
(FPCore (x)
:precision binary64
(let* ((t_0 (/ 1.0 (fabs x)))
(t_1 (* (* t_0 t_0) t_0))
(t_2 (* (* t_1 t_0) t_0)))
(*
(* (/ 1.0 (sqrt PI)) (exp (* (fabs x) (fabs x))))
(+
(+ (+ t_0 (* (/ 1.0 2.0) t_1)) (* (/ 3.0 4.0) t_2))
(* (/ 15.0 8.0) (* (* t_2 t_0) t_0))))))
double code(double x) {
double t_0 = 1.0 / fabs(x);
double t_1 = (t_0 * t_0) * t_0;
double t_2 = (t_1 * t_0) * t_0;
return ((1.0 / sqrt(((double) M_PI))) * exp((fabs(x) * fabs(x)))) * (((t_0 + ((1.0 / 2.0) * t_1)) + ((3.0 / 4.0) * t_2)) + ((15.0 / 8.0) * ((t_2 * t_0) * t_0)));
}
public static double code(double x) {
double t_0 = 1.0 / Math.abs(x);
double t_1 = (t_0 * t_0) * t_0;
double t_2 = (t_1 * t_0) * t_0;
return ((1.0 / Math.sqrt(Math.PI)) * Math.exp((Math.abs(x) * Math.abs(x)))) * (((t_0 + ((1.0 / 2.0) * t_1)) + ((3.0 / 4.0) * t_2)) + ((15.0 / 8.0) * ((t_2 * t_0) * t_0)));
}
def code(x): t_0 = 1.0 / math.fabs(x) t_1 = (t_0 * t_0) * t_0 t_2 = (t_1 * t_0) * t_0 return ((1.0 / math.sqrt(math.pi)) * math.exp((math.fabs(x) * math.fabs(x)))) * (((t_0 + ((1.0 / 2.0) * t_1)) + ((3.0 / 4.0) * t_2)) + ((15.0 / 8.0) * ((t_2 * t_0) * t_0)))
function code(x) t_0 = Float64(1.0 / abs(x)) t_1 = Float64(Float64(t_0 * t_0) * t_0) t_2 = Float64(Float64(t_1 * t_0) * t_0) return Float64(Float64(Float64(1.0 / sqrt(pi)) * exp(Float64(abs(x) * abs(x)))) * Float64(Float64(Float64(t_0 + Float64(Float64(1.0 / 2.0) * t_1)) + Float64(Float64(3.0 / 4.0) * t_2)) + Float64(Float64(15.0 / 8.0) * Float64(Float64(t_2 * t_0) * t_0)))) end
function tmp = code(x) t_0 = 1.0 / abs(x); t_1 = (t_0 * t_0) * t_0; t_2 = (t_1 * t_0) * t_0; tmp = ((1.0 / sqrt(pi)) * exp((abs(x) * abs(x)))) * (((t_0 + ((1.0 / 2.0) * t_1)) + ((3.0 / 4.0) * t_2)) + ((15.0 / 8.0) * ((t_2 * t_0) * t_0))); end
code[x_] := Block[{t$95$0 = N[(1.0 / N[Abs[x], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(t$95$0 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t$95$1 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]}, N[(N[(N[(1.0 / N[Sqrt[Pi], $MachinePrecision]), $MachinePrecision] * N[Exp[N[(N[Abs[x], $MachinePrecision] * N[Abs[x], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(N[(N[(t$95$0 + N[(N[(1.0 / 2.0), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision] + N[(N[(3.0 / 4.0), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(N[(15.0 / 8.0), $MachinePrecision] * N[(N[(t$95$2 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \frac{1}{\left|x\right|}\\
t_1 := \left(t\_0 \cdot t\_0\right) \cdot t\_0\\
t_2 := \left(t\_1 \cdot t\_0\right) \cdot t\_0\\
\left(\frac{1}{\sqrt{\pi}} \cdot e^{\left|x\right| \cdot \left|x\right|}\right) \cdot \left(\left(\left(t\_0 + \frac{1}{2} \cdot t\_1\right) + \frac{3}{4} \cdot t\_2\right) + \frac{15}{8} \cdot \left(\left(t\_2 \cdot t\_0\right) \cdot t\_0\right)\right)
\end{array}
\end{array}
Sampling outcomes in binary64 precision:
Herbie found 6 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (x)
:precision binary64
(let* ((t_0 (/ 1.0 (fabs x)))
(t_1 (* (* t_0 t_0) t_0))
