2log (problem 3.3.6)

Percentage Accurate: 24.0% → 99.3%

Time: 13.0s

Alternatives: 13

Speedup: 68.3×

Specification

\[N > 1 \land N < 10^{+40}\]

Math

\[\begin{array}{l} \\ \log \left(N + 1\right) - \log N \end{array} \]

FPCore

(FPCore (N) :precision binary64 (- (log (+ N 1.0)) (log N)))

C

double code(double N) {
	return log((N + 1.0)) - log(N);
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    code = log((n + 1.0d0)) - log(n)
end function

Java

public static double code(double N) {
	return Math.log((N + 1.0)) - Math.log(N);
}

Python

def code(N):
	return math.log((N + 1.0)) - math.log(N)

Julia

function code(N)
	return Float64(log(Float64(N + 1.0)) - log(N))
end

MATLAB

function tmp = code(N)
	tmp = log((N + 1.0)) - log(N);
end

Wolfram

code[N_] := N[(N[Log[N[(N + 1.0), $MachinePrecision]], $MachinePrecision] - N[Log[N], $MachinePrecision]), $MachinePrecision]

Initial Program: 24.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \log \left(N + 1\right) - \log N \end{array} \]

FPCore

(FPCore (N) :precision binary64 (- (log (+ N 1.0)) (log N)))

C

double code(double N) {
	return log((N + 1.0)) - log(N);
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    code = log((n + 1.0d0)) - log(n)
end function

Java

public static double code(double N) {
	return Math.log((N + 1.0)) - Math.log(N);
}

Python

def code(N):
	return math.log((N + 1.0)) - math.log(N)

Julia

function code(N)
	return Float64(log(Float64(N + 1.0)) - log(N))
end

MATLAB

function tmp = code(N)
	tmp = log((N + 1.0)) - log(N);
end

Wolfram

code[N_] := N[(N[Log[N[(N + 1.0), $MachinePrecision]], $MachinePrecision] - N[Log[N], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\log \left(N + 1\right) - \log N \leq 0.001:\\ \;\;\;\;\frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{-0.5}{{N}^{2}} + \frac{-0.25}{{N}^{4}}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= (- (log (+ N 1.0)) (log N)) 0.001)
   (+
    (/ 1.0 N)
    (+
     (/ 0.3333333333333333 (pow N 3.0))
     (+ (/ -0.5 (pow N 2.0)) (/ -0.25 (pow N 4.0)))))
   (- (log (/ N (+ N 1.0))))))

C

double code(double N) {
	double tmp;
	if ((log((N + 1.0)) - log(N)) <= 0.001) {
		tmp = (1.0 / N) + ((0.3333333333333333 / pow(N, 3.0)) + ((-0.5 / pow(N, 2.0)) + (-0.25 / pow(N, 4.0))));
	} else {
		tmp = -log((N / (N + 1.0)));
	}
	return tmp;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((log((n + 1.0d0)) - log(n)) <= 0.001d0) then
        tmp = (1.0d0 / n) + ((0.3333333333333333d0 / (n ** 3.0d0)) + (((-0.5d0) / (n ** 2.0d0)) + ((-0.25d0) / (n ** 4.0d0))))
    else
        tmp = -log((n / (n + 1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double N) {
	double tmp;
	if ((Math.log((N + 1.0)) - Math.log(N)) <= 0.001) {
		tmp = (1.0 / N) + ((0.3333333333333333 / Math.pow(N, 3.0)) + ((-0.5 / Math.pow(N, 2.0)) + (-0.25 / Math.pow(N, 4.0))));
	} else {
		tmp = -Math.log((N / (N + 1.0)));
	}
	return tmp;
}

Python

def code(N):
	tmp = 0
	if (math.log((N + 1.0)) - math.log(N)) <= 0.001:
		tmp = (1.0 / N) + ((0.3333333333333333 / math.pow(N, 3.0)) + ((-0.5 / math.pow(N, 2.0)) + (-0.25 / math.pow(N, 4.0))))
	else:
		tmp = -math.log((N / (N + 1.0)))
	return tmp

Julia

function code(N)
	tmp = 0.0
	if (Float64(log(Float64(N + 1.0)) - log(N)) <= 0.001)
		tmp = Float64(Float64(1.0 / N) + Float64(Float64(0.3333333333333333 / (N ^ 3.0)) + Float64(Float64(-0.5 / (N ^ 2.0)) + Float64(-0.25 / (N ^ 4.0)))));
	else
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(N)
	tmp = 0.0;
	if ((log((N + 1.0)) - log(N)) <= 0.001)
		tmp = (1.0 / N) + ((0.3333333333333333 / (N ^ 3.0)) + ((-0.5 / (N ^ 2.0)) + (-0.25 / (N ^ 4.0))));
	else
		tmp = -log((N / (N + 1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[N_] := If[LessEqual[N[(N[Log[N[(N + 1.0), $MachinePrecision]], $MachinePrecision] - N[Log[N], $MachinePrecision]), $MachinePrecision], 0.001], N[(N[(1.0 / N), $MachinePrecision] + N[(N[(0.3333333333333333 / N[Power[N, 3.0], $MachinePrecision]), $MachinePrecision] + N[(N[(-0.5 / N[Power[N, 2.0], $MachinePrecision]), $MachinePrecision] + N[(-0.25 / N[Power[N, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision])]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (log.f64 (+.f64 N 1)) (log.f64 N)) < 1e-3

   1. Initial program 17.9%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 17.9%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 17.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 17.9%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing

   5. Taylor expanded in N around inf 99.8%

      \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) - \left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)} \]
   6. Step-by-step derivation

      1. sub-neg 99.8%

         \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) + \left(-\left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right)} \]

      2. +-commutative 99.8%

         \[\leadsto \color{blue}{\left(\frac{1}{N} + 0.3333333333333333 \cdot \frac{1}{{N}^{3}}\right)} + \left(-\left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right) \]

      3. associate-+l+ 99.8%

         \[\leadsto \color{blue}{\frac{1}{N} + \left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \left(-\left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right)\right)} \]

      4. associate-*r/ 99.8%

         \[\leadsto \frac{1}{N} + \left(\color{blue}{\frac{0.3333333333333333 \cdot 1}{{N}^{3}}} + \left(-\left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right)\right) \]

      5. metadata-eval 99.8%

         \[\leadsto \frac{1}{N} + \left(\frac{\color{blue}{0.3333333333333333}}{{N}^{3}} + \left(-\left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right)\right) \]

      6. +-commutative 99.8%

         \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(-\color{blue}{\left(0.5 \cdot \frac{1}{{N}^{2}} + 0.25 \cdot \frac{1}{{N}^{4}}\right)}\right)\right) \]

      7. distribute-neg-in 99.8%

         \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\left(\left(-0.5 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.25 \cdot \frac{1}{{N}^{4}}\right)\right)}\right) \]

      8. associate-*r/ 99.8%

         \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\left(-\color{blue}{\frac{0.5 \cdot 1}{{N}^{2}}}\right) + \left(-0.25 \cdot \frac{1}{{N}^{4}}\right)\right)\right) \]

      9. metadata-eval 99.8%

         \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\left(-\frac{\color{blue}{0.5}}{{N}^{2}}\right) + \left(-0.25 \cdot \frac{1}{{N}^{4}}\right)\right)\right) \]

      10. distribute-neg-frac 99.8%

          \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\color{blue}{\frac{-0.5}{{N}^{2}}} + \left(-0.25 \cdot \frac{1}{{N}^{4}}\right)\right)\right) \]

      11. metadata-eval 99.8%

          \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{\color{blue}{-0.5}}{{N}^{2}} + \left(-0.25 \cdot \frac{1}{{N}^{4}}\right)\right)\right) \]

      12. associate-*r/ 99.8%

          \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{-0.5}{{N}^{2}} + \left(-\color{blue}{\frac{0.25 \cdot 1}{{N}^{4}}}\right)\right)\right) \]

      13. metadata-eval 99.8%

          \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{-0.5}{{N}^{2}} + \left(-\frac{\color{blue}{0.25}}{{N}^{4}}\right)\right)\right) \]

      14. distribute-neg-frac 99.8%

          \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{-0.5}{{N}^{2}} + \color{blue}{\frac{-0.25}{{N}^{4}}}\right)\right) \]

   7. Simplified 99.8%

      \[\leadsto \color{blue}{\frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{-0.5}{{N}^{2}} + \frac{-0.25}{{N}^{4}}\right)\right)} \]

   if 1e-3 < (-.f64 (log.f64 (+.f64 N 1)) (log.f64 N))

   1. Initial program 93.4%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 93.4%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 93.6%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 93.6%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 93.7%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 93.7%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 93.6%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 93.4%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 93.4%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 94.9%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 94.6%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 95.4%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 95.4%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\log \left(N + 1\right) - \log N \leq 0.001:\\ \;\;\;\;\frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{-0.5}{{N}^{2}} + \frac{-0.25}{{N}^{4}}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.3% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\log \left(N + 1\right) - \log N \leq 0.001:\\ \;\;\;\;\frac{0.3333333333333333}{{N}^{3}} + \left(\left(\frac{1}{N} - \frac{0.25}{{N}^{4}}\right) - \frac{0.5}{{N}^{2}}\right)\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= (- (log (+ N 1.0)) (log N)) 0.001)
   (+
    (/ 0.3333333333333333 (pow N 3.0))
    (- (- (/ 1.0 N) (/ 0.25 (pow N 4.0))) (/ 0.5 (pow N 2.0))))
   (- (log (/ N (+ N 1.0))))))

