
(FPCore (x) :precision binary64 (let* ((t_0 (* (tan x) (tan x)))) (/ (- 1.0 t_0) (+ 1.0 t_0))))
double code(double x) {
double t_0 = tan(x) * tan(x);
return (1.0 - t_0) / (1.0 + t_0);
}
real(8) function code(x)
real(8), intent (in) :: x
real(8) :: t_0
t_0 = tan(x) * tan(x)
code = (1.0d0 - t_0) / (1.0d0 + t_0)
end function
public static double code(double x) {
double t_0 = Math.tan(x) * Math.tan(x);
return (1.0 - t_0) / (1.0 + t_0);
}
def code(x): t_0 = math.tan(x) * math.tan(x) return (1.0 - t_0) / (1.0 + t_0)
function code(x) t_0 = Float64(tan(x) * tan(x)) return Float64(Float64(1.0 - t_0) / Float64(1.0 + t_0)) end
function tmp = code(x) t_0 = tan(x) * tan(x); tmp = (1.0 - t_0) / (1.0 + t_0); end
code[x_] := Block[{t$95$0 = N[(N[Tan[x], $MachinePrecision] * N[Tan[x], $MachinePrecision]), $MachinePrecision]}, N[(N[(1.0 - t$95$0), $MachinePrecision] / N[(1.0 + t$95$0), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \tan x \cdot \tan x\\
\frac{1 - t\_0}{1 + t\_0}
\end{array}
\end{array}
Sampling outcomes in binary64 precision:
Herbie found 7 alternatives:
| Alternative | Accuracy | Speedup |
|---|
(FPCore (x) :precision binary64 (let* ((t_0 (* (tan x) (tan x)))) (/ (- 1.0 t_0) (+ 1.0 t_0))))
double code(double x) {
double t_0 = tan(x) * tan(x);
return (1.0 - t_0) / (1.0 + t_0);
}
real(8) function code(x)
real(8), intent (in) :: x
real(8) :: t_0
t_0 = tan(x) * tan(x)
code = (1.0d0 - t_0) / (1.0d0 + t_0)
end function
public static double code(double x) {
double t_0 = Math.tan(x) * Math.tan(x);
return (1.0 - t_0) / (1.0 + t_0);
}
def code(x): t_0 = math.tan(x) * math.tan(x) return (1.0 - t_0) / (1.0 + t_0)
function code(x) t_0 = Float64(tan(x) * tan(x)) return Float64(Float64(1.0 - t_0) / Float64(1.0 + t_0)) end
function tmp = code(x) t_0 = tan(x) * tan(x); tmp = (1.0 - t_0) / (1.0 + t_0); end
code[x_] := Block[{t$95$0 = N[(N[Tan[x], $MachinePrecision] * N[Tan[x], $MachinePrecision]), $MachinePrecision]}, N[(N[(1.0 - t$95$0), $MachinePrecision] / N[(1.0 + t$95$0), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \tan x \cdot \tan x\\
\frac{1 - t\_0}{1 + t\_0}
\end{array}
\end{array}
(FPCore (x)
:precision binary64
(let* ((t_0 (cos (* 2.0 x))) (t_1 (* t_0 -0.5)) (t_2 (* 0.5 t_0)))
(/
(+ 1.0 (/ (- t_2 0.5) (+ 0.5 t_2)))
(+ 1.0 (/ (- -0.5 t_1) (+ -0.5 t_1))))))
double code(double x) {
double t_0 = cos((2.0 * x));
double t_1 = t_0 * -0.5;
double t_2 = 0.5 * t_0;
return (1.0 + ((t_2 - 0.5) / (0.5 + t_2))) / (1.0 + ((-0.5 - t_1) / (-0.5 + t_1)));
}
real(8) function code(x)
real(8), intent (in) :: x
real(8) :: t_0
real(8) :: t_1
real(8) :: t_2
t_0 = cos((2.0d0 * x))
t_1 = t_0 * (-0.5d0)
t_2 = 0.5d0 * t_0
code = (1.0d0 + ((t_2 - 0.5d0) / (0.5d0 + t_2))) / (1.0d0 + (((-0.5d0) - t_1) / ((-0.5d0) + t_1)))
end function
public static double code(double x) {
double t_0 = Math.cos((2.0 * x));
double t_1 = t_0 * -0.5;
double t_2 = 0.5 * t_0;
return (1.0 + ((t_2 - 0.5) / (0.5 + t_2))) / (1.0 + ((-0.5 - t_1) / (-0.5 + t_1)));
}
def code(x): t_0 = math.cos((2.0 * x)) t_1 = t_0 * -0.5 t_2 = 0.5 * t_0 return (1.0 + ((t_2 - 0.5) / (0.5 + t_2))) / (1.0 + ((-0.5 - t_1) / (-0.5 + t_1)))
