
(FPCore (t l k) :precision binary64 (/ 2.0 (* (* (* (/ (pow t 3.0) (* l l)) (sin k)) (tan k)) (- (+ 1.0 (pow (/ k t) 2.0)) 1.0))))
double code(double t, double l, double k) {
return 2.0 / ((((pow(t, 3.0) / (l * l)) * sin(k)) * tan(k)) * ((1.0 + pow((k / t), 2.0)) - 1.0));
}
real(8) function code(t, l, k)
real(8), intent (in) :: t
real(8), intent (in) :: l
real(8), intent (in) :: k
code = 2.0d0 / (((((t ** 3.0d0) / (l * l)) * sin(k)) * tan(k)) * ((1.0d0 + ((k / t) ** 2.0d0)) - 1.0d0))
end function
public static double code(double t, double l, double k) {
return 2.0 / ((((Math.pow(t, 3.0) / (l * l)) * Math.sin(k)) * Math.tan(k)) * ((1.0 + Math.pow((k / t), 2.0)) - 1.0));
}
def code(t, l, k): return 2.0 / ((((math.pow(t, 3.0) / (l * l)) * math.sin(k)) * math.tan(k)) * ((1.0 + math.pow((k / t), 2.0)) - 1.0))
function code(t, l, k) return Float64(2.0 / Float64(Float64(Float64(Float64((t ^ 3.0) / Float64(l * l)) * sin(k)) * tan(k)) * Float64(Float64(1.0 + (Float64(k / t) ^ 2.0)) - 1.0))) end
function tmp = code(t, l, k) tmp = 2.0 / (((((t ^ 3.0) / (l * l)) * sin(k)) * tan(k)) * ((1.0 + ((k / t) ^ 2.0)) - 1.0)); end
code[t_, l_, k_] := N[(2.0 / N[(N[(N[(N[(N[Power[t, 3.0], $MachinePrecision] / N[(l * l), $MachinePrecision]), $MachinePrecision] * N[Sin[k], $MachinePrecision]), $MachinePrecision] * N[Tan[k], $MachinePrecision]), $MachinePrecision] * N[(N[(1.0 + N[Power[N[(k / t), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{2}{\left(\left(\frac{{t}^{3}}{\ell \cdot \ell} \cdot \sin k\right) \cdot \tan k\right) \cdot \left(\left(1 + {\left(\frac{k}{t}\right)}^{2}\right) - 1\right)}
\end{array}
Sampling outcomes in binary64 precision:
Herbie found 13 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (t l k) :precision binary64 (/ 2.0 (* (* (* (/ (pow t 3.0) (* l l)) (sin k)) (tan k)) (- (+ 1.0 (pow (/ k t) 2.0)) 1.0))))
double code(double t, double l, double k) {
return 2.0 / ((((pow(t, 3.0) / (l * l)) * sin(k)) * tan(k)) * ((1.0 + pow((k / t), 2.0)) - 1.0));
}
real(8) function code(t, l, k)
real(8), intent (in) :: t
real(8), intent (in) :: l
real(8), intent (in) :: k
code = 2.0d0 / (((((t ** 3.0d0) / (l * l)) * sin(k)) * tan(k)) * ((1.0d0 + ((k / t) ** 2.0d0)) - 1.0d0))
end function
public static double code(double t, double l, double k) {
return 2.0 / ((((Math.pow(t, 3.0) / (l * l)) * Math.sin(k)) * Math.tan(k)) * ((1.0 + Math.pow((k / t), 2.0)) - 1.0));
}
def code(t, l, k): return 2.0 / ((((math.pow(t, 3.0) / (l * l)) * math.sin(k)) * math.tan(k)) * ((1.0 + math.pow((k / t), 2.0)) - 1.0))
function code(t, l, k) return Float64(2.0 / Float64(Float64(Float64(Float64((t ^ 3.0) / Float64(l * l)) * sin(k)) * tan(k)) * Float64(Float64(1.0 + (Float64(k / t) ^ 2.0)) - 1.0))) end
function tmp = code(t, l, k) tmp = 2.0 / (((((t ^ 3.0) / (l * l)) * sin(k)) * tan(k)) * ((1.0 + ((k / t) ^ 2.0)) - 1.0)); end
code[t_, l_, k_] := N[(2.0 / N[(N[(N[(N[(N[Power[t, 3.0], $MachinePrecision] / N[(l * l), $MachinePrecision]), $MachinePrecision] * N[Sin[k], $MachinePrecision]), $MachinePrecision] * N[Tan[k], $MachinePrecision]), $MachinePrecision] * N[(N[(1.0 + N[Power[N[(k / t), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{2}{\left(\left(\frac{{t}^{3}}{\ell \cdot \ell} \cdot \sin k\right) \cdot \tan k\right) \cdot \left(\left(1 + {\left(\frac{k}{t}\right)}^{2}\right) - 1\right)}
\end{array}
(FPCore (t l k) :precision binary64 (* (/ (/ 2.0 t) (/ (* k (sin k)) l)) (/ (/ l k) (tan k))))
double code(double t, double l, double k) {
return ((2.0 / t) / ((k * sin(k)) / l)) * ((l / k) / tan(k));
}
real(8) function code(t, l, k)
real(8), intent (in) :: t
real(8), intent (in) :: l
real(8), intent (in) :: k
code = ((2.0d0 / t) / ((k * sin(k)) / l)) * ((l / k) / tan(k))
end function
public static double code(double t, double l, double k) {
return ((2.0 / t) / ((k * Math.sin(k)) / l)) * ((l / k) / Math.tan(k));
