VandenBroeck and Keller, Equation (20)

Percentage Accurate: 6.4% → 99.0%

Time: 17.9s

Alternatives: 6

Speedup: 4.8×

• could not determine a ground truth

  1. f = 1450012264.484864

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\pi}{4} \cdot f\\ t_1 := e^{t\_0}\\ t_2 := e^{-t\_0}\\ -\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{t\_1 + t\_2}{t\_1 - t\_2}\right) \end{array} \end{array} \]

FPCore

(FPCore (f)
 :precision binary64
 (let* ((t_0 (* (/ PI 4.0) f)) (t_1 (exp t_0)) (t_2 (exp (- t_0))))
   (- (* (/ 1.0 (/ PI 4.0)) (log (/ (+ t_1 t_2) (- t_1 t_2)))))))

C

double code(double f) {
	double t_0 = (((double) M_PI) / 4.0) * f;
	double t_1 = exp(t_0);
	double t_2 = exp(-t_0);
	return -((1.0 / (((double) M_PI) / 4.0)) * log(((t_1 + t_2) / (t_1 - t_2))));
}

Java

public static double code(double f) {
	double t_0 = (Math.PI / 4.0) * f;
	double t_1 = Math.exp(t_0);
	double t_2 = Math.exp(-t_0);
	return -((1.0 / (Math.PI / 4.0)) * Math.log(((t_1 + t_2) / (t_1 - t_2))));
}

Python

def code(f):
	t_0 = (math.pi / 4.0) * f
	t_1 = math.exp(t_0)
	t_2 = math.exp(-t_0)
	return -((1.0 / (math.pi / 4.0)) * math.log(((t_1 + t_2) / (t_1 - t_2))))

Julia

function code(f)
	t_0 = Float64(Float64(pi / 4.0) * f)
	t_1 = exp(t_0)
	t_2 = exp(Float64(-t_0))
	return Float64(-Float64(Float64(1.0 / Float64(pi / 4.0)) * log(Float64(Float64(t_1 + t_2) / Float64(t_1 - t_2)))))
end

MATLAB

function tmp = code(f)
	t_0 = (pi / 4.0) * f;
	t_1 = exp(t_0);
	t_2 = exp(-t_0);
	tmp = -((1.0 / (pi / 4.0)) * log(((t_1 + t_2) / (t_1 - t_2))));
end

Wolfram

code[f_] := Block[{t$95$0 = N[(N[(Pi / 4.0), $MachinePrecision] * f), $MachinePrecision]}, Block[{t$95$1 = N[Exp[t$95$0], $MachinePrecision]}, Block[{t$95$2 = N[Exp[(-t$95$0)], $MachinePrecision]}, (-N[(N[(1.0 / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision] * N[Log[N[(N[(t$95$1 + t$95$2), $MachinePrecision] / N[(t$95$1 - t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision])]]]

Initial Program: 6.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\pi}{4} \cdot f\\ t_1 := e^{t\_0}\\ t_2 := e^{-t\_0}\\ -\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{t\_1 + t\_2}{t\_1 - t\_2}\right) \end{array} \end{array} \]

FPCore

(FPCore (f)
 :precision binary64
 (let* ((t_0 (* (/ PI 4.0) f)) (t_1 (exp t_0)) (t_2 (exp (- t_0))))
   (- (* (/ 1.0 (/ PI 4.0)) (log (/ (+ t_1 t_2) (- t_1 t_2)))))))

C

double code(double f) {
	double t_0 = (((double) M_PI) / 4.0) * f;
	double t_1 = exp(t_0);
	double t_2 = exp(-t_0);
	return -((1.0 / (((double) M_PI) / 4.0)) * log(((t_1 + t_2) / (t_1 - t_2))));
}

Java

public static double code(double f) {
	double t_0 = (Math.PI / 4.0) * f;
	double t_1 = Math.exp(t_0);
	double t_2 = Math.exp(-t_0);
	return -((1.0 / (Math.PI / 4.0)) * Math.log(((t_1 + t_2) / (t_1 - t_2))));
}

Python

def code(f):
	t_0 = (math.pi / 4.0) * f
	t_1 = math.exp(t_0)
	t_2 = math.exp(-t_0)
	return -((1.0 / (math.pi / 4.0)) * math.log(((t_1 + t_2) / (t_1 - t_2))))

