Linear.V2:$cdot from linear-1.19.1.3, A

Average Error: 0.0 → 0.0

Time: 2.6s

Precision: binary64

Cost: 6720

Math

\[x \cdot y + z \cdot t \]

↓

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

FPCore

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

↓

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

C

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

↓

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

Julia

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

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 0.0

   \[x \cdot y + z \cdot t \]

2. Simplified 0.0

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, y, z \cdot t\right)} \]
   Proof
   (fma.f64 x y (*.f64 z t)): 0 points increase in error, 0 points decrease in error
   (Rewrite<= fma-def_binary64 (+.f64 (*.f64 x y) (*.f64 z t))): 0 points increase in error, 0 points decrease in error

3. Final simplification 0.0

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

Alternatives

Alternative 1
Error	23.2
Cost	986

\[\begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{-186}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;y \leq 9 \cdot 10^{-108} \lor \neg \left(y \leq 5.6 \cdot 10^{-70}\right) \land \left(y \leq 6.8 \cdot 10^{-32} \lor \neg \left(y \leq 10^{+27}\right) \land y \leq 4 \cdot 10^{+60}\right):\\ \;\;\;\;z \cdot t\\ \mathbf{else}:\\ \;\;\;\;x \cdot y\\ \end{array} \]

Alternative 2
Error	0.0
Cost	448

\[z \cdot t + x \cdot y \]

Alternative 3
Error	31.0
Cost	192

\[z \cdot t \]

Reproduce

herbie shell --seed 2022343 
(FPCore (x y z t)
  :name "Linear.V2:$cdot from linear-1.19.1.3, A"
  :precision binary64
  (+ (* x y) (* z t)))