
(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 4 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 (* (* (- 1.0 (* v v)) PI) (sqrt (fma v (* v -6.0) 2.0)))))
double code(double v) {
return 1.3333333333333333 / (((1.0 - (v * v)) * ((double) M_PI)) * sqrt(fma(v, (v * -6.0), 2.0)));
}
function code(v) return Float64(1.3333333333333333 / Float64(Float64(Float64(1.0 - Float64(v * v)) * pi) * sqrt(fma(v, Float64(v * -6.0), 2.0)))) end
code[v_] := N[(1.3333333333333333 / N[(N[(N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision] * Pi), $MachinePrecision] * N[Sqrt[N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1.3333333333333333}{\left(\left(1 - v \cdot v\right) \cdot \pi\right) \cdot \sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)}}
\end{array}
Initial program 98.4%
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 rewrites98.4%
lift-*.f64N/A
*-commutativeN/A
lift-fma.f64N/A
+-commutativeN/A
*-commutativeN/A
metadata-evalN/A
cancel-sign-sub-invN/A
lift-*.f64N/A
lift--.f64N/A
lift-*.f64N/A
lift-*.f64N/A
associate-*l*N/A
associate-*r*N/A
lower-*.f64N/A
Applied rewrites100.0%
Applied rewrites100.0%
lift--.f64N/A
lift-*.f64N/A
cancel-sign-sub-invN/A
distribute-rgt1-inN/A
+-commutativeN/A
sub-negN/A
lift--.f64N/A
lower-*.f64100.0
Applied rewrites100.0%
(FPCore (v) :precision binary64 (/ (/ 4.0 (* PI 3.0)) (sqrt (fma (* v v) -6.0 2.0))))
double code(double v) {
return (4.0 / (((double) M_PI) * 3.0)) / sqrt(fma((v * v), -6.0, 2.0));
}
function code(v) return Float64(Float64(4.0 / Float64(pi * 3.0)) / sqrt(fma(Float64(v * v), -6.0, 2.0))) end
code[v_] := N[(N[(4.0 / N[(Pi * 3.0), $MachinePrecision]), $MachinePrecision] / N[Sqrt[N[(N[(v * v), $MachinePrecision] * -6.0 + 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{4}{\pi \cdot 3}}{\sqrt{\mathsf{fma}\left(v \cdot v, -6, 2\right)}}
\end{array}
Initial program 98.5%
Taylor expanded in v around 0
*-commutativeN/A
lower-*.f64N/A
lower-PI.f6497.6
Applied rewrites97.6%
lift-/.f64N/A
lift-*.f64N/A
associate-/r*N/A
lower-/.f64N/A
lower-/.f6499.1
lift--.f64N/A
lift-*.f64N/A
cancel-sign-sub-invN/A
metadata-evalN/A
*-commutativeN/A
+-commutativeN/A
lift-fma.f64N/A
Applied rewrites99.1%
Final simplification99.1%
herbie shell --seed 2024223
(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)))))))