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

Average Error: 0.0 → 0.0

Time: 2.4s

Precision: binary64

Cost: 6720

Math

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

↓

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

FPCore

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

↓

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

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(z, t, (x * y));
}

Julia

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

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 0.0

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

2. Applied egg-rr 0.0

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

3. Final simplification 0.0

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

Alternatives

Alternative 1
Error	22.1
Cost	456

\[\begin{array}{l} \mathbf{if}\;z \leq -2.209469513786305 \cdot 10^{+58}:\\ \;\;\;\;z \cdot t\\ \mathbf{elif}\;z \leq 2.848464196701326 \cdot 10^{-161}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;z \cdot t\\ \end{array} \]

Alternative 2
Error	0.0
Cost	448

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

Alternative 3
Error	30.5
Cost	192

\[z \cdot t \]

Reproduce

herbie shell --seed 2022300 
(FPCore (x y z t)
  :name "Linear.V2:$cdot from linear-1.19.1.3, A"
  :precision binary64
  (+ (* x y) (* z t)))