C

double code(double N) {
	double tmp;
	if ((log((N + 1.0)) - log(N)) <= 0.001) {
		tmp = (0.3333333333333333 / pow(N, 3.0)) + (((1.0 / N) - (0.25 / pow(N, 4.0))) - (0.5 / pow(N, 2.0)));
	} else {
		tmp = -log((N / (N + 1.0)));
	}
	return tmp;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((log((n + 1.0d0)) - log(n)) <= 0.001d0) then
        tmp = (0.3333333333333333d0 / (n ** 3.0d0)) + (((1.0d0 / n) - (0.25d0 / (n ** 4.0d0))) - (0.5d0 / (n ** 2.0d0)))
    else
        tmp = -log((n / (n + 1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double N) {
	double tmp;
	if ((Math.log((N + 1.0)) - Math.log(N)) <= 0.001) {
		tmp = (0.3333333333333333 / Math.pow(N, 3.0)) + (((1.0 / N) - (0.25 / Math.pow(N, 4.0))) - (0.5 / Math.pow(N, 2.0)));
	} else {
		tmp = -Math.log((N / (N + 1.0)));
	}
	return tmp;
}

Python

def code(N):
	tmp = 0
	if (math.log((N + 1.0)) - math.log(N)) <= 0.001:
		tmp = (0.3333333333333333 / math.pow(N, 3.0)) + (((1.0 / N) - (0.25 / math.pow(N, 4.0))) - (0.5 / math.pow(N, 2.0)))
	else:
		tmp = -math.log((N / (N + 1.0)))
	return tmp

Julia

function code(N)
	tmp = 0.0
	if (Float64(log(Float64(N + 1.0)) - log(N)) <= 0.001)
		tmp = Float64(Float64(0.3333333333333333 / (N ^ 3.0)) + Float64(Float64(Float64(1.0 / N) - Float64(0.25 / (N ^ 4.0))) - Float64(0.5 / (N ^ 2.0))));
	else
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(N)
	tmp = 0.0;
	if ((log((N + 1.0)) - log(N)) <= 0.001)
		tmp = (0.3333333333333333 / (N ^ 3.0)) + (((1.0 / N) - (0.25 / (N ^ 4.0))) - (0.5 / (N ^ 2.0)));
	else
		tmp = -log((N / (N + 1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[N_] := If[LessEqual[N[(N[Log[N[(N + 1.0), $MachinePrecision]], $MachinePrecision] - N[Log[N], $MachinePrecision]), $MachinePrecision], 0.001], N[(N[(0.3333333333333333 / N[Power[N, 3.0], $MachinePrecision]), $MachinePrecision] + N[(N[(N[(1.0 / N), $MachinePrecision] - N[(0.25 / N[Power[N, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(0.5 / N[Power[N, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision])]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (log.f64 (+.f64 N 1)) (log.f64 N)) < 1e-3

   1. Initial program 17.9%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 17.9%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 17.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 17.9%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 17.9%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 17.9%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   7. Taylor expanded in N around inf 99.8%

      \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) - \left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)} \]
   8. Step-by-step derivation

      1. associate--l+ 99.8%

         \[\leadsto \color{blue}{0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \left(\frac{1}{N} - \left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right)} \]

      2. associate-*r/ 99.8%

         \[\leadsto \color{blue}{\frac{0.3333333333333333 \cdot 1}{{N}^{3}}} + \left(\frac{1}{N} - \left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right) \]

      3. metadata-eval 99.8%

         \[\leadsto \frac{\color{blue}{0.3333333333333333}}{{N}^{3}} + \left(\frac{1}{N} - \left(0.25 \cdot \frac{1}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right) \]

      4. associate-*r/ 99.8%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \left(\color{blue}{\frac{0.25 \cdot 1}{{N}^{4}}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right) \]

      5. metadata-eval 99.8%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \left(\frac{\color{blue}{0.25}}{{N}^{4}} + 0.5 \cdot \frac{1}{{N}^{2}}\right)\right) \]

      6. associate--r+ 99.8%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\left(\left(\frac{1}{N} - \frac{0.25}{{N}^{4}}\right) - 0.5 \cdot \frac{1}{{N}^{2}}\right)} \]

      7. associate-*r/ 99.8%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\left(\frac{1}{N} - \frac{0.25}{{N}^{4}}\right) - \color{blue}{\frac{0.5 \cdot 1}{{N}^{2}}}\right) \]

      8. metadata-eval 99.8%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\left(\frac{1}{N} - \frac{0.25}{{N}^{4}}\right) - \frac{\color{blue}{0.5}}{{N}^{2}}\right) \]

   9. Simplified 99.8%

      \[\leadsto \color{blue}{\frac{0.3333333333333333}{{N}^{3}} + \left(\left(\frac{1}{N} - \frac{0.25}{{N}^{4}}\right) - \frac{0.5}{{N}^{2}}\right)} \]

   if 1e-3 < (-.f64 (log.f64 (+.f64 N 1)) (log.f64 N))

   1. Initial program 93.4%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 93.4%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 93.6%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 93.6%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 93.7%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 93.7%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 93.6%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 93.4%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 93.4%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 94.9%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 94.6%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 95.4%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 95.4%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\log \left(N + 1\right) - \log N \leq 0.001:\\ \;\;\;\;\frac{0.3333333333333333}{{N}^{3}} + \left(\left(\frac{1}{N} - \frac{0.25}{{N}^{4}}\right) - \frac{0.5}{{N}^{2}}\right)\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 98.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\log \left(N + 1\right) - \log N \leq 8 \cdot 10^{-5}:\\ \;\;\;\;\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{0.5}{{N}^{2}}\right)\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= (- (log (+ N 1.0)) (log N)) 8e-5)
   (+ (/ 0.3333333333333333 (pow N 3.0)) (- (/ 1.0 N) (/ 0.5 (pow N 2.0))))
   (- (log (/ N (+ N 1.0))))))

C

double code(double N) {
	double tmp;
	if ((log((N + 1.0)) - log(N)) <= 8e-5) {
		tmp = (0.3333333333333333 / pow(N, 3.0)) + ((1.0 / N) - (0.5 / pow(N, 2.0)));
	} else {
		tmp = -log((N / (N + 1.0)));
	}
	return tmp;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    real(8) :: tmp
    if ((log((n + 1.0d0)) - log(n)) <= 8d-5) then
        tmp = (0.3333333333333333d0 / (n ** 3.0d0)) + ((1.0d0 / n) - (0.5d0 / (n ** 2.0d0)))
    else
        tmp = -log((n / (n + 1.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double N) {
	double tmp;
	if ((Math.log((N + 1.0)) - Math.log(N)) <= 8e-5) {
		tmp = (0.3333333333333333 / Math.pow(N, 3.0)) + ((1.0 / N) - (0.5 / Math.pow(N, 2.0)));
	} else {
		tmp = -Math.log((N / (N + 1.0)));
	}
	return tmp;
}

Python

def code(N):
	tmp = 0
	if (math.log((N + 1.0)) - math.log(N)) <= 8e-5:
		tmp = (0.3333333333333333 / math.pow(N, 3.0)) + ((1.0 / N) - (0.5 / math.pow(N, 2.0)))
	else:
		tmp = -math.log((N / (N + 1.0)))
	return tmp

Julia

function code(N)
	tmp = 0.0
	if (Float64(log(Float64(N + 1.0)) - log(N)) <= 8e-5)
		tmp = Float64(Float64(0.3333333333333333 / (N ^ 3.0)) + Float64(Float64(1.0 / N) - Float64(0.5 / (N ^ 2.0))));
	else
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(N)
	tmp = 0.0;
	if ((log((N + 1.0)) - log(N)) <= 8e-5)
		tmp = (0.3333333333333333 / (N ^ 3.0)) + ((1.0 / N) - (0.5 / (N ^ 2.0)));
	else
		tmp = -log((N / (N + 1.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[N_] := If[LessEqual[N[(N[Log[N[(N + 1.0), $MachinePrecision]], $MachinePrecision] - N[Log[N], $MachinePrecision]), $MachinePrecision], 8e-5], N[(N[(0.3333333333333333 / N[Power[N, 3.0], $MachinePrecision]), $MachinePrecision] + N[(N[(1.0 / N), $MachinePrecision] - N[(0.5 / N[Power[N, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision])]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (log.f64 (+.f64 N 1)) (log.f64 N)) < 8.00000000000000065e-5

   1. Initial program 16.8%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 16.8%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 16.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 16.9%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing

   5. Taylor expanded in N around inf 99.7%

      \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) - 0.5 \cdot \frac{1}{{N}^{2}}} \]
   6. Step-by-step derivation

      1. associate--l+ 99.7%

         \[\leadsto \color{blue}{0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right)} \]

      2. associate-*r/ 99.7%

         \[\leadsto \color{blue}{\frac{0.3333333333333333 \cdot 1}{{N}^{3}}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      3. metadata-eval 99.7%

         \[\leadsto \frac{\color{blue}{0.3333333333333333}}{{N}^{3}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      4. associate-*r/ 99.7%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \color{blue}{\frac{0.5 \cdot 1}{{N}^{2}}}\right) \]

      5. metadata-eval 99.7%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{\color{blue}{0.5}}{{N}^{2}}\right) \]

   7. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{0.5}{{N}^{2}}\right)} \]

   if 8.00000000000000065e-5 < (-.f64 (log.f64 (+.f64 N 1)) (log.f64 N))

   1. Initial program 91.0%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 91.2%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 91.3%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 91.3%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 91.0%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 93.3%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 93.0%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 93.9%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 93.9%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\log \left(N + 1\right) - \log N \leq 8 \cdot 10^{-5}:\\ \;\;\;\;\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{0.5}{{N}^{2}}\right)\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.0% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;N \leq 7200:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{\mathsf{fma}\left(0.20833333333333334, {N}^{-2}, \frac{-0.5}{N}\right)}}{N}\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= N 7200.0)
   (- (log (/ N (+ N 1.0))))
   (/ (exp (fma 0.20833333333333334 (pow N -2.0) (/ -0.5 N))) N)))

