
(FPCore (x) :precision binary64 (- (/ PI 2.0) (* 2.0 (asin (sqrt (/ (- 1.0 x) 2.0))))))
double code(double x) {
return (((double) M_PI) / 2.0) - (2.0 * asin(sqrt(((1.0 - x) / 2.0))));
}
public static double code(double x) {
return (Math.PI / 2.0) - (2.0 * Math.asin(Math.sqrt(((1.0 - x) / 2.0))));
}
def code(x): return (math.pi / 2.0) - (2.0 * math.asin(math.sqrt(((1.0 - x) / 2.0))))
function code(x) return Float64(Float64(pi / 2.0) - Float64(2.0 * asin(sqrt(Float64(Float64(1.0 - x) / 2.0))))) end
function tmp = code(x) tmp = (pi / 2.0) - (2.0 * asin(sqrt(((1.0 - x) / 2.0)))); end
code[x_] := N[(N[(Pi / 2.0), $MachinePrecision] - N[(2.0 * N[ArcSin[N[Sqrt[N[(N[(1.0 - x), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)
\end{array}
Sampling outcomes in binary64 precision:
Herbie found 5 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (x) :precision binary64 (- (/ PI 2.0) (* 2.0 (asin (sqrt (/ (- 1.0 x) 2.0))))))
double code(double x) {
return (((double) M_PI) / 2.0) - (2.0 * asin(sqrt(((1.0 - x) / 2.0))));
}
public static double code(double x) {
return (Math.PI / 2.0) - (2.0 * Math.asin(Math.sqrt(((1.0 - x) / 2.0))));
}
def code(x): return (math.pi / 2.0) - (2.0 * math.asin(math.sqrt(((1.0 - x) / 2.0))))
function code(x) return Float64(Float64(pi / 2.0) - Float64(2.0 * asin(sqrt(Float64(Float64(1.0 - x) / 2.0))))) end
function tmp = code(x) tmp = (pi / 2.0) - (2.0 * asin(sqrt(((1.0 - x) / 2.0)))); end
code[x_] := N[(N[(Pi / 2.0), $MachinePrecision] - N[(2.0 * N[ArcSin[N[Sqrt[N[(N[(1.0 - x), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\pi}{2} - 2 \cdot \sin^{-1} \left(\sqrt{\frac{1 - x}{2}}\right)
\end{array}
(FPCore (x)
:precision binary64
(let* ((t_0 (acos (sqrt (fma -0.5 x 0.5)))) (t_1 (fma -2.0 t_0 (* -0.5 PI))))
(*
(- (* t_1 (* 0.25 (* PI PI))) (* t_1 (pow (* -2.0 t_0) 2.0)))
(pow t_1 -2.0))))
double code(double x) {
double t_0 = acos(sqrt(fma(-0.5, x, 0.5)));
double t_1 = fma(-2.0, t_0, (-0.5 * ((double) M_PI)));
return ((t_1 * (0.25 * (((double) M_PI) * ((double) M_PI)))) - (t_1 * pow((-2.0 * t_0), 2.0))) * pow(t_1, -2.0);
}
function code(x) t_0 = acos(sqrt(fma(-0.5, x, 0.5))) t_1 = fma(-2.0, t_0, Float64(-0.5 * pi)) return Float64(Float64(Float64(t_1 * Float64(0.25 * Float64(pi * pi))) - Float64(t_1 * (Float64(-2.0 * t_0) ^ 2.0))) * (t_1 ^ -2.0)) end
code[x_] := Block[{t$95$0 = N[ArcCos[N[Sqrt[N[(-0.5 * x + 0.5), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(-2.0 * t$95$0 + N[(-0.5 * Pi), $MachinePrecision]), $MachinePrecision]}, N[(N[(N[(t$95$1 * N[(0.25 * N[(Pi * Pi), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(t$95$1 * N[Power[N[(-2.0 * t$95$0), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Power[t$95$1, -2.0], $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos^{-1} \left(\sqrt{\mathsf{fma}\left(-0.5, x, 0.5\right)}\right)\\
