2log (problem 3.3.6)

Percentage Accurate: 23.9% → 99.5%

Time: 7.7s

Alternatives: 8

Speedup: 17.3×

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: 23.9% 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: 99.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\log \left(1 + N\right) - \log N \leq 0.001:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(-N, -1, \frac{\frac{\frac{0.041666666666666664}{N} - 0.08333333333333333}{N} - -0.5}{N} \cdot N\right)}\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{N}{1 + N}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= (- (log (+ 1.0 N)) (log N)) 0.001)
   (/
    1.0
    (fma
     (- N)
     -1.0
     (*
      (/ (- (/ (- (/ 0.041666666666666664 N) 0.08333333333333333) N) -0.5) N)
      N)))
   (- (log (/ N (+ 1.0 N))))))

C

double code(double N) {
	double tmp;
	if ((log((1.0 + N)) - log(N)) <= 0.001) {
		tmp = 1.0 / fma(-N, -1.0, ((((((0.041666666666666664 / N) - 0.08333333333333333) / N) - -0.5) / N) * N));
	} else {
		tmp = -log((N / (1.0 + N)));
	}
	return tmp;
}

Julia

function code(N)
	tmp = 0.0
	if (Float64(log(Float64(1.0 + N)) - log(N)) <= 0.001)
		tmp = Float64(1.0 / fma(Float64(-N), -1.0, Float64(Float64(Float64(Float64(Float64(Float64(0.041666666666666664 / N) - 0.08333333333333333) / N) - -0.5) / N) * N)));
	else
		tmp = Float64(-log(Float64(N / Float64(1.0 + N))));
	end
	return tmp
end

Wolfram

code[N_] := If[LessEqual[N[(N[Log[N[(1.0 + N), $MachinePrecision]], $MachinePrecision] - N[Log[N], $MachinePrecision]), $MachinePrecision], 0.001], N[(1.0 / N[((-N) * -1.0 + N[(N[(N[(N[(N[(N[(0.041666666666666664 / N), $MachinePrecision] - 0.08333333333333333), $MachinePrecision] / N), $MachinePrecision] - -0.5), $MachinePrecision] / N), $MachinePrecision] * N), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], (-N[Log[N[(N / N[(1.0 + N), $MachinePrecision]), $MachinePrecision]], $MachinePrecision])]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (log.f64 (+.f64 N #s(literal 1 binary64))) (log.f64 N)) < 1e-3

   1. Initial program 20.9%

      \[\log \left(N + 1\right) - \log N \]
   2. Add Preprocessing

   3. Taylor expanded in N around inf

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

   5. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}{N}} \]
   6. Step-by-step derivation

   7. Applied rewrites 99.8%

      \[\leadsto \frac{1}{\color{blue}{\frac{N}{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}}} \]

   8. Taylor expanded in N around -inf

      \[\leadsto \frac{1}{-1 \cdot \color{blue}{\left(N \cdot \left(-1 \cdot \frac{\frac{1}{2} + -1 \cdot \frac{\frac{1}{12} - \frac{1}{24} \cdot \frac{1}{N}}{N}}{N} - 1\right)\right)}} \]
   9. Step-by-step derivation

   10. Applied rewrites 99.7%

       \[\leadsto \frac{1}{\left(-N\right) \cdot \color{blue}{\left(-1 - \frac{0.5 - \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)}} \]
   11. Step-by-step derivation

   12. Applied rewrites 99.9%

       \[\leadsto \frac{1}{\mathsf{fma}\left(-N, -1, \left(-N\right) \cdot \frac{-0.5 + \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)} \]

   if 1e-3 < (-.f64 (log.f64 (+.f64 N #s(literal 1 binary64))) (log.f64 N))

   1. Initial program 92.5%

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

      1. lift--.f64 N/A

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

      2. lift-log.f64 N/A

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

      3. lift-log.f64 N/A

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

      4. diff-log N/A

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

      5. lift-+.f64 N/A

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

      6. flip-+ N/A

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

      7. associate-/l/ N/A

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

      8. clear-num N/A

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

      9. log-rec N/A

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

      10. lower-neg.f64 N/A

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

      11. lower-log.f64 N/A

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

      12. distribute-rgt-out-- N/A

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

      13. lower-/.f64 N/A

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

      14. distribute-rgt-out-- N/A

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

      15. *-commutative N/A

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

      16. lower-*.f64 N/A

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

      17. lower--.f64 N/A

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

      18. metadata-eval N/A

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

      19. sub-neg N/A

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

      20. lower-fma.f64 N/A

          \[\leadsto -\log \left(\frac{\left(N - 1\right) \cdot N}{\color{blue}{\mathsf{fma}\left(N, N, \mathsf{neg}\left(1\right)\right)}}\right) \]

