
(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)))))))
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))));
}
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))));
}
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))))
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
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
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])]]]
\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}
Sampling outcomes in binary64 precision:
Herbie found 8 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(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)))))))
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))));
}
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))));
}
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))))
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
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
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])]]]
\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 (f) :precision binary64 (/ (log (tanh (/ PI (/ 4.0 f)))) (/ PI 4.0)))
double code(double f) {
return log(tanh((((double) M_PI) / (4.0 / f)))) / (((double) M_PI) / 4.0);
}
public static double code(double f) {
return Math.log(Math.tanh((Math.PI / (4.0 / f)))) / (Math.PI / 4.0);
}
def code(f): return math.log(math.tanh((math.pi / (4.0 / f)))) / (math.pi / 4.0)
function code(f) return Float64(log(tanh(Float64(pi / Float64(4.0 / f)))) / Float64(pi / 4.0)) end
function tmp = code(f) tmp = log(tanh((pi / (4.0 / f)))) / (pi / 4.0); end
code[f_] := N[(N[Log[N[Tanh[N[(Pi / N[(4.0 / f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision] / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\log \tanh \left(\frac{\pi}{\frac{4}{f}}\right)}{\frac{\pi}{4}}
\end{array}
Initial program 6.9%
*-commutativeN/A
un-div-invN/A
distribute-neg-fracN/A
/-lowering-/.f64N/A
Applied egg-rr99.4%
(FPCore (f) :precision binary64 (/ 4.0 (/ PI (log (tanh (/ PI (/ 4.0 f)))))))
double code(double f) {
return 4.0 / (((double) M_PI) / log(tanh((((double) M_PI) / (4.0 / f)))));
}
public static double code(double f) {
return 4.0 / (Math.PI / Math.log(Math.tanh((Math.PI / (4.0 / f)))));
}
def code(f): return 4.0 / (math.pi / math.log(math.tanh((math.pi / (4.0 / f)))))
function code(f) return Float64(4.0 / Float64(pi / log(tanh(Float64(pi / Float64(4.0 / f)))))) end
function tmp = code(f) tmp = 4.0 / (pi / log(tanh((pi / (4.0 / f))))); end
code[f_] := N[(4.0 / N[(Pi / N[Log[N[Tanh[N[(Pi / N[(4.0 / f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{4}{\frac{\pi}{\log \tanh \left(\frac{\pi}{\frac{4}{f}}\right)}}
\end{array}
Initial program 6.9%
*-commutativeN/A
un-div-invN/A
distribute-neg-fracN/A
/-lowering-/.f64N/A
Applied egg-rr99.4%
associate-/r/N/A
*-commutativeN/A
clear-numN/A
un-div-invN/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
log-lowering-log.f64N/A
tanh-lowering-tanh.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
/-lowering-/.f6499.3%
Applied egg-rr99.3%
(FPCore (f)
:precision binary64
(let* ((t_0 (+ (* PI 0.125) (* -2.0 (* PI 0.020833333333333332))))
(t_1 (* t_0 (* f f))))
(/
(log
(/
(/ (- (/ 16.0 (* PI PI)) (* t_0 (* (* f f) t_1))) (- (/ 4.0 PI) t_1))
f))
(/ PI -4.0))))
double code(double f) {
double t_0 = (((double) M_PI) * 0.125) + (-2.0 * (((double) M_PI) * 0.020833333333333332));
double t_1 = t_0 * (f * f);