C

double code(double N) {
	double tmp;
	if (N <= 7200.0) {
		tmp = -log((N / (N + 1.0)));
	} else {
		tmp = exp(fma(0.20833333333333334, pow(N, -2.0), (-0.5 / N))) / N;
	}
	return tmp;
}

Julia

function code(N)
	tmp = 0.0
	if (N <= 7200.0)
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	else
		tmp = Float64(exp(fma(0.20833333333333334, (N ^ -2.0), Float64(-0.5 / N))) / N);
	end
	return tmp
end

Wolfram

code[N_] := If[LessEqual[N, 7200.0], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), N[(N[Exp[N[(0.20833333333333334 * N[Power[N, -2.0], $MachinePrecision] + N[(-0.5 / N), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if N < 7200

   1. Initial program 91.7%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 91.7%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 91.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 91.9%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 92.0%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 92.0%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 91.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 91.7%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 91.7%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 93.4%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 93.1%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 94.3%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 94.3%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   if 7200 < N

   1. Initial program 17.1%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 17.1%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 17.1%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 17.1%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 17.1%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 17.1%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   7. Taylor expanded in N around inf 94.5%

      \[\leadsto e^{\color{blue}{\left(\log \left(\frac{1}{N}\right) + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) - 0.5 \cdot \frac{1}{N}}} \]
   8. Step-by-step derivation

      1. sub-neg 94.5%

         \[\leadsto e^{\color{blue}{\left(\log \left(\frac{1}{N}\right) + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.5 \cdot \frac{1}{N}\right)}} \]

      2. log-rec 94.5%

         \[\leadsto e^{\left(\color{blue}{\left(-\log N\right)} + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      3. +-commutative 94.5%

         \[\leadsto e^{\color{blue}{\left(0.20833333333333334 \cdot \frac{1}{{N}^{2}} + \left(-\log N\right)\right)} + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      4. unsub-neg 94.5%

         \[\leadsto e^{\color{blue}{\left(0.20833333333333334 \cdot \frac{1}{{N}^{2}} - \log N\right)} + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      5. associate-*r/ 94.5%

         \[\leadsto e^{\left(\color{blue}{\frac{0.20833333333333334 \cdot 1}{{N}^{2}}} - \log N\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      6. metadata-eval 94.5%

         \[\leadsto e^{\left(\frac{\color{blue}{0.20833333333333334}}{{N}^{2}} - \log N\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      7. associate-*r/ 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \left(-\color{blue}{\frac{0.5 \cdot 1}{N}}\right)} \]

      8. metadata-eval 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \left(-\frac{\color{blue}{0.5}}{N}\right)} \]

      9. distribute-neg-frac 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \color{blue}{\frac{-0.5}{N}}} \]

      10. metadata-eval 94.5%

          \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{\color{blue}{-0.5}}{N}} \]

   9. Simplified 94.5%

      \[\leadsto e^{\color{blue}{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{-0.5}{N}}} \]
   10. Step-by-step derivation

       1. expm1-log1p-u 94.5%

          \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{-0.5}{N}}\right)\right)} \]

       2. expm1-undefine 19.8%

          \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{-0.5}{N}}\right)} - 1} \]

       3. exp-sum 19.8%

          \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{e^{\frac{0.20833333333333334}{{N}^{2}} - \log N} \cdot e^{\frac{-0.5}{N}}}\right)} - 1 \]

       4. exp-diff 19.8%

          \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\frac{e^{\frac{0.20833333333333334}{{N}^{2}}}}{e^{\log N}}} \cdot e^{\frac{-0.5}{N}}\right)} - 1 \]

       5. add-exp-log 19.8%

          \[\leadsto e^{\mathsf{log1p}\left(\frac{e^{\frac{0.20833333333333334}{{N}^{2}}}}{\color{blue}{N}} \cdot e^{\frac{-0.5}{N}}\right)} - 1 \]

       6. div-inv 19.8%

          \[\leadsto e^{\mathsf{log1p}\left(\frac{e^{\color{blue}{0.20833333333333334 \cdot \frac{1}{{N}^{2}}}}}{N} \cdot e^{\frac{-0.5}{N}}\right)} - 1 \]

       7. pow-flip 19.8%

          \[\leadsto e^{\mathsf{log1p}\left(\frac{e^{0.20833333333333334 \cdot \color{blue}{{N}^{\left(-2\right)}}}}{N} \cdot e^{\frac{-0.5}{N}}\right)} - 1 \]

       8. metadata-eval 19.8%

          \[\leadsto e^{\mathsf{log1p}\left(\frac{e^{0.20833333333333334 \cdot {N}^{\color{blue}{-2}}}}{N} \cdot e^{\frac{-0.5}{N}}\right)} - 1 \]

       9. exp-prod 19.8%

          \[\leadsto e^{\mathsf{log1p}\left(\frac{\color{blue}{{\left(e^{0.20833333333333334}\right)}^{\left({N}^{-2}\right)}}}{N} \cdot e^{\frac{-0.5}{N}}\right)} - 1 \]

   11. Applied egg-rr 19.8%

       \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{{\left(e^{0.20833333333333334}\right)}^{\left({N}^{-2}\right)}}{N} \cdot e^{\frac{-0.5}{N}}\right)} - 1} \]
   12. Step-by-step derivation

       1. log1p-undefine 19.8%

          \[\leadsto e^{\color{blue}{\log \left(1 + \frac{{\left(e^{0.20833333333333334}\right)}^{\left({N}^{-2}\right)}}{N} \cdot e^{\frac{-0.5}{N}}\right)}} - 1 \]

       2. rem-exp-log 19.8%

          \[\leadsto \color{blue}{\left(1 + \frac{{\left(e^{0.20833333333333334}\right)}^{\left({N}^{-2}\right)}}{N} \cdot e^{\frac{-0.5}{N}}\right)} - 1 \]

       3. exp-prod 19.8%

          \[\leadsto \left(1 + \frac{\color{blue}{e^{0.20833333333333334 \cdot {N}^{-2}}}}{N} \cdot e^{\frac{-0.5}{N}}\right) - 1 \]

       4. *-commutative 19.8%

          \[\leadsto \left(1 + \frac{e^{\color{blue}{{N}^{-2} \cdot 0.20833333333333334}}}{N} \cdot e^{\frac{-0.5}{N}}\right) - 1 \]

       5. rem-exp-log 19.8%

          \[\leadsto \left(1 + \frac{e^{{N}^{-2} \cdot 0.20833333333333334}}{\color{blue}{e^{\log N}}} \cdot e^{\frac{-0.5}{N}}\right) - 1 \]

       6. exp-diff 19.8%

          \[\leadsto \left(1 + \color{blue}{e^{{N}^{-2} \cdot 0.20833333333333334 - \log N}} \cdot e^{\frac{-0.5}{N}}\right) - 1 \]

       7. exp-sum 19.8%

          \[\leadsto \left(1 + \color{blue}{e^{\left({N}^{-2} \cdot 0.20833333333333334 - \log N\right) + \frac{-0.5}{N}}}\right) - 1 \]

       8. associate-+r- 21.0%

          \[\leadsto \color{blue}{1 + \left(e^{\left({N}^{-2} \cdot 0.20833333333333334 - \log N\right) + \frac{-0.5}{N}} - 1\right)} \]

       9. expm1-undefine 21.0%

          \[\leadsto 1 + \color{blue}{\mathsf{expm1}\left(\left({N}^{-2} \cdot 0.20833333333333334 - \log N\right) + \frac{-0.5}{N}\right)} \]

       10. rem-exp-log 21.0%

           \[\leadsto \color{blue}{e^{\log \left(1 + \mathsf{expm1}\left(\left({N}^{-2} \cdot 0.20833333333333334 - \log N\right) + \frac{-0.5}{N}\right)\right)}} \]

       11. log1p-define 21.0%

           \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\left({N}^{-2} \cdot 0.20833333333333334 - \log N\right) + \frac{-0.5}{N}\right)\right)}} \]

       12. log1p-expm1 94.5%

           \[\leadsto e^{\color{blue}{\left({N}^{-2} \cdot 0.20833333333333334 - \log N\right) + \frac{-0.5}{N}}} \]

   13. Simplified 99.7%

       \[\leadsto \color{blue}{\frac{e^{\mathsf{fma}\left(0.20833333333333334, {N}^{-2}, \frac{-0.5}{N}\right)}}{N}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;N \leq 7200:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{e^{\mathsf{fma}\left(0.20833333333333334, {N}^{-2}, \frac{-0.5}{N}\right)}}{N}\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;N \leq 10500:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \frac{-0.5}{{N}^{2}}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= N 10500.0)
   (- (log (/ N (+ N 1.0))))
   (+ (/ 1.0 N) (+ (/ 0.3333333333333333 (pow N 3.0)) (/ -0.5 (pow N 2.0))))))

C

double code(double N) {
	double tmp;
	if (N <= 10500.0) {
		tmp = -log((N / (N + 1.0)));
	} else {
		tmp = (1.0 / N) + ((0.3333333333333333 / pow(N, 3.0)) + (-0.5 / pow(N, 2.0)));
	}
	return tmp;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    real(8) :: tmp
    if (n <= 10500.0d0) then
        tmp = -log((n / (n + 1.0d0)))
    else
        tmp = (1.0d0 / n) + ((0.3333333333333333d0 / (n ** 3.0d0)) + ((-0.5d0) / (n ** 2.0d0)))
    end if
    code = tmp
end function

Java

public static double code(double N) {
	double tmp;
	if (N <= 10500.0) {
		tmp = -Math.log((N / (N + 1.0)));
	} else {
		tmp = (1.0 / N) + ((0.3333333333333333 / Math.pow(N, 3.0)) + (-0.5 / Math.pow(N, 2.0)));
	}
	return tmp;
}