(t_2 (* (* t_1 t_0) t_0)))
(*
(* (/ 1.0 (sqrt PI)) (exp (* (fabs x) (fabs x))))
(+
(+ (+ t_0 (* (/ 1.0 2.0) t_1)) (* (/ 3.0 4.0) t_2))
(* (/ 15.0 8.0) (* (* t_2 t_0) t_0))))))
double code(double x) {
double t_0 = 1.0 / fabs(x);
double t_1 = (t_0 * t_0) * t_0;
double t_2 = (t_1 * t_0) * t_0;
return ((1.0 / sqrt(((double) M_PI))) * exp((fabs(x) * fabs(x)))) * (((t_0 + ((1.0 / 2.0) * t_1)) + ((3.0 / 4.0) * t_2)) + ((15.0 / 8.0) * ((t_2 * t_0) * t_0)));
}
public static double code(double x) {
double t_0 = 1.0 / Math.abs(x);
double t_1 = (t_0 * t_0) * t_0;
double t_2 = (t_1 * t_0) * t_0;
return ((1.0 / Math.sqrt(Math.PI)) * Math.exp((Math.abs(x) * Math.abs(x)))) * (((t_0 + ((1.0 / 2.0) * t_1)) + ((3.0 / 4.0) * t_2)) + ((15.0 / 8.0) * ((t_2 * t_0) * t_0)));
}
def code(x): t_0 = 1.0 / math.fabs(x) t_1 = (t_0 * t_0) * t_0 t_2 = (t_1 * t_0) * t_0 return ((1.0 / math.sqrt(math.pi)) * math.exp((math.fabs(x) * math.fabs(x)))) * (((t_0 + ((1.0 / 2.0) * t_1)) + ((3.0 / 4.0) * t_2)) + ((15.0 / 8.0) * ((t_2 * t_0) * t_0)))
function code(x) t_0 = Float64(1.0 / abs(x)) t_1 = Float64(Float64(t_0 * t_0) * t_0) t_2 = Float64(Float64(t_1 * t_0) * t_0) return Float64(Float64(Float64(1.0 / sqrt(pi)) * exp(Float64(abs(x) * abs(x)))) * Float64(Float64(Float64(t_0 + Float64(Float64(1.0 / 2.0) * t_1)) + Float64(Float64(3.0 / 4.0) * t_2)) + Float64(Float64(15.0 / 8.0) * Float64(Float64(t_2 * t_0) * t_0)))) end
function tmp = code(x) t_0 = 1.0 / abs(x); t_1 = (t_0 * t_0) * t_0; t_2 = (t_1 * t_0) * t_0; tmp = ((1.0 / sqrt(pi)) * exp((abs(x) * abs(x)))) * (((t_0 + ((1.0 / 2.0) * t_1)) + ((3.0 / 4.0) * t_2)) + ((15.0 / 8.0) * ((t_2 * t_0) * t_0))); end
code[x_] := Block[{t$95$0 = N[(1.0 / N[Abs[x], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(N[(t$95$0 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]}, Block[{t$95$2 = N[(N[(t$95$1 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]}, N[(N[(N[(1.0 / N[Sqrt[Pi], $MachinePrecision]), $MachinePrecision] * N[Exp[N[(N[Abs[x], $MachinePrecision] * N[Abs[x], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(N[(N[(t$95$0 + N[(N[(1.0 / 2.0), $MachinePrecision] * t$95$1), $MachinePrecision]), $MachinePrecision] + N[(N[(3.0 / 4.0), $MachinePrecision] * t$95$2), $MachinePrecision]), $MachinePrecision] + N[(N[(15.0 / 8.0), $MachinePrecision] * N[(N[(t$95$2 * t$95$0), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \frac{1}{\left|x\right|}\\
t_1 := \left(t\_0 \cdot t\_0\right) \cdot t\_0\\
t_2 := \left(t\_1 \cdot t\_0\right) \cdot t\_0\\
\left(\frac{1}{\sqrt{\pi}} \cdot e^{\left|x\right| \cdot \left|x\right|}\right) \cdot \left(\left(\left(t\_0 + \frac{1}{2} \cdot t\_1\right) + \frac{3}{4} \cdot t\_2\right) + \frac{15}{8} \cdot \left(\left(t\_2 \cdot t\_0\right) \cdot t\_0\right)\right)
\end{array}
\end{array}
(FPCore (x) :precision binary64 (* (/ (exp (* x x)) (sqrt PI)) (+ (/ 0.75 (pow x 5.0)) (+ (+ (/ 1.0 x) (/ 1.875 (pow x 7.0))) (/ 0.5 (pow x 3.0))))))
double code(double x) {