function code(x) t_0 = cos(Float64(2.0 * x)) t_1 = Float64(t_0 * -0.5) t_2 = Float64(0.5 * t_0) return Float64(Float64(1.0 + Float64(Float64(t_2 - 0.5) / Float64(0.5 + t_2))) / Float64(1.0 + Float64(Float64(-0.5 - t_1) / Float64(-0.5 + t_1)))) end
function tmp = code(x) t_0 = cos((2.0 * x)); t_1 = t_0 * -0.5; t_2 = 0.5 * t_0; tmp = (1.0 + ((t_2 - 0.5) / (0.5 + t_2))) / (1.0 + ((-0.5 - t_1) / (-0.5 + t_1))); end
code[x_] := Block[{t$95$0 = N[Cos[N[(2.0 * x), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(t$95$0 * -0.5), $MachinePrecision]}, Block[{t$95$2 = N[(0.5 * t$95$0), $MachinePrecision]}, N[(N[(1.0 + N[(N[(t$95$2 - 0.5), $MachinePrecision] / N[(0.5 + t$95$2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[(-0.5 - t$95$1), $MachinePrecision] / N[(-0.5 + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \left(2 \cdot x\right)\\
t_1 := t\_0 \cdot -0.5\\
t_2 := 0.5 \cdot t\_0\\
\frac{1 + \frac{t\_2 - 0.5}{0.5 + t\_2}}{1 + \frac{-0.5 - t\_1}{-0.5 + t\_1}}
\end{array}
\end{array}
Initial program 99.5%
tan-quotN/A
tan-quotN/A
frac-timesN/A
/-lowering-/.f64N/A
sqr-sin-aN/A
--lowering--.f64N/A
*-lowering-*.f64N/A
cos-lowering-cos.f64N/A
*-lowering-*.f64N/A
sqr-cos-aN/A
+-lowering-+.f64N/A
*-lowering-*.f64N/A
cos-lowering-cos.f64N/A
*-lowering-*.f6498.9%
Applied egg-rr98.9%
tan-quotN/A
tan-quotN/A
frac-timesN/A
sqr-sin-aN/A
sqr-cos-aN/A
div-subN/A
frac-2negN/A
frac-2negN/A
sub-divN/A
/-lowering-/.f64N/A
Applied egg-rr99.6%
Final simplification99.6%
(FPCore (x) :precision binary64 (let* ((t_0 (pow (tan x) 2.0))) (/ (/ 1.0 (+ 1.0 t_0)) (/ 1.0 (- 1.0 t_0)))))
double code(double x) {
double t_0 = pow(tan(x), 2.0);
return (1.0 / (1.0 + t_0)) / (1.0 / (1.0 - t_0));
}
real(8) function code(x)
real(8), intent (in) :: x
real(8) :: t_0
t_0 = tan(x) ** 2.0d0
code = (1.0d0 / (1.0d0 + t_0)) / (1.0d0 / (1.0d0 - t_0))
end function
public static double code(double x) {
double t_0 = Math.pow(Math.tan(x), 2.0);
return (1.0 / (1.0 + t_0)) / (1.0 / (1.0 - t_0));
}
def code(x): t_0 = math.pow(math.tan(x), 2.0) return (1.0 / (1.0 + t_0)) / (1.0 / (1.0 - t_0))
function code(x) t_0 = tan(x) ^ 2.0 return Float64(Float64(1.0 / Float64(1.0 + t_0)) / Float64(1.0 / Float64(1.0 - t_0))) end
function tmp = code(x) t_0 = tan(x) ^ 2.0; tmp = (1.0 / (1.0 + t_0)) / (1.0 / (1.0 - t_0)); end
code[x_] := Block[{t$95$0 = N[Power[N[Tan[x], $MachinePrecision], 2.0], $MachinePrecision]}, N[(N[(1.0 / N[(1.0 + t$95$0), $MachinePrecision]), $MachinePrecision] / N[(1.0 / N[(1.0 - t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\tan x}^{2}\\
\frac{\frac{1}{1 + t\_0}}{\frac{1}{1 - t\_0}}
\end{array}
\end{array}
Initial program 99.5%
flip--N/A
clear-numN/A
associate-/l/N/A
associate-/r*N/A
/-lowering-/.f64N/A
/-lowering-/.f64N/A
+-lowering-+.f64N/A
pow2N/A
pow-lowering-pow.f64N/A
tan-lowering-tan.f64N/A
clear-numN/A
Applied egg-rr99.5%
(FPCore (x) :precision binary64 (let* ((t_0 (pow (tan x) 2.0))) (/ (- 1.0 t_0) (+ 1.0 t_0))))
double code(double x) {
double t_0 = pow(tan(x), 2.0);
return (1.0 - t_0) / (1.0 + t_0);
}