Python

def code(N):
	tmp = 0
	if N <= 10500.0:
		tmp = -math.log((N / (N + 1.0)))
	else:
		tmp = (1.0 / N) + ((0.3333333333333333 / math.pow(N, 3.0)) + (-0.5 / math.pow(N, 2.0)))
	return tmp

Julia

function code(N)
	tmp = 0.0
	if (N <= 10500.0)
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	else
		tmp = Float64(Float64(1.0 / N) + Float64(Float64(0.3333333333333333 / (N ^ 3.0)) + Float64(-0.5 / (N ^ 2.0))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(N)
	tmp = 0.0;
	if (N <= 10500.0)
		tmp = -log((N / (N + 1.0)));
	else
		tmp = (1.0 / N) + ((0.3333333333333333 / (N ^ 3.0)) + (-0.5 / (N ^ 2.0)));
	end
	tmp_2 = tmp;
end

Wolfram

code[N_] := If[LessEqual[N, 10500.0], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), N[(N[(1.0 / N), $MachinePrecision] + N[(N[(0.3333333333333333 / N[Power[N, 3.0], $MachinePrecision]), $MachinePrecision] + N[(-0.5 / N[Power[N, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if N < 10500

   1. Initial program 91.0%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 91.2%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 91.3%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 91.3%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 91.0%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 93.3%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 93.0%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 93.9%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 93.9%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   if 10500 < N

   1. Initial program 16.8%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 16.8%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 16.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 16.9%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing

   5. Taylor expanded in N around inf 99.7%

      \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) - 0.5 \cdot \frac{1}{{N}^{2}}} \]
   6. Step-by-step derivation

      1. sub-neg 99.7%

         \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) + \left(-0.5 \cdot \frac{1}{{N}^{2}}\right)} \]

      2. +-commutative 99.7%

         \[\leadsto \color{blue}{\left(\frac{1}{N} + 0.3333333333333333 \cdot \frac{1}{{N}^{3}}\right)} + \left(-0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      3. associate-+l+ 99.7%

         \[\leadsto \color{blue}{\frac{1}{N} + \left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \left(-0.5 \cdot \frac{1}{{N}^{2}}\right)\right)} \]

      4. associate-*r/ 99.7%

         \[\leadsto \frac{1}{N} + \left(\color{blue}{\frac{0.3333333333333333 \cdot 1}{{N}^{3}}} + \left(-0.5 \cdot \frac{1}{{N}^{2}}\right)\right) \]

      5. metadata-eval 99.7%

         \[\leadsto \frac{1}{N} + \left(\frac{\color{blue}{0.3333333333333333}}{{N}^{3}} + \left(-0.5 \cdot \frac{1}{{N}^{2}}\right)\right) \]

      6. associate-*r/ 99.7%

         \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(-\color{blue}{\frac{0.5 \cdot 1}{{N}^{2}}}\right)\right) \]

      7. metadata-eval 99.7%

         \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \left(-\frac{\color{blue}{0.5}}{{N}^{2}}\right)\right) \]

      8. distribute-neg-frac 99.7%

         \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{-0.5}{{N}^{2}}}\right) \]

      9. metadata-eval 99.7%

         \[\leadsto \frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{-0.5}}{{N}^{2}}\right) \]

   7. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \frac{-0.5}{{N}^{2}}\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;N \leq 10500:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{N} + \left(\frac{0.3333333333333333}{{N}^{3}} + \frac{-0.5}{{N}^{2}}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 98.7% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;N \leq 10500:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{0.3333333333333333}{{N}^{3}} + {N}^{-2} \cdot \left(N + -0.5\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= N 10500.0)
   (- (log (/ N (+ N 1.0))))
   (+ (/ 0.3333333333333333 (pow N 3.0)) (* (pow N -2.0) (+ N -0.5)))))

C

double code(double N) {
	double tmp;
	if (N <= 10500.0) {
		tmp = -log((N / (N + 1.0)));
	} else {
		tmp = (0.3333333333333333 / pow(N, 3.0)) + (pow(N, -2.0) * (N + -0.5));
	}
	return tmp;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    real(8) :: tmp
    if (n <= 10500.0d0) then
        tmp = -log((n / (n + 1.0d0)))
    else
        tmp = (0.3333333333333333d0 / (n ** 3.0d0)) + ((n ** (-2.0d0)) * (n + (-0.5d0)))
    end if
    code = tmp
end function

Java

public static double code(double N) {
	double tmp;
	if (N <= 10500.0) {
		tmp = -Math.log((N / (N + 1.0)));
	} else {
		tmp = (0.3333333333333333 / Math.pow(N, 3.0)) + (Math.pow(N, -2.0) * (N + -0.5));
	}
	return tmp;
}

Python

def code(N):
	tmp = 0
	if N <= 10500.0:
		tmp = -math.log((N / (N + 1.0)))
	else:
		tmp = (0.3333333333333333 / math.pow(N, 3.0)) + (math.pow(N, -2.0) * (N + -0.5))
	return tmp

Julia

function code(N)
	tmp = 0.0
	if (N <= 10500.0)
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	else
		tmp = Float64(Float64(0.3333333333333333 / (N ^ 3.0)) + Float64((N ^ -2.0) * Float64(N + -0.5)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(N)
	tmp = 0.0;
	if (N <= 10500.0)
		tmp = -log((N / (N + 1.0)));
	else
		tmp = (0.3333333333333333 / (N ^ 3.0)) + ((N ^ -2.0) * (N + -0.5));
	end
	tmp_2 = tmp;
end

Wolfram

code[N_] := If[LessEqual[N, 10500.0], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), N[(N[(0.3333333333333333 / N[Power[N, 3.0], $MachinePrecision]), $MachinePrecision] + N[(N[Power[N, -2.0], $MachinePrecision] * N[(N + -0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if N < 10500

   1. Initial program 91.0%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 91.2%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 91.3%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 91.3%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 91.0%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 93.3%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 93.0%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 93.9%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 93.9%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   if 10500 < N

   1. Initial program 16.8%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 16.8%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 16.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 16.9%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing

   5. Taylor expanded in N around inf 99.7%

      \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) - 0.5 \cdot \frac{1}{{N}^{2}}} \]
   6. Step-by-step derivation

      1. associate--l+ 99.7%

         \[\leadsto \color{blue}{0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right)} \]

      2. associate-*r/ 99.7%

         \[\leadsto \color{blue}{\frac{0.3333333333333333 \cdot 1}{{N}^{3}}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      3. metadata-eval 99.7%

         \[\leadsto \frac{\color{blue}{0.3333333333333333}}{{N}^{3}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      4. associate-*r/ 99.7%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \color{blue}{\frac{0.5 \cdot 1}{{N}^{2}}}\right) \]

      5. metadata-eval 99.7%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{\color{blue}{0.5}}{{N}^{2}}\right) \]

   7. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{0.5}{{N}^{2}}\right)} \]
   8. Step-by-step derivation

      1. frac-sub 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{1 \cdot {N}^{2} - N \cdot 0.5}{N \cdot {N}^{2}}} \]

      2. *-un-lft-identity 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{{N}^{2}} - N \cdot 0.5}{N \cdot {N}^{2}} \]

      3. unpow2 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{{N}^{2} - N \cdot 0.5}{N \cdot \color{blue}{\left(N \cdot N\right)}} \]

      4. cube-mult 99.3%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{{N}^{2} - N \cdot 0.5}{\color{blue}{{N}^{3}}} \]

   9. Applied egg-rr 99.3%

      \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{{N}^{2} - N \cdot 0.5}{{N}^{3}}} \]
   10. Step-by-step derivation

       1. unpow2 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{N \cdot N} - N \cdot 0.5}{{N}^{3}} \]

       2. distribute-lft-out-- 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{N \cdot \left(N - 0.5\right)}}{{N}^{3}} \]

   11. Simplified 99.3%

       \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{N \cdot \left(N - 0.5\right)}{{N}^{3}}} \]
   12. Step-by-step derivation

       1. *-un-lft-identity 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{1 \cdot \frac{N \cdot \left(N - 0.5\right)}{{N}^{3}}} \]

       2. associate-/l* 99.2%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + 1 \cdot \color{blue}{\left(N \cdot \frac{N - 0.5}{{N}^{3}}\right)} \]

       3. div-inv 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + 1 \cdot \left(N \cdot \color{blue}{\left(\left(N - 0.5\right) \cdot \frac{1}{{N}^{3}}\right)}\right) \]

       4. sub-neg 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + 1 \cdot \left(N \cdot \left(\color{blue}{\left(N + \left(-0.5\right)\right)} \cdot \frac{1}{{N}^{3}}\right)\right) \]

       5. metadata-eval 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + 1 \cdot \left(N \cdot \left(\left(N + \color{blue}{-0.5}\right) \cdot \frac{1}{{N}^{3}}\right)\right) \]

       6. pow-flip 99.4%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + 1 \cdot \left(N \cdot \left(\left(N + -0.5\right) \cdot \color{blue}{{N}^{\left(-3\right)}}\right)\right) \]

       7. metadata-eval 99.4%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + 1 \cdot \left(N \cdot \left(\left(N + -0.5\right) \cdot {N}^{\color{blue}{-3}}\right)\right) \]

   13. Applied egg-rr 99.4%

       \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{1 \cdot \left(N \cdot \left(\left(N + -0.5\right) \cdot {N}^{-3}\right)\right)} \]
   14. Step-by-step derivation

       1. *-lft-identity 99.4%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{N \cdot \left(\left(N + -0.5\right) \cdot {N}^{-3}\right)} \]

       2. associate-*r* 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\left(N \cdot \left(N + -0.5\right)\right) \cdot {N}^{-3}} \]

