 10}}{\log \left(\sqrt{\color{blue}{{re}^{2}} + im \cdot im}\right)}} \]

   6. unpow2 52.0%

      \[\leadsto \frac{{\log 10}^{-0.5}}{\frac{\sqrt{\log 10}}{\log \left(\sqrt{{re}^{2} + \color{blue}{{im}^{2}}}\right)}} \]

   7. +-commutative 52.0%

      \[\leadsto \frac{{\log 10}^{-0.5}}{\frac{\sqrt{\log 10}}{\log \left(\sqrt{\color{blue}{{im}^{2} + {re}^{2}}}\right)}} \]

   8. unpow2 52.0%

      \[\leadsto \frac{{\log 10}^{-0.5}}{\frac{\sqrt{\log 10}}{\log \left(\sqrt{\color{blue}{im \cdot im} + {re}^{2}}\right)}} \]

   9. unpow2 52.0%

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

   10. hypot-define 99.1%

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

8. Simplified 99.1%

   \[\leadsto \color{blue}{\frac{{\log 10}^{-0.5}}{\frac{\sqrt{\log 10}}{\log \left(\mathsf{hypot}\left(im, re\right)\right)}}} \]
9. Add Preprocessing

Alternative 2: 99.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ {\left(\frac{\log 0.1}{-\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (pow (/ (log 0.1) (- (log (hypot re im)))) -1.0))

C

double code(double re, double im) {
	return pow((log(0.1) / -log(hypot(re, im))), -1.0);
}

Java

public static double code(double re, double im) {
	return Math.pow((Math.log(0.1) / -Math.log(Math.hypot(re, im))), -1.0);
}

Python

def code(re, im):
	return math.pow((math.log(0.1) / -math.log(math.hypot(re, im))), -1.0)

Julia

function code(re, im)
	return Float64(log(0.1) / Float64(-log(hypot(re, im)))) ^ -1.0
end

MATLAB

function tmp = code(re, im)
	tmp = (log(0.1) / -log(hypot(re, im))) ^ -1.0;
end

Wolfram

code[re_, im_] := N[Power[N[(N[Log[0.1], $MachinePrecision] / (-N[Log[N[Sqrt[re ^ 2 + im ^ 2], $MachinePrecision]], $MachinePrecision])), $MachinePrecision], -1.0], $MachinePrecision]

Derivation

1. Initial program 52.0%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]
2. Step-by-step derivation

   1. +-commutative 52.0%

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

   2. +-commutative 52.0%

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

   3. sqr-neg 52.0%

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

   4. sqr-neg 52.0%

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

   5. sqr-neg 52.0%

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

   6. sqr-neg 52.0%

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

   7. hypot-define 99.0%

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

3. Simplified 99.0%

   \[\leadsto \color{blue}{\frac{\log \left(\mathsf{hypot}\left(re, im\right)\right)}{\log 10}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. clear-num 99.1%

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

   2. inv-pow 99.1%

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

6. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{{\left(\frac{\log 10}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1}} \]
7. Step-by-step derivation

   1. metadata-eval 99.1%

      \[\leadsto {\left(\frac{\log \color{blue}{\left(\frac{1}{0.1}\right)}}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1} \]

   2. metadata-eval 99.1%

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

   3. rem-exp-log 99.1%

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

   4. neg-log 99.1%

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

   5. neg-log 99.1%

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

   6. add-log-exp 99.1%

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

   7. neg-log 99.1%

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

   8. metadata-eval 99.1%

      \[\leadsto {\left(\frac{-\log \color{blue}{0.1}}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1} \]

8. Applied egg-rr 99.1%

   \[\leadsto {\left(\frac{\color{blue}{-\log 0.1}}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1} \]

9. Final simplification 99.1%

   \[\leadsto {\left(\frac{\log 0.1}{-\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1} \]
10. Add Preprocessing

Alternative 3: 99.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ {\left(\frac{\log 10}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (pow (/ (log 10.0) (log (hypot re im))) -1.0))

C

double code(double re, double im) {
	return pow((log(10.0) / log(hypot(re, im))), -1.0);
}

Java

public static double code(double re, double im) {
	return Math.pow((Math.log(10.0) / Math.log(Math.hypot(re, im))), -1.0);
}

Python

def code(re, im):
	return math.pow((math.log(10.0) / math.log(math.hypot(re, im))), -1.0)

Julia

function code(re, im)
	return Float64(log(10.0) / log(hypot(re, im))) ^ -1.0
end

MATLAB

function tmp = code(re, im)
	tmp = (log(10.0) / log(hypot(re, im))) ^ -1.0;
end

Wolfram

code[re_, im_] := N[Power[N[(N[Log[10.0], $MachinePrecision] / N[Log[N[Sqrt[re ^ 2 + im ^ 2], $MachinePrecision]], $MachinePrecision]), $MachinePrecision], -1.0], $MachinePrecision]