real(8) function code(x)
real(8), intent (in) :: x
real(8) :: t_0
t_0 = tan(x) ** 2.0d0
code = (1.0d0 - t_0) / (1.0d0 + t_0)
end function
public static double code(double x) {
double t_0 = Math.pow(Math.tan(x), 2.0);
return (1.0 - t_0) / (1.0 + t_0);
}
def code(x): t_0 = math.pow(math.tan(x), 2.0) return (1.0 - t_0) / (1.0 + t_0)
function code(x) t_0 = tan(x) ^ 2.0 return Float64(Float64(1.0 - t_0) / Float64(1.0 + t_0)) end
function tmp = code(x) t_0 = tan(x) ^ 2.0; tmp = (1.0 - t_0) / (1.0 + t_0); end
code[x_] := Block[{t$95$0 = N[Power[N[Tan[x], $MachinePrecision], 2.0], $MachinePrecision]}, N[(N[(1.0 - t$95$0), $MachinePrecision] / N[(1.0 + t$95$0), $MachinePrecision]), $MachinePrecision]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := {\tan x}^{2}\\
\frac{1 - t\_0}{1 + t\_0}
\end{array}
\end{array}
Initial program 99.5%
/-lowering-/.f64N/A
--lowering--.f64N/A
pow2N/A
pow-lowering-pow.f64N/A
tan-lowering-tan.f64N/A
+-lowering-+.f64N/A
pow2N/A
pow-lowering-pow.f64N/A
tan-lowering-tan.f6499.5%
Applied egg-rr99.5%
(FPCore (x)
:precision binary64
(let* ((t_0 (cos (* 2.0 x))) (t_1 (* 0.5 t_0)))
(/
(+ 1.0 (/ (- t_1 0.5) (+ 0.5 t_1)))
(+ 1.0 (/ (- -0.5 (* t_0 -0.5)) -1.0)))))
double code(double x) {
double t_0 = cos((2.0 * x));
double t_1 = 0.5 * t_0;
return (1.0 + ((t_1 - 0.5) / (0.5 + t_1))) / (1.0 + ((-0.5 - (t_0 * -0.5)) / -1.0));
}
real(8) function code(x)
real(8), intent (in) :: x
real(8) :: t_0
real(8) :: t_1
t_0 = cos((2.0d0 * x))
t_1 = 0.5d0 * t_0
code = (1.0d0 + ((t_1 - 0.5d0) / (0.5d0 + t_1))) / (1.0d0 + (((-0.5d0) - (t_0 * (-0.5d0))) / (-1.0d0)))
end function
public static double code(double x) {
double t_0 = Math.cos((2.0 * x));
double t_1 = 0.5 * t_0;
return (1.0 + ((t_1 - 0.5) / (0.5 + t_1))) / (1.0 + ((-0.5 - (t_0 * -0.5)) / -1.0));
}
def code(x): t_0 = math.cos((2.0 * x)) t_1 = 0.5 * t_0 return (1.0 + ((t_1 - 0.5) / (0.5 + t_1))) / (1.0 + ((-0.5 - (t_0 * -0.5)) / -1.0))
function code(x) t_0 = cos(Float64(2.0 * x)) t_1 = Float64(0.5 * t_0) return Float64(Float64(1.0 + Float64(Float64(t_1 - 0.5) / Float64(0.5 + t_1))) / Float64(1.0 + Float64(Float64(-0.5 - Float64(t_0 * -0.5)) / -1.0))) end
function tmp = code(x) t_0 = cos((2.0 * x)); t_1 = 0.5 * t_0; tmp = (1.0 + ((t_1 - 0.5) / (0.5 + t_1))) / (1.0 + ((-0.5 - (t_0 * -0.5)) / -1.0)); end
code[x_] := Block[{t$95$0 = N[Cos[N[(2.0 * x), $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[(0.5 * t$95$0), $MachinePrecision]}, N[(N[(1.0 + N[(N[(t$95$1 - 0.5), $MachinePrecision] / N[(0.5 + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[(-0.5 - N[(t$95$0 * -0.5), $MachinePrecision]), $MachinePrecision] / -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]
\begin{array}{l}
\\
\begin{array}{l}
t_0 := \cos \left(2 \cdot x\right)\\
t_1 := 0.5 \cdot t\_0\\
\frac{1 + \frac{t\_1 - 0.5}{0.5 + t\_1}}{1 + \frac{-0.5 - t\_0 \cdot -0.5}{-1}}
\end{array}
\end{array}
Initial program 99.5%
tan-quotN/A
tan-quotN/A
frac-timesN/A
/-lowering-/.f64N/A
sqr-sin-aN/A
--lowering--.f64N/A
*-lowering-*.f64N/A
cos-lowering-cos.f64N/A
*-lowering-*.f64N/A
sqr-cos-aN/A
+-lowering-+.f64N/A