       3. *-commutative 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{{N}^{-3} \cdot \left(N \cdot \left(N + -0.5\right)\right)} \]

       4. associate-*r* 99.4%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\left({N}^{-3} \cdot N\right) \cdot \left(N + -0.5\right)} \]

       5. pow-plus 99.5%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{{N}^{\left(-3 + 1\right)}} \cdot \left(N + -0.5\right) \]

       6. metadata-eval 99.5%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + {N}^{\color{blue}{-2}} \cdot \left(N + -0.5\right) \]

   15. Simplified 99.5%

       \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{{N}^{-2} \cdot \left(N + -0.5\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;N \leq 10500:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{0.3333333333333333}{{N}^{3}} + {N}^{-2} \cdot \left(N + -0.5\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 98.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;N \leq 10500:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;{N}^{-3} \cdot \mathsf{fma}\left(N, N + -0.5, 0.3333333333333333\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= N 10500.0)
   (- (log (/ N (+ N 1.0))))
   (* (pow N -3.0) (fma N (+ N -0.5) 0.3333333333333333))))

C

double code(double N) {
	double tmp;
	if (N <= 10500.0) {
		tmp = -log((N / (N + 1.0)));
	} else {
		tmp = pow(N, -3.0) * fma(N, (N + -0.5), 0.3333333333333333);
	}
	return tmp;
}

Julia

function code(N)
	tmp = 0.0
	if (N <= 10500.0)
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	else
		tmp = Float64((N ^ -3.0) * fma(N, Float64(N + -0.5), 0.3333333333333333));
	end
	return tmp
end

Wolfram

code[N_] := If[LessEqual[N, 10500.0], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), N[(N[Power[N, -3.0], $MachinePrecision] * N[(N * N[(N + -0.5), $MachinePrecision] + 0.3333333333333333), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if N < 10500

   1. Initial program 91.0%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 91.2%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 91.3%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 91.3%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 91.0%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 93.3%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 93.0%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 93.9%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 93.9%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   if 10500 < N

   1. Initial program 16.8%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 16.8%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 16.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 16.9%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing

   5. Taylor expanded in N around inf 99.7%

      \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) - 0.5 \cdot \frac{1}{{N}^{2}}} \]
   6. Step-by-step derivation

      1. associate--l+ 99.7%

         \[\leadsto \color{blue}{0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right)} \]

      2. associate-*r/ 99.7%

         \[\leadsto \color{blue}{\frac{0.3333333333333333 \cdot 1}{{N}^{3}}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      3. metadata-eval 99.7%

         \[\leadsto \frac{\color{blue}{0.3333333333333333}}{{N}^{3}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      4. associate-*r/ 99.7%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \color{blue}{\frac{0.5 \cdot 1}{{N}^{2}}}\right) \]

      5. metadata-eval 99.7%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{\color{blue}{0.5}}{{N}^{2}}\right) \]

   7. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{0.5}{{N}^{2}}\right)} \]
   8. Step-by-step derivation

      1. frac-sub 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{1 \cdot {N}^{2} - N \cdot 0.5}{N \cdot {N}^{2}}} \]

      2. *-un-lft-identity 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{{N}^{2}} - N \cdot 0.5}{N \cdot {N}^{2}} \]

      3. unpow2 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{{N}^{2} - N \cdot 0.5}{N \cdot \color{blue}{\left(N \cdot N\right)}} \]

      4. cube-mult 99.3%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{{N}^{2} - N \cdot 0.5}{\color{blue}{{N}^{3}}} \]

   9. Applied egg-rr 99.3%

      \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{{N}^{2} - N \cdot 0.5}{{N}^{3}}} \]
   10. Step-by-step derivation

       1. unpow2 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{N \cdot N} - N \cdot 0.5}{{N}^{3}} \]

       2. distribute-lft-out-- 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{N \cdot \left(N - 0.5\right)}}{{N}^{3}} \]

   11. Simplified 99.3%

       \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{N \cdot \left(N - 0.5\right)}{{N}^{3}}} \]
   12. Step-by-step derivation

       1. *-un-lft-identity 99.3%

          \[\leadsto \color{blue}{1 \cdot \left(\frac{0.3333333333333333}{{N}^{3}} + \frac{N \cdot \left(N - 0.5\right)}{{N}^{3}}\right)} \]

       2. +-commutative 99.3%

          \[\leadsto 1 \cdot \color{blue}{\left(\frac{N \cdot \left(N - 0.5\right)}{{N}^{3}} + \frac{0.3333333333333333}{{N}^{3}}\right)} \]

       3. div-inv 99.2%

          \[\leadsto 1 \cdot \left(\color{blue}{\left(N \cdot \left(N - 0.5\right)\right) \cdot \frac{1}{{N}^{3}}} + \frac{0.3333333333333333}{{N}^{3}}\right) \]

       4. div-inv 99.2%

          \[\leadsto 1 \cdot \left(\left(N \cdot \left(N - 0.5\right)\right) \cdot \frac{1}{{N}^{3}} + \color{blue}{0.3333333333333333 \cdot \frac{1}{{N}^{3}}}\right) \]

       5. distribute-rgt-out 99.2%

          \[\leadsto 1 \cdot \color{blue}{\left(\frac{1}{{N}^{3}} \cdot \left(N \cdot \left(N - 0.5\right) + 0.3333333333333333\right)\right)} \]

       6. pow-flip 99.3%

          \[\leadsto 1 \cdot \left(\color{blue}{{N}^{\left(-3\right)}} \cdot \left(N \cdot \left(N - 0.5\right) + 0.3333333333333333\right)\right) \]

       7. metadata-eval 99.3%

          \[\leadsto 1 \cdot \left({N}^{\color{blue}{-3}} \cdot \left(N \cdot \left(N - 0.5\right) + 0.3333333333333333\right)\right) \]

       8. sub-neg 99.3%

          \[\leadsto 1 \cdot \left({N}^{-3} \cdot \left(N \cdot \color{blue}{\left(N + \left(-0.5\right)\right)} + 0.3333333333333333\right)\right) \]

       9. metadata-eval 99.3%

          \[\leadsto 1 \cdot \left({N}^{-3} \cdot \left(N \cdot \left(N + \color{blue}{-0.5}\right) + 0.3333333333333333\right)\right) \]

   13. Applied egg-rr 99.3%

       \[\leadsto \color{blue}{1 \cdot \left({N}^{-3} \cdot \left(N \cdot \left(N + -0.5\right) + 0.3333333333333333\right)\right)} \]
   14. Step-by-step derivation

       1. *-lft-identity 99.3%

          \[\leadsto \color{blue}{{N}^{-3} \cdot \left(N \cdot \left(N + -0.5\right) + 0.3333333333333333\right)} \]

       2. fma-define 99.4%

          \[\leadsto {N}^{-3} \cdot \color{blue}{\mathsf{fma}\left(N, N + -0.5, 0.3333333333333333\right)} \]

   15. Simplified 99.4%

       \[\leadsto \color{blue}{{N}^{-3} \cdot \mathsf{fma}\left(N, N + -0.5, 0.3333333333333333\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;N \leq 10500:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;{N}^{-3} \cdot \mathsf{fma}\left(N, N + -0.5, 0.3333333333333333\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 98.6% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;N \leq 10500:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;{N}^{-3} \cdot \left(0.3333333333333333 + N \cdot \left(N + -0.5\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= N 10500.0)
   (- (log (/ N (+ N 1.0))))
   (* (pow N -3.0) (+ 0.3333333333333333 (* N (+ N -0.5))))))

C

double code(double N) {
	double tmp;
	if (N <= 10500.0) {
		tmp = -log((N / (N + 1.0)));
	} else {
		tmp = pow(N, -3.0) * (0.3333333333333333 + (N * (N + -0.5)));
	}
	return tmp;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    real(8) :: tmp
    if (n <= 10500.0d0) then
        tmp = -log((n / (n + 1.0d0)))
    else
        tmp = (n ** (-3.0d0)) * (0.3333333333333333d0 + (n * (n + (-0.5d0))))
    end if
    code = tmp
end function

Java

public static double code(double N) {
	double tmp;
	if (N <= 10500.0) {
		tmp = -Math.log((N / (N + 1.0)));
	} else {
		tmp = Math.pow(N, -3.0) * (0.3333333333333333 + (N * (N + -0.5)));
	}
	return tmp;
}

Python

def code(N):
	tmp = 0
	if N <= 10500.0:
		tmp = -math.log((N / (N + 1.0)))
	else:
		tmp = math.pow(N, -3.0) * (0.3333333333333333 + (N * (N + -0.5)))
	return tmp

Julia

function code(N)
	tmp = 0.0
	if (N <= 10500.0)
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	else
		tmp = Float64((N ^ -3.0) * Float64(0.3333333333333333 + Float64(N * Float64(N + -0.5))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(N)
	tmp = 0.0;
	if (N <= 10500.0)
		tmp = -log((N / (N + 1.0)));
	else
		tmp = (N ^ -3.0) * (0.3333333333333333 + (N * (N + -0.5)));
	end
	tmp_2 = tmp;
end

Wolfram

code[N_] := If[LessEqual[N, 10500.0], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), N[(N[Power[N, -3.0], $MachinePrecision] * N[(0.3333333333333333 + N[(N * N[(N + -0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if N < 10500

   1. Initial program 91.0%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 91.2%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 91.3%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 91.3%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 91.2%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 91.0%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 91.0%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 93.3%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 93.0%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 93.9%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 93.9%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   if 10500 < N

   1. Initial program 16.8%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 16.8%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 16.9%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 16.9%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing

   5. Taylor expanded in N around inf 99.7%

      \[\leadsto \color{blue}{\left(0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \frac{1}{N}\right) - 0.5 \cdot \frac{1}{{N}^{2}}} \]
   6. Step-by-step derivation

