
(FPCore (v) :precision binary64 (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))
double code(double v) {
return 4.0 / (((3.0 * ((double) M_PI)) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
}
public static double code(double v) {
return 4.0 / (((3.0 * Math.PI) * (1.0 - (v * v))) * Math.sqrt((2.0 - (6.0 * (v * v)))));
}
def code(v): return 4.0 / (((3.0 * math.pi) * (1.0 - (v * v))) * math.sqrt((2.0 - (6.0 * (v * v)))))
function code(v) return Float64(4.0 / Float64(Float64(Float64(3.0 * pi) * Float64(1.0 - Float64(v * v))) * sqrt(Float64(2.0 - Float64(6.0 * Float64(v * v)))))) end
function tmp = code(v) tmp = 4.0 / (((3.0 * pi) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v))))); end
code[v_] := N[(4.0 / N[(N[(N[(3.0 * Pi), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sqrt[N[(2.0 - N[(6.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}}
\end{array}
Sampling outcomes in binary64 precision:
Herbie found 6 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (v) :precision binary64 (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))
double code(double v) {
return 4.0 / (((3.0 * ((double) M_PI)) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
}
public static double code(double v) {
return 4.0 / (((3.0 * Math.PI) * (1.0 - (v * v))) * Math.sqrt((2.0 - (6.0 * (v * v)))));
}
def code(v): return 4.0 / (((3.0 * math.pi) * (1.0 - (v * v))) * math.sqrt((2.0 - (6.0 * (v * v)))))
function code(v) return Float64(4.0 / Float64(Float64(Float64(3.0 * pi) * Float64(1.0 - Float64(v * v))) * sqrt(Float64(2.0 - Float64(6.0 * Float64(v * v)))))) end
function tmp = code(v) tmp = 4.0 / (((3.0 * pi) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v))))); end
code[v_] := N[(4.0 / N[(N[(N[(3.0 * Pi), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sqrt[N[(2.0 - N[(6.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}}
\end{array}
(FPCore (v) :precision binary64 (/ 1.3333333333333333 (* PI (* (sqrt (fma v (* v -6.0) 2.0)) (- 1.0 (* v v))))))
double code(double v) {
return 1.3333333333333333 / (((double) M_PI) * (sqrt(fma(v, (v * -6.0), 2.0)) * (1.0 - (v * v))));
}
function code(v) return Float64(1.3333333333333333 / Float64(pi * Float64(sqrt(fma(v, Float64(v * -6.0), 2.0)) * Float64(1.0 - Float64(v * v))))) end
code[v_] := N[(1.3333333333333333 / N[(Pi * N[(N[Sqrt[N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1.3333333333333333}{\pi \cdot \left(\sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)} \cdot \left(1 - v \cdot v\right)\right)}
\end{array}
Initial program 98.4%
lift-*.f64N/A
lift-*.f64N/A
sub-negN/A
+-commutativeN/A
lift-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
lower-fma.f64N/A
metadata-eval98.4
Applied egg-rr98.4%
lift-PI.f64N/A
lift-*.f64N/A
lift-*.f64N/A
lift--.f64N/A
lift-*.f64N/A
lift-fma.f64N/A
lift-sqrt.f64N/A
associate-*l*N/A
lift-*.f64N/A
*-commutativeN/A
associate-*l*N/A
lower-*.f64N/A
lower-*.f64N/A
lift-fma.f64N/A
lift-*.f64N/A
Applied egg-rr100.0%
lift-PI.f64N/A
lift-*.f64N/A
lift-fma.f64N/A
lift-sqrt.f64N/A
lift-*.f64N/A
lift--.f64N/A
associate-*r*N/A
lift--.f64N/A
sub-negN/A
distribute-rgt-inN/A
*-lft-identityN/A
Applied egg-rr100.0%
(FPCore (v) :precision binary64 (/ -1.3333333333333333 (* (sqrt (fma v (* v -6.0) 2.0)) (* PI (fma v v -1.0)))))
double code(double v) {
return -1.3333333333333333 / (sqrt(fma(v, (v * -6.0), 2.0)) * (((double) M_PI) * fma(v, v, -1.0)));
}
function code(v) return Float64(-1.3333333333333333 / Float64(sqrt(fma(v, Float64(v * -6.0), 2.0)) * Float64(pi * fma(v, v, -1.0)))) end
code[v_] := N[(-1.3333333333333333 / N[(N[Sqrt[N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision] * N[(Pi * N[(v * v + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{-1.3333333333333333}{\sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)} \cdot \left(\pi \cdot \mathsf{fma}\left(v, v, -1\right)\right)}
\end{array}
Initial program 98.4%
lift-*.f64N/A
lift-*.f64N/A
sub-negN/A
+-commutativeN/A
lift-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
lower-fma.f64N/A
metadata-eval98.4
Applied egg-rr98.4%
lift-PI.f64N/A
lift-*.f64N/A
lift-*.f64N/A
lift--.f64N/A
lift-*.f64N/A
lift-fma.f64N/A
lift-sqrt.f64N/A
associate-*l*N/A
lift-*.f64N/A
*-commutativeN/A
associate-*l*N/A