Julia

function code(f)
	t_0 = Float64(Float64(pi / 4.0) * f)
	t_1 = exp(t_0)
	t_2 = exp(Float64(-t_0))
	return Float64(-Float64(Float64(1.0 / Float64(pi / 4.0)) * log(Float64(Float64(t_1 + t_2) / Float64(t_1 - t_2)))))
end

MATLAB

function tmp = code(f)
	t_0 = (pi / 4.0) * f;
	t_1 = exp(t_0);
	t_2 = exp(-t_0);
	tmp = -((1.0 / (pi / 4.0)) * log(((t_1 + t_2) / (t_1 - t_2))));
end

Wolfram

code[f_] := Block[{t$95$0 = N[(N[(Pi / 4.0), $MachinePrecision] * f), $MachinePrecision]}, Block[{t$95$1 = N[Exp[t$95$0], $MachinePrecision]}, Block[{t$95$2 = N[Exp[(-t$95$0)], $MachinePrecision]}, (-N[(N[(1.0 / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision] * N[Log[N[(N[(t$95$1 + t$95$2), $MachinePrecision] / N[(t$95$1 - t$95$2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision])]]]

Alternative 1: 99.0% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \frac{\log \tanh \left(\left(\pi \cdot 0.25\right) \cdot f\right)}{\pi \cdot 0.25} \end{array} \]

FPCore

(FPCore (f) :precision binary64 (/ (log (tanh (* (* PI 0.25) f))) (* PI 0.25)))

C

double code(double f) {
	return log(tanh(((((double) M_PI) * 0.25) * f))) / (((double) M_PI) * 0.25);
}

Java

public static double code(double f) {
	return Math.log(Math.tanh(((Math.PI * 0.25) * f))) / (Math.PI * 0.25);
}

Python

def code(f):
	return math.log(math.tanh(((math.pi * 0.25) * f))) / (math.pi * 0.25)

Julia

function code(f)
	return Float64(log(tanh(Float64(Float64(pi * 0.25) * f))) / Float64(pi * 0.25))
end

MATLAB

function tmp = code(f)
	tmp = log(tanh(((pi * 0.25) * f))) / (pi * 0.25);
end

Wolfram

code[f_] := N[(N[Log[N[Tanh[N[(N[(Pi * 0.25), $MachinePrecision] * f), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] / N[(Pi * 0.25), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.5%

   \[-\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{e^{\frac{\pi}{4} \cdot f} + e^{-\frac{\pi}{4} \cdot f}}{e^{\frac{\pi}{4} \cdot f} - e^{-\frac{\pi}{4} \cdot f}}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-neg.f64 N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)\right)} \]

   2. lift-*.f64 N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   4. lift-/.f64 N/A

      \[\leadsto \mathsf{neg}\left(\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   5. un-div-inv N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

4. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\frac{\log \tanh \left(\left(\pi \cdot 0.25\right) \cdot f\right)}{\pi \cdot 0.25}} \]
5. Add Preprocessing

Alternative 2: 96.4% accurate, 3.3× speedup

Math

\[\begin{array}{l} \\ \log \left(\frac{\mathsf{fma}\left(f, f \cdot \mathsf{fma}\left(0.005208333333333333 \cdot \left(2 \cdot \left(\pi + \pi\right)\right), -2, \left(\pi + \pi\right) \cdot 0.0625\right), \frac{4}{\pi}\right)}{f}\right) \cdot \frac{-1}{\frac{\pi}{4}} \end{array} \]

FPCore

(FPCore (f)
 :precision binary64
 (*
  (log
   (/
    (fma
     f
     (*
      f
      (fma
       (* 0.005208333333333333 (* 2.0 (+ PI PI)))
       -2.0
       (* (+ PI PI) 0.0625)))
     (/ 4.0 PI))
    f))
  (/ -1.0 (/ PI 4.0))))

C

double code(double f) {
	return log((fma(f, (f * fma((0.005208333333333333 * (2.0 * (((double) M_PI) + ((double) M_PI)))), -2.0, ((((double) M_PI) + ((double) M_PI)) * 0.0625))), (4.0 / ((double) M_PI))) / f)) * (-1.0 / (((double) M_PI) / 4.0));
}