      1. associate--l+ 99.7%

         \[\leadsto \color{blue}{0.3333333333333333 \cdot \frac{1}{{N}^{3}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right)} \]

      2. associate-*r/ 99.7%

         \[\leadsto \color{blue}{\frac{0.3333333333333333 \cdot 1}{{N}^{3}}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      3. metadata-eval 99.7%

         \[\leadsto \frac{\color{blue}{0.3333333333333333}}{{N}^{3}} + \left(\frac{1}{N} - 0.5 \cdot \frac{1}{{N}^{2}}\right) \]

      4. associate-*r/ 99.7%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \color{blue}{\frac{0.5 \cdot 1}{{N}^{2}}}\right) \]

      5. metadata-eval 99.7%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{\color{blue}{0.5}}{{N}^{2}}\right) \]

   7. Simplified 99.7%

      \[\leadsto \color{blue}{\frac{0.3333333333333333}{{N}^{3}} + \left(\frac{1}{N} - \frac{0.5}{{N}^{2}}\right)} \]
   8. Step-by-step derivation

      1. frac-sub 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{1 \cdot {N}^{2} - N \cdot 0.5}{N \cdot {N}^{2}}} \]

      2. *-un-lft-identity 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{{N}^{2}} - N \cdot 0.5}{N \cdot {N}^{2}} \]

      3. unpow2 99.4%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{{N}^{2} - N \cdot 0.5}{N \cdot \color{blue}{\left(N \cdot N\right)}} \]

      4. cube-mult 99.3%

         \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{{N}^{2} - N \cdot 0.5}{\color{blue}{{N}^{3}}} \]

   9. Applied egg-rr 99.3%

      \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{{N}^{2} - N \cdot 0.5}{{N}^{3}}} \]
   10. Step-by-step derivation

       1. unpow2 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{N \cdot N} - N \cdot 0.5}{{N}^{3}} \]

       2. distribute-lft-out-- 99.3%

          \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \frac{\color{blue}{N \cdot \left(N - 0.5\right)}}{{N}^{3}} \]

   11. Simplified 99.3%

       \[\leadsto \frac{0.3333333333333333}{{N}^{3}} + \color{blue}{\frac{N \cdot \left(N - 0.5\right)}{{N}^{3}}} \]
   12. Step-by-step derivation

       1. +-commutative 99.3%

          \[\leadsto \color{blue}{\frac{N \cdot \left(N - 0.5\right)}{{N}^{3}} + \frac{0.3333333333333333}{{N}^{3}}} \]

       2. div-inv 99.2%

          \[\leadsto \color{blue}{\left(N \cdot \left(N - 0.5\right)\right) \cdot \frac{1}{{N}^{3}}} + \frac{0.3333333333333333}{{N}^{3}} \]

       3. div-inv 99.2%

          \[\leadsto \left(N \cdot \left(N - 0.5\right)\right) \cdot \frac{1}{{N}^{3}} + \color{blue}{0.3333333333333333 \cdot \frac{1}{{N}^{3}}} \]

       4. distribute-rgt-out 99.2%

          \[\leadsto \color{blue}{\frac{1}{{N}^{3}} \cdot \left(N \cdot \left(N - 0.5\right) + 0.3333333333333333\right)} \]

       5. pow-flip 99.3%

          \[\leadsto \color{blue}{{N}^{\left(-3\right)}} \cdot \left(N \cdot \left(N - 0.5\right) + 0.3333333333333333\right) \]

       6. metadata-eval 99.3%

          \[\leadsto {N}^{\color{blue}{-3}} \cdot \left(N \cdot \left(N - 0.5\right) + 0.3333333333333333\right) \]

       7. sub-neg 99.3%

          \[\leadsto {N}^{-3} \cdot \left(N \cdot \color{blue}{\left(N + \left(-0.5\right)\right)} + 0.3333333333333333\right) \]

       8. metadata-eval 99.3%

          \[\leadsto {N}^{-3} \cdot \left(N \cdot \left(N + \color{blue}{-0.5}\right) + 0.3333333333333333\right) \]

   13. Applied egg-rr 99.3%

       \[\leadsto \color{blue}{{N}^{-3} \cdot \left(N \cdot \left(N + -0.5\right) + 0.3333333333333333\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;N \leq 10500:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;{N}^{-3} \cdot \left(0.3333333333333333 + N \cdot \left(N + -0.5\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 97.9% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;N \leq 250000:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{N} + \frac{\frac{-0.5}{N}}{N}\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= N 250000.0) (- (log (/ N (+ N 1.0)))) (+ (/ 1.0 N) (/ (/ -0.5 N) N))))

C

double code(double N) {
	double tmp;
	if (N <= 250000.0) {
		tmp = -log((N / (N + 1.0)));
	} else {
		tmp = (1.0 / N) + ((-0.5 / N) / N);
	}
	return tmp;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    real(8) :: tmp
    if (n <= 250000.0d0) then
        tmp = -log((n / (n + 1.0d0)))
    else
        tmp = (1.0d0 / n) + (((-0.5d0) / n) / n)
    end if
    code = tmp
end function

Java

public static double code(double N) {
	double tmp;
	if (N <= 250000.0) {
		tmp = -Math.log((N / (N + 1.0)));
	} else {
		tmp = (1.0 / N) + ((-0.5 / N) / N);
	}
	return tmp;
}

Python

def code(N):
	tmp = 0
	if N <= 250000.0:
		tmp = -math.log((N / (N + 1.0)))
	else:
		tmp = (1.0 / N) + ((-0.5 / N) / N)
	return tmp

Julia

function code(N)
	tmp = 0.0
	if (N <= 250000.0)
		tmp = Float64(-log(Float64(N / Float64(N + 1.0))));
	else
		tmp = Float64(Float64(1.0 / N) + Float64(Float64(-0.5 / N) / N));
	end
	return tmp
end

MATLAB

function tmp_2 = code(N)
	tmp = 0.0;
	if (N <= 250000.0)
		tmp = -log((N / (N + 1.0)));
	else
		tmp = (1.0 / N) + ((-0.5 / N) / N);
	end
	tmp_2 = tmp;
end

Wolfram

code[N_] := If[LessEqual[N, 250000.0], (-N[Log[N[(N / N[(N + 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), N[(N[(1.0 / N), $MachinePrecision] + N[(N[(-0.5 / N), $MachinePrecision] / N), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if N < 2.5e5

   1. Initial program 87.2%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 87.2%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 87.3%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 87.3%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 87.4%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 87.4%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]
   7. Step-by-step derivation

      1. rem-exp-log 87.3%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]

      2. log1p-undefine 87.2%

         \[\leadsto \color{blue}{\log \left(1 + N\right)} - \log N \]

      3. +-commutative 87.2%

         \[\leadsto \log \color{blue}{\left(N + 1\right)} - \log N \]

      4. diff-log 90.1%

         \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

      5. clear-num 89.9%

         \[\leadsto \log \color{blue}{\left(\frac{1}{\frac{N}{N + 1}}\right)} \]

      6. log-rec 90.9%

         \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   8. Applied egg-rr 90.9%

      \[\leadsto \color{blue}{-\log \left(\frac{N}{N + 1}\right)} \]

   if 2.5e5 < N

   1. Initial program 15.0%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 15.0%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 15.1%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 15.1%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 15.1%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 15.1%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   7. Taylor expanded in N around inf 94.5%

      \[\leadsto e^{\color{blue}{\left(\log \left(\frac{1}{N}\right) + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) - 0.5 \cdot \frac{1}{N}}} \]
   8. Step-by-step derivation

      1. sub-neg 94.5%

         \[\leadsto e^{\color{blue}{\left(\log \left(\frac{1}{N}\right) + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.5 \cdot \frac{1}{N}\right)}} \]

      2. log-rec 94.5%

         \[\leadsto e^{\left(\color{blue}{\left(-\log N\right)} + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      3. +-commutative 94.5%

         \[\leadsto e^{\color{blue}{\left(0.20833333333333334 \cdot \frac{1}{{N}^{2}} + \left(-\log N\right)\right)} + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      4. unsub-neg 94.5%

         \[\leadsto e^{\color{blue}{\left(0.20833333333333334 \cdot \frac{1}{{N}^{2}} - \log N\right)} + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      5. associate-*r/ 94.5%

         \[\leadsto e^{\left(\color{blue}{\frac{0.20833333333333334 \cdot 1}{{N}^{2}}} - \log N\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      6. metadata-eval 94.5%

         \[\leadsto e^{\left(\frac{\color{blue}{0.20833333333333334}}{{N}^{2}} - \log N\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      7. associate-*r/ 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \left(-\color{blue}{\frac{0.5 \cdot 1}{N}}\right)} \]

      8. metadata-eval 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \left(-\frac{\color{blue}{0.5}}{N}\right)} \]

      9. distribute-neg-frac 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \color{blue}{\frac{-0.5}{N}}} \]

      10. metadata-eval 94.5%

          \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{\color{blue}{-0.5}}{N}} \]

   9. Simplified 94.5%

      \[\leadsto e^{\color{blue}{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{-0.5}{N}}} \]

   10. Taylor expanded in N around inf 94.0%

       \[\leadsto \color{blue}{e^{--1 \cdot \log \left(\frac{1}{N}\right)} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N}} \]
   11. Step-by-step derivation