lower-*.f64N/A
lower-*.f64N/A
lift-fma.f64N/A
lift-*.f64N/A
Applied egg-rr100.0%
lift-PI.f64N/A
lift-*.f64N/A
lift-fma.f64N/A
lift-sqrt.f64N/A
lift-*.f64N/A
lift--.f64N/A
associate-*r*N/A
lift--.f64N/A
sub-negN/A
distribute-rgt-inN/A
*-lft-identityN/A
Applied egg-rr100.0%
Applied egg-rr100.0%
(FPCore (v) :precision binary64 (* (/ 4.0 (* PI (sqrt (fma v (* v -6.0) 2.0)))) 0.3333333333333333))
double code(double v) {
return (4.0 / (((double) M_PI) * sqrt(fma(v, (v * -6.0), 2.0)))) * 0.3333333333333333;
}
function code(v) return Float64(Float64(4.0 / Float64(pi * sqrt(fma(v, Float64(v * -6.0), 2.0)))) * 0.3333333333333333) end
code[v_] := N[(N[(4.0 / N[(Pi * N[Sqrt[N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.3333333333333333), $MachinePrecision]
\begin{array}{l}
\\
\frac{4}{\pi \cdot \sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)}} \cdot 0.3333333333333333
\end{array}
Initial program 98.4%
Taylor expanded in v around 0
*-commutativeN/A
lower-*.f64N/A
lower-PI.f6497.1
Simplified97.1%
lift-PI.f64N/A
lift-*.f64N/A
lift-*.f64N/A
lift-*.f64N/A
lift--.f64N/A
lift-sqrt.f64N/A
associate-/r*N/A
lift--.f64N/A
lift-*.f64N/A
cancel-sign-sub-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
+-commutativeN/A
lift-fma.f64N/A
Applied egg-rr98.6%
lift-PI.f64N/A
lift-*.f64N/A
lift-fma.f64N/A
lift-sqrt.f64N/A
metadata-evalN/A
lift-*.f64N/A
associate-*l/N/A
lower-*.f64N/A
lower-/.f6498.6
Applied egg-rr98.6%
(FPCore (v) :precision binary64 (/ 1.3333333333333333 (* PI (sqrt (fma v (* v -6.0) 2.0)))))
double code(double v) {
return 1.3333333333333333 / (((double) M_PI) * sqrt(fma(v, (v * -6.0), 2.0)));
}
function code(v) return Float64(1.3333333333333333 / Float64(pi * sqrt(fma(v, Float64(v * -6.0), 2.0)))) end
code[v_] := N[(1.3333333333333333 / N[(Pi * N[Sqrt[N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1.3333333333333333}{\pi \cdot \sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)}}
\end{array}
Initial program 98.4%
Taylor expanded in v around 0
*-commutativeN/A
lower-*.f64N/A
lower-PI.f6497.1
Simplified97.1%
lift-PI.f64N/A
lift-*.f64N/A
lift-*.f64N/A
lift-*.f64N/A
lift--.f64N/A
lift-sqrt.f64N/A
associate-/r*N/A
lift--.f64N/A
lift-*.f64N/A
cancel-sign-sub-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
+-commutativeN/A
lift-fma.f64N/A
Applied egg-rr98.6%
(FPCore (v) :precision binary64 (/ 1.3333333333333333 (* PI (sqrt (fma v (- v) 2.0)))))
double code(double v) {
return 1.3333333333333333 / (((double) M_PI) * sqrt(fma(v, -v, 2.0)));
}
function code(v) return Float64(1.3333333333333333 / Float64(pi * sqrt(fma(v, Float64(-v), 2.0)))) end
code[v_] := N[(1.3333333333333333 / N[(Pi * N[Sqrt[N[(v * (-v) + 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1.3333333333333333}{\pi \cdot \sqrt{\mathsf{fma}\left(v, -v, 2\right)}}
\end{array}
Initial program 98.4%
Taylor expanded in v around 0
*-commutativeN/A
lower-*.f64N/A
lower-PI.f6497.1
Simplified97.1%
lift-PI.f64N/A
lift-*.f64N/A
lift-*.f64N/A
lift-*.f64N/A
lift--.f64N/A
lift-sqrt.f64N/A
associate-/r*N/A
lift--.f64N/A
lift-*.f64N/A
cancel-sign-sub-invN/A
metadata-evalN/A
*-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
+-commutativeN/A
lift-fma.f64N/A
Applied egg-rr98.6%
Taylor expanded in v around -inf
mul-1-negN/A
lower-neg.f6498.6
Simplified98.6%
(FPCore (v) :precision binary64 (/ 1.3333333333333333 (* PI (sqrt 2.0))))
double code(double v) {
return 1.3333333333333333 / (((double) M_PI) * sqrt(2.0));
}
public static double code(double v) {
return 1.3333333333333333 / (Math.PI * Math.sqrt(2.0));
}
def code(v): return 1.3333333333333333 / (math.pi * math.sqrt(2.0))
function code(v) return Float64(1.3333333333333333 / Float64(pi * sqrt(2.0))) end
function tmp = code(v) tmp = 1.3333333333333333 / (pi * sqrt(2.0)); end
code[v_] := N[(1.3333333333333333 / N[(Pi * N[Sqrt[2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1.3333333333333333}{\pi \cdot \sqrt{2}}
\end{array}
Initial program 98.4%
Applied egg-rr98.6%
Taylor expanded in v around 0
lower-*.f64N/A
lower-PI.f64N/A
lower-sqrt.f6498.6
Simplified98.6%
herbie shell --seed 2024214
(FPCore (v)
:name "Falkner and Boettcher, Equation (22+)"
:precision binary64
(/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))