t_1 := \mathsf{fma}\left(-2, t\_0, -0.5 \cdot \pi\right)\\
\left(t\_1 \cdot \left(0.25 \cdot \left(\pi \cdot \pi\right)\right) - t\_1 \cdot {\left(-2 \cdot t\_0\right)}^{2}\right) \cdot {t\_1}^{-2}
\end{array}
\end{array}
Initial program 7.5%
lift-asin.f64N/A
asin-acosN/A
lift-PI.f64N/A
lift-/.f64N/A
sub-negN/A
lift-/.f64N/A
div-invN/A
metadata-evalN/A
lower-fma.f64N/A
lower-neg.f64N/A
lower-acos.f648.6
lift-/.f64N/A
lift--.f64N/A
div-subN/A
metadata-evalN/A
sub-negN/A
+-commutativeN/A
div-invN/A
metadata-evalN/A
distribute-rgt-neg-inN/A
metadata-evalN/A
metadata-evalN/A
metadata-evalN/A
lower-fma.f64N/A
Applied rewrites8.6%
Taylor expanded in x around 0
cancel-sign-sub-invN/A
metadata-evalN/A
sub-negN/A
metadata-evalN/A
cancel-sign-sub-invN/A
distribute-lft-inN/A
associate-+r+N/A
associate-*r*N/A
metadata-evalN/A
distribute-rgt-outN/A
metadata-evalN/A
Applied rewrites8.6%
Applied rewrites8.7%
Applied rewrites8.7%
Final simplification8.7%
(FPCore (x) :precision binary64 (let* ((t_0 (acos (sqrt (fma -0.5 x 0.5)))) (t_1 (fma -2.0 t_0 (* -0.5 PI)))) (fma (* PI 0.25) (/ PI t_1) (/ (* (pow t_0 2.0) -4.0) t_1))))
double code(double x) {
double t_0 = acos(sqrt(fma(-0.5, x, 0.5)));
double t_1 = fma(-2.0, t_0, (-0.5 * ((double) M_PI)));
return fma((((double) M_PI) * 0.25), (((double) M_PI) / t_1), ((pow(t_0, 2.0) * -4.0) / t_1));
}
function code(x) t_0 = acos(sqrt(fma(-0.5, x, 0.5))) t_1 = fma(-2.0, t_0, Float64(-0.5 * pi)) return fma(Float64(pi * 0.25), Float64(pi / t_1), Float64(Float64((t_0 ^ 2.0) * -4.0) / t_1)) end
code[x_] := Block[{t$95$0 = N[ArcCos[N[Sqrt[N[(-0.5 * x + 0.5), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(-2.0 * t$95$0 + N[(-0.5 * Pi), $MachinePrecision]), $MachinePrecision]}, N[(N[(Pi * 0.25), $MachinePrecision] * N[(Pi / t$95$1), $MachinePrecision] + N[(N[(N[Power[t$95$0, 2.0], $MachinePrecision] * -4.0), $MachinePrecision] / t$95$1), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos^{-1} \left(\sqrt{\mathsf{fma}\left(-0.5, x, 0.5\right)}\right)\\
t_1 := \mathsf{fma}\left(-2, t\_0, -0.5 \cdot \pi\right)\\
\mathsf{fma}\left(\pi \cdot 0.25, \frac{\pi}{t\_1}, \frac{{t\_0}^{2} \cdot -4}{t\_1}\right)
\end{array}
\end{array}
Initial program 7.5%
lift-asin.f64N/A
asin-acosN/A
lift-PI.f64N/A
lift-/.f64N/A
sub-negN/A
lift-/.f64N/A
div-invN/A
metadata-evalN/A
lower-fma.f64N/A
lower-neg.f64N/A
lower-acos.f648.6
lift-/.f64N/A
lift--.f64N/A
div-subN/A
metadata-evalN/A
