math.log/2 on complex, real part

Percentage Accurate: 50.6% → 99.1%

Time: 28.4s

Alternatives: 4

Speedup: 1.1×

Specification

Math

\[\begin{array}{l} \\ \frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \end{array} \]

FPCore

(FPCore (re im base)
 :precision binary64
 (/
  (+ (* (log (sqrt (+ (* re re) (* im im)))) (log base)) (* (atan2 im re) 0.0))
  (+ (* (log base) (log base)) (* 0.0 0.0))))

C

double code(double re, double im, double base) {
	return ((log(sqrt(((re * re) + (im * im)))) * log(base)) + (atan2(im, re) * 0.0)) / ((log(base) * log(base)) + (0.0 * 0.0));
}

Fortran

real(8) function code(re, im, base)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8), intent (in) :: base
    code = ((log(sqrt(((re * re) + (im * im)))) * log(base)) + (atan2(im, re) * 0.0d0)) / ((log(base) * log(base)) + (0.0d0 * 0.0d0))
end function

Java

public static double code(double re, double im, double base) {
	return ((Math.log(Math.sqrt(((re * re) + (im * im)))) * Math.log(base)) + (Math.atan2(im, re) * 0.0)) / ((Math.log(base) * Math.log(base)) + (0.0 * 0.0));
}

Python

def code(re, im, base):
	return ((math.log(math.sqrt(((re * re) + (im * im)))) * math.log(base)) + (math.atan2(im, re) * 0.0)) / ((math.log(base) * math.log(base)) + (0.0 * 0.0))

Julia

function code(re, im, base)
	return Float64(Float64(Float64(log(sqrt(Float64(Float64(re * re) + Float64(im * im)))) * log(base)) + Float64(atan(im, re) * 0.0)) / Float64(Float64(log(base) * log(base)) + Float64(0.0 * 0.0)))
end

MATLAB

function tmp = code(re, im, base)
	tmp = ((log(sqrt(((re * re) + (im * im)))) * log(base)) + (atan2(im, re) * 0.0)) / ((log(base) * log(base)) + (0.0 * 0.0));
end

Wolfram

code[re_, im_, base_] := N[(N[(N[(N[Log[N[Sqrt[N[(N[(re * re), $MachinePrecision] + N[(im * im), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * N[Log[base], $MachinePrecision]), $MachinePrecision] + N[(N[ArcTan[im / re], $MachinePrecision] * 0.0), $MachinePrecision]), $MachinePrecision] / N[(N[(N[Log[base], $MachinePrecision] * N[Log[base], $MachinePrecision]), $MachinePrecision] + N[(0.0 * 0.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 50.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \end{array} \]

FPCore

(FPCore (re im base)
 :precision binary64
 (/
  (+ (* (log (sqrt (+ (* re re) (* im im)))) (log base)) (* (atan2 im re) 0.0))
  (+ (* (log base) (log base)) (* 0.0 0.0))))

C

double code(double re, double im, double base) {
	return ((log(sqrt(((re * re) + (im * im)))) * log(base)) + (atan2(im, re) * 0.0)) / ((log(base) * log(base)) + (0.0 * 0.0));
}

Fortran

real(8) function code(re, im, base)
    real(8), intent (in) :: re
    real(8), intent (in) :: im
    real(8), intent (in) :: base
    code = ((log(sqrt(((re * re) + (im * im)))) * log(base)) + (atan2(im, re) * 0.0d0)) / ((log(base) * log(base)) + (0.0d0 * 0.0d0))
end function

Java

public static double code(double re, double im, double base) {
	return ((Math.log(Math.sqrt(((re * re) + (im * im)))) * Math.log(base)) + (Math.atan2(im, re) * 0.0)) / ((Math.log(base) * Math.log(base)) + (0.0 * 0.0));
}

Python

def code(re, im, base):
	return ((math.log(math.sqrt(((re * re) + (im * im)))) * math.log(base)) + (math.atan2(im, re) * 0.0)) / ((math.log(base) * math.log(base)) + (0.0 * 0.0))

Julia

function code(re, im, base)
	return Float64(Float64(Float64(log(sqrt(Float64(Float64(re * re) + Float64(im * im)))) * log(base)) + Float64(atan(im, re) * 0.0)) / Float64(Float64(log(base) * log(base)) + Float64(0.0 * 0.0)))
end