       1. log-rec 94.0%

          \[\leadsto e^{--1 \cdot \color{blue}{\left(-\log N\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       2. distribute-rgt-neg-in 94.0%

          \[\leadsto e^{\color{blue}{-1 \cdot \left(-\left(-\log N\right)\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       3. exp-prod 93.8%

          \[\leadsto \color{blue}{{\left(e^{-1}\right)}^{\left(-\left(-\log N\right)\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       4. remove-double-neg 93.8%

          \[\leadsto {\left(e^{-1}\right)}^{\color{blue}{\log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       5. exp-prod 94.0%

          \[\leadsto \color{blue}{e^{-1 \cdot \log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       6. neg-mul-1 94.0%

          \[\leadsto e^{\color{blue}{-\log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       7. log-rec 94.0%

          \[\leadsto e^{\color{blue}{\log \left(\frac{1}{N}\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       8. rem-exp-log 98.9%

          \[\leadsto \color{blue}{\frac{1}{N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       9. associate-*r/ 98.9%

          \[\leadsto \frac{1}{N} + \color{blue}{\frac{-0.5 \cdot e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N}} \]

   12. Simplified 98.9%

       \[\leadsto \color{blue}{\frac{1}{N} + \frac{\frac{-0.5}{N}}{N}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;N \leq 250000:\\ \;\;\;\;-\log \left(\frac{N}{N + 1}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{N} + \frac{\frac{-0.5}{N}}{N}\\ \end{array} \]
5. Add Preprocessing

Alternative 10: 97.7% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;N \leq 240000:\\ \;\;\;\;\log \left(1 + \frac{1}{N}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{N} + \frac{\frac{-0.5}{N}}{N}\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= N 240000.0) (log (+ 1.0 (/ 1.0 N))) (+ (/ 1.0 N) (/ (/ -0.5 N) N))))

C

double code(double N) {
	double tmp;
	if (N <= 240000.0) {
		tmp = log((1.0 + (1.0 / N)));
	} else {
		tmp = (1.0 / N) + ((-0.5 / N) / N);
	}
	return tmp;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    real(8) :: tmp
    if (n <= 240000.0d0) then
        tmp = log((1.0d0 + (1.0d0 / n)))
    else
        tmp = (1.0d0 / n) + (((-0.5d0) / n) / n)
    end if
    code = tmp
end function

Java

public static double code(double N) {
	double tmp;
	if (N <= 240000.0) {
		tmp = Math.log((1.0 + (1.0 / N)));
	} else {
		tmp = (1.0 / N) + ((-0.5 / N) / N);
	}
	return tmp;
}

Python

def code(N):
	tmp = 0
	if N <= 240000.0:
		tmp = math.log((1.0 + (1.0 / N)))
	else:
		tmp = (1.0 / N) + ((-0.5 / N) / N)
	return tmp

Julia

function code(N)
	tmp = 0.0
	if (N <= 240000.0)
		tmp = log(Float64(1.0 + Float64(1.0 / N)));
	else
		tmp = Float64(Float64(1.0 / N) + Float64(Float64(-0.5 / N) / N));
	end
	return tmp
end

MATLAB

function tmp_2 = code(N)
	tmp = 0.0;
	if (N <= 240000.0)
		tmp = log((1.0 + (1.0 / N)));
	else
		tmp = (1.0 / N) + ((-0.5 / N) / N);
	end
	tmp_2 = tmp;
end

Wolfram

code[N_] := If[LessEqual[N, 240000.0], N[Log[N[(1.0 + N[(1.0 / N), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], N[(N[(1.0 / N), $MachinePrecision] + N[(N[(-0.5 / N), $MachinePrecision] / N), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if N < 2.4e5

   1. Initial program 87.8%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 87.8%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 88.0%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 88.0%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-log-exp 87.9%

         \[\leadsto \color{blue}{\log \left(e^{\mathsf{log1p}\left(N\right)}\right)} - \log N \]

      2. log1p-expm1-u 87.9%

         \[\leadsto \log \left(e^{\mathsf{log1p}\left(N\right)}\right) - \color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\log N\right)\right)} \]

      3. log1p-undefine 87.9%

         \[\leadsto \log \left(e^{\mathsf{log1p}\left(N\right)}\right) - \color{blue}{\log \left(1 + \mathsf{expm1}\left(\log N\right)\right)} \]

      4. diff-log 87.9%

         \[\leadsto \color{blue}{\log \left(\frac{e^{\mathsf{log1p}\left(N\right)}}{1 + \mathsf{expm1}\left(\log N\right)}\right)} \]

      5. log1p-undefine 87.9%

         \[\leadsto \log \left(\frac{e^{\color{blue}{\log \left(1 + N\right)}}}{1 + \mathsf{expm1}\left(\log N\right)}\right) \]

      6. rem-exp-log 87.6%

         \[\leadsto \log \left(\frac{\color{blue}{1 + N}}{1 + \mathsf{expm1}\left(\log N\right)}\right) \]

      7. +-commutative 87.6%

         \[\leadsto \log \left(\frac{\color{blue}{N + 1}}{1 + \mathsf{expm1}\left(\log N\right)}\right) \]

      8. add-exp-log 87.6%

         \[\leadsto \log \left(\frac{N + 1}{\color{blue}{e^{\log \left(1 + \mathsf{expm1}\left(\log N\right)\right)}}}\right) \]

      9. log1p-undefine 87.6%

         \[\leadsto \log \left(\frac{N + 1}{e^{\color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\log N\right)\right)}}}\right) \]

      10. log1p-expm1-u 87.6%

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

      11. add-exp-log 90.6%

          \[\leadsto \log \left(\frac{N + 1}{\color{blue}{N}}\right) \]

   6. Applied egg-rr 90.6%

      \[\leadsto \color{blue}{\log \left(\frac{N + 1}{N}\right)} \]

   7. Taylor expanded in N around 0 90.7%

      \[\leadsto \log \color{blue}{\left(1 + \frac{1}{N}\right)} \]

   if 2.4e5 < N

   1. Initial program 15.3%

      \[\log \left(N + 1\right) - \log N \]
   2. Step-by-step derivation

      1. +-commutative 15.3%

         \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

      2. log1p-define 15.3%

         \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

   3. Simplified 15.3%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. add-exp-log 15.3%

         \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   6. Applied egg-rr 15.3%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

   7. Taylor expanded in N around inf 94.5%

      \[\leadsto e^{\color{blue}{\left(\log \left(\frac{1}{N}\right) + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) - 0.5 \cdot \frac{1}{N}}} \]
   8. Step-by-step derivation

      1. sub-neg 94.5%

         \[\leadsto e^{\color{blue}{\left(\log \left(\frac{1}{N}\right) + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.5 \cdot \frac{1}{N}\right)}} \]

      2. log-rec 94.5%

         \[\leadsto e^{\left(\color{blue}{\left(-\log N\right)} + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      3. +-commutative 94.5%

         \[\leadsto e^{\color{blue}{\left(0.20833333333333334 \cdot \frac{1}{{N}^{2}} + \left(-\log N\right)\right)} + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      4. unsub-neg 94.5%

         \[\leadsto e^{\color{blue}{\left(0.20833333333333334 \cdot \frac{1}{{N}^{2}} - \log N\right)} + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      5. associate-*r/ 94.5%

         \[\leadsto e^{\left(\color{blue}{\frac{0.20833333333333334 \cdot 1}{{N}^{2}}} - \log N\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      6. metadata-eval 94.5%

         \[\leadsto e^{\left(\frac{\color{blue}{0.20833333333333334}}{{N}^{2}} - \log N\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

      7. associate-*r/ 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \left(-\color{blue}{\frac{0.5 \cdot 1}{N}}\right)} \]

      8. metadata-eval 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \left(-\frac{\color{blue}{0.5}}{N}\right)} \]

      9. distribute-neg-frac 94.5%

         \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \color{blue}{\frac{-0.5}{N}}} \]

      10. metadata-eval 94.5%

          \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{\color{blue}{-0.5}}{N}} \]

   9. Simplified 94.5%

      \[\leadsto e^{\color{blue}{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{-0.5}{N}}} \]

   10. Taylor expanded in N around inf 93.9%

       \[\leadsto \color{blue}{e^{--1 \cdot \log \left(\frac{1}{N}\right)} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N}} \]
   11. Step-by-step derivation

       1. log-rec 93.9%

          \[\leadsto e^{--1 \cdot \color{blue}{\left(-\log N\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       2. distribute-rgt-neg-in 93.9%

          \[\leadsto e^{\color{blue}{-1 \cdot \left(-\left(-\log N\right)\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       3. exp-prod 93.7%

          \[\leadsto \color{blue}{{\left(e^{-1}\right)}^{\left(-\left(-\log N\right)\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       4. remove-double-neg 93.7%

          \[\leadsto {\left(e^{-1}\right)}^{\color{blue}{\log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       5. exp-prod 93.9%

          \[\leadsto \color{blue}{e^{-1 \cdot \log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       6. neg-mul-1 93.9%

          \[\leadsto e^{\color{blue}{-\log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       7. log-rec 93.9%

          \[\leadsto e^{\color{blue}{\log \left(\frac{1}{N}\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       8. rem-exp-log 98.8%

          \[\leadsto \color{blue}{\frac{1}{N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

       9. associate-*r/ 98.8%

          \[\leadsto \frac{1}{N} + \color{blue}{\frac{-0.5 \cdot e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N}} \]

   12. Simplified 98.8%

       \[\leadsto \color{blue}{\frac{1}{N} + \frac{\frac{-0.5}{N}}{N}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;N \leq 240000:\\ \;\;\;\;\log \left(1 + \frac{1}{N}\right)\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{N} + \frac{\frac{-0.5}{N}}{N}\\ \end{array} \]
5. Add Preprocessing

Alternative 11: 92.2% accurate, 22.8× speedup

Math

\[\begin{array}{l} \\ \frac{1}{N} + \frac{\frac{-0.5}{N}}{N} \end{array} \]

FPCore

(FPCore (N) :precision binary64 (+ (/ 1.0 N) (/ (/ -0.5 N) N)))

C

double code(double N) {
	return (1.0 / N) + ((-0.5 / N) / N);
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    code = (1.0d0 / n) + (((-0.5d0) / n) / n)
end function