Julia

function code(f)
	return Float64(log(Float64(fma(f, Float64(f * fma(Float64(0.005208333333333333 * Float64(2.0 * Float64(pi + pi))), -2.0, Float64(Float64(pi + pi) * 0.0625))), Float64(4.0 / pi)) / f)) * Float64(-1.0 / Float64(pi / 4.0)))
end

Wolfram

code[f_] := N[(N[Log[N[(N[(f * N[(f * N[(N[(0.005208333333333333 * N[(2.0 * N[(Pi + Pi), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -2.0 + N[(N[(Pi + Pi), $MachinePrecision] * 0.0625), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(4.0 / Pi), $MachinePrecision]), $MachinePrecision] / f), $MachinePrecision]], $MachinePrecision] * N[(-1.0 / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.5%

   \[-\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{e^{\frac{\pi}{4} \cdot f} + e^{-\frac{\pi}{4} \cdot f}}{e^{\frac{\pi}{4} \cdot f} - e^{-\frac{\pi}{4} \cdot f}}\right) \]
2. Add Preprocessing

3. Taylor expanded in f around 0

   \[\leadsto \mathsf{neg}\left(\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \color{blue}{\left(\frac{f \cdot \left(\frac{-1}{4} \cdot \frac{\mathsf{PI}\left(\right)}{\frac{1}{4} \cdot \mathsf{PI}\left(\right) - \frac{-1}{4} \cdot \mathsf{PI}\left(\right)} + \left(\frac{1}{4} \cdot \frac{\mathsf{PI}\left(\right)}{\frac{1}{4} \cdot \mathsf{PI}\left(\right) - \frac{-1}{4} \cdot \mathsf{PI}\left(\right)} + f \cdot \left(\frac{1}{16} \cdot \frac{{\mathsf{PI}\left(\right)}^{2}}{\frac{1}{4} \cdot \mathsf{PI}\left(\right) - \frac{-1}{4} \cdot \mathsf{PI}\left(\right)} - 2 \cdot \frac{\frac{1}{384} \cdot {\mathsf{PI}\left(\right)}^{3} - \frac{-1}{384} \cdot {\mathsf{PI}\left(\right)}^{3}}{{\left(\frac{1}{4} \cdot \mathsf{PI}\left(\right) - \frac{-1}{4} \cdot \mathsf{PI}\left(\right)\right)}^{2}}\right)\right)\right) + 2 \cdot \frac{1}{\frac{1}{4} \cdot \mathsf{PI}\left(\right) - \frac{-1}{4} \cdot \mathsf{PI}\left(\right)}}{f}\right)}\right) \]

5. Applied rewrites 95.4%

   \[\leadsto -\frac{1}{\frac{\pi}{4}} \cdot \log \color{blue}{\left(\frac{\mathsf{fma}\left(f, f \cdot \mathsf{fma}\left(0.005208333333333333 \cdot \left(2 \cdot \left(\pi + \pi\right)\right), -2, 0.0625 \cdot \left(\pi + \pi\right)\right), \frac{4}{\pi}\right)}{f}\right)} \]

6. Final simplification 95.4%

   \[\leadsto \log \left(\frac{\mathsf{fma}\left(f, f \cdot \mathsf{fma}\left(0.005208333333333333 \cdot \left(2 \cdot \left(\pi + \pi\right)\right), -2, \left(\pi + \pi\right) \cdot 0.0625\right), \frac{4}{\pi}\right)}{f}\right) \cdot \frac{-1}{\frac{\pi}{4}} \]
7. Add Preprocessing

Alternative 3: 96.5% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ \frac{4 \cdot \log \left(f \cdot \left(\pi \cdot \mathsf{fma}\left(\pi \cdot \pi, f \cdot \left(f \cdot -0.005208333333333333\right), 0.25\right)\right)\right)}{\pi} \end{array} \]

FPCore

(FPCore (f)
 :precision binary64
 (/
  (*
   4.0
   (log (* f (* PI (fma (* PI PI) (* f (* f -0.005208333333333333)) 0.25)))))
  PI))

C

double code(double f) {
	return (4.0 * log((f * (((double) M_PI) * fma((((double) M_PI) * ((double) M_PI)), (f * (f * -0.005208333333333333)), 0.25))))) / ((double) M_PI);
}

