
(FPCore (v t) :precision binary64 (/ (- 1.0 (* 5.0 (* v v))) (* (* (* PI t) (sqrt (* 2.0 (- 1.0 (* 3.0 (* v v)))))) (- 1.0 (* v v)))))
double code(double v, double t) {
return (1.0 - (5.0 * (v * v))) / (((((double) M_PI) * t) * sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
}
public static double code(double v, double t) {
return (1.0 - (5.0 * (v * v))) / (((Math.PI * t) * Math.sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
}
def code(v, t): return (1.0 - (5.0 * (v * v))) / (((math.pi * t) * math.sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)))
function code(v, t) return Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(Float64(Float64(pi * t) * sqrt(Float64(2.0 * Float64(1.0 - Float64(3.0 * Float64(v * v)))))) * Float64(1.0 - Float64(v * v)))) end
function tmp = code(v, t) tmp = (1.0 - (5.0 * (v * v))) / (((pi * t) * sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v))); end
code[v_, t_] := N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(Pi * t), $MachinePrecision] * N[Sqrt[N[(2.0 * N[(1.0 - N[(3.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)}
\end{array}
Sampling outcomes in binary64 precision:
Herbie found 10 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (v t) :precision binary64 (/ (- 1.0 (* 5.0 (* v v))) (* (* (* PI t) (sqrt (* 2.0 (- 1.0 (* 3.0 (* v v)))))) (- 1.0 (* v v)))))
double code(double v, double t) {
return (1.0 - (5.0 * (v * v))) / (((((double) M_PI) * t) * sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
}
public static double code(double v, double t) {
return (1.0 - (5.0 * (v * v))) / (((Math.PI * t) * Math.sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
}
def code(v, t): return (1.0 - (5.0 * (v * v))) / (((math.pi * t) * math.sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)))
function code(v, t) return Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(Float64(Float64(pi * t) * sqrt(Float64(2.0 * Float64(1.0 - Float64(3.0 * Float64(v * v)))))) * Float64(1.0 - Float64(v * v)))) end
function tmp = code(v, t) tmp = (1.0 - (5.0 * (v * v))) / (((pi * t) * sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v))); end
code[v_, t_] := N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(Pi * t), $MachinePrecision] * N[Sqrt[N[(2.0 * N[(1.0 - N[(3.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)}
\end{array}
(FPCore (v t) :precision binary64 (/ (/ (+ 1.0 (* v (* v -5.0))) (* (sqrt (+ 2.0 (* (* v v) -6.0))) (* PI (- 1.0 (* v v))))) t))
double code(double v, double t) {
return ((1.0 + (v * (v * -5.0))) / (sqrt((2.0 + ((v * v) * -6.0))) * (((double) M_PI) * (1.0 - (v * v))))) / t;
}
public static double code(double v, double t) {
return ((1.0 + (v * (v * -5.0))) / (Math.sqrt((2.0 + ((v * v) * -6.0))) * (Math.PI * (1.0 - (v * v))))) / t;
}
def code(v, t): return ((1.0 + (v * (v * -5.0))) / (math.sqrt((2.0 + ((v * v) * -6.0))) * (math.pi * (1.0 - (v * v))))) / t
function code(v, t) return Float64(Float64(Float64(1.0 + Float64(v * Float64(v * -5.0))) / Float64(sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0))) * Float64(pi * Float64(1.0 - Float64(v * v))))) / t) end
function tmp = code(v, t) tmp = ((1.0 + (v * (v * -5.0))) / (sqrt((2.0 + ((v * v) * -6.0))) * (pi * (1.0 - (v * v))))) / t; end
code[v_, t_] := N[(N[(N[(1.0 + N[(v * N[(v * -5.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[Sqrt[N[(2.0 + N[(N[(v * v), $MachinePrecision] * -6.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[(Pi * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{1 + v \cdot \left(v \cdot -5\right)}{\sqrt{2 + \left(v \cdot v\right) \cdot -6} \cdot \left(\pi \cdot \left(1 - v \cdot v\right)\right)}}{t}
\end{array}
Initial program 99.1%
/-lowering-/.f64N/A
sub-negN/A
+-lowering-+.f64N/A
associate-*r*N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
metadata-evalN/A
associate-*l*N/A
*-commutativeN/A
associate-*l*N/A
*-lowering-*.f64N/A