sub-negN/A
+-commutativeN/A
div-invN/A
metadata-evalN/A
distribute-rgt-neg-inN/A
metadata-evalN/A
metadata-evalN/A
metadata-evalN/A
lower-fma.f64N/A
Applied rewrites8.6%
Taylor expanded in x around 0
cancel-sign-sub-invN/A
metadata-evalN/A
sub-negN/A
metadata-evalN/A
cancel-sign-sub-invN/A
distribute-lft-inN/A
associate-+r+N/A
associate-*r*N/A
metadata-evalN/A
distribute-rgt-outN/A
metadata-evalN/A
Applied rewrites8.6%
Applied rewrites8.7%
Applied rewrites8.7%
Final simplification8.7%
(FPCore (x) :precision binary64 (let* ((t_0 (acos (sqrt (fma -0.5 x 0.5))))) (/ (fma 0.25 (* PI PI) (* (pow t_0 2.0) -4.0)) (fma -2.0 t_0 (* -0.5 PI)))))
double code(double x) {
double t_0 = acos(sqrt(fma(-0.5, x, 0.5)));
return fma(0.25, (((double) M_PI) * ((double) M_PI)), (pow(t_0, 2.0) * -4.0)) / fma(-2.0, t_0, (-0.5 * ((double) M_PI)));
}
function code(x) t_0 = acos(sqrt(fma(-0.5, x, 0.5))) return Float64(fma(0.25, Float64(pi * pi), Float64((t_0 ^ 2.0) * -4.0)) / fma(-2.0, t_0, Float64(-0.5 * pi))) end
code[x_] := Block[{t$95$0 = N[ArcCos[N[Sqrt[N[(-0.5 * x + 0.5), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]}, N[(N[(0.25 * N[(Pi * Pi), $MachinePrecision] + N[(N[Power[t$95$0, 2.0], $MachinePrecision] * -4.0), $MachinePrecision]), $MachinePrecision] / N[(-2.0 * t$95$0 + N[(-0.5 * Pi), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos^{-1} \left(\sqrt{\mathsf{fma}\left(-0.5, x, 0.5\right)}\right)\\
\frac{\mathsf{fma}\left(0.25, \pi \cdot \pi, {t\_0}^{2} \cdot -4\right)}{\mathsf{fma}\left(-2, t\_0, -0.5 \cdot \pi\right)}
\end{array}
\end{array}
Initial program 7.5%
lift-asin.f64N/A
asin-acosN/A
lift-PI.f64N/A
lift-/.f64N/A
sub-negN/A
lift-/.f64N/A
div-invN/A
metadata-evalN/A
lower-fma.f64N/A
lower-neg.f64N/A
lower-acos.f648.6
lift-/.f64N/A
lift--.f64N/A
div-subN/A
metadata-evalN/A
sub-negN/A
+-commutativeN/A
div-invN/A
metadata-evalN/A
distribute-rgt-neg-inN/A
metadata-evalN/A
metadata-evalN/A
metadata-evalN/A
lower-fma.f64N/A
Applied rewrites8.6%
Taylor expanded in x around 0
cancel-sign-sub-invN/A
metadata-evalN/A
sub-negN/A
metadata-evalN/A
cancel-sign-sub-invN/A
distribute-lft-inN/A
associate-+r+N/A
associate-*r*N/A
metadata-evalN/A
distribute-rgt-outN/A
metadata-evalN/A
Applied rewrites8.6%
Applied rewrites8.7%
Applied rewrites8.6%
Final simplification8.6%
(FPCore (x) :precision binary64 (fma PI -0.5 (* 2.0 (acos (sqrt (fma -0.5 x 0.5))))))
double code(double x) {
return fma(((double) M_PI), -0.5, (2.0 * acos(sqrt(fma(-0.5, x, 0.5)))));
}
function code(x) return fma(pi, -0.5, Float64(2.0 * acos(sqrt(fma(-0.5, x, 0.5))))) end