      21. metadata-eval 94.3

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

   4. Applied rewrites 94.3%

      \[\leadsto \color{blue}{-\log \left(\frac{\left(N - 1\right) \cdot N}{\mathsf{fma}\left(N, N, -1\right)}\right)} \]
   5. Step-by-step derivation

      1. lift-/.f64 N/A

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

      2. clear-num N/A

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

      3. inv-pow N/A

         \[\leadsto -\log \color{blue}{\left({\left(\frac{\mathsf{fma}\left(N, N, -1\right)}{\left(N - 1\right) \cdot N}\right)}^{-1}\right)} \]

      4. lift-fma.f64 N/A

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

      5. metadata-eval N/A

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

      6. sub-neg N/A

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

      7. metadata-eval N/A

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

      8. lift-*.f64 N/A

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

      9. associate-/r* N/A

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

      10. lift--.f64 N/A

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

      11. flip-+ N/A

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

      12. div-inv N/A

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

      13. lift-/.f64 N/A

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

      14. unpow-prod-down N/A

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

      15. inv-pow N/A

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

      16. inv-pow N/A

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

      17. lift-/.f64 N/A

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

      18. clear-num N/A

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

      19. times-frac N/A

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

      20. *-lft-identity N/A

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

      21. distribute-rgt1-in N/A

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

      22. *-rgt-identity N/A

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

      23. +-commutative N/A

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

      24. lower-/.f64 N/A

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

      25. +-commutative N/A

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

      26. lower-+.f64 95.6

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

   6. Applied rewrites 95.6%

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

4. Final simplification 99.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\log \left(1 + N\right) - \log N \leq 0.001:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(-N, -1, \frac{\frac{\frac{0.041666666666666664}{N} - 0.08333333333333333}{N} - -0.5}{N} \cdot N\right)}\\ \mathbf{else}:\\ \;\;\;\;-\log \left(\frac{N}{1 + N}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 99.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\log \left(1 + N\right) - \log N \leq 0.001:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(-N, -1, \frac{\frac{\frac{0.041666666666666664}{N} - 0.08333333333333333}{N} - -0.5}{N} \cdot N\right)}\\ \mathbf{else}:\\ \;\;\;\;\log \left(1 + \frac{1}{N}\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (if (<= (- (log (+ 1.0 N)) (log N)) 0.001)
   (/
    1.0
    (fma
     (- N)
     -1.0
     (*
      (/ (- (/ (- (/ 0.041666666666666664 N) 0.08333333333333333) N) -0.5) N)
      N)))
   (log (+ 1.0 (/ 1.0 N)))))

C

double code(double N) {
	double tmp;
	if ((log((1.0 + N)) - log(N)) <= 0.001) {
		tmp = 1.0 / fma(-N, -1.0, ((((((0.041666666666666664 / N) - 0.08333333333333333) / N) - -0.5) / N) * N));
	} else {
		tmp = log((1.0 + (1.0 / N)));
	}
	return tmp;
}

Julia

function code(N)
	tmp = 0.0
	if (Float64(log(Float64(1.0 + N)) - log(N)) <= 0.001)
		tmp = Float64(1.0 / fma(Float64(-N), -1.0, Float64(Float64(Float64(Float64(Float64(Float64(0.041666666666666664 / N) - 0.08333333333333333) / N) - -0.5) / N) * N)));
	else
		tmp = log(Float64(1.0 + Float64(1.0 / N)));
	end
	return tmp
end

Wolfram

code[N_] := If[LessEqual[N[(N[Log[N[(1.0 + N), $MachinePrecision]], $MachinePrecision] - N[Log[N], $MachinePrecision]), $MachinePrecision], 0.001], N[(1.0 / N[((-N) * -1.0 + N[(N[(N[(N[(N[(N[(0.041666666666666664 / N), $MachinePrecision] - 0.08333333333333333), $MachinePrecision] / N), $MachinePrecision] - -0.5), $MachinePrecision] / N), $MachinePrecision] * N), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[Log[N[(1.0 + N[(1.0 / N), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (log.f64 (+.f64 N #s(literal 1 binary64))) (log.f64 N)) < 1e-3