return log(((((16.0 / (((double) M_PI) * ((double) M_PI))) - (t_0 * ((f * f) * t_1))) / ((4.0 / ((double) M_PI)) - t_1)) / f)) / (((double) M_PI) / -4.0);
}
public static double code(double f) {
double t_0 = (Math.PI * 0.125) + (-2.0 * (Math.PI * 0.020833333333333332));
double t_1 = t_0 * (f * f);
return Math.log(((((16.0 / (Math.PI * Math.PI)) - (t_0 * ((f * f) * t_1))) / ((4.0 / Math.PI) - t_1)) / f)) / (Math.PI / -4.0);
}
def code(f): t_0 = (math.pi * 0.125) + (-2.0 * (math.pi * 0.020833333333333332)) t_1 = t_0 * (f * f) return math.log(((((16.0 / (math.pi * math.pi)) - (t_0 * ((f * f) * t_1))) / ((4.0 / math.pi) - t_1)) / f)) / (math.pi / -4.0)
function code(f) t_0 = Float64(Float64(pi * 0.125) + Float64(-2.0 * Float64(pi * 0.020833333333333332))) t_1 = Float64(t_0 * Float64(f * f)) return Float64(log(Float64(Float64(Float64(Float64(16.0 / Float64(pi * pi)) - Float64(t_0 * Float64(Float64(f * f) * t_1))) / Float64(Float64(4.0 / pi) - t_1)) / f)) / Float64(pi / -4.0)) end
function tmp = code(f) t_0 = (pi * 0.125) + (-2.0 * (pi * 0.020833333333333332)); t_1 = t_0 * (f * f); tmp = log(((((16.0 / (pi * pi)) - (t_0 * ((f * f) * t_1))) / ((4.0 / pi) - t_1)) / f)) / (pi / -4.0); end
code[f_] := Block[{t$95$0 = N[(N[(Pi * 0.125), $MachinePrecision] + N[(-2.0 * N[(Pi * 0.020833333333333332), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * N[(f * f), $MachinePrecision]), $MachinePrecision]}, N[(N[Log[N[(N[(N[(N[(16.0 / N[(Pi * Pi), $MachinePrecision]), $MachinePrecision] - N[(t$95$0 * N[(N[(f * f), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(4.0 / Pi), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision] / f), $MachinePrecision]], $MachinePrecision] / N[(Pi / -4.0), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \pi \cdot 0.125 + -2 \cdot \left(\pi \cdot 0.020833333333333332\right)\\
t_1 := t\_0 \cdot \left(f \cdot f\right)\\
\frac{\log \left(\frac{\frac{\frac{16}{\pi \cdot \pi} - t\_0 \cdot \left(\left(f \cdot f\right) \cdot t\_1\right)}{\frac{4}{\pi} - t\_1}}{f}\right)}{\frac{\pi}{-4}}
\end{array}
\end{array}
Initial program 6.9%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified6.9%
Taylor expanded in f around 0
Simplified95.4%
Applied egg-rr95.4%
/-lowering-/.f64N/A
Applied egg-rr95.4%
(FPCore (f)
:precision binary64
(/
(log
(/
f
(+
(* (+ (* PI 0.125) (* -2.0 (* PI 0.020833333333333332))) (* f f))
(/ 4.0 PI))))
(/ PI 4.0)))
double code(double f) {
return log((f / ((((((double) M_PI) * 0.125) + (-2.0 * (((double) M_PI) * 0.020833333333333332))) * (f * f)) + (4.0 / ((double) M_PI))))) / (((double) M_PI) / 4.0);
}
public static double code(double f) {
return Math.log((f / ((((Math.PI * 0.125) + (-2.0 * (Math.PI * 0.020833333333333332))) * (f * f)) + (4.0 / Math.PI)))) / (Math.PI / 4.0);
}
def code(f): return math.log((f / ((((math.pi * 0.125) + (-2.0 * (math.pi * 0.020833333333333332))) * (f * f)) + (4.0 / math.pi)))) / (math.pi / 4.0)
function code(f) return Float64(log(Float64(f / Float64(Float64(Float64(Float64(pi * 0.125) + Float64(-2.0 * Float64(pi * 0.020833333333333332))) * Float64(f * f)) + Float64(4.0 / pi)))) / Float64(pi / 4.0)) end
function tmp = code(f) tmp = log((f / ((((pi * 0.125) + (-2.0 * (pi * 0.020833333333333332))) * (f * f)) + (4.0 / pi)))) / (pi / 4.0); end