Simplified99.2%
Applied egg-rr99.9%
(FPCore (v t) :precision binary64 (/ (+ (/ (/ 1.0 PI) t) (/ (* (/ (* v v) t) -4.0) PI)) (sqrt (+ 2.0 (* (* v v) -6.0)))))
double code(double v, double t) {
return (((1.0 / ((double) M_PI)) / t) + ((((v * v) / t) * -4.0) / ((double) M_PI))) / sqrt((2.0 + ((v * v) * -6.0)));
}
public static double code(double v, double t) {
return (((1.0 / Math.PI) / t) + ((((v * v) / t) * -4.0) / Math.PI)) / Math.sqrt((2.0 + ((v * v) * -6.0)));
}
def code(v, t): return (((1.0 / math.pi) / t) + ((((v * v) / t) * -4.0) / math.pi)) / math.sqrt((2.0 + ((v * v) * -6.0)))
function code(v, t) return Float64(Float64(Float64(Float64(1.0 / pi) / t) + Float64(Float64(Float64(Float64(v * v) / t) * -4.0) / pi)) / sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0)))) end
function tmp = code(v, t) tmp = (((1.0 / pi) / t) + ((((v * v) / t) * -4.0) / pi)) / sqrt((2.0 + ((v * v) * -6.0))); end
code[v_, t_] := N[(N[(N[(N[(1.0 / Pi), $MachinePrecision] / t), $MachinePrecision] + N[(N[(N[(N[(v * v), $MachinePrecision] / t), $MachinePrecision] * -4.0), $MachinePrecision] / Pi), $MachinePrecision]), $MachinePrecision] / N[Sqrt[N[(2.0 + N[(N[(v * v), $MachinePrecision] * -6.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{\frac{1}{\pi}}{t} + \frac{\frac{v \cdot v}{t} \cdot -4}{\pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}}
\end{array}
Initial program 99.1%
/-lowering-/.f64N/A
sub-negN/A
+-lowering-+.f64N/A
associate-*r*N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
metadata-evalN/A
associate-*l*N/A
*-commutativeN/A
associate-*l*N/A
*-lowering-*.f64N/A
Simplified99.2%
Applied egg-rr99.3%
Taylor expanded in v around 0
+-commutativeN/A
+-lowering-+.f64N/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
*-commutativeN/A
associate-/r*N/A
associate-*l/N/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
/-lowering-/.f64N/A
unpow2N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6499.1%
Simplified99.1%
*-commutativeN/A
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f6499.5%
Applied egg-rr99.5%
(FPCore (v t) :precision binary64 (/ (/ (+ (/ 1.0 PI) (/ (* (* v v) -4.0) PI)) t) (sqrt (+ 2.0 (* (* v v) -6.0)))))
double code(double v, double t) {
return (((1.0 / ((double) M_PI)) + (((v * v) * -4.0) / ((double) M_PI))) / t) / sqrt((2.0 + ((v * v) * -6.0)));
}
public static double code(double v, double t) {
return (((1.0 / Math.PI) + (((v * v) * -4.0) / Math.PI)) / t) / Math.sqrt((2.0 + ((v * v) * -6.0)));
}
def code(v, t): return (((1.0 / math.pi) + (((v * v) * -4.0) / math.pi)) / t) / math.sqrt((2.0 + ((v * v) * -6.0)))
function code(v, t) return Float64(Float64(Float64(Float64(1.0 / pi) + Float64(Float64(Float64(v * v) * -4.0) / pi)) / t) / sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0)))) end
function tmp = code(v, t) tmp = (((1.0 / pi) + (((v * v) * -4.0) / pi)) / t) / sqrt((2.0 + ((v * v) * -6.0))); end
code[v_, t_] := N[(N[(N[(N[(1.0 / Pi), $MachinePrecision] + N[(N[(N[(v * v), $MachinePrecision] * -4.0), $MachinePrecision] / Pi), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision] / N[Sqrt[N[(2.0 + N[(N[(v * v), $MachinePrecision] * -6.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{\frac{1}{\pi} + \frac{\left(v \cdot v\right) \cdot -4}{\pi}}{t}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}}
\end{array}
Initial program 99.1%
/-lowering-/.f64N/A
sub-negN/A
+-lowering-+.f64N/A
associate-*r*N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
metadata-evalN/A
associate-*l*N/A
*-commutativeN/A
associate-*l*N/A
*-lowering-*.f64N/A
Simplified99.2%
Applied egg-rr99.3%
Taylor expanded in v around 0
+-commutativeN/A
+-lowering-+.f64N/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
*-commutativeN/A
associate-/r*N/A
associate-*l/N/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
/-lowering-/.f64N/A
unpow2N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6499.1%
