Average Error: 10.4 → 1.1
Time: 4.9s
Precision: binary64
\[x + \frac{y \cdot \left(z - t\right)}{z - a} \]
↓
\[x + \frac{y}{\frac{z}{z - t} - \frac{a}{z - t}} \]
(FPCore (x y z t a) :precision binary64 (+ x (/ (* y (- z t)) (- z a))))
↓
(FPCore (x y z t a)
 :precision binary64
 (+ x (/ y (- (/ z (- z t)) (/ a (- z t))))))
double code(double x, double y, double z, double t, double a) {
	return x + ((y * (z - t)) / (z - a));
}
↓
double code(double x, double y, double z, double t, double a) {
	return x + (y / ((z / (z - t)) - (a / (z - t))));
}

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

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

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

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

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

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

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

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Bits error versus a

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original10.4
Target1.1
Herbie1.1
\[x + \frac{y}{\frac{z - a}{z - t}} \]

Derivation

  1. Initial program 10.4

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

  2. Simplified1.2

    \[\leadsto \color{blue}{\mathsf{fma}\left(y, \frac{z - t}{z - a}, x\right)} \]

  3. Applied egg-rr1.1

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

  4. Taylor expanded in a around 0 1.1

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

  5. Final simplification1.1

    \[\leadsto x + \frac{y}{\frac{z}{z - t} - \frac{a}{z - t}} \]

Reproduce

herbie shell --seed 2022153 
(FPCore (x y z t a)
  :name "Graphics.Rendering.Plot.Render.Plot.Axis:renderAxisTicks from plot-0.2.3.4, A"
  :precision binary64

  :herbie-target
  (+ x (/ y (/ (- z a) (- z t))))

  (+ x (/ (* y (- z t)) (- z a))))