Julia

function code(f)
	return Float64(Float64(4.0 * log(Float64(f * Float64(pi * fma(Float64(pi * pi), Float64(f * Float64(f * -0.005208333333333333)), 0.25))))) / pi)
end

Wolfram

code[f_] := N[(N[(4.0 * N[Log[N[(f * N[(Pi * N[(N[(Pi * Pi), $MachinePrecision] * N[(f * N[(f * -0.005208333333333333), $MachinePrecision]), $MachinePrecision] + 0.25), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / Pi), $MachinePrecision]

Derivation

1. Initial program 6.5%

   \[-\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{e^{\frac{\pi}{4} \cdot f} + e^{-\frac{\pi}{4} \cdot f}}{e^{\frac{\pi}{4} \cdot f} - e^{-\frac{\pi}{4} \cdot f}}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-neg.f64 N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)\right)} \]

   2. lift-*.f64 N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   4. lift-/.f64 N/A

      \[\leadsto \mathsf{neg}\left(\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   5. un-div-inv N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

4. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\frac{\log \tanh \left(\left(\pi \cdot 0.25\right) \cdot f\right)}{\pi \cdot 0.25}} \]

6. Applied rewrites 96.0%

   \[\leadsto \frac{\log \color{blue}{\left(\mathsf{expm1}\left(\pi \cdot \left(f \cdot 0.5\right)\right) \cdot \frac{1}{1 + e^{\pi \cdot \left(f \cdot 0.5\right)}}\right)}}{\pi \cdot 0.25} \]

7. Taylor expanded in f around 0

   \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right) + {f}^{2} \cdot \left(\frac{1}{96} \cdot {\mathsf{PI}\left(\right)}^{3} - \frac{1}{64} \cdot {\mathsf{PI}\left(\right)}^{3}\right)\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]
8. Step-by-step derivation

   1. lower-*.f64 N/A

      \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right) + {f}^{2} \cdot \left(\frac{1}{96} \cdot {\mathsf{PI}\left(\right)}^{3} - \frac{1}{64} \cdot {\mathsf{PI}\left(\right)}^{3}\right)\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   2. +-commutative N/A

      \[\leadsto \frac{\log \left(f \cdot \color{blue}{\left({f}^{2} \cdot \left(\frac{1}{96} \cdot {\mathsf{PI}\left(\right)}^{3} - \frac{1}{64} \cdot {\mathsf{PI}\left(\right)}^{3}\right) + \frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)}\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   3. *-commutative N/A

      \[\leadsto \frac{\log \left(f \cdot \left(\color{blue}{\left(\frac{1}{96} \cdot {\mathsf{PI}\left(\right)}^{3} - \frac{1}{64} \cdot {\mathsf{PI}\left(\right)}^{3}\right) \cdot {f}^{2}} + \frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

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

      \[\leadsto \frac{\log \left(f \cdot \left(\color{blue}{\left({\mathsf{PI}\left(\right)}^{3} \cdot \left(\frac{1}{96} - \frac{1}{64}\right)\right)} \cdot {f}^{2} + \frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   5. metadata-eval N/A

      \[\leadsto \frac{\log \left(f \cdot \left(\left({\mathsf{PI}\left(\right)}^{3} \cdot \color{blue}{\frac{-1}{192}}\right) \cdot {f}^{2} + \frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   6. metadata-eval N/A

      \[\leadsto \frac{\log \left(f \cdot \left(\left({\mathsf{PI}\left(\right)}^{3} \cdot \color{blue}{\left(\frac{-1}{128} + \frac{1}{384}\right)}\right) \cdot {f}^{2} + \frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   7. associate-*l* N/A

      \[\leadsto \frac{\log \left(f \cdot \left(\color{blue}{{\mathsf{PI}\left(\right)}^{3} \cdot \left(\left(\frac{-1}{128} + \frac{1}{384}\right) \cdot {f}^{2}\right)} + \frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   8. lower-fma.f64 N/A

      \[\leadsto \frac{\log \left(f \cdot \color{blue}{\mathsf{fma}\left({\mathsf{PI}\left(\right)}^{3}, \left(\frac{-1}{128} + \frac{1}{384}\right) \cdot {f}^{2}, \frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)}\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