   1. Initial program 20.9%

      \[\log \left(N + 1\right) - \log N \]
   2. Add Preprocessing

   3. Taylor expanded in N around inf

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

   5. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\frac{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}{N}} \]
   6. Step-by-step derivation

   7. Applied rewrites 99.8%

      \[\leadsto \frac{1}{\color{blue}{\frac{N}{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}}} \]

   8. Taylor expanded in N around -inf

      \[\leadsto \frac{1}{-1 \cdot \color{blue}{\left(N \cdot \left(-1 \cdot \frac{\frac{1}{2} + -1 \cdot \frac{\frac{1}{12} - \frac{1}{24} \cdot \frac{1}{N}}{N}}{N} - 1\right)\right)}} \]
   9. Step-by-step derivation

   10. Applied rewrites 99.7%

       \[\leadsto \frac{1}{\left(-N\right) \cdot \color{blue}{\left(-1 - \frac{0.5 - \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)}} \]
   11. Step-by-step derivation

   12. Applied rewrites 99.9%

       \[\leadsto \frac{1}{\mathsf{fma}\left(-N, -1, \left(-N\right) \cdot \frac{-0.5 + \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)} \]

   if 1e-3 < (-.f64 (log.f64 (+.f64 N #s(literal 1 binary64))) (log.f64 N))

   1. Initial program 92.5%

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

      1. lift--.f64 N/A

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

      2. lift-log.f64 N/A

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

      3. lift-log.f64 N/A

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

      4. diff-log N/A

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

      5. lower-log.f64 N/A

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

      6. lower-/.f64 95.0

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

      7. lift-+.f64 N/A

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

      8. +-commutative N/A

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

      9. lower-+.f64 95.0

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

   4. Applied rewrites 95.0%

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

   5. Taylor expanded in N around inf

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

      1. +-commutative N/A

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

      2. lower-+.f64 N/A

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

      3. lower-/.f64 95.0

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

   7. Applied rewrites 95.0%

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

4. Final simplification 99.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\log \left(1 + N\right) - \log N \leq 0.001:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(-N, -1, \frac{\frac{\frac{0.041666666666666664}{N} - 0.08333333333333333}{N} - -0.5}{N} \cdot N\right)}\\ \mathbf{else}:\\ \;\;\;\;\log \left(1 + \frac{1}{N}\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 96.8% accurate, 3.2× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\mathsf{fma}\left(-N, -1, \frac{\frac{\frac{0.041666666666666664}{N} - 0.08333333333333333}{N} - -0.5}{N} \cdot N\right)} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (/
  1.0
  (fma
   (- N)
   -1.0
   (*
    (/ (- (/ (- (/ 0.041666666666666664 N) 0.08333333333333333) N) -0.5) N)
    N))))

C

double code(double N) {
	return 1.0 / fma(-N, -1.0, ((((((0.041666666666666664 / N) - 0.08333333333333333) / N) - -0.5) / N) * N));
}

Julia

function code(N)
	return Float64(1.0 / fma(Float64(-N), -1.0, Float64(Float64(Float64(Float64(Float64(Float64(0.041666666666666664 / N) - 0.08333333333333333) / N) - -0.5) / N) * N)))
end

Wolfram

code[N_] := N[(1.0 / N[((-N) * -1.0 + N[(N[(N[(N[(N[(N[(0.041666666666666664 / N), $MachinePrecision] - 0.08333333333333333), $MachinePrecision] / N), $MachinePrecision] - -0.5), $MachinePrecision] / N), $MachinePrecision] * N), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 26.8%

   \[\log \left(N + 1\right) - \log N \]
2. Add Preprocessing

3. Taylor expanded in N around inf

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

5. Applied rewrites 95.9%

   \[\leadsto \color{blue}{\frac{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}{N}} \]
6. Step-by-step derivation

7. Applied rewrites 95.9%

   \[\leadsto \frac{1}{\color{blue}{\frac{N}{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}}} \]

8. Taylor expanded in N around -inf

   \[\leadsto \frac{1}{-1 \cdot \color{blue}{\left(N \cdot \left(-1 \cdot \frac{\frac{1}{2} + -1 \cdot \frac{\frac{1}{12} - \frac{1}{24} \cdot \frac{1}{N}}{N}}{N} - 1\right)\right)}} \]
9. Step-by-step derivation