return (exp((x * x)) / sqrt(((double) M_PI))) * ((0.75 / pow(x, 5.0)) + (((1.0 / x) + (1.875 / pow(x, 7.0))) + (0.5 / pow(x, 3.0))));
}
public static double code(double x) {
return (Math.exp((x * x)) / Math.sqrt(Math.PI)) * ((0.75 / Math.pow(x, 5.0)) + (((1.0 / x) + (1.875 / Math.pow(x, 7.0))) + (0.5 / Math.pow(x, 3.0))));
}
def code(x): return (math.exp((x * x)) / math.sqrt(math.pi)) * ((0.75 / math.pow(x, 5.0)) + (((1.0 / x) + (1.875 / math.pow(x, 7.0))) + (0.5 / math.pow(x, 3.0))))
function code(x) return Float64(Float64(exp(Float64(x * x)) / sqrt(pi)) * Float64(Float64(0.75 / (x ^ 5.0)) + Float64(Float64(Float64(1.0 / x) + Float64(1.875 / (x ^ 7.0))) + Float64(0.5 / (x ^ 3.0))))) end
function tmp = code(x) tmp = (exp((x * x)) / sqrt(pi)) * ((0.75 / (x ^ 5.0)) + (((1.0 / x) + (1.875 / (x ^ 7.0))) + (0.5 / (x ^ 3.0)))); end
code[x_] := N[(N[(N[Exp[N[(x * x), $MachinePrecision]], $MachinePrecision] / N[Sqrt[Pi], $MachinePrecision]), $MachinePrecision] * N[(N[(0.75 / N[Power[x, 5.0], $MachinePrecision]), $MachinePrecision] + N[(N[(N[(1.0 / x), $MachinePrecision] + N[(1.875 / N[Power[x, 7.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(0.5 / N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{e^{x \cdot x}}{\sqrt{\pi}} \cdot \left(\frac{0.75}{{x}^{5}} + \left(\left(\frac{1}{x} + \frac{1.875}{{x}^{7}}\right) + \frac{0.5}{{x}^{3}}\right)\right)
\end{array}
Initial program 100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
Taylor expanded in x around inf 100.0%
+-commutative100.0%
associate-+l+100.0%
rem-square-sqrt100.0%
fabs-sqr100.0%
rem-square-sqrt100.0%
associate-*r/100.0%
metadata-eval100.0%
rem-square-sqrt100.0%
fabs-sqr100.0%
rem-square-sqrt100.0%
pow-plus100.0%
metadata-eval100.0%
Simplified100.0%
(FPCore (x) :precision binary64 (* (+ (/ 1.0 x) (+ (/ 0.75 (pow x 5.0)) (/ 1.875 (pow x 7.0)))) (* (exp (pow x 2.0)) (pow PI -0.5))))
double code(double x) {
return ((1.0 / x) + ((0.75 / pow(x, 5.0)) + (1.875 / pow(x, 7.0)))) * (exp(pow(x, 2.0)) * pow(((double) M_PI), -0.5));
}
public static double code(double x) {
return ((1.0 / x) + ((0.75 / Math.pow(x, 5.0)) + (1.875 / Math.pow(x, 7.0)))) * (Math.exp(Math.pow(x, 2.0)) * Math.pow(Math.PI, -0.5));
}
def code(x): return ((1.0 / x) + ((0.75 / math.pow(x, 5.0)) + (1.875 / math.pow(x, 7.0)))) * (math.exp(math.pow(x, 2.0)) * math.pow(math.pi, -0.5))
function code(x) return Float64(Float64(Float64(1.0 / x) + Float64(Float64(0.75 / (x ^ 5.0)) + Float64(1.875 / (x ^ 7.0)))) * Float64(exp((x ^ 2.0)) * (pi ^ -0.5))) end
function tmp = code(x) tmp = ((1.0 / x) + ((0.75 / (x ^ 5.0)) + (1.875 / (x ^ 7.0)))) * (exp((x ^ 2.0)) * (pi ^ -0.5)); end
code[x_] := N[(N[(N[(1.0 / x), $MachinePrecision] + N[(N[(0.75 / N[Power[x, 5.0], $MachinePrecision]), $MachinePrecision] + N[(1.875 / N[Power[x, 7.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(N[Exp[N[Power[x, 2.0], $MachinePrecision]], $MachinePrecision] * N[Power[Pi, -0.5], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\left(\frac{1}{x} + \left(\frac{0.75}{{x}^{5}} + \frac{1.875}{{x}^{7}}\right)\right) \cdot \left(e^{{x}^{2}} \cdot {\pi}^{-0.5}\right)
