\[\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}}
\]
↓
\[1 - \left(\frac{1}{x \cdot 9} + \sqrt{\frac{0.1111111111111111}{x}} \cdot y\right)
\]
(FPCore (x y)
:precision binary64
(- (- 1.0 (/ 1.0 (* x 9.0))) (/ y (* 3.0 (sqrt x)))))
↓
(FPCore (x y)
:precision binary64
(- 1.0 (+ (/ 1.0 (* x 9.0)) (* (sqrt (/ 0.1111111111111111 x)) y))))
double code(double x, double y) {
return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * sqrt(x)));
}
↓
double code(double x, double y) {
return 1.0 - ((1.0 / (x * 9.0)) + (sqrt((0.1111111111111111 / x)) * y));
}
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = (1.0d0 - (1.0d0 / (x * 9.0d0))) - (y / (3.0d0 * sqrt(x)))
end function
↓
real(8) function code(x, y)
real(8), intent (in) :: x
real(8), intent (in) :: y
code = 1.0d0 - ((1.0d0 / (x * 9.0d0)) + (sqrt((0.1111111111111111d0 / x)) * y))
end function
public static double code(double x, double y) {
return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * Math.sqrt(x)));
}
↓
public static double code(double x, double y) {
return 1.0 - ((1.0 / (x * 9.0)) + (Math.sqrt((0.1111111111111111 / x)) * y));
}
def code(x, y):
return (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * math.sqrt(x)))
↓
def code(x, y):
return 1.0 - ((1.0 / (x * 9.0)) + (math.sqrt((0.1111111111111111 / x)) * y))
function code(x, y)
return Float64(Float64(1.0 - Float64(1.0 / Float64(x * 9.0))) - Float64(y / Float64(3.0 * sqrt(x))))
end
↓
function code(x, y)
return Float64(1.0 - Float64(Float64(1.0 / Float64(x * 9.0)) + Float64(sqrt(Float64(0.1111111111111111 / x)) * y)))
end
function tmp = code(x, y)
tmp = (1.0 - (1.0 / (x * 9.0))) - (y / (3.0 * sqrt(x)));
end
↓
function tmp = code(x, y)
tmp = 1.0 - ((1.0 / (x * 9.0)) + (sqrt((0.1111111111111111 / x)) * y));
end
code[x_, y_] := N[(N[(1.0 - N[(1.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(y / N[(3.0 * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_] := N[(1.0 - N[(N[(1.0 / N[(x * 9.0), $MachinePrecision]), $MachinePrecision] + N[(N[Sqrt[N[(0.1111111111111111 / x), $MachinePrecision]], $MachinePrecision] * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
\left(1 - \frac{1}{x \cdot 9}\right) - \frac{y}{3 \cdot \sqrt{x}}
↓
1 - \left(\frac{1}{x \cdot 9} + \sqrt{\frac{0.1111111111111111}{x}} \cdot y\right)
Alternatives
| Alternative 1 |
|---|
| Error | 6.4% |
|---|
| Cost | 7113 |
|---|
\[\begin{array}{l}
\mathbf{if}\;y \leq -28000000000000 \lor \neg \left(y \leq 4.5 \cdot 10^{+26}\right):\\
\;\;\;\;1 + \frac{y}{\sqrt{x}} \cdot -0.3333333333333333\\
\mathbf{else}:\\
\;\;\;\;1 - {\left(x \cdot 9\right)}^{-1}\\
\end{array}
\]
| Alternative 2 |
|---|
| Error | 8.32% |
|---|
| Cost | 7112 |
|---|
\[\begin{array}{l}
\mathbf{if}\;y \leq -1.32 \cdot 10^{+70}:\\
\;\;\;\;\frac{y}{\sqrt{x}} \cdot -0.3333333333333333\\
\mathbf{elif}\;y \leq 4.6 \cdot 10^{+50}:\\
\;\;\;\;1 - {\left(x \cdot 9\right)}^{-1}\\
\mathbf{else}:\\
\;\;\;\;y \cdot \left(-0.3333333333333333 \cdot \sqrt{\frac{1}{x}}\right)\\
\end{array}
\]
| Alternative 3 |
|---|
| Error | 0.36% |
|---|
| Cost | 7104 |
|---|
\[1 - \left(\frac{0.1111111111111111}{x} + \sqrt{\frac{0.1111111111111111}{x}} \cdot y\right)
\]
| Alternative 4 |
|---|
| Error | 8.36% |
|---|
| Cost | 7049 |
|---|
\[\begin{array}{l}
\mathbf{if}\;y \leq -2.2 \cdot 10^{+70} \lor \neg \left(y \leq 7 \cdot 10^{+50}\right):\\
\;\;\;\;\frac{y}{\sqrt{x}} \cdot -0.3333333333333333\\
\mathbf{else}:\\
\;\;\;\;1 - {\left(x \cdot 9\right)}^{-1}\\
\end{array}
\]
| Alternative 5 |
|---|
| Error | 8.47% |
|---|
| Cost | 6985 |
|---|
\[\begin{array}{l}
\mathbf{if}\;y \leq -2.5 \cdot 10^{+68} \lor \neg \left(y \leq 6.6 \cdot 10^{+50}\right):\\
\;\;\;\;\frac{y}{\sqrt{x}} \cdot -0.3333333333333333\\
\mathbf{else}:\\
\;\;\;\;1 + \frac{-0.1111111111111111}{x}\\
\end{array}
\]
| Alternative 6 |
|---|
| Error | 34.67% |
|---|
| Cost | 324 |
|---|
\[\begin{array}{l}
\mathbf{if}\;x \leq 0.112:\\
\;\;\;\;\frac{-0.1111111111111111}{x}\\
\mathbf{else}:\\
\;\;\;\;1\\
\end{array}
\]
| Alternative 7 |
|---|
| Error | 33.47% |
|---|
| Cost | 320 |
|---|
\[1 + \frac{-0.1111111111111111}{x}
\]
| Alternative 8 |
|---|
| Error | 66.15% |
|---|
| Cost | 64 |
|---|
\[1
\]