Graphics.Rasterific.Linear:$cquadrance from Rasterific-0.6.1

Average Error: 0.0 → 0.0

Time: 1.7s

Precision: binary64

Cost: 6720

\[ \begin{array}{c}[x, y] = \mathsf{sort}([x, y])\\ \end{array} \]

Math

\[x \cdot x + y \cdot y \]

↓

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

FPCore

(FPCore (x y) :precision binary64 (+ (* x x) (* y y)))

↓

(FPCore (x y) :precision binary64 (fma y y (* x x)))

C

double code(double x, double y) {
	return (x * x) + (y * y);
}

↓

double code(double x, double y) {
	return fma(y, y, (x * x));
}

Julia

function code(x, y)
	return Float64(Float64(x * x) + Float64(y * y))
end

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 0.0

   \[x \cdot x + y \cdot y \]

2. Simplified 0.0

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

3. Taylor expanded in x around 0 0.0

   \[\leadsto \color{blue}{{y}^{2} + {x}^{2}} \]

4. Simplified 0.0

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

5. Final simplification 0.0

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

Alternatives

Alternative 1
Error	7.9
Cost	588

\[\begin{array}{l} \mathbf{if}\;y \leq 1.5177658240149548 \cdot 10^{-148}:\\ \;\;\;\;x \cdot x\\ \mathbf{elif}\;y \leq 1.0619158744720323 \cdot 10^{-92}:\\ \;\;\;\;y \cdot y\\ \mathbf{elif}\;y \leq 5.488429879176881 \cdot 10^{-81}:\\ \;\;\;\;x \cdot x\\ \mathbf{else}:\\ \;\;\;\;y \cdot y\\ \end{array} \]

Alternative 2
Error	0.0
Cost	448

\[x \cdot x + y \cdot y \]

Alternative 3
Error	27.5
Cost	192

\[x \cdot x \]

Reproduce

herbie shell --seed 2022325 
(FPCore (x y)
  :name "Graphics.Rasterific.Linear:$cquadrance from Rasterific-0.6.1"
  :precision binary64
  (+ (* x x) (* y y)))