Average Error: 11.7 → 0.1
Time: 3.7s
Precision: binary64
\[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]
↓
\[x + \frac{-2}{\frac{1}{\frac{y}{2 \cdot z}} - \frac{t}{z}} \]
(FPCore (x y z t)
 :precision binary64
 (- x (/ (* (* y 2.0) z) (- (* (* z 2.0) z) (* y t)))))
↓
(FPCore (x y z t)
 :precision binary64
 (+ x (/ -2.0 (- (/ 1.0 (/ y (* 2.0 z))) (/ t z)))))
double code(double x, double y, double z, double t) {
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
}
↓
double code(double x, double y, double z, double t) {
	return x + (-2.0 / ((1.0 / (y / (2.0 * z))) - (t / z)));
}

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x - (((y * 2.0d0) * z) / (((z * 2.0d0) * z) - (y * t)))
end function
↓
real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = x + ((-2.0d0) / ((1.0d0 / (y / (2.0d0 * z))) - (t / z)))
end function

public static double code(double x, double y, double z, double t) {
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
}
↓
public static double code(double x, double y, double z, double t) {
	return x + (-2.0 / ((1.0 / (y / (2.0 * z))) - (t / z)));
}

def code(x, y, z, t):
	return x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)))
↓
def code(x, y, z, t):
	return x + (-2.0 / ((1.0 / (y / (2.0 * z))) - (t / z)))

function code(x, y, z, t)
	return Float64(x - Float64(Float64(Float64(y * 2.0) * z) / Float64(Float64(Float64(z * 2.0) * z) - Float64(y * t))))
end
↓
function code(x, y, z, t)
	return Float64(x + Float64(-2.0 / Float64(Float64(1.0 / Float64(y / Float64(2.0 * z))) - Float64(t / z))))
end

function tmp = code(x, y, z, t)
	tmp = x - (((y * 2.0) * z) / (((z * 2.0) * z) - (y * t)));
end
↓
function tmp = code(x, y, z, t)
	tmp = x + (-2.0 / ((1.0 / (y / (2.0 * z))) - (t / z)));
end

code[x_, y_, z_, t_] := N[(x - N[(N[(N[(y * 2.0), $MachinePrecision] * z), $MachinePrecision] / N[(N[(N[(z * 2.0), $MachinePrecision] * z), $MachinePrecision] - N[(y * t), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
↓
code[x_, y_, z_, t_] := N[(x + N[(-2.0 / N[(N[(1.0 / N[(y / N[(2.0 * z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(t / z), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t}
↓
x + \frac{-2}{\frac{1}{\frac{y}{2 \cdot z}} - \frac{t}{z}}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original11.7
Target0.1
Herbie0.1
\[x - \frac{1}{\frac{z}{y} - \frac{\frac{t}{2}}{z}} \]

Derivation

  1. Initial program 11.7

    \[x - \frac{\left(y \cdot 2\right) \cdot z}{\left(z \cdot 2\right) \cdot z - y \cdot t} \]

  2. Simplified0.1

    \[\leadsto \color{blue}{x + \frac{-2}{2 \cdot \frac{z}{y} - \frac{t}{z}}} \]

  3. Applied egg-rr0.1

    \[\leadsto x + \frac{-2}{\color{blue}{\frac{1}{\frac{y}{2 \cdot z}}} - \frac{t}{z}} \]

  4. Final simplification0.1

    \[\leadsto x + \frac{-2}{\frac{1}{\frac{y}{2 \cdot z}} - \frac{t}{z}} \]

Reproduce

herbie shell --seed 2022160 
(FPCore (x y z t)
  :name "Numeric.AD.Rank1.Halley:findZero from ad-4.2.4"
  :precision binary64

  :herbie-target
  (- x (/ 1.0 (- (/ z y) (/ (/ t 2.0) z))))

  (- x (/ (* (* y 2.0) z) (- (* (* z 2.0) z) (* y t)))))