Derivation

1. Initial program 52.0%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]
2. Step-by-step derivation

   1. +-commutative 52.0%

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

   2. +-commutative 52.0%

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

   3. sqr-neg 52.0%

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

   4. sqr-neg 52.0%

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

   5. sqr-neg 52.0%

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

   6. sqr-neg 52.0%

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

   7. hypot-define 99.0%

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

3. Simplified 99.0%

   \[\leadsto \color{blue}{\frac{\log \left(\mathsf{hypot}\left(re, im\right)\right)}{\log 10}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. clear-num 99.1%

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

   2. inv-pow 99.1%

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

6. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{{\left(\frac{\log 10}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1}} \]
7. Add Preprocessing

Alternative 4: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\frac{\mathsf{log1p}\left(9\right)}{\log \left(\mathsf{hypot}\left(im, re\right)\right)}} \end{array} \]

FPCore

(FPCore (re im)
 :precision binary64
 (/ 1.0 (/ (log1p 9.0) (log (hypot im re)))))

C

double code(double re, double im) {
	return 1.0 / (log1p(9.0) / log(hypot(im, re)));
}

Java

public static double code(double re, double im) {
	return 1.0 / (Math.log1p(9.0) / Math.log(Math.hypot(im, re)));
}

Python

def code(re, im):
	return 1.0 / (math.log1p(9.0) / math.log(math.hypot(im, re)))

Julia

function code(re, im)
	return Float64(1.0 / Float64(log1p(9.0) / log(hypot(im, re))))
end

Wolfram

code[re_, im_] := N[(1.0 / N[(N[Log[1 + 9.0], $MachinePrecision] / N[Log[N[Sqrt[im ^ 2 + re ^ 2], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.0%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]
2. Step-by-step derivation

   1. +-commutative 52.0%

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

   2. +-commutative 52.0%

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

   3. sqr-neg 52.0%

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

   4. sqr-neg 52.0%

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

   5. sqr-neg 52.0%

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

   6. sqr-neg 52.0%

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

   7. hypot-define 99.0%

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

3. Simplified 99.0%

   \[\leadsto \color{blue}{\frac{\log \left(\mathsf{hypot}\left(re, im\right)\right)}{\log 10}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. clear-num 99.1%

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

   2. inv-pow 99.1%

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

6. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{{\left(\frac{\log 10}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}\right)}^{-1}} \]
7. Step-by-step derivation

   1. unpow-1 99.1%

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

   2. div-inv 99.0%

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

   3. div-inv 99.1%

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

   4. log1p-expm1-u 99.1%

      \[\leadsto \frac{1}{\frac{\color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\log 10\right)\right)}}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}} \]

   5. expm1-undefine 99.1%

      \[\leadsto \frac{1}{\frac{\mathsf{log1p}\left(\color{blue}{e^{\log 10} - 1}\right)}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}} \]

   6. rem-exp-log 99.1%

      \[\leadsto \frac{1}{\frac{\mathsf{log1p}\left(\color{blue}{10} - 1\right)}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}} \]

   7. metadata-eval 99.1%

      \[\leadsto \frac{1}{\frac{\mathsf{log1p}\left(\color{blue}{9}\right)}{\log \left(\mathsf{hypot}\left(re, im\right)\right)}} \]

   8. hypot-undefine 52.0%

      \[\leadsto \frac{1}{\frac{\mathsf{log1p}\left(9\right)}{\log \color{blue}{\left(\sqrt{re \cdot re + im \cdot im}\right)}}} \]

   9. +-commutative 52.0%

      \[\leadsto \frac{1}{\frac{\mathsf{log1p}\left(9\right)}{\log \left(\sqrt{\color{blue}{im \cdot im + re \cdot re}}\right)}} \]

   10. hypot-undefine 99.1%

       \[\leadsto \frac{1}{\frac{\mathsf{log1p}\left(9\right)}{\log \color{blue}{\left(\mathsf{hypot}\left(im, re\right)\right)}}} \]

8. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{log1p}\left(9\right)}{\log \left(\mathsf{hypot}\left(im, re\right)\right)}}} \]
9. Add Preprocessing

Alternative 5: 99.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right)}{\log 10} \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (/ (log (hypot re im)) (log 10.0)))

C

double code(double re, double im) {
	return log(hypot(re, im)) / log(10.0);
}

Java

public static double code(double re, double im) {
	return Math.log(Math.hypot(re, im)) / Math.log(10.0);
}

Python

def code(re, im):
	return math.log(math.hypot(re, im)) / math.log(10.0)