MATLAB

function tmp = code(re, im, base)
	tmp = ((log(sqrt(((re * re) + (im * im)))) * log(base)) + (atan2(im, re) * 0.0)) / ((log(base) * log(base)) + (0.0 * 0.0));
end

Wolfram

code[re_, im_, base_] := N[(N[(N[(N[Log[N[Sqrt[N[(N[(re * re), $MachinePrecision] + N[(im * im), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] * N[Log[base], $MachinePrecision]), $MachinePrecision] + N[(N[ArcTan[im / re], $MachinePrecision] * 0.0), $MachinePrecision]), $MachinePrecision] / N[(N[(N[Log[base], $MachinePrecision] * N[Log[base], $MachinePrecision]), $MachinePrecision] + N[(0.0 * 0.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\frac{1}{{\log base}^{-2}} + 0 \cdot 0} \end{array} \]

FPCore

(FPCore (re im base)
 :precision binary64
 (/
  (+ (* (log (hypot re im)) (log base)) (* (atan2 im re) 0.0))
  (+ (/ 1.0 (pow (log base) -2.0)) (* 0.0 0.0))))

C

double code(double re, double im, double base) {
	return ((log(hypot(re, im)) * log(base)) + (atan2(im, re) * 0.0)) / ((1.0 / pow(log(base), -2.0)) + (0.0 * 0.0));
}

Java

public static double code(double re, double im, double base) {
	return ((Math.log(Math.hypot(re, im)) * Math.log(base)) + (Math.atan2(im, re) * 0.0)) / ((1.0 / Math.pow(Math.log(base), -2.0)) + (0.0 * 0.0));
}

Python

def code(re, im, base):
	return ((math.log(math.hypot(re, im)) * math.log(base)) + (math.atan2(im, re) * 0.0)) / ((1.0 / math.pow(math.log(base), -2.0)) + (0.0 * 0.0))

Julia

function code(re, im, base)
	return Float64(Float64(Float64(log(hypot(re, im)) * log(base)) + Float64(atan(im, re) * 0.0)) / Float64(Float64(1.0 / (log(base) ^ -2.0)) + Float64(0.0 * 0.0)))
end

MATLAB

function tmp = code(re, im, base)
	tmp = ((log(hypot(re, im)) * log(base)) + (atan2(im, re) * 0.0)) / ((1.0 / (log(base) ^ -2.0)) + (0.0 * 0.0));
end

Wolfram

code[re_, im_, base_] := N[(N[(N[(N[Log[N[Sqrt[re ^ 2 + im ^ 2], $MachinePrecision]], $MachinePrecision] * N[Log[base], $MachinePrecision]), $MachinePrecision] + N[(N[ArcTan[im / re], $MachinePrecision] * 0.0), $MachinePrecision]), $MachinePrecision] / N[(N[(1.0 / N[Power[N[Log[base], $MachinePrecision], -2.0], $MachinePrecision]), $MachinePrecision] + N[(0.0 * 0.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 51.9%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. accelerator-lowering-hypot.f64 99.2

      \[\leadsto \frac{\log \color{blue}{\left(\mathsf{hypot}\left(re, im\right)\right)} \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \]

4. Applied egg-rr 99.2%

   \[\leadsto \frac{\log \color{blue}{\left(\mathsf{hypot}\left(re, im\right)\right)} \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \]
5. Step-by-step derivation

   1. pow2 N/A

      \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\color{blue}{{\log base}^{2}} + 0 \cdot 0} \]

   2. metadata-eval N/A

      \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{{\log base}^{\color{blue}{\left(\mathsf{neg}\left(-2\right)\right)}} + 0 \cdot 0} \]

   3. pow-flip N/A

      \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\color{blue}{\frac{1}{{\log base}^{-2}}} + 0 \cdot 0} \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\color{blue}{\frac{1}{{\log base}^{-2}}} + 0 \cdot 0} \]

   5. pow-lowering-pow.f64 N/A

      \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\frac{1}{\color{blue}{{\log base}^{-2}}} + 0 \cdot 0} \]

   6. log-lowering-log.f64 99.2

      \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\frac{1}{{\color{blue}{\log base}}^{-2}} + 0 \cdot 0} \]

6. Applied egg-rr 99.2%

   \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\color{blue}{\frac{1}{{\log base}^{-2}}} + 0 \cdot 0} \]
7. Add Preprocessing