9. Applied rewrites 95.4%

   \[\leadsto \frac{\log \color{blue}{\left(f \cdot \mathsf{fma}\left(\pi \cdot \left(\pi \cdot \pi\right), -0.005208333333333333 \cdot \left(f \cdot f\right), 0.25 \cdot \pi\right)\right)}}{\pi \cdot 0.25} \]

11. Applied rewrites 95.4%

    \[\leadsto \color{blue}{\frac{\log \left(f \cdot \left(\pi \cdot \mathsf{fma}\left(\pi \cdot \pi, f \cdot \left(f \cdot -0.005208333333333333\right), 0.25\right)\right)\right) \cdot 4}{\pi}} \]

12. Final simplification 95.4%

    \[\leadsto \frac{4 \cdot \log \left(f \cdot \left(\pi \cdot \mathsf{fma}\left(\pi \cdot \pi, f \cdot \left(f \cdot -0.005208333333333333\right), 0.25\right)\right)\right)}{\pi} \]
13. Add Preprocessing

Alternative 4: 96.0% accurate, 4.4× speedup

Math

\[\begin{array}{l} \\ \frac{\log \left(\frac{\pi}{\frac{4}{f}}\right)}{\pi \cdot 0.25} \end{array} \]

FPCore

(FPCore (f) :precision binary64 (/ (log (/ PI (/ 4.0 f))) (* PI 0.25)))

C

double code(double f) {
	return log((((double) M_PI) / (4.0 / f))) / (((double) M_PI) * 0.25);
}

Java

public static double code(double f) {
	return Math.log((Math.PI / (4.0 / f))) / (Math.PI * 0.25);
}

Python

def code(f):
	return math.log((math.pi / (4.0 / f))) / (math.pi * 0.25)

Julia

function code(f)
	return Float64(log(Float64(pi / Float64(4.0 / f))) / Float64(pi * 0.25))
end

MATLAB

function tmp = code(f)
	tmp = log((pi / (4.0 / f))) / (pi * 0.25);
end

Wolfram

code[f_] := N[(N[Log[N[(Pi / N[(4.0 / f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[(Pi * 0.25), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.5%

   \[-\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{e^{\frac{\pi}{4} \cdot f} + e^{-\frac{\pi}{4} \cdot f}}{e^{\frac{\pi}{4} \cdot f} - e^{-\frac{\pi}{4} \cdot f}}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-neg.f64 N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)\right)} \]

   2. lift-*.f64 N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   4. lift-/.f64 N/A

      \[\leadsto \mathsf{neg}\left(\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   5. un-div-inv N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

4. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\frac{\log \tanh \left(\left(\pi \cdot 0.25\right) \cdot f\right)}{\pi \cdot 0.25}} \]

5. Taylor expanded in f around 0

   \[\leadsto \frac{\log \color{blue}{\left(\frac{1}{4} \cdot \left(f \cdot \mathsf{PI}\left(\right)\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]
6. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{\log \color{blue}{\left(\left(f \cdot \mathsf{PI}\left(\right)\right) \cdot \frac{1}{4}\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   2. associate-*r* N/A

      \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(\mathsf{PI}\left(\right) \cdot \frac{1}{4}\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   3. *-commutative N/A

      \[\leadsto \frac{\log \left(f \cdot \color{blue}{\left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)}\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   4. lower-*.f64 N/A

      \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   5. lower-*.f64 N/A

      \[\leadsto \frac{\log \left(f \cdot \color{blue}{\left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)}\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   6. lower-PI.f64 95.2

      \[\leadsto \frac{\log \left(f \cdot \left(0.25 \cdot \color{blue}{\pi}\right)\right)}{\pi \cdot 0.25} \]

7. Applied rewrites 95.2%

   \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(0.25 \cdot \pi\right)\right)}}{\pi \cdot 0.25} \]
8. Step-by-step derivation

9. Applied rewrites 95.2%

   \[\leadsto \frac{\log \left(\frac{\pi}{\color{blue}{\frac{4}{f}}}\right)}{\pi \cdot 0.25} \]
10. Add Preprocessing

Alternative 5: 96.1% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \frac{\log \left(\left(\pi \cdot 0.25\right) \cdot f\right)}{\pi \cdot 0.25} \end{array} \]