Java

public static double code(double N) {
	return (1.0 / N) + ((-0.5 / N) / N);
}

Python

def code(N):
	return (1.0 / N) + ((-0.5 / N) / N)

Julia

function code(N)
	return Float64(Float64(1.0 / N) + Float64(Float64(-0.5 / N) / N))
end

MATLAB

function tmp = code(N)
	tmp = (1.0 / N) + ((-0.5 / N) / N);
end

Wolfram

code[N_] := N[(N[(1.0 / N), $MachinePrecision] + N[(N[(-0.5 / N), $MachinePrecision] / N), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 24.1%

   \[\log \left(N + 1\right) - \log N \]
2. Step-by-step derivation

   1. +-commutative 24.1%

      \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

   2. log1p-define 24.1%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

3. Simplified 24.1%

   \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. add-exp-log 24.1%

      \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

6. Applied egg-rr 24.1%

   \[\leadsto \color{blue}{e^{\log \left(\mathsf{log1p}\left(N\right) - \log N\right)}} \]

7. Taylor expanded in N around inf 90.1%

   \[\leadsto e^{\color{blue}{\left(\log \left(\frac{1}{N}\right) + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) - 0.5 \cdot \frac{1}{N}}} \]
8. Step-by-step derivation

   1. sub-neg 90.1%

      \[\leadsto e^{\color{blue}{\left(\log \left(\frac{1}{N}\right) + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.5 \cdot \frac{1}{N}\right)}} \]

   2. log-rec 90.1%

      \[\leadsto e^{\left(\color{blue}{\left(-\log N\right)} + 0.20833333333333334 \cdot \frac{1}{{N}^{2}}\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

   3. +-commutative 90.1%

      \[\leadsto e^{\color{blue}{\left(0.20833333333333334 \cdot \frac{1}{{N}^{2}} + \left(-\log N\right)\right)} + \left(-0.5 \cdot \frac{1}{N}\right)} \]

   4. unsub-neg 90.1%

      \[\leadsto e^{\color{blue}{\left(0.20833333333333334 \cdot \frac{1}{{N}^{2}} - \log N\right)} + \left(-0.5 \cdot \frac{1}{N}\right)} \]

   5. associate-*r/ 90.1%

      \[\leadsto e^{\left(\color{blue}{\frac{0.20833333333333334 \cdot 1}{{N}^{2}}} - \log N\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

   6. metadata-eval 90.1%

      \[\leadsto e^{\left(\frac{\color{blue}{0.20833333333333334}}{{N}^{2}} - \log N\right) + \left(-0.5 \cdot \frac{1}{N}\right)} \]

   7. associate-*r/ 90.1%

      \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \left(-\color{blue}{\frac{0.5 \cdot 1}{N}}\right)} \]

   8. metadata-eval 90.1%

      \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \left(-\frac{\color{blue}{0.5}}{N}\right)} \]

   9. distribute-neg-frac 90.1%

      \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \color{blue}{\frac{-0.5}{N}}} \]

   10. metadata-eval 90.1%

       \[\leadsto e^{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{\color{blue}{-0.5}}{N}} \]

9. Simplified 90.1%

   \[\leadsto e^{\color{blue}{\left(\frac{0.20833333333333334}{{N}^{2}} - \log N\right) + \frac{-0.5}{N}}} \]

10. Taylor expanded in N around inf 87.9%

    \[\leadsto \color{blue}{e^{--1 \cdot \log \left(\frac{1}{N}\right)} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N}} \]
11. Step-by-step derivation

    1. log-rec 87.9%

       \[\leadsto e^{--1 \cdot \color{blue}{\left(-\log N\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

    2. distribute-rgt-neg-in 87.9%

       \[\leadsto e^{\color{blue}{-1 \cdot \left(-\left(-\log N\right)\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

    3. exp-prod 87.8%

       \[\leadsto \color{blue}{{\left(e^{-1}\right)}^{\left(-\left(-\log N\right)\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

    4. remove-double-neg 87.8%

       \[\leadsto {\left(e^{-1}\right)}^{\color{blue}{\log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

    5. exp-prod 87.9%

       \[\leadsto \color{blue}{e^{-1 \cdot \log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

    6. neg-mul-1 87.9%

       \[\leadsto e^{\color{blue}{-\log N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

    7. log-rec 87.9%

       \[\leadsto e^{\color{blue}{\log \left(\frac{1}{N}\right)}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

    8. rem-exp-log 92.2%

       \[\leadsto \color{blue}{\frac{1}{N}} + -0.5 \cdot \frac{e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N} \]

    9. associate-*r/ 92.2%

       \[\leadsto \frac{1}{N} + \color{blue}{\frac{-0.5 \cdot e^{--1 \cdot \log \left(\frac{1}{N}\right)}}{N}} \]

12. Simplified 92.2%

    \[\leadsto \color{blue}{\frac{1}{N} + \frac{\frac{-0.5}{N}}{N}} \]

13. Final simplification 92.2%

    \[\leadsto \frac{1}{N} + \frac{\frac{-0.5}{N}}{N} \]
14. Add Preprocessing

Alternative 12: 84.3% accurate, 68.3× speedup

Math

\[\begin{array}{l} \\ \frac{1}{N} \end{array} \]

FPCore

(FPCore (N) :precision binary64 (/ 1.0 N))

C

double code(double N) {
	return 1.0 / N;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    code = 1.0d0 / n
end function

Java

public static double code(double N) {
	return 1.0 / N;
}

Python

def code(N):
	return 1.0 / N

Julia

function code(N)
	return Float64(1.0 / N)
end

MATLAB

function tmp = code(N)
	tmp = 1.0 / N;
end

Wolfram

code[N_] := N[(1.0 / N), $MachinePrecision]

Derivation

1. Initial program 24.1%

   \[\log \left(N + 1\right) - \log N \]
2. Step-by-step derivation

   1. +-commutative 24.1%

      \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

   2. log1p-define 24.1%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

3. Simplified 24.1%

   \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
4. Add Preprocessing

5. Taylor expanded in N around inf 84.2%

   \[\leadsto \color{blue}{\frac{1}{N}} \]

6. Final simplification 84.2%

   \[\leadsto \frac{1}{N} \]
7. Add Preprocessing

Alternative 13: 3.3% accurate, 205.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (N) :precision binary64 0.0)

C

double code(double N) {
	return 0.0;
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    code = 0.0d0
end function

Java

public static double code(double N) {
	return 0.0;
}

Python

def code(N):
	return 0.0

Julia

function code(N)
	return 0.0
end

MATLAB

function tmp = code(N)
	tmp = 0.0;
end

Wolfram

code[N_] := 0.0

Derivation

1. Initial program 24.1%

   \[\log \left(N + 1\right) - \log N \]
2. Step-by-step derivation

   1. +-commutative 24.1%

      \[\leadsto \log \color{blue}{\left(1 + N\right)} - \log N \]

   2. log1p-define 24.1%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right)} - \log N \]

3. Simplified 24.1%

   \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) - \log N} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. sub-neg 24.1%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(N\right) + \left(-\log N\right)} \]

   2. +-commutative 24.1%

      \[\leadsto \color{blue}{\left(-\log N\right) + \mathsf{log1p}\left(N\right)} \]

   3. add-sqr-sqrt 24.2%

      \[\leadsto \left(-\color{blue}{\sqrt{\log N} \cdot \sqrt{\log N}}\right) + \mathsf{log1p}\left(N\right) \]

   4. distribute-rgt-neg-in 24.2%

      \[\leadsto \color{blue}{\sqrt{\log N} \cdot \left(-\sqrt{\log N}\right)} + \mathsf{log1p}\left(N\right) \]

   5. fma-define 25.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\sqrt{\log N}, -\sqrt{\log N}, \mathsf{log1p}\left(N\right)\right)} \]

6. Applied egg-rr 25.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\sqrt{\log N}, -\sqrt{\log N}, \mathsf{log1p}\left(N\right)\right)} \]

7. Taylor expanded in N around inf 3.3%

   \[\leadsto \color{blue}{\log \left(\frac{1}{N}\right) + -1 \cdot \log \left(\frac{1}{N}\right)} \]
8. Step-by-step derivation

   1. log-rec 3.3%

      \[\leadsto \color{blue}{\left(-\log N\right)} + -1 \cdot \log \left(\frac{1}{N}\right) \]

   2. log-rec 3.3%

      \[\leadsto \left(-\log N\right) + -1 \cdot \color{blue}{\left(-\log N\right)} \]

   3. distribute-rgt1-in 3.3%

      \[\leadsto \color{blue}{\left(-1 + 1\right) \cdot \left(-\log N\right)} \]

   4. metadata-eval 3.3%

      \[\leadsto \color{blue}{0} \cdot \left(-\log N\right) \]

   5. mul0-lft 3.3%

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

9. Simplified 3.3%

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

10. Final simplification 3.3%

    \[\leadsto 0 \]
11. Add Preprocessing

Developer target: 99.8% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{log1p}\left(\frac{1}{N}\right) \end{array} \]

FPCore

(FPCore (N) :precision binary64 (log1p (/ 1.0 N)))

C

double code(double N) {
	return log1p((1.0 / N));
}

Java

public static double code(double N) {
	return Math.log1p((1.0 / N));
}

Python

def code(N):
	return math.log1p((1.0 / N))

Julia

function code(N)
	return log1p(Float64(1.0 / N))
end

Wolfram

code[N_] := N[Log[1 + N[(1.0 / N), $MachinePrecision]], $MachinePrecision]

Reproduce

herbie shell --seed 2024044 
(FPCore (N)
  :name "2log (problem 3.3.6)"
  :precision binary64
  :pre (and (> N 1.0) (< N 1e+40))

  :herbie-target
  (log1p (/ 1.0 N))

  (- (log (+ N 1.0)) (log N)))