*-lowering-*.f64N/A
cos-lowering-cos.f64N/A
*-lowering-*.f6498.9%
Applied egg-rr98.9%
tan-quotN/A
tan-quotN/A
frac-timesN/A
sqr-sin-aN/A
sqr-cos-aN/A
div-subN/A
frac-2negN/A
frac-2negN/A
sub-divN/A
/-lowering-/.f64N/A
Applied egg-rr99.6%
Taylor expanded in x around 0
Simplified60.4%
Final simplification60.4%
(FPCore (x) :precision binary64 (/ 1.0 (/ 1.0 (- 1.0 (pow (tan x) 2.0)))))
double code(double x) {
return 1.0 / (1.0 / (1.0 - pow(tan(x), 2.0)));
}
real(8) function code(x)
real(8), intent (in) :: x
code = 1.0d0 / (1.0d0 / (1.0d0 - (tan(x) ** 2.0d0)))
end function
public static double code(double x) {
return 1.0 / (1.0 / (1.0 - Math.pow(Math.tan(x), 2.0)));
}
def code(x): return 1.0 / (1.0 / (1.0 - math.pow(math.tan(x), 2.0)))
function code(x) return Float64(1.0 / Float64(1.0 / Float64(1.0 - (tan(x) ^ 2.0)))) end
function tmp = code(x) tmp = 1.0 / (1.0 / (1.0 - (tan(x) ^ 2.0))); end
code[x_] := N[(1.0 / N[(1.0 / N[(1.0 - N[Power[N[Tan[x], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
\frac{1}{\frac{1}{1 - {\tan x}^{2}}}
\end{array}
Initial program 99.5%
+-commutativeN/A
fma-defineN/A
fma-lowering-fma.f64N/A
tan-lowering-tan.f64N/A
tan-lowering-tan.f6499.5%
Applied egg-rr99.5%
flip--N/A
metadata-evalN/A
pow2N/A
pow-prod-downN/A
pow-sqrN/A
metadata-evalN/A
pow2N/A
clear-numN/A
/-lowering-/.f64N/A
pow2N/A
clear-numN/A
Applied egg-rr99.5%
Taylor expanded in x around 0
Simplified58.6%
Final simplification58.6%
(FPCore (x) :precision binary64 (- 1.0 (pow (tan x) 2.0)))
double code(double x) {
return 1.0 - pow(tan(x), 2.0);
}
real(8) function code(x)
real(8), intent (in) :: x
code = 1.0d0 - (tan(x) ** 2.0d0)
end function
public static double code(double x) {
return 1.0 - Math.pow(Math.tan(x), 2.0);
}
def code(x): return 1.0 - math.pow(math.tan(x), 2.0)
function code(x) return Float64(1.0 - (tan(x) ^ 2.0)) end
function tmp = code(x) tmp = 1.0 - (tan(x) ^ 2.0); end
code[x_] := N[(1.0 - N[Power[N[Tan[x], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]
\begin{array}{l}
\\
1 - {\tan x}^{2}
\end{array}
Initial program 99.5%
+-commutativeN/A
fma-defineN/A
fma-lowering-fma.f64N/A
tan-lowering-tan.f64N/A
tan-lowering-tan.f6499.5%
Applied egg-rr99.5%
flip--N/A
metadata-evalN/A
pow2N/A
pow-prod-downN/A
pow-sqrN/A
metadata-evalN/A
pow2N/A
clear-numN/A
/-lowering-/.f64N/A
pow2N/A
clear-numN/A
Applied egg-rr99.5%
Taylor expanded in x around 0
Simplified58.6%
/-rgt-identityN/A
remove-double-divN/A
--lowering--.f64N/A
pow-lowering-pow.f64N/A
tan-lowering-tan.f6458.6%
Applied egg-rr58.6%
(FPCore (x) :precision binary64 1.0)
double code(double x) {
return 1.0;
}
real(8) function code(x)
real(8), intent (in) :: x
code = 1.0d0
end function
public static double code(double x) {
return 1.0;
}
def code(x): return 1.0
function code(x) return 1.0 end
function tmp = code(x) tmp = 1.0; end
code[x_] := 1.0
\begin{array}{l}
\\
1
\end{array}
Initial program 99.5%
Taylor expanded in x around 0
Simplified54.1%
herbie shell --seed 2024138
(FPCore (x)
:name "Trigonometry B"
:precision binary64
(/ (- 1.0 (* (tan x) (tan x))) (+ 1.0 (* (tan x) (tan x)))))