FPCore

(FPCore (f) :precision binary64 (/ (log (* (* PI 0.25) f)) (* PI 0.25)))

C

double code(double f) {
	return log(((((double) M_PI) * 0.25) * f)) / (((double) M_PI) * 0.25);
}

Java

public static double code(double f) {
	return Math.log(((Math.PI * 0.25) * f)) / (Math.PI * 0.25);
}

Python

def code(f):
	return math.log(((math.pi * 0.25) * f)) / (math.pi * 0.25)

Julia

function code(f)
	return Float64(log(Float64(Float64(pi * 0.25) * f)) / Float64(pi * 0.25))
end

MATLAB

function tmp = code(f)
	tmp = log(((pi * 0.25) * f)) / (pi * 0.25);
end

Wolfram

code[f_] := N[(N[Log[N[(N[(Pi * 0.25), $MachinePrecision] * f), $MachinePrecision]], $MachinePrecision] / N[(Pi * 0.25), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.5%

   \[-\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{e^{\frac{\pi}{4} \cdot f} + e^{-\frac{\pi}{4} \cdot f}}{e^{\frac{\pi}{4} \cdot f} - e^{-\frac{\pi}{4} \cdot f}}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-neg.f64 N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)\right)} \]

   2. lift-*.f64 N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   4. lift-/.f64 N/A

      \[\leadsto \mathsf{neg}\left(\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   5. un-div-inv N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

4. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\frac{\log \tanh \left(\left(\pi \cdot 0.25\right) \cdot f\right)}{\pi \cdot 0.25}} \]

5. Taylor expanded in f around 0

   \[\leadsto \frac{\log \color{blue}{\left(\frac{1}{4} \cdot \left(f \cdot \mathsf{PI}\left(\right)\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]
6. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{\log \color{blue}{\left(\left(f \cdot \mathsf{PI}\left(\right)\right) \cdot \frac{1}{4}\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   2. associate-*r* N/A

      \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(\mathsf{PI}\left(\right) \cdot \frac{1}{4}\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   3. *-commutative N/A

      \[\leadsto \frac{\log \left(f \cdot \color{blue}{\left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)}\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   4. lower-*.f64 N/A

      \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   5. lower-*.f64 N/A

      \[\leadsto \frac{\log \left(f \cdot \color{blue}{\left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)}\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   6. lower-PI.f64 95.2

      \[\leadsto \frac{\log \left(f \cdot \left(0.25 \cdot \color{blue}{\pi}\right)\right)}{\pi \cdot 0.25} \]

7. Applied rewrites 95.2%

   \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(0.25 \cdot \pi\right)\right)}}{\pi \cdot 0.25} \]

8. Final simplification 95.2%

   \[\leadsto \frac{\log \left(\left(\pi \cdot 0.25\right) \cdot f\right)}{\pi \cdot 0.25} \]
9. Add Preprocessing

Alternative 6: 96.0% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \frac{4}{\pi} \cdot \log \left(0.25 \cdot \left(\pi \cdot f\right)\right) \end{array} \]

FPCore

(FPCore (f) :precision binary64 (* (/ 4.0 PI) (log (* 0.25 (* PI f)))))

C

double code(double f) {
	return (4.0 / ((double) M_PI)) * log((0.25 * (((double) M_PI) * f)));
}

Java

public static double code(double f) {
	return (4.0 / Math.PI) * Math.log((0.25 * (Math.PI * f)));
}

Python

def code(f):
	return (4.0 / math.pi) * math.log((0.25 * (math.pi * f)))

Julia

function code(f)
	return Float64(Float64(4.0 / pi) * log(Float64(0.25 * Float64(pi * f))))
end

MATLAB

function tmp = code(f)
	tmp = (4.0 / pi) * log((0.25 * (pi * f)));
end

Wolfram

code[f_] := N[(N[(4.0 / Pi), $MachinePrecision] * N[Log[N[(0.25 * N[(Pi * f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 6.5%

   \[-\frac{1}{\frac{\pi}{4}} \cdot \log \left(\frac{e^{\frac{\pi}{4} \cdot f} + e^{-\frac{\pi}{4} \cdot f}}{e^{\frac{\pi}{4} \cdot f} - e^{-\frac{\pi}{4} \cdot f}}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-neg.f64 N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)\right)} \]