code[f_] := N[(N[Log[N[(f / N[(N[(N[(N[(Pi * 0.125), $MachinePrecision] + N[(-2.0 * N[(Pi * 0.020833333333333332), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(f * f), $MachinePrecision]), $MachinePrecision] + N[(4.0 / Pi), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[(Pi / 4.0), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\log \left(\frac{f}{\left(\pi \cdot 0.125 + -2 \cdot \left(\pi \cdot 0.020833333333333332\right)\right) \cdot \left(f \cdot f\right) + \frac{4}{\pi}}\right)}{\frac{\pi}{4}}
\end{array}
Initial program 6.9%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified6.9%
Taylor expanded in f around 0
Simplified95.4%
Applied egg-rr95.4%
Applied egg-rr95.4%
Final simplification95.4%
(FPCore (f) :precision binary64 (/ (log (/ (+ (/ 4.0 PI) (* f (* f (* PI 0.08333333333333333)))) f)) (/ PI -4.0)))
double code(double f) {
return log((((4.0 / ((double) M_PI)) + (f * (f * (((double) M_PI) * 0.08333333333333333)))) / f)) / (((double) M_PI) / -4.0);
}
public static double code(double f) {
return Math.log((((4.0 / Math.PI) + (f * (f * (Math.PI * 0.08333333333333333)))) / f)) / (Math.PI / -4.0);
}
def code(f): return math.log((((4.0 / math.pi) + (f * (f * (math.pi * 0.08333333333333333)))) / f)) / (math.pi / -4.0)
function code(f) return Float64(log(Float64(Float64(Float64(4.0 / pi) + Float64(f * Float64(f * Float64(pi * 0.08333333333333333)))) / f)) / Float64(pi / -4.0)) end
function tmp = code(f) tmp = log((((4.0 / pi) + (f * (f * (pi * 0.08333333333333333)))) / f)) / (pi / -4.0); end
code[f_] := N[(N[Log[N[(N[(N[(4.0 / Pi), $MachinePrecision] + N[(f * N[(f * N[(Pi * 0.08333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / f), $MachinePrecision]], $MachinePrecision] / N[(Pi / -4.0), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\log \left(\frac{\frac{4}{\pi} + f \cdot \left(f \cdot \left(\pi \cdot 0.08333333333333333\right)\right)}{f}\right)}{\frac{\pi}{-4}}
\end{array}
Initial program 6.9%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified6.9%
Taylor expanded in f around 0
Simplified95.4%
Taylor expanded in f around 0
/-lowering-/.f64N/A
+-lowering-+.f64N/A
unpow2N/A
associate-*l*N/A
*-lowering-*.f64N/A
*-lowering-*.f64N/A
distribute-rgt-outN/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
metadata-evalN/A
associate-*r/N/A
metadata-evalN/A
/-lowering-/.f64N/A
PI-lowering-PI.f6495.4%
Simplified95.4%
Final simplification95.4%
(FPCore (f) :precision binary64 (/ (log (/ 4.0 (* PI f))) (/ PI -4.0)))
double code(double f) {
return log((4.0 / (((double) M_PI) * f))) / (((double) M_PI) / -4.0);
}
public static double code(double f) {
return Math.log((4.0 / (Math.PI * f))) / (Math.PI / -4.0);
}
def code(f): return math.log((4.0 / (math.pi * f))) / (math.pi / -4.0)
function code(f) return Float64(log(Float64(4.0 / Float64(pi * f))) / Float64(pi / -4.0)) end
function tmp = code(f) tmp = log((4.0 / (pi * f))) / (pi / -4.0); end
code[f_] := N[(N[Log[N[(4.0 / N[(Pi * f), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] / N[(Pi / -4.0), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\log \left(\frac{4}{\pi \cdot f}\right)}{\frac{\pi}{-4}}
\end{array}
Initial program 6.9%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified6.9%
Taylor expanded in f around 0
/-lowering-/.f64N/A
*-commutativeN/A
distribute-rgt-out--N/A
metadata-evalN/A
associate-*l*N/A
metadata-evalN/A
*-inversesN/A
metadata-evalN/A
times-fracN/A
*-commutativeN/A
metadata-evalN/A
distribute-rgt-out--N/A
associate-*r/N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