\end{array}
Initial program 100.0%
Simplified100.0%
Taylor expanded in x around inf 99.7%
*-commutative99.7%
+-commutative99.7%
+-commutative99.7%
associate-+l+99.7%
associate-*r/99.7%
metadata-eval99.7%
associate-*r/99.7%
metadata-eval99.7%
Simplified99.7%
Taylor expanded in x around inf 99.7%
*-commutative99.7%
*-commutative99.7%
unpow-199.7%
metadata-eval99.7%
pow-sqr99.7%
rem-sqrt-square99.7%
rem-square-sqrt99.7%
fabs-sqr99.7%
rem-square-sqrt99.7%
associate-*l*99.7%
Simplified99.7%
Final simplification99.7%
(FPCore (x) :precision binary64 (* (/ (exp (* x x)) (sqrt PI)) (+ (/ 1.0 x) (+ (/ 0.75 (pow x 5.0)) (/ 0.5 (pow x 3.0))))))
double code(double x) {
return (exp((x * x)) / sqrt(((double) M_PI))) * ((1.0 / x) + ((0.75 / pow(x, 5.0)) + (0.5 / pow(x, 3.0))));
}
public static double code(double x) {
return (Math.exp((x * x)) / Math.sqrt(Math.PI)) * ((1.0 / x) + ((0.75 / Math.pow(x, 5.0)) + (0.5 / Math.pow(x, 3.0))));
}
def code(x): return (math.exp((x * x)) / math.sqrt(math.pi)) * ((1.0 / x) + ((0.75 / math.pow(x, 5.0)) + (0.5 / math.pow(x, 3.0))))
function code(x) return Float64(Float64(exp(Float64(x * x)) / sqrt(pi)) * Float64(Float64(1.0 / x) + Float64(Float64(0.75 / (x ^ 5.0)) + Float64(0.5 / (x ^ 3.0))))) end
function tmp = code(x) tmp = (exp((x * x)) / sqrt(pi)) * ((1.0 / x) + ((0.75 / (x ^ 5.0)) + (0.5 / (x ^ 3.0)))); end
code[x_] := N[(N[(N[Exp[N[(x * x), $MachinePrecision]], $MachinePrecision] / N[Sqrt[Pi], $MachinePrecision]), $MachinePrecision] * N[(N[(1.0 / x), $MachinePrecision] + N[(N[(0.75 / N[Power[x, 5.0], $MachinePrecision]), $MachinePrecision] + N[(0.5 / N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{e^{x \cdot x}}{\sqrt{\pi}} \cdot \left(\frac{1}{x} + \left(\frac{0.75}{{x}^{5}} + \frac{0.5}{{x}^{3}}\right)\right)
\end{array}
Initial program 100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
Taylor expanded in x around inf 99.7%
+-commutative99.7%
+-commutative99.7%
associate-+l+99.7%
rem-square-sqrt99.7%
fabs-sqr99.7%
rem-square-sqrt99.7%
rem-square-sqrt99.7%
fabs-sqr99.7%
rem-square-sqrt99.7%
pow-plus99.7%
metadata-eval99.7%
Simplified99.7%
(FPCore (x) :precision binary64 (* (/ (exp (* x x)) (sqrt PI)) (+ (/ 1.0 x) (/ 0.5 (pow x 3.0)))))
double code(double x) {
return (exp((x * x)) / sqrt(((double) M_PI))) * ((1.0 / x) + (0.5 / pow(x, 3.0)));
}
public static double code(double x) {
return (Math.exp((x * x)) / Math.sqrt(Math.PI)) * ((1.0 / x) + (0.5 / Math.pow(x, 3.0)));
}
def code(x): return (math.exp((x * x)) / math.sqrt(math.pi)) * ((1.0 / x) + (0.5 / math.pow(x, 3.0)))
function code(x) return Float64(Float64(exp(Float64(x * x)) / sqrt(pi)) * Float64(Float64(1.0 / x) + Float64(0.5 / (x ^ 3.0)))) end
function tmp = code(x) tmp = (exp((x * x)) / sqrt(pi)) * ((1.0 / x) + (0.5 / (x ^ 3.0))); end
code[x_] := N[(N[(N[Exp[N[(x * x), $MachinePrecision]], $MachinePrecision] / N[Sqrt[Pi], $MachinePrecision]), $MachinePrecision] * N[(N[(1.0 / x), $MachinePrecision] + N[(0.5 / N[Power[x, 3.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{e^{x \cdot x}}{\sqrt{\pi}} \cdot \left(\frac{1}{x} + \frac{0.5}{{x}^{3}}\right)
