Average Error: 0.3 → 0.3
Time: 6.3s
Precision: binary64
\[\left(\left(x \cdot 0.5 - y\right) \cdot \sqrt{z \cdot 2}\right) \cdot e^{\frac{t \cdot t}{2}}\]
↓
\[\left(x \cdot 0.5 - y\right) \cdot \left({\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)} \cdot \left(\sqrt{z \cdot 2} \cdot {\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)}\right)\right)\]
\left(\left(x \cdot 0.5 - y\right) \cdot \sqrt{z \cdot 2}\right) \cdot e^{\frac{t \cdot t}{2}}
↓
\left(x \cdot 0.5 - y\right) \cdot \left({\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)} \cdot \left(\sqrt{z \cdot 2} \cdot {\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)}\right)\right)
double code(double x, double y, double z, double t) {
	return ((double) (((double) (((double) (((double) (x * 0.5)) - y)) * ((double) sqrt(((double) (z * 2.0)))))) * ((double) exp(((double) (((double) (t * t)) / 2.0))))));
}
↓
double code(double x, double y, double z, double t) {
	return ((double) (((double) (((double) (x * 0.5)) - y)) * ((double) (((double) pow(((double) sqrt(((double) exp(t)))), ((double) (t / 2.0)))) * ((double) (((double) sqrt(((double) (z * 2.0)))) * ((double) pow(((double) sqrt(((double) exp(t)))), ((double) (t / 2.0))))))))));
}

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

Original0.3
Target0.3
Herbie0.3
\[\left(\left(x \cdot 0.5 - y\right) \cdot \sqrt{z \cdot 2}\right) \cdot {\left(e^{1}\right)}^{\left(\frac{t \cdot t}{2}\right)}\]

Derivation

  1. Initial program 0.3

    \[\left(\left(x \cdot 0.5 - y\right) \cdot \sqrt{z \cdot 2}\right) \cdot e^{\frac{t \cdot t}{2}}\]

  2. Simplified0.3

    \[\leadsto \color{blue}{\left(x \cdot 0.5 - y\right) \cdot \left(\sqrt{z \cdot 2} \cdot {\left(e^{t}\right)}^{\left(\frac{t}{2}\right)}\right)}\]
  3. Using strategy rm

  4. Applied add-sqr-sqrt0.3

    \[\leadsto \left(x \cdot 0.5 - y\right) \cdot \left(\sqrt{z \cdot 2} \cdot {\color{blue}{\left(\sqrt{e^{t}} \cdot \sqrt{e^{t}}\right)}}^{\left(\frac{t}{2}\right)}\right)\]

  5. Applied unpow-prod-down0.3

    \[\leadsto \left(x \cdot 0.5 - y\right) \cdot \left(\sqrt{z \cdot 2} \cdot \color{blue}{\left({\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)} \cdot {\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)}\right)}\right)\]

  6. Applied associate-*r*0.3

    \[\leadsto \left(x \cdot 0.5 - y\right) \cdot \color{blue}{\left(\left(\sqrt{z \cdot 2} \cdot {\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)}\right) \cdot {\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)}\right)}\]

  7. Final simplification0.3

    \[\leadsto \left(x \cdot 0.5 - y\right) \cdot \left({\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)} \cdot \left(\sqrt{z \cdot 2} \cdot {\left(\sqrt{e^{t}}\right)}^{\left(\frac{t}{2}\right)}\right)\right)\]

Reproduce

herbie shell --seed 2020184 
(FPCore (x y z t)
  :name "Data.Number.Erf:$cinvnormcdf from erf-2.0.0.0, A"
  :precision binary64

  :herbie-target
  (* (* (- (* x 0.5) y) (sqrt (* z 2.0))) (pow (exp 1.0) (/ (* t t) 2.0)))

  (* (* (- (* x 0.5) y) (sqrt (* z 2.0))) (exp (/ (* t t) 2.0))))