   2. lift-*.f64 N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}} \cdot \log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}\right) \]

   3. *-commutative N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   4. lift-/.f64 N/A

      \[\leadsto \mathsf{neg}\left(\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right) \cdot \color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

   5. un-div-inv N/A

      \[\leadsto \mathsf{neg}\left(\color{blue}{\frac{\log \left(\frac{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} + e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}{e^{\frac{\mathsf{PI}\left(\right)}{4} \cdot f} - e^{\mathsf{neg}\left(\frac{\mathsf{PI}\left(\right)}{4} \cdot f\right)}}\right)}{\frac{\mathsf{PI}\left(\right)}{4}}}\right) \]

4. Applied rewrites 98.7%

   \[\leadsto \color{blue}{\frac{\log \tanh \left(\left(\pi \cdot 0.25\right) \cdot f\right)}{\pi \cdot 0.25}} \]

5. Taylor expanded in f around 0

   \[\leadsto \frac{\log \color{blue}{\left(\frac{1}{4} \cdot \left(f \cdot \mathsf{PI}\left(\right)\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]
6. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{\log \color{blue}{\left(\left(f \cdot \mathsf{PI}\left(\right)\right) \cdot \frac{1}{4}\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   2. associate-*r* N/A

      \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(\mathsf{PI}\left(\right) \cdot \frac{1}{4}\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   3. *-commutative N/A

      \[\leadsto \frac{\log \left(f \cdot \color{blue}{\left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)}\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   4. lower-*.f64 N/A

      \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   5. lower-*.f64 N/A

      \[\leadsto \frac{\log \left(f \cdot \color{blue}{\left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)}\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \]

   6. lower-PI.f64 95.2

      \[\leadsto \frac{\log \left(f \cdot \left(0.25 \cdot \color{blue}{\pi}\right)\right)}{\pi \cdot 0.25} \]

7. Applied rewrites 95.2%

   \[\leadsto \frac{\log \color{blue}{\left(f \cdot \left(0.25 \cdot \pi\right)\right)}}{\pi \cdot 0.25} \]
8. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\log \left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}}} \]

   2. clear-num N/A

      \[\leadsto \color{blue}{\frac{1}{\frac{\mathsf{PI}\left(\right) \cdot \frac{1}{4}}{\log \left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)}}} \]

   3. associate-/r/ N/A

      \[\leadsto \color{blue}{\frac{1}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \cdot \log \left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)} \]

   4. lower-*.f64 N/A

      \[\leadsto \color{blue}{\frac{1}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}} \cdot \log \left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right)} \]

   5. lift-*.f64 N/A

      \[\leadsto \frac{1}{\color{blue}{\mathsf{PI}\left(\right) \cdot \frac{1}{4}}} \cdot \log \left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right) \]

   6. *-commutative N/A

      \[\leadsto \frac{1}{\color{blue}{\frac{1}{4} \cdot \mathsf{PI}\left(\right)}} \cdot \log \left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right) \]

   7. associate-/r* N/A

      \[\leadsto \color{blue}{\frac{\frac{1}{\frac{1}{4}}}{\mathsf{PI}\left(\right)}} \cdot \log \left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right) \]

   8. metadata-eval N/A

      \[\leadsto \frac{\color{blue}{4}}{\mathsf{PI}\left(\right)} \cdot \log \left(f \cdot \left(\frac{1}{4} \cdot \mathsf{PI}\left(\right)\right)\right) \]

   9. lower-/.f64 95.0

      \[\leadsto \color{blue}{\frac{4}{\pi}} \cdot \log \left(f \cdot \left(0.25 \cdot \pi\right)\right) \]

9. Applied rewrites 95.0%

   \[\leadsto \color{blue}{\frac{4}{\pi} \cdot \log \left(0.25 \cdot \left(\pi \cdot f\right)\right)} \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2024226 
(FPCore (f)
  :name "VandenBroeck and Keller, Equation (20)"
  :precision binary64
  (- (* (/ 1.0 (/ PI 4.0)) (log (/ (+ (exp (* (/ PI 4.0) f)) (exp (- (* (/ PI 4.0) f)))) (- (exp (* (/ PI 4.0) f)) (exp (- (* (/ PI 4.0) f)))))))))