
(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 5 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 (- 1.0 (* v v)))) (sqrt (fma v (* v -6.0) 2.0))))
double code(double v) {
return (1.3333333333333333 / (((double) M_PI) * (1.0 - (v * v)))) / sqrt(fma(v, (v * -6.0), 2.0));
}
function code(v) return Float64(Float64(1.3333333333333333 / Float64(pi * Float64(1.0 - Float64(v * v)))) / sqrt(fma(v, Float64(v * -6.0), 2.0))) end
code[v_] := N[(N[(1.3333333333333333 / N[(Pi * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[Sqrt[N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)}}
\end{array}
Initial program 98.5%
lift--.f64N/A
sub-negN/A
+-commutativeN/A
lift-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
lower-fma.f64N/A
metadata-eval98.5
Applied rewrites98.5%
lift-/.f64N/A
lift-*.f64N/A
associate-/r*N/A
lift-fma.f64N/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
+-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
*-commutativeN/A
metadata-evalN/A
cancel-sign-sub-invN/A
lift-*.f64N/A
lift--.f64N/A
Applied rewrites100.0%
(FPCore (v) :precision binary64 (/ 1.3333333333333333 (* (fma v (- v) 1.0) (* PI (sqrt (fma v (* v -6.0) 2.0))))))
double code(double v) {
return 1.3333333333333333 / (fma(v, -v, 1.0) * (((double) M_PI) * sqrt(fma(v, (v * -6.0), 2.0))));
}
function code(v) return Float64(1.3333333333333333 / Float64(fma(v, Float64(-v), 1.0) * Float64(pi * sqrt(fma(v, Float64(v * -6.0), 2.0))))) end
code[v_] := N[(1.3333333333333333 / N[(N[(v * (-v) + 1.0), $MachinePrecision] * N[(Pi * N[Sqrt[N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1.3333333333333333}{\mathsf{fma}\left(v, -v, 1\right) \cdot \left(\pi \cdot \sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)}\right)}
\end{array}
Initial program 98.5%
lift--.f64N/A
sub-negN/A
+-commutativeN/A
lift-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
lower-fma.f64N/A
metadata-eval98.5
Applied rewrites98.5%
lift-/.f64N/A
lift-*.f64N/A
associate-/r*N/A
lift-fma.f64N/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
+-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
*-commutativeN/A
metadata-evalN/A
cancel-sign-sub-invN/A
lift-*.f64N/A
lift--.f64N/A
Applied rewrites100.0%
lift-*.f64N/A
lift-*.f64N/A
*-commutativeN/A
associate-*l*N/A
lower-*.f64N/A
lift--.f64N/A
sub-negN/A
+-commutativeN/A
lift-*.f64N/A
distribute-rgt-neg-inN/A
lower-fma.f64N/A
lower-neg.f64N/A
lower-*.f64100.0
Applied rewrites100.0%
(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 98.5%
lift--.f64N/A
sub-negN/A
+-commutativeN/A
lift-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
lower-fma.f64N/A
metadata-eval98.5
Applied rewrites98.5%
lift-/.f64N/A
lift-*.f64N/A
associate-/r*N/A
lift-fma.f64N/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
+-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
*-commutativeN/A
metadata-evalN/A
cancel-sign-sub-invN/A
lift-*.f64N/A
lift--.f64N/A
Applied rewrites100.0%
lift-*.f64N/A
lift-*.f64N/A
*-commutativeN/A
associate-*l*N/A
lower-*.f64N/A
lift--.f64N/A
sub-negN/A
+-commutativeN/A
lift-*.f64N/A
distribute-rgt-neg-inN/A
lower-fma.f64N/A
lower-neg.f64N/A
lower-*.f64100.0
Applied rewrites100.0%
Applied rewrites100.0%
(FPCore (v) :precision binary64 (/ 1.3333333333333333 (* (sqrt (fma v (* v -6.0) 2.0)) PI)))
double code(double v) {
return 1.3333333333333333 / (sqrt(fma(v, (v * -6.0), 2.0)) * ((double) M_PI));
}
function code(v) return Float64(1.3333333333333333 / Float64(sqrt(fma(v, Float64(v * -6.0), 2.0)) * pi)) end
code[v_] := N[(1.3333333333333333 / N[(N[Sqrt[N[(v * N[(v * -6.0), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision] * Pi), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1.3333333333333333}{\sqrt{\mathsf{fma}\left(v, v \cdot -6, 2\right)} \cdot \pi}
\end{array}
Initial program 98.5%
lift--.f64N/A
sub-negN/A
+-commutativeN/A
lift-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
lower-fma.f64N/A
metadata-eval98.5
Applied rewrites98.5%
lift-/.f64N/A
lift-*.f64N/A
associate-/r*N/A
lift-fma.f64N/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
+-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
lift-*.f64N/A
*-commutativeN/A
metadata-evalN/A
cancel-sign-sub-invN/A
lift-*.f64N/A
lift--.f64N/A
Applied rewrites100.0%
Taylor expanded in v around 0
lower-PI.f6499.3
Applied rewrites99.3%
Final simplification99.3%
(FPCore (v) :precision binary64 (* (sqrt 0.5) (/ 1.3333333333333333 PI)))
double code(double v) {
return sqrt(0.5) * (1.3333333333333333 / ((double) M_PI));
}
public static double code(double v) {
return Math.sqrt(0.5) * (1.3333333333333333 / Math.PI);
}
def code(v): return math.sqrt(0.5) * (1.3333333333333333 / math.pi)
function code(v) return Float64(sqrt(0.5) * Float64(1.3333333333333333 / pi)) end
function tmp = code(v) tmp = sqrt(0.5) * (1.3333333333333333 / pi); end
code[v_] := N[(N[Sqrt[0.5], $MachinePrecision] * N[(1.3333333333333333 / Pi), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\sqrt{0.5} \cdot \frac{1.3333333333333333}{\pi}
\end{array}
Initial program 98.5%
Taylor expanded in v around 0
associate-*r/N/A
lower-/.f64N/A
lower-*.f64N/A
lower-sqrt.f64N/A
lower-PI.f6497.7
Applied rewrites97.7%
Applied rewrites99.3%
herbie shell --seed 2024222
(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)))))))