10. Applied rewrites 96.3%

    \[\leadsto \frac{1}{\left(-N\right) \cdot \color{blue}{\left(-1 - \frac{0.5 - \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)}} \]
11. Step-by-step derivation

12. Applied rewrites 96.4%

    \[\leadsto \frac{1}{\mathsf{fma}\left(-N, -1, \left(-N\right) \cdot \frac{-0.5 + \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)} \]

13. Final simplification 96.4%

    \[\leadsto \frac{1}{\mathsf{fma}\left(-N, -1, \frac{\frac{\frac{0.041666666666666664}{N} - 0.08333333333333333}{N} - -0.5}{N} \cdot N\right)} \]
14. Add Preprocessing

Alternative 4: 96.7% accurate, 3.5× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\left(\frac{0.5 - \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N} - -1\right) \cdot N} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (/
  1.0
  (*
   (-
    (/ (- 0.5 (/ (- 0.08333333333333333 (/ 0.041666666666666664 N)) N)) N)
    -1.0)
   N)))

C

double code(double N) {
	return 1.0 / ((((0.5 - ((0.08333333333333333 - (0.041666666666666664 / N)) / N)) / N) - -1.0) * N);
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    code = 1.0d0 / ((((0.5d0 - ((0.08333333333333333d0 - (0.041666666666666664d0 / n)) / n)) / n) - (-1.0d0)) * n)
end function

Java

public static double code(double N) {
	return 1.0 / ((((0.5 - ((0.08333333333333333 - (0.041666666666666664 / N)) / N)) / N) - -1.0) * N);
}

Python

def code(N):
	return 1.0 / ((((0.5 - ((0.08333333333333333 - (0.041666666666666664 / N)) / N)) / N) - -1.0) * N)

Julia

function code(N)
	return Float64(1.0 / Float64(Float64(Float64(Float64(0.5 - Float64(Float64(0.08333333333333333 - Float64(0.041666666666666664 / N)) / N)) / N) - -1.0) * N))
end

MATLAB

function tmp = code(N)
	tmp = 1.0 / ((((0.5 - ((0.08333333333333333 - (0.041666666666666664 / N)) / N)) / N) - -1.0) * N);
end

Wolfram

code[N_] := N[(1.0 / N[(N[(N[(N[(0.5 - N[(N[(0.08333333333333333 - N[(0.041666666666666664 / N), $MachinePrecision]), $MachinePrecision] / N), $MachinePrecision]), $MachinePrecision] / N), $MachinePrecision] - -1.0), $MachinePrecision] * N), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 26.8%

   \[\log \left(N + 1\right) - \log N \]
2. Add Preprocessing

3. Taylor expanded in N around inf

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

5. Applied rewrites 95.9%

   \[\leadsto \color{blue}{\frac{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}{N}} \]
6. Step-by-step derivation

7. Applied rewrites 95.9%

   \[\leadsto \frac{1}{\color{blue}{\frac{N}{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}}} \]

8. Taylor expanded in N around -inf

   \[\leadsto \frac{1}{-1 \cdot \color{blue}{\left(N \cdot \left(-1 \cdot \frac{\frac{1}{2} + -1 \cdot \frac{\frac{1}{12} - \frac{1}{24} \cdot \frac{1}{N}}{N}}{N} - 1\right)\right)}} \]
9. Step-by-step derivation

10. Applied rewrites 96.3%

    \[\leadsto \frac{1}{\left(-N\right) \cdot \color{blue}{\left(-1 - \frac{0.5 - \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)}} \]

11. Final simplification 96.3%

    \[\leadsto \frac{1}{\left(\frac{0.5 - \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N} - -1\right) \cdot N} \]
12. Add Preprocessing

Alternative 5: 96.6% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\frac{\frac{\mathsf{fma}\left(\mathsf{fma}\left(0.5 + N, N, -0.08333333333333333\right), N, 0.041666666666666664\right)}{N}}{N}} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (/
  1.0
  (/
   (/ (fma (fma (+ 0.5 N) N -0.08333333333333333) N 0.041666666666666664) N)
   N)))

C

double code(double N) {
	return 1.0 / ((fma(fma((0.5 + N), N, -0.08333333333333333), N, 0.041666666666666664) / N) / N);
}

Julia

function code(N)
	return Float64(1.0 / Float64(Float64(fma(fma(Float64(0.5 + N), N, -0.08333333333333333), N, 0.041666666666666664) / N) / N))
end

