Graphics.Rasterific.QuadraticFormula:discriminant from Rasterific-0.6.1

Average Error: 0.0 → 0.0

Time: 2.3s

Precision: binary64

Cost: 6848

Math

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

↓

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

FPCore

(FPCore (x y z) :precision binary64 (- (* x x) (* (* y 4.0) z)))

↓

(FPCore (x y z) :precision binary64 (fma (* y -4.0) z (* x x)))

C

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 * -4.0), z, (x * x));
}

Julia

function code(x, y, z)
	return Float64(Float64(x * x) - Float64(Float64(y * 4.0) * z))
end

↓

function code(x, y, z)
	return fma(Float64(y * -4.0), z, Float64(x * x))
end

Wolfram

code[x_, y_, z_] := N[(N[(x * x), $MachinePrecision] - N[(N[(y * 4.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_, z_] := N[(N[(y * -4.0), $MachinePrecision] * z + N[(x * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 0.0

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

2. Applied egg-rr 0.0

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

3. Final simplification 0.0

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

Alternatives

Alternative 1
Error	0.0
Cost	6848

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

Alternative 2
Error	11.4
Cost	584

\[\begin{array}{l} \mathbf{if}\;x \leq -1.3 \cdot 10^{-60}:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;x \leq 5.2 \cdot 10^{-8}:\\ \;\;\;\;-4 \cdot \left(y \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot x\\ \end{array} \]

Alternative 3
Error	0.0
Cost	576

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

Alternative 4
Error	35.3
Cost	192

\[x \cdot x \]

Reproduce

herbie shell --seed 2022332 
(FPCore (x y z)
  :name "Graphics.Rasterific.QuadraticFormula:discriminant from Rasterific-0.6.1"
  :precision binary64
  (- (* x x) (* (* y 4.0) z)))