\end{array}
Initial program 100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
Taylor expanded in x around inf 99.7%
rem-square-sqrt99.7%
fabs-sqr99.7%
rem-square-sqrt99.7%
associate-*r/99.7%
metadata-eval99.7%
rem-square-sqrt99.7%
fabs-sqr99.7%
rem-square-sqrt99.7%
pow-plus99.7%
metadata-eval99.7%
Simplified99.7%
(FPCore (x) :precision binary64 (* (/ (exp (* x x)) (sqrt PI)) (/ 1.0 x)))
double code(double x) {
return (exp((x * x)) / sqrt(((double) M_PI))) * (1.0 / x);
}
public static double code(double x) {
return (Math.exp((x * x)) / Math.sqrt(Math.PI)) * (1.0 / x);
}
def code(x): return (math.exp((x * x)) / math.sqrt(math.pi)) * (1.0 / x)
function code(x) return Float64(Float64(exp(Float64(x * x)) / sqrt(pi)) * Float64(1.0 / x)) end
function tmp = code(x) tmp = (exp((x * x)) / sqrt(pi)) * (1.0 / x); end
code[x_] := N[(N[(N[Exp[N[(x * x), $MachinePrecision]], $MachinePrecision] / N[Sqrt[Pi], $MachinePrecision]), $MachinePrecision] * N[(1.0 / x), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{e^{x \cdot x}}{\sqrt{\pi}} \cdot \frac{1}{x}
\end{array}
Initial program 100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
Taylor expanded in x around inf 99.7%
rem-square-sqrt99.7%
fabs-sqr99.7%
rem-square-sqrt99.7%
Simplified99.7%
(FPCore (x) :precision binary64 (* (/ 1.0 x) (/ 1.0 (sqrt PI))))
double code(double x) {
return (1.0 / x) * (1.0 / sqrt(((double) M_PI)));
}
public static double code(double x) {
return (1.0 / x) * (1.0 / Math.sqrt(Math.PI));
}
def code(x): return (1.0 / x) * (1.0 / math.sqrt(math.pi))
function code(x) return Float64(Float64(1.0 / x) * Float64(1.0 / sqrt(pi))) end
function tmp = code(x) tmp = (1.0 / x) * (1.0 / sqrt(pi)); end
code[x_] := N[(N[(1.0 / x), $MachinePrecision] * N[(1.0 / N[Sqrt[Pi], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1}{x} \cdot \frac{1}{\sqrt{\pi}}
\end{array}
Initial program 100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
*-un-lft-identity100.0%
inv-pow100.0%
pow-pow100.0%
add-sqr-sqrt100.0%
fabs-sqr100.0%
add-sqr-sqrt100.0%
metadata-eval100.0%
Applied egg-rr100.0%
*-lft-identity100.0%
Simplified100.0%
Taylor expanded in x around inf 99.7%
rem-square-sqrt99.7%
fabs-sqr99.7%
rem-square-sqrt99.7%
Simplified99.7%
Taylor expanded in x around 0 2.3%
Final simplification2.3%
herbie shell --seed 2024129
(FPCore (x)
:name "Jmat.Real.erfi, branch x greater than or equal to 5"
:precision binary64
:pre (>= x 0.5)
(* (* (/ 1.0 (sqrt PI)) (exp (* (fabs x) (fabs x)))) (+ (+ (+ (/ 1.0 (fabs x)) (* (/ 1.0 2.0) (* (* (/ 1.0 (fabs x)) (/ 1.0 (fabs x))) (/ 1.0 (fabs x))))) (* (/ 3.0 4.0) (* (* (* (* (/ 1.0 (fabs x)) (/ 1.0 (fabs x))) (/ 1.0 (fabs x))) (/ 1.0 (fabs x))) (/ 1.0 (fabs x))))) (* (/ 15.0 8.0) (* (* (* (* (* (* (/ 1.0 (fabs x)) (/ 1.0 (fabs x))) (/ 1.0 (fabs x))) (/ 1.0 (fabs x))) (/ 1.0 (fabs x))) (/ 1.0 (fabs x))) (/ 1.0 (fabs x)))))))