}
def code(t, l, k): return ((2.0 / t) / ((k * math.sin(k)) / l)) * ((l / k) / math.tan(k))
function code(t, l, k) return Float64(Float64(Float64(2.0 / t) / Float64(Float64(k * sin(k)) / l)) * Float64(Float64(l / k) / tan(k))) end
function tmp = code(t, l, k) tmp = ((2.0 / t) / ((k * sin(k)) / l)) * ((l / k) / tan(k)); end
code[t_, l_, k_] := N[(N[(N[(2.0 / t), $MachinePrecision] / N[(N[(k * N[Sin[k], $MachinePrecision]), $MachinePrecision] / l), $MachinePrecision]), $MachinePrecision] * N[(N[(l / k), $MachinePrecision] / N[Tan[k], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{\frac{2}{t}}{\frac{k \cdot \sin k}{\ell}} \cdot \frac{\frac{\ell}{k}}{\tan k}
\end{array}
Initial program 34.0%
lift-*.f64N/A
*-commutativeN/A
lift-*.f64N/A
lift-*.f64N/A
associate-*l*N/A
associate-*r*N/A
lower-*.f64N/A
Applied rewrites28.2%
lift-*.f64N/A
lift-/.f64N/A
associate-*r/N/A
lift-*.f64N/A
lift-/.f64N/A
div-invN/A
associate-*l*N/A
lift-*.f64N/A
pow2N/A
pow-flipN/A
lift-*.f64N/A
lift-*.f64N/A
cube-unmultN/A
pow-prod-upN/A
metadata-evalN/A
metadata-evalN/A
unpow1N/A
*-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
Applied rewrites93.9%
lift-/.f64N/A
lift-*.f64N/A
lift-*.f64N/A
associate-*l*N/A
associate-/r*N/A
lower-/.f64N/A
div-invN/A
lift-/.f64N/A
clear-numN/A
lower-*.f64N/A
lower-/.f64N/A
lower-*.f6495.2
Applied rewrites95.2%
lift-/.f64N/A
lift-*.f64N/A
lift-/.f64N/A
associate-*r/N/A
lift-*.f64N/A
times-fracN/A
lift-*.f64N/A
lift-*.f64N/A
associate-*r*N/A
times-fracN/A
lower-*.f64N/A
lower-/.f64N/A
lower-/.f64N/A
lift-/.f64N/A
associate-*l/N/A
lower-/.f64N/A
lower-*.f64N/A
lower-/.f64N/A
lower-/.f6498.5
Applied rewrites98.5%
(FPCore (t l k) :precision binary64 (/ (* 2.0 (/ l (* t k))) (* (sin k) (* (tan k) (/ k l)))))
double code(double t, double l, double k) {
return (2.0 * (l / (t * k))) / (sin(k) * (tan(k) * (k / l)));
}
real(8) function code(t, l, k)
real(8), intent (in) :: t
real(8), intent (in) :: l
real(8), intent (in) :: k
code = (2.0d0 * (l / (t * k))) / (sin(k) * (tan(k) * (k / l)))
end function
public static double code(double t, double l, double k) {
return (2.0 * (l / (t * k))) / (Math.sin(k) * (Math.tan(k) * (k / l)));
}
def code(t, l, k): return (2.0 * (l / (t * k))) / (math.sin(k) * (math.tan(k) * (k / l)))
function code(t, l, k) return Float64(Float64(2.0 * Float64(l / Float64(t * k))) / Float64(sin(k) * Float64(tan(k) * Float64(k / l)))) end
function tmp = code(t, l, k) tmp = (2.0 * (l / (t * k))) / (sin(k) * (tan(k) * (k / l))); end
code[t_, l_, k_] := N[(N[(2.0 * N[(l / N[(t * k), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[Sin[k], $MachinePrecision] * N[(N[Tan[k], $MachinePrecision] * N[(k / l), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{2 \cdot \frac{\ell}{t \cdot k}}{\sin k \cdot \left(\tan k \cdot \frac{k}{\ell}\right)}
\end{array}
Initial program 35.2%
lift-*.f64N/A
*-commutativeN/A
lift-*.f64N/A
lift-*.f64N/A
associate-*l*N/A
associate-*r*N/A
lower-*.f64N/A
Applied rewrites28.9%
lift-*.f64N/A
lift-/.f64N/A
associate-*r/N/A
lift-*.f64N/A
lift-/.f64N/A
div-invN/A
associate-*l*N/A
lift-*.f64N/A
pow2N/A
pow-flipN/A
lift-*.f64N/A
lift-*.f64N/A
cube-unmultN/A
pow-prod-upN/A
metadata-evalN/A
metadata-evalN/A
unpow1N/A
*-commutativeN/A
lift-*.f64N/A
associate-*r*N/A
Applied rewrites92.5%
lift-/.f64N/A
lift-*.f64N/A
lift-*.f64N/A
associate-*l*N/A
associate-/r*N/A
lower-/.f64N/A
div-invN/A
lift-/.f64N/A
clear-numN/A
lower-*.f64N/A
lower-/.f64N/A
lower-*.f6493.4
Applied rewrites93.4%
lift-*.f64N/A
lift-*.f64N/A
*-commutativeN/A
associate-*r*N/A
lower-*.f64N/A
lower-*.f6493.9
Applied rewrites93.9%
Final simplification93.9%
herbie shell --seed 2024233
(FPCore (t l k)
:name "Toniolo and Linder, Equation (10-)"
:precision binary64
(/ 2.0 (* (* (* (/ (pow t 3.0) (* l l)) (sin k)) (tan k)) (- (+ 1.0 (pow (/ k t) 2.0)) 1.0))))