Wolfram

code[N_] := N[(1.0 / N[(N[(N[(N[(N[(0.5 + N), $MachinePrecision] * N + -0.08333333333333333), $MachinePrecision] * N + 0.041666666666666664), $MachinePrecision] / N), $MachinePrecision] / N), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 26.8%

   \[\log \left(N + 1\right) - \log N \]
2. Add Preprocessing

3. Taylor expanded in N around inf

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

5. Applied rewrites 95.9%

   \[\leadsto \color{blue}{\frac{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}{N}} \]
6. Step-by-step derivation

7. Applied rewrites 95.9%

   \[\leadsto \frac{1}{\color{blue}{\frac{N}{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}}} \]

8. Taylor expanded in N around -inf

   \[\leadsto \frac{1}{-1 \cdot \color{blue}{\left(N \cdot \left(-1 \cdot \frac{\frac{1}{2} + -1 \cdot \frac{\frac{1}{12} - \frac{1}{24} \cdot \frac{1}{N}}{N}}{N} - 1\right)\right)}} \]
9. Step-by-step derivation

10. Applied rewrites 96.3%

    \[\leadsto \frac{1}{\left(-N\right) \cdot \color{blue}{\left(-1 - \frac{0.5 - \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)}} \]

11. Taylor expanded in N around 0

    \[\leadsto \frac{1}{\frac{\frac{1}{24} + N \cdot \left(N \cdot \left(\frac{1}{2} + N\right) - \frac{1}{12}\right)}{{N}^{\color{blue}{2}}}} \]
12. Step-by-step derivation

13. Applied rewrites 96.2%

    \[\leadsto \frac{1}{\frac{\frac{\mathsf{fma}\left(\mathsf{fma}\left(0.5 + N, N, -0.08333333333333333\right), N, 0.041666666666666664\right)}{N}}{N}} \]
14. Add Preprocessing

Alternative 6: 95.6% accurate, 7.1× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\left(0.5 - \frac{0.08333333333333333}{N}\right) + N} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (/ 1.0 (+ (- 0.5 (/ 0.08333333333333333 N)) N)))

C

double code(double N) {
	return 1.0 / ((0.5 - (0.08333333333333333 / N)) + N);
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    code = 1.0d0 / ((0.5d0 - (0.08333333333333333d0 / n)) + n)
end function

Java

public static double code(double N) {
	return 1.0 / ((0.5 - (0.08333333333333333 / N)) + N);
}

Python

def code(N):
	return 1.0 / ((0.5 - (0.08333333333333333 / N)) + N)

Julia

function code(N)
	return Float64(1.0 / Float64(Float64(0.5 - Float64(0.08333333333333333 / N)) + N))
end

MATLAB

function tmp = code(N)
	tmp = 1.0 / ((0.5 - (0.08333333333333333 / N)) + N);
end

Wolfram

code[N_] := N[(1.0 / N[(N[(0.5 - N[(0.08333333333333333 / N), $MachinePrecision]), $MachinePrecision] + N), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 26.8%

   \[\log \left(N + 1\right) - \log N \]
2. Add Preprocessing

3. Taylor expanded in N around inf

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

5. Applied rewrites 95.9%

   \[\leadsto \color{blue}{\frac{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}{N}} \]
6. Step-by-step derivation

7. Applied rewrites 95.9%

   \[\leadsto \frac{1}{\color{blue}{\frac{N}{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}}} \]

8. Taylor expanded in N around -inf

   \[\leadsto \frac{1}{-1 \cdot \color{blue}{\left(N \cdot \left(-1 \cdot \frac{\frac{1}{2} + -1 \cdot \frac{\frac{1}{12} - \frac{1}{24} \cdot \frac{1}{N}}{N}}{N} - 1\right)\right)}} \]
9. Step-by-step derivation

10. Applied rewrites 96.3%

    \[\leadsto \frac{1}{\left(-N\right) \cdot \color{blue}{\left(-1 - \frac{0.5 - \frac{0.08333333333333333 - \frac{0.041666666666666664}{N}}{N}}{N}\right)}} \]

11. Taylor expanded in N around inf

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

13. Applied rewrites 95.3%

    \[\leadsto \frac{1}{\left(0.5 - \frac{0.08333333333333333}{N}\right) + \color{blue}{N}} \]
14. Add Preprocessing

