Linear.V3:$cdot from linear-1.19.1.3, B

Average Accuracy: 100.0% → 100.0%

Time: 6.2s

Precision: binary64

Cost: 13248

Math

\[\left(x \cdot y + z \cdot t\right) + a \cdot b \]

↓

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

FPCore

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

↓

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

C

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

↓

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

Julia

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

↓

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

Wolfram

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

↓

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

Derivation

1. Initial program 100.0%

   \[\left(x \cdot y + z \cdot t\right) + a \cdot b \]

2. Simplified 100.0%

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

   [Start] 100.0	\[ \left(x \cdot y + z \cdot t\right) + a \cdot b \]
   associate-+l+ [=>] 100.0	\[ \color{blue}{x \cdot y + \left(z \cdot t + a \cdot b\right)} \]
   fma-def [=>] 100.0	\[ \color{blue}{\mathsf{fma}\left(x, y, z \cdot t + a \cdot b\right)} \]
   fma-def [=>] 100.0	\[ \mathsf{fma}\left(x, y, \color{blue}{\mathsf{fma}\left(z, t, a \cdot b\right)}\right) \]

3. Final simplification 100.0%

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

Alternatives

Alternative 1
Accuracy	51.1%
Cost	1492

\[\begin{array}{l} \mathbf{if}\;a \cdot b \leq -2.25 \cdot 10^{+106}:\\ \;\;\;\;a \cdot b\\ \mathbf{elif}\;a \cdot b \leq -3.9 \cdot 10^{-44}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;a \cdot b \leq -3.15 \cdot 10^{-158}:\\ \;\;\;\;z \cdot t\\ \mathbf{elif}\;a \cdot b \leq 1.75 \cdot 10^{-289}:\\ \;\;\;\;x \cdot y\\ \mathbf{elif}\;a \cdot b \leq 1.55 \cdot 10^{-60}:\\ \;\;\;\;z \cdot t\\ \mathbf{else}:\\ \;\;\;\;a \cdot b\\ \end{array} \]

Alternative 2
Accuracy	62.9%
Cost	1243

\[\begin{array}{l} \mathbf{if}\;x \leq -1.85 \cdot 10^{+238} \lor \neg \left(x \leq -4.5 \cdot 10^{+210}\right) \land \left(x \leq -7.4 \cdot 10^{+153} \lor \neg \left(x \leq -1.02 \cdot 10^{+90} \lor \neg \left(x \leq -1.6 \cdot 10^{+78}\right) \land x \leq 2.3 \cdot 10^{-159}\right)\right):\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;a \cdot b + z \cdot t\\ \end{array} \]

Alternative 3
Accuracy	75.2%
Cost	978

\[\begin{array}{l} \mathbf{if}\;y \leq -1.7 \cdot 10^{-209} \lor \neg \left(y \leq 1.35 \cdot 10^{-71}\right) \land \left(y \leq 1.9 \cdot 10^{+48} \lor \neg \left(y \leq 1.42 \cdot 10^{+75}\right)\right):\\ \;\;\;\;a \cdot b + x \cdot y\\ \mathbf{else}:\\ \;\;\;\;a \cdot b + z \cdot t\\ \end{array} \]

Alternative 4
Accuracy	85.1%
Cost	968

\[\begin{array}{l} \mathbf{if}\;a \cdot b \leq -5 \cdot 10^{+69}:\\ \;\;\;\;a \cdot b + x \cdot y\\ \mathbf{elif}\;a \cdot b \leq 3.7:\\ \;\;\;\;x \cdot y + z \cdot t\\ \mathbf{else}:\\ \;\;\;\;a \cdot b + z \cdot t\\ \end{array} \]

Alternative 5
Accuracy	51.9%
Cost	712

\[\begin{array}{l} \mathbf{if}\;a \cdot b \leq -4.9 \cdot 10^{+68}:\\ \;\;\;\;a \cdot b\\ \mathbf{elif}\;a \cdot b \leq 3.5 \cdot 10^{-62}:\\ \;\;\;\;z \cdot t\\ \mathbf{else}:\\ \;\;\;\;a \cdot b\\ \end{array} \]

Alternative 6
Accuracy	100.0%
Cost	704

\[a \cdot b + \left(x \cdot y + z \cdot t\right) \]

Alternative 7
Accuracy	34.1%
Cost	192

\[a \cdot b \]

Reproduce

herbie shell --seed 2023133 
(FPCore (x y z t a b)
  :name "Linear.V3:$cdot from linear-1.19.1.3, B"
  :precision binary64
  (+ (+ (* x y) (* z t)) (* a b)))