Diagrams.Solve.Polynomial:quartForm from diagrams-solve-0.1, B

Average Accuracy: 100.0% → 100.0%

Time: 10.3s

Precision: binary64

Cost: 13248

Math

\[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]

↓

\[\mathsf{fma}\left(z, -0.5 \cdot y, \mathsf{fma}\left(0.125, x, t\right)\right) \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))

↓

(FPCore (x y z t) :precision binary64 (fma z (* -0.5 y) (fma 0.125 x t)))

C

double code(double x, double y, double z, double t) {
	return (((1.0 / 8.0) * x) - ((y * z) / 2.0)) + t;
}

↓

double code(double x, double y, double z, double t) {
	return fma(z, (-0.5 * y), fma(0.125, x, t));
}

Julia

function code(x, y, z, t)
	return Float64(Float64(Float64(Float64(1.0 / 8.0) * x) - Float64(Float64(y * z) / 2.0)) + t)
end

↓

function code(x, y, z, t)
	return fma(z, Float64(-0.5 * y), fma(0.125, x, t))
end

Wolfram

code[x_, y_, z_, t_] := N[(N[(N[(N[(1.0 / 8.0), $MachinePrecision] * x), $MachinePrecision] - N[(N[(y * z), $MachinePrecision] / 2.0), $MachinePrecision]), $MachinePrecision] + t), $MachinePrecision]

↓

code[x_, y_, z_, t_] := N[(z * N[(-0.5 * y), $MachinePrecision] + N[(0.125 * x + t), $MachinePrecision]), $MachinePrecision]

Target

Original	100.0%
Target	100.0%
Herbie	100.0%

\[\left(\frac{x}{8} + t\right) - \frac{z}{2} \cdot y \]

Derivation

1. Initial program 100.0%

   \[\left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]

2. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(z, -0.5 \cdot y, \mathsf{fma}\left(0.125, x, t\right)\right)} \]
   Proof

   [Start] 100.0	\[ \left(\frac{1}{8} \cdot x - \frac{y \cdot z}{2}\right) + t \]
   sub-neg [=>] 100.0	\[ \color{blue}{\left(\frac{1}{8} \cdot x + \left(-\frac{y \cdot z}{2}\right)\right)} + t \]
   +-commutative [=>] 100.0	\[ \color{blue}{\left(\left(-\frac{y \cdot z}{2}\right) + \frac{1}{8} \cdot x\right)} + t \]
   associate-+l+ [=>] 100.0	\[ \color{blue}{\left(-\frac{y \cdot z}{2}\right) + \left(\frac{1}{8} \cdot x + t\right)} \]
   neg-mul-1 [=>] 100.0	\[ \color{blue}{-1 \cdot \frac{y \cdot z}{2}} + \left(\frac{1}{8} \cdot x + t\right) \]
   associate-*l/ [<=] 100.0	\[ -1 \cdot \color{blue}{\left(\frac{y}{2} \cdot z\right)} + \left(\frac{1}{8} \cdot x + t\right) \]
   associate-*r* [=>] 100.0	\[ \color{blue}{\left(-1 \cdot \frac{y}{2}\right) \cdot z} + \left(\frac{1}{8} \cdot x + t\right) \]
   *-commutative [=>] 100.0	\[ \color{blue}{z \cdot \left(-1 \cdot \frac{y}{2}\right)} + \left(\frac{1}{8} \cdot x + t\right) \]
   +-commutative [<=] 100.0	\[ z \cdot \left(-1 \cdot \frac{y}{2}\right) + \color{blue}{\left(t + \frac{1}{8} \cdot x\right)} \]
   fma-def [=>] 100.0	\[ \color{blue}{\mathsf{fma}\left(z, -1 \cdot \frac{y}{2}, t + \frac{1}{8} \cdot x\right)} \]
   associate-*r/ [=>] 100.0	\[ \mathsf{fma}\left(z, \color{blue}{\frac{-1 \cdot y}{2}}, t + \frac{1}{8} \cdot x\right) \]
   associate-/l* [=>] 99.9	\[ \mathsf{fma}\left(z, \color{blue}{\frac{-1}{\frac{2}{y}}}, t + \frac{1}{8} \cdot x\right) \]
   associate-/r/ [=>] 100.0	\[ \mathsf{fma}\left(z, \color{blue}{\frac{-1}{2} \cdot y}, t + \frac{1}{8} \cdot x\right) \]
   metadata-eval [=>] 100.0	\[ \mathsf{fma}\left(z, \color{blue}{-0.5} \cdot y, t + \frac{1}{8} \cdot x\right) \]
   +-commutative [=>] 100.0	\[ \mathsf{fma}\left(z, -0.5 \cdot y, \color{blue}{\frac{1}{8} \cdot x + t}\right) \]
   fma-def [=>] 100.0	\[ \mathsf{fma}\left(z, -0.5 \cdot y, \color{blue}{\mathsf{fma}\left(\frac{1}{8}, x, t\right)}\right) \]
   metadata-eval [=>] 100.0	\[ \mathsf{fma}\left(z, -0.5 \cdot y, \mathsf{fma}\left(\color{blue}{0.125}, x, t\right)\right) \]

3. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(z, -0.5 \cdot y, \mathsf{fma}\left(0.125, x, t\right)\right) \]

Alternatives

Alternative 1
Accuracy	54.8%
Cost	1380

\[\begin{array}{l} t_1 := -0.5 \cdot \left(z \cdot y\right)\\ \mathbf{if}\;x \leq -3.2 \cdot 10^{+47}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;x \leq -1.35 \cdot 10^{-13}:\\ \;\;\;\;t\\ \mathbf{elif}\;x \leq -1.35 \cdot 10^{-24}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{elif}\;x \leq -5.8 \cdot 10^{-75}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -2.1 \cdot 10^{-104}:\\ \;\;\;\;t\\ \mathbf{elif}\;x \leq -4.2 \cdot 10^{-137}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq -1.75 \cdot 10^{-197}:\\ \;\;\;\;t\\ \mathbf{elif}\;x \leq -1.25 \cdot 10^{-246}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 5000000000:\\ \;\;\;\;t\\ \mathbf{else}:\\ \;\;\;\;0.125 \cdot x\\ \end{array} \]

Alternative 2
Accuracy	86.0%
Cost	968

\[\begin{array}{l} t_1 := -0.5 \cdot \left(z \cdot y\right)\\ \mathbf{if}\;z \cdot y \leq -3.6 \cdot 10^{+85}:\\ \;\;\;\;0.125 \cdot x + t_1\\ \mathbf{elif}\;z \cdot y \leq 1.55 \cdot 10^{+94}:\\ \;\;\;\;t + 0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t + t_1\\ \end{array} \]

Alternative 3
Accuracy	81.4%
Cost	841

\[\begin{array}{l} \mathbf{if}\;z \cdot y \leq -2.4 \cdot 10^{+82} \lor \neg \left(z \cdot y \leq 2.3 \cdot 10^