Alternative 7: 93.1% accurate, 13.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 26.8%

   \[\log \left(N + 1\right) - \log N \]
2. Add Preprocessing

3. Taylor expanded in N around inf

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

5. Applied rewrites 95.9%

   \[\leadsto \color{blue}{\frac{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}{N}} \]
6. Step-by-step derivation

7. Applied rewrites 95.9%

   \[\leadsto \frac{1}{\color{blue}{\frac{N}{\frac{-0.5 - \frac{\frac{0.25}{N} - 0.3333333333333333}{N}}{N} - -1}}} \]

8. Taylor expanded in N around inf

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

10. Applied rewrites 92.5%

    \[\leadsto \frac{1}{\mathsf{fma}\left(\frac{0.5}{N}, \color{blue}{N}, N\right)} \]
11. Step-by-step derivation

12. Applied rewrites 92.5%

    \[\leadsto \frac{1}{N + 0.5} \]

13. Final simplification 92.5%

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

Alternative 8: 84.4% accurate, 17.3× 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 26.8%

   \[\log \left(N + 1\right) - \log N \]
2. Add Preprocessing

3. Taylor expanded in N around inf

   \[\leadsto \color{blue}{\frac{1}{N}} \]
4. Step-by-step derivation

   1. lower-/.f64 82.6

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

5. Applied rewrites 82.6%

   \[\leadsto \color{blue}{\frac{1}{N}} \]
6. Add Preprocessing

Developer Target 1: 96.3% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \left(\left(\frac{1}{N} + \frac{-1}{2 \cdot {N}^{2}}\right) + \frac{1}{3 \cdot {N}^{3}}\right) + \frac{-1}{4 \cdot {N}^{4}} \end{array} \]

FPCore

(FPCore (N)
 :precision binary64
 (+
  (+ (+ (/ 1.0 N) (/ -1.0 (* 2.0 (pow N 2.0)))) (/ 1.0 (* 3.0 (pow N 3.0))))
  (/ -1.0 (* 4.0 (pow N 4.0)))))

C

double code(double N) {
	return (((1.0 / N) + (-1.0 / (2.0 * pow(N, 2.0)))) + (1.0 / (3.0 * pow(N, 3.0)))) + (-1.0 / (4.0 * pow(N, 4.0)));
}

Fortran

real(8) function code(n)
    real(8), intent (in) :: n
    code = (((1.0d0 / n) + ((-1.0d0) / (2.0d0 * (n ** 2.0d0)))) + (1.0d0 / (3.0d0 * (n ** 3.0d0)))) + ((-1.0d0) / (4.0d0 * (n ** 4.0d0)))
end function

Java

public static double code(double N) {
	return (((1.0 / N) + (-1.0 / (2.0 * Math.pow(N, 2.0)))) + (1.0 / (3.0 * Math.pow(N, 3.0)))) + (-1.0 / (4.0 * Math.pow(N, 4.0)));
}

Python

def code(N):
	return (((1.0 / N) + (-1.0 / (2.0 * math.pow(N, 2.0)))) + (1.0 / (3.0 * math.pow(N, 3.0)))) + (-1.0 / (4.0 * math.pow(N, 4.0)))

Julia

function code(N)
	return Float64(Float64(Float64(Float64(1.0 / N) + Float64(-1.0 / Float64(2.0 * (N ^ 2.0)))) + Float64(1.0 / Float64(3.0 * (N ^ 3.0)))) + Float64(-1.0 / Float64(4.0 * (N ^ 4.0))))
end

MATLAB

function tmp = code(N)
	tmp = (((1.0 / N) + (-1.0 / (2.0 * (N ^ 2.0)))) + (1.0 / (3.0 * (N ^ 3.0)))) + (-1.0 / (4.0 * (N ^ 4.0)));
end

Wolfram

code[N_] := N[(N[(N[(N[(1.0 / N), $MachinePrecision] + N[(-1.0 / N[(2.0 * N[Power[N, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(1.0 / N[(3.0 * N[Power[N, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(-1.0 / N[(4.0 * N[Power[N, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024249 
(FPCore (N)
  :name "2log (problem 3.3.6)"
  :precision binary64
  :pre (and (> N 1.0) (< N 1e+40))

  :alt
  (! :herbie-platform default (+ (/ 1 N) (/ -1 (* 2 (pow N 2))) (/ 1 (* 3 (pow N 3))) (/ -1 (* 4 (pow N 4)))))

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