?

Average Accuracy: 98.6% → 99.5%
Time: 2.6s
Precision: binary64
Cost: 6848

?

\[x \cdot x - \left(y \cdot 4\right) \cdot z \]
\[\mathsf{fma}\left(y, z \cdot -4, x \cdot x\right) \]
(FPCore (x y z) :precision binary64 (- (* x x) (* (* y 4.0) z)))
(FPCore (x y z) :precision binary64 (fma y (* z -4.0) (* x x)))
double code(double x, double y, double z) {
	return (x * x) - ((y * 4.0) * z);
}
double code(double x, double y, double z) {
	return fma(y, (z * -4.0), (x * x));
}
function code(x, y, z)
	return Float64(Float64(x * x) - Float64(Float64(y * 4.0) * z))
end
function code(x, y, z)
	return fma(y, Float64(z * -4.0), Float64(x * x))
end
code[x_, y_, z_] := N[(N[(x * x), $MachinePrecision] - N[(N[(y * 4.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]
code[x_, y_, z_] := N[(y * N[(z * -4.0), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision]
x \cdot x - \left(y \cdot 4\right) \cdot z
\mathsf{fma}\left(y, z \cdot -4, x \cdot x\right)

Error?

Bogosity?

Bogosity

Derivation?

  1. Initial program 97.7%

    \[x \cdot x - \left(y \cdot 4\right) \cdot z \]
  2. Simplified99.2%

    \[\leadsto \color{blue}{\mathsf{fma}\left(y, z \cdot -4, x \cdot x\right)} \]
    Proof

    [Start]97.7

    \[ x \cdot x - \left(y \cdot 4\right) \cdot z \]

    sub-neg [=>]97.7

    \[ \color{blue}{x \cdot x + \left(-\left(y \cdot 4\right) \cdot z\right)} \]

    +-commutative [=>]97.7

    \[ \color{blue}{\left(-\left(y \cdot 4\right) \cdot z\right) + x \cdot x} \]

    associate-*l* [=>]97.7

    \[ \left(-\color{blue}{y \cdot \left(4 \cdot z\right)}\right) + x \cdot x \]

    distribute-rgt-neg-in [=>]97.7

    \[ \color{blue}{y \cdot \left(-4 \cdot z\right)} + x \cdot x \]

    fma-def [=>]99.2

    \[ \color{blue}{\mathsf{fma}\left(y, -4 \cdot z, x \cdot x\right)} \]

    *-commutative [=>]99.2

    \[ \mathsf{fma}\left(y, -\color{blue}{z \cdot 4}, x \cdot x\right) \]

    distribute-rgt-neg-in [=>]99.2

    \[ \mathsf{fma}\left(y, \color{blue}{z \cdot \left(-4\right)}, x \cdot x\right) \]

    metadata-eval [=>]99.2

    \[ \mathsf{fma}\left(y, z \cdot \color{blue}{-4}, x \cdot x\right) \]
  3. Final simplification99.2%

    \[\leadsto \mathsf{fma}\left(y, z \cdot -4, x \cdot x\right) \]

Alternatives

Alternative 1
Accuracy99.0%
Cost6848
\[\mathsf{fma}\left(x, x, z \cdot \left(y \cdot -4\right)\right) \]
Alternative 2
Accuracy99.3%
Cost836
\[\begin{array}{l} \mathbf{if}\;x \cdot x \leq 1.8 \cdot 10^{+277}:\\ \;\;\;\;x \cdot x - z \cdot \left(y \cdot 4\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \]
Alternative 3
Accuracy84.9%
Cost584
\[\begin{array}{l} \mathbf{if}\;x \leq -8.5 \cdot 10^{-23}:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;x \leq 7.4 \cdot 10^{-9}:\\ \;\;\;\;y \cdot \left(z \cdot -4\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \]
Alternative 4
Accuracy53.9%
Cost192
\[x \cdot x \]

Error

Reproduce?

herbie shell --seed 2023160 
(FPCore (x y z)
  :name "Graphics.Rasterific.QuadraticFormula:discriminant from Rasterific-0.6.1"
  :precision binary64
  (- (* x x) (* (* y 4.0) z)))