Simplified99.1%
Taylor expanded in t around 0
/-lowering-/.f64N/A
+-lowering-+.f64N/A
associate-*r/N/A
/-lowering-/.f64N/A
*-commutativeN/A
*-lowering-*.f64N/A
unpow2N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f6499.5%
Simplified99.5%
Final simplification99.5%
(FPCore (v t) :precision binary64 (/ (/ 1.0 (/ PI (sqrt 0.5))) t))
double code(double v, double t) {
return (1.0 / (((double) M_PI) / sqrt(0.5))) / t;
}
public static double code(double v, double t) {
return (1.0 / (Math.PI / Math.sqrt(0.5))) / t;
}
def code(v, t): return (1.0 / (math.pi / math.sqrt(0.5))) / t
function code(v, t) return Float64(Float64(1.0 / Float64(pi / sqrt(0.5))) / t) end
function tmp = code(v, t) tmp = (1.0 / (pi / sqrt(0.5))) / t; end
code[v_, t_] := N[(N[(1.0 / N[(Pi / N[Sqrt[0.5], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / t), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{1}{\frac{\pi}{\sqrt{0.5}}}}{t}
\end{array}
Initial program 99.1%
/-lowering-/.f64N/A
sub-negN/A
+-lowering-+.f64N/A
associate-*r*N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
metadata-evalN/A
associate-*l*N/A
*-commutativeN/A
associate-*l*N/A
*-lowering-*.f64N/A
Simplified99.2%
Taylor expanded in v around 0
/-lowering-/.f64N/A
sqrt-lowering-sqrt.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6498.3%
Simplified98.3%
*-commutativeN/A
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
sqrt-lowering-sqrt.f64N/A
PI-lowering-PI.f6498.5%
Applied egg-rr98.5%
clear-numN/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
sqrt-lowering-sqrt.f6499.5%
Applied egg-rr99.5%
(FPCore (v t) :precision binary64 (/ (/ 1.0 PI) (* t (sqrt 2.0))))
double code(double v, double t) {
return (1.0 / ((double) M_PI)) / (t * sqrt(2.0));
}
public static double code(double v, double t) {
return (1.0 / Math.PI) / (t * Math.sqrt(2.0));
}
def code(v, t): return (1.0 / math.pi) / (t * math.sqrt(2.0))
function code(v, t) return Float64(Float64(1.0 / pi) / Float64(t * sqrt(2.0))) end
function tmp = code(v, t) tmp = (1.0 / pi) / (t * sqrt(2.0)); end
code[v_, t_] := N[(N[(1.0 / Pi), $MachinePrecision] / N[(t * N[Sqrt[2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{1}{\pi}}{t \cdot \sqrt{2}}
\end{array}
Initial program 99.1%
Taylor expanded in v around 0
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
sqrt-lowering-sqrt.f6499.1%
Simplified99.1%
associate-/l/N/A
associate-*r*N/A
*-commutativeN/A
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
PI-lowering-PI.f64N/A
*-lowering-*.f64N/A
sqrt-lowering-sqrt.f6499.2%
Applied egg-rr99.2%
(FPCore (v t) :precision binary64 (/ (/ 1.0 t) (* PI (sqrt 2.0))))
double code(double v, double t) {
return (1.0 / t) / (((double) M_PI) * sqrt(2.0));
}
public static double code(double v, double t) {
return (1.0 / t) / (Math.PI * Math.sqrt(2.0));
}
def code(v, t): return (1.0 / t) / (math.pi * math.sqrt(2.0))
function code(v, t) return Float64(Float64(1.0 / t) / Float64(pi * sqrt(2.0))) end
function tmp = code(v, t) tmp = (1.0 / t) / (pi * sqrt(2.0)); end
code[v_, t_] := N[(N[(1.0 / t), $MachinePrecision] / N[(Pi * N[Sqrt[2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{1}{t}}{\pi \cdot \sqrt{2}}
\end{array}
Initial program 99.1%
Taylor expanded in v around 0
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
sqrt-lowering-sqrt.f6499.1%
Simplified99.1%
(FPCore (v t) :precision binary64 (/ 1.0 (* (sqrt 2.0) (* PI t))))
double code(double v, double t) {
return 1.0 / (sqrt(2.0) * (((double) M_PI) * t));
}
public static double code(double v, double t) {
return 1.0 / (Math.sqrt(2.0) * (Math.PI * t));
}
def code(v, t): return 1.0 / (math.sqrt(2.0) * (math.pi * t))
function code(v, t) return Float64(1.0 / Float64(sqrt(2.0) * Float64(pi * t))) end
function tmp = code(v, t) tmp = 1.0 / (sqrt(2.0) * (pi * t)); end
code[v_, t_] := N[(1.0 / N[(N[Sqrt[2.0], $MachinePrecision] * N[(Pi * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1}{\sqrt{2} \cdot \left(\pi \cdot t\right)}
