Data.Colour.CIE:cieLABView from colour-2.3.3, B

Average Accuracy: 100.0% → 100.0%

Time: 6.9s

Precision: binary64

Cost: 6720

Math

\[500 \cdot \left(x - y\right) \]

↓

\[\mathsf{fma}\left(500, x, -500 \cdot y\right) \]

FPCore

(FPCore (x y) :precision binary64 (* 500.0 (- x y)))

↓

(FPCore (x y) :precision binary64 (fma 500.0 x (* -500.0 y)))

C

double code(double x, double y) {
	return 500.0 * (x - y);
}

↓

double code(double x, double y) {
	return fma(500.0, x, (-500.0 * y));
}

Julia

function code(x, y)
	return Float64(500.0 * Float64(x - y))
end

↓

function code(x, y)
	return fma(500.0, x, Float64(-500.0 * y))
end

Wolfram

code[x_, y_] := N[(500.0 * N[(x - y), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_] := N[(500.0 * x + N[(-500.0 * y), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[500 \cdot \left(x - y\right) \]

2. Taylor expanded in x around 0 100.0%

   \[\leadsto \color{blue}{500 \cdot x + -500 \cdot y} \]

3. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(500, x, -500 \cdot y\right)} \]
   Proof

   [Start] 100.0	\[ 500 \cdot x + -500 \cdot y \]
   fma-def [=>] 100.0	\[ \color{blue}{\mathsf{fma}\left(500, x, -500 \cdot y\right)} \]

4. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(500, x, -500 \cdot y\right) \]

Alternatives

Alternative 1
Accuracy	74.4%
Cost	456

\[\begin{array}{l} \mathbf{if}\;y \leq -1.15 \cdot 10^{+58}:\\ \;\;\;\;-500 \cdot y\\ \mathbf{elif}\;y \leq 3.8 \cdot 10^{+60}:\\ \;\;\;\;500 \cdot x\\ \mathbf{else}:\\ \;\;\;\;-500 \cdot y\\ \end{array} \]

Alternative 2
Accuracy	100.0%
Cost	320

\[500 \cdot \left(x - y\right) \]

Alternative 3
Accuracy	49.8%
Cost	192

\[-500 \cdot y \]

Reproduce

herbie shell --seed 2023130 
(FPCore (x y)
  :name "Data.Colour.CIE:cieLABView from colour-2.3.3, B"
  :precision binary64
  (* 500.0 (- x y)))