Alternative 2: 99.2% accurate, 1.1× speedup

Math

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

FPCore

(FPCore (re im base)
 :precision binary64
 (/ (* (log (hypot re im)) (log base)) (* (log base) (log base))))

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 51.9%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. accelerator-lowering-hypot.f64 99.2

      \[\leadsto \frac{\log \color{blue}{\left(\mathsf{hypot}\left(re, im\right)\right)} \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \]

4. Applied egg-rr 99.2%

   \[\leadsto \frac{\log \color{blue}{\left(\mathsf{hypot}\left(re, im\right)\right)} \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \]
5. Step-by-step derivation

   1. mul0-rgt 99.2

      \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \color{blue}{0}}{\log base \cdot \log base + 0 \cdot 0} \]

6. Applied egg-rr 99.2%

   \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + \color{blue}{0}}{\log base \cdot \log base + 0 \cdot 0} \]
7. Step-by-step derivation

   1. metadata-eval 99.2

      \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + 0}{\log base \cdot \log base + \color{blue}{0}} \]

8. Applied egg-rr 99.2%

   \[\leadsto \frac{\log \left(\mathsf{hypot}\left(re, im\right)\right) \cdot \log base + 0}{\log base \cdot \log base + \color{blue}{0}} \]

9. Final simplification 99.2%

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

Alternative 3: 27.4% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \frac{\log \left(\frac{1}{\frac{1}{im}}\right)}{\log base} \end{array} \]

FPCore

(FPCore (re im base)
 :precision binary64
 (/ (log (/ 1.0 (/ 1.0 im))) (log base)))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 51.9%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \]
2. Add Preprocessing

3. Taylor expanded in re around 0

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

   1. /-lowering-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\log im}{\log base}} \]

   2. log-lowering-log.f64 N/A

      \[\leadsto \frac{\color{blue}{\log im}}{\log base} \]

   3. log-lowering-log.f64 28.1

      \[\leadsto \frac{\log im}{\color{blue}{\log base}} \]

5. Simplified 28.1%

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

   1. remove-double-neg N/A

      \[\leadsto \frac{\color{blue}{\mathsf{neg}\left(\left(\mathsf{neg}\left(\log im\right)\right)\right)}}{\log base} \]

   2. neg-log N/A

      \[\leadsto \frac{\mathsf{neg}\left(\color{blue}{\log \left(\frac{1}{im}\right)}\right)}{\log base} \]

   3. neg-log N/A

      \[\leadsto \frac{\color{blue}{\log \left(\frac{1}{\frac{1}{im}}\right)}}{\log base} \]

   4. log-lowering-log.f64 N/A

      \[\leadsto \frac{\color{blue}{\log \left(\frac{1}{\frac{1}{im}}\right)}}{\log base} \]

   5. /-lowering-/.f64 N/A

      \[\leadsto \frac{\log \color{blue}{\left(\frac{1}{\frac{1}{im}}\right)}}{\log base} \]

   6. /-lowering-/.f64 28.1

      \[\leadsto \frac{\log \left(\frac{1}{\color{blue}{\frac{1}{im}}}\right)}{\log base} \]

7. Applied egg-rr 28.1%

   \[\leadsto \frac{\color{blue}{\log \left(\frac{1}{\frac{1}{im}}\right)}}{\log base} \]
8. Add Preprocessing

Alternative 4: 27.4% accurate, 2.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 51.9%

   \[\frac{\log \left(\sqrt{re \cdot re + im \cdot im}\right) \cdot \log base + \tan^{-1}_* \frac{im}{re} \cdot 0}{\log base \cdot \log base + 0 \cdot 0} \]
2. Add Preprocessing

3. Taylor expanded in re around 0

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

   1. /-lowering-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\log im}{\log base}} \]

   2. log-lowering-log.f64 N/A

      \[\leadsto \frac{\color{blue}{\log im}}{\log base} \]

   3. log-lowering-log.f64 28.1

      \[\leadsto \frac{\log im}{\color{blue}{\log base}} \]

5. Simplified 28.1%

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

Reproduce

herbie shell --seed 2024196 
(FPCore (re im base)
  :name "math.log/2 on complex, real part"
  :precision binary64
  (/ (+ (* (log (sqrt (+ (* re re) (* im im)))) (log base)) (* (atan2 im re) 0.0)) (+ (* (log base) (log base)) (* 0.0 0.0))))