Simplified94.9%
clear-numN/A
associate-*r*N/A
associate-/l*N/A
metadata-evalN/A
metadata-evalN/A
div-invN/A
clear-numN/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6494.9%
Applied egg-rr94.9%
(FPCore (f) :precision binary64 (/ -4.0 (/ PI (log (/ (/ 4.0 f) PI)))))
double code(double f) {
return -4.0 / (((double) M_PI) / log(((4.0 / f) / ((double) M_PI))));
}
public static double code(double f) {
return -4.0 / (Math.PI / Math.log(((4.0 / f) / Math.PI)));
}
def code(f): return -4.0 / (math.pi / math.log(((4.0 / f) / math.pi)))
function code(f) return Float64(-4.0 / Float64(pi / log(Float64(Float64(4.0 / f) / pi)))) end
function tmp = code(f) tmp = -4.0 / (pi / log(((4.0 / f) / pi))); end
code[f_] := N[(-4.0 / N[(Pi / N[Log[N[(N[(4.0 / f), $MachinePrecision] / Pi), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{-4}{\frac{\pi}{\log \left(\frac{\frac{4}{f}}{\pi}\right)}}
\end{array}
Initial program 6.9%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified6.9%
Taylor expanded in f around 0
/-lowering-/.f64N/A
*-commutativeN/A
distribute-rgt-out--N/A
metadata-evalN/A
associate-*l*N/A
metadata-evalN/A
*-inversesN/A
metadata-evalN/A
times-fracN/A
*-commutativeN/A
metadata-evalN/A
distribute-rgt-out--N/A
associate-*r/N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
Simplified94.9%
associate-/r/N/A
*-commutativeN/A
clear-numN/A
un-div-invN/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
clear-numN/A
associate-*r*N/A
associate-/l*N/A
metadata-evalN/A
metadata-evalN/A
div-invN/A
clear-numN/A
associate-/l/N/A
log-lowering-log.f64N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f6494.8%
Applied egg-rr94.8%
(FPCore (f) :precision binary64 (* (log (/ (/ 4.0 f) PI)) (/ -4.0 PI)))
double code(double f) {
return log(((4.0 / f) / ((double) M_PI))) * (-4.0 / ((double) M_PI));
}
public static double code(double f) {
return Math.log(((4.0 / f) / Math.PI)) * (-4.0 / Math.PI);
}
def code(f): return math.log(((4.0 / f) / math.pi)) * (-4.0 / math.pi)
function code(f) return Float64(log(Float64(Float64(4.0 / f) / pi)) * Float64(-4.0 / pi)) end
function tmp = code(f) tmp = log(((4.0 / f) / pi)) * (-4.0 / pi); end
code[f_] := N[(N[Log[N[(N[(4.0 / f), $MachinePrecision] / Pi), $MachinePrecision]], $MachinePrecision] * N[(-4.0 / Pi), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\log \left(\frac{\frac{4}{f}}{\pi}\right) \cdot \frac{-4}{\pi}
\end{array}
Initial program 6.9%
distribute-lft-neg-inN/A
distribute-neg-frac2N/A
associate-*l/N/A
*-lft-identityN/A
/-lowering-/.f64N/A
Simplified6.9%
Taylor expanded in f around 0
/-lowering-/.f64N/A
*-commutativeN/A
distribute-rgt-out--N/A
metadata-evalN/A
associate-*l*N/A
metadata-evalN/A
*-inversesN/A
metadata-evalN/A
times-fracN/A
*-commutativeN/A
metadata-evalN/A
distribute-rgt-out--N/A
associate-*r/N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
Simplified94.9%
div-invN/A
associate-/r/N/A
metadata-evalN/A
distribute-rgt-neg-inN/A
associate-/r/N/A
*-commutativeN/A
*-lowering-*.f64N/A
clear-numN/A
distribute-neg-fracN/A
metadata-evalN/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
clear-numN/A
associate-*r*N/A
associate-/l*N/A
metadata-evalN/A
metadata-evalN/A
div-invN/A
clear-numN/A
associate-/l/N/A
Applied egg-rr94.8%
Final simplification94.8%
herbie shell --seed 2024145
(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)))))))))