code[x_] := N[(Pi * -0.5 + N[(2.0 * N[ArcCos[N[Sqrt[N[(-0.5 * x + 0.5), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\mathsf{fma}\left(\pi, -0.5, 2 \cdot \cos^{-1} \left(\sqrt{\mathsf{fma}\left(-0.5, x, 0.5\right)}\right)\right)
\end{array}
Initial program 7.5%
lift-asin.f64N/A
asin-acosN/A
lift-PI.f64N/A
lift-/.f64N/A
sub-negN/A
lift-/.f64N/A
div-invN/A
metadata-evalN/A
lower-fma.f64N/A
lower-neg.f64N/A
lower-acos.f648.6
lift-/.f64N/A
lift--.f64N/A
div-subN/A
metadata-evalN/A
sub-negN/A
+-commutativeN/A
div-invN/A
metadata-evalN/A
distribute-rgt-neg-inN/A
metadata-evalN/A
metadata-evalN/A
metadata-evalN/A
lower-fma.f64N/A
Applied rewrites8.6%
Taylor expanded in x around 0
cancel-sign-sub-invN/A
metadata-evalN/A
sub-negN/A
metadata-evalN/A
cancel-sign-sub-invN/A
distribute-lft-inN/A
associate-+r+N/A
associate-*r*N/A
metadata-evalN/A
distribute-rgt-outN/A
metadata-evalN/A
Applied rewrites8.6%
(FPCore (x) :precision binary64 (fma PI -0.5 (* 2.0 (acos (sqrt 0.5)))))
double code(double x) {
return fma(((double) M_PI), -0.5, (2.0 * acos(sqrt(0.5))));
}
function code(x) return fma(pi, -0.5, Float64(2.0 * acos(sqrt(0.5)))) end
code[x_] := N[(Pi * -0.5 + N[(2.0 * N[ArcCos[N[Sqrt[0.5], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\mathsf{fma}\left(\pi, -0.5, 2 \cdot \cos^{-1} \left(\sqrt{0.5}\right)\right)
\end{array}
Initial program 7.5%
lift-asin.f64N/A
asin-acosN/A
lift-PI.f64N/A
lift-/.f64N/A
sub-negN/A
lift-/.f64N/A
div-invN/A
metadata-evalN/A
lower-fma.f64N/A
lower-neg.f64N/A
lower-acos.f648.6
lift-/.f64N/A
lift--.f64N/A
div-subN/A
metadata-evalN/A
sub-negN/A
+-commutativeN/A
div-invN/A
metadata-evalN/A
distribute-rgt-neg-inN/A
metadata-evalN/A
metadata-evalN/A
metadata-evalN/A
lower-fma.f64N/A
Applied rewrites8.6%
Taylor expanded in x around 0
cancel-sign-sub-invN/A
metadata-evalN/A
sub-negN/A
metadata-evalN/A
cancel-sign-sub-invN/A
distribute-lft-inN/A
associate-+r+N/A
associate-*r*N/A
metadata-evalN/A
distribute-rgt-outN/A
metadata-evalN/A
Applied rewrites8.6%
Taylor expanded in x around 0
Applied rewrites5.2%
(FPCore (x) :precision binary64 (asin x))
double code(double x) {
return asin(x);
}
real(8) function code(x)
real(8), intent (in) :: x
code = asin(x)
end function
public static double code(double x) {
return Math.asin(x);
}
def code(x): return math.asin(x)
function code(x) return asin(x) end
function tmp = code(x) tmp = asin(x); end
code[x_] := N[ArcSin[x], $MachinePrecision]
\begin{array}{l}
\\
\sin^{-1} x
\end{array}
herbie shell --seed 2024237
(FPCore (x)
:name "Ian Simplification"
:precision binary64
:alt
(! :herbie-platform default (asin x))
(- (/ PI 2.0) (* 2.0 (asin (sqrt (/ (- 1.0 x) 2.0))))))