Julia

function code(re, im)
	return Float64(log(hypot(re, im)) / log(10.0))
end

MATLAB

function tmp = code(re, im)
	tmp = log(hypot(re, im)) / log(10.0);
end

Wolfram

code[re_, im_] := N[(N[Log[N[Sqrt[re ^ 2 + im ^ 2], $MachinePrecision]], $MachinePrecision] / N[Log[10.0], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.0%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]
2. Step-by-step derivation

   1. +-commutative 52.0%

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

   2. +-commutative 52.0%

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

   3. sqr-neg 52.0%

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

   4. sqr-neg 52.0%

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

   5. sqr-neg 52.0%

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

   6. sqr-neg 52.0%

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

   7. hypot-define 99.0%

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

3. Simplified 99.0%

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

Alternative 6: 26.9% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \frac{-1}{\frac{\log 0.1}{\log im}} \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (/ -1.0 (/ (log 0.1) (log im))))

C

double code(double re, double im) {
	return -1.0 / (log(0.1) / log(im));
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = (-1.0d0) / (log(0.1d0) / log(im))
end function

Java

public static double code(double re, double im) {
	return -1.0 / (Math.log(0.1) / Math.log(im));
}

Python

def code(re, im):
	return -1.0 / (math.log(0.1) / math.log(im))

Julia

function code(re, im)
	return Float64(-1.0 / Float64(log(0.1) / log(im)))
end

MATLAB

function tmp = code(re, im)
	tmp = -1.0 / (log(0.1) / log(im));
end

Wolfram

code[re_, im_] := N[(-1.0 / N[(N[Log[0.1], $MachinePrecision] / N[Log[im], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.0%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]
2. Step-by-step derivation

   1. +-commutative 52.0%

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

   2. +-commutative 52.0%

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

   3. sqr-neg 52.0%

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

   4. sqr-neg 52.0%

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

   5. sqr-neg 52.0%

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

   6. sqr-neg 52.0%

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

   7. hypot-define 99.0%

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

3. Simplified 99.0%

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

5. Taylor expanded in re around 0 28.1%

   \[\leadsto \color{blue}{\frac{\log im}{\log 10}} \]
6. Step-by-step derivation

   1. add-exp-log 17.9%

      \[\leadsto \color{blue}{e^{\log \left(\frac{\log im}{\log 10}\right)}} \]

   2. log1p-expm1-u 17.9%

      \[\leadsto e^{\log \left(\frac{\log im}{\color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\log 10\right)\right)}}\right)} \]

   3. expm1-undefine 17.9%

      \[\leadsto e^{\log \left(\frac{\log im}{\mathsf{log1p}\left(\color{blue}{e^{\log 10} - 1}\right)}\right)} \]

   4. rem-exp-log 17.9%

      \[\leadsto e^{\log \left(\frac{\log im}{\mathsf{log1p}\left(\color{blue}{10} - 1\right)}\right)} \]

   5. metadata-eval 17.9%

      \[\leadsto e^{\log \left(\frac{\log im}{\mathsf{log1p}\left(\color{blue}{9}\right)}\right)} \]

7. Applied egg-rr 17.9%

   \[\leadsto \color{blue}{e^{\log \left(\frac{\log im}{\mathsf{log1p}\left(9\right)}\right)}} \]

9. Applied egg-rr 28.1%

   \[\leadsto \color{blue}{\frac{-1}{\frac{\log 0.1}{\log im}}} \]
10. Add Preprocessing

Alternative 7: 26.9% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \frac{\log im}{\log 10} \end{array} \]

FPCore

(FPCore (re im) :precision binary64 (/ (log im) (log 10.0)))

C

double code(double re, double im) {
	return log(im) / log(10.0);
}

Fortran

real(8) function code(re, im)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    code = log(im) / log(10.0d0)
end function

Java

public static double code(double re, double im) {
	return Math.log(im) / Math.log(10.0);
}

Python

def code(re, im):
	return math.log(im) / math.log(10.0)

Julia

function code(re, im)
	return Float64(log(im) / log(10.0))
end

MATLAB

function tmp = code(re, im)
	tmp = log(im) / log(10.0);
end

Wolfram

code[re_, im_] := N[(N[Log[im], $MachinePrecision] / N[Log[10.0], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.0%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right)}{\log 10} \]
2. Step-by-step derivation

   1. +-commutative 52.0%

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

   2. +-commutative 52.0%

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

   3. sqr-neg 52.0%

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

   4. sqr-neg 52.0%

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

   5. sqr-neg 52.0%

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

   6. sqr-neg 52.0%

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

   7. hypot-define 99.0%

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

3. Simplified 99.0%

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

5. Taylor expanded in re around 0 28.1%

   \[\leadsto \color{blue}{\frac{\log im}{\log 10}} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024110 
(FPCore (re im)
  :name "math.log10 on complex, real part"
  :precision binary64
  (/ (log (sqrt (+ (* re re) (* im im)))) (log 10.0)))