| Alternative 1 | |
|---|---|
| Error | 0.4 |
| Cost | 26176 |
\[\frac{1}{1 + {\tan x}^{2}} - {\sin x}^{2}
\]
(FPCore (x) :precision binary64 (/ (- 1.0 (* (tan x) (tan x))) (+ 1.0 (* (tan x) (tan x)))))
(FPCore (x) :precision binary64 (/ (- 1.0 (pow (tan x) 2.0)) (fma (tan x) (tan x) 1.0)))
double code(double x) {
return (1.0 - (tan(x) * tan(x))) / (1.0 + (tan(x) * tan(x)));
}
double code(double x) {
return (1.0 - pow(tan(x), 2.0)) / fma(tan(x), tan(x), 1.0);
}
function code(x) return Float64(Float64(1.0 - Float64(tan(x) * tan(x))) / Float64(1.0 + Float64(tan(x) * tan(x)))) end
function code(x) return Float64(Float64(1.0 - (tan(x) ^ 2.0)) / fma(tan(x), tan(x), 1.0)) end
code[x_] := N[(N[(1.0 - N[(N[Tan[x], $MachinePrecision] * N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[Tan[x], $MachinePrecision] * N[Tan[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
code[x_] := N[(N[(1.0 - N[Power[N[Tan[x], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / N[(N[Tan[x], $MachinePrecision] * N[Tan[x], $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]
\frac{1 - \tan x \cdot \tan x}{1 + \tan x \cdot \tan x}
\frac{1 - {\tan x}^{2}}{\mathsf{fma}\left(\tan x, \tan x, 1\right)}
Initial program 0.3
Applied egg-rr0.5
Applied egg-rr0.5
Applied egg-rr0.3
Final simplification0.3
| Alternative 1 | |
|---|---|
| Error | 0.4 |
| Cost | 26176 |
| Alternative 2 | |
|---|---|
| Error | 0.3 |
| Cost | 26176 |
| Alternative 3 | |
|---|---|
| Error | 26.2 |
| Cost | 25920 |
| Alternative 4 | |
|---|---|
| Error | 26.2 |
| Cost | 13056 |
| Alternative 5 | |
|---|---|
| Error | 28.9 |
| Cost | 64 |

herbie shell --seed 2022228
(FPCore (x)
:name "Trigonometry B"
:precision binary64
(/ (- 1.0 (* (tan x) (tan x))) (+ 1.0 (* (tan x) (tan x)))))