\end{array}
Initial program 99.1%
Taylor expanded in v around 0
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
sqrt-lowering-sqrt.f6499.1%
Simplified99.1%
associate-/l/N/A
associate-*r*N/A
*-commutativeN/A
/-lowering-/.f64N/A
associate-*r*N/A
*-commutativeN/A
*-lowering-*.f64N/A
*-commutativeN/A
*-lowering-*.f64N/A
PI-lowering-PI.f64N/A
sqrt-lowering-sqrt.f6498.7%
Applied egg-rr98.7%
Final simplification98.7%
(FPCore (v t) :precision binary64 (/ (/ (sqrt 0.5) PI) t))
double code(double v, double t) {
return (sqrt(0.5) / ((double) M_PI)) / t;
}
public static double code(double v, double t) {
return (Math.sqrt(0.5) / Math.PI) / t;
}
def code(v, t): return (math.sqrt(0.5) / math.pi) / t
function code(v, t) return Float64(Float64(sqrt(0.5) / pi) / t) end
function tmp = code(v, t) tmp = (sqrt(0.5) / pi) / t; end
code[v_, t_] := N[(N[(N[Sqrt[0.5], $MachinePrecision] / Pi), $MachinePrecision] / t), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{\sqrt{0.5}}{\pi}}{t}
\end{array}
Initial program 99.1%
/-lowering-/.f64N/A
sub-negN/A
+-lowering-+.f64N/A
associate-*r*N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
metadata-evalN/A
associate-*l*N/A
*-commutativeN/A
associate-*l*N/A
*-lowering-*.f64N/A
Simplified99.2%
Taylor expanded in v around 0
/-lowering-/.f64N/A
sqrt-lowering-sqrt.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6498.3%
Simplified98.3%
*-commutativeN/A
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
sqrt-lowering-sqrt.f64N/A
PI-lowering-PI.f6498.5%
Applied egg-rr98.5%
(FPCore (v t) :precision binary64 (/ (/ (sqrt 0.5) t) PI))
double code(double v, double t) {
return (sqrt(0.5) / t) / ((double) M_PI);
}
public static double code(double v, double t) {
return (Math.sqrt(0.5) / t) / Math.PI;
}
def code(v, t): return (math.sqrt(0.5) / t) / math.pi
function code(v, t) return Float64(Float64(sqrt(0.5) / t) / pi) end
function tmp = code(v, t) tmp = (sqrt(0.5) / t) / pi; end
code[v_, t_] := N[(N[(N[Sqrt[0.5], $MachinePrecision] / t), $MachinePrecision] / Pi), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{\sqrt{0.5}}{t}}{\pi}
\end{array}
Initial program 99.1%
/-lowering-/.f64N/A
sub-negN/A
+-lowering-+.f64N/A
associate-*r*N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
metadata-evalN/A
associate-*l*N/A
*-commutativeN/A
associate-*l*N/A
*-lowering-*.f64N/A
Simplified99.2%
Taylor expanded in v around 0
/-lowering-/.f64N/A
sqrt-lowering-sqrt.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6498.3%
Simplified98.3%
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
sqrt-lowering-sqrt.f64N/A
PI-lowering-PI.f6498.5%
Applied egg-rr98.5%
(FPCore (v t) :precision binary64 (/ (sqrt 0.5) (* PI t)))
double code(double v, double t) {
return sqrt(0.5) / (((double) M_PI) * t);
}
public static double code(double v, double t) {
return Math.sqrt(0.5) / (Math.PI * t);
}
def code(v, t): return math.sqrt(0.5) / (math.pi * t)
function code(v, t) return Float64(sqrt(0.5) / Float64(pi * t)) end
function tmp = code(v, t) tmp = sqrt(0.5) / (pi * t); end
code[v_, t_] := N[(N[Sqrt[0.5], $MachinePrecision] / N[(Pi * t), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\sqrt{0.5}}{\pi \cdot t}
\end{array}
Initial program 99.1%
/-lowering-/.f64N/A
sub-negN/A
+-lowering-+.f64N/A
associate-*r*N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
*-commutativeN/A
distribute-rgt-neg-inN/A
*-lowering-*.f64N/A
metadata-evalN/A
associate-*l*N/A
*-commutativeN/A
associate-*l*N/A
*-lowering-*.f64N/A
Simplified99.2%
Taylor expanded in v around 0
/-lowering-/.f64N/A
sqrt-lowering-sqrt.f64N/A
*-lowering-*.f64N/A
PI-lowering-PI.f6498.3%
Simplified98.3%
Final simplification98.3%
herbie shell --seed 2024144
(FPCore (v t)
:name "Falkner and Boettcher, Equation (20:1,3)"
:precision binary64
(/ (- 1.0 (* 5.0 (* v v))) (* (* (* PI t) (sqrt (* 2.0 (- 1.0 (* 3.0 (* v v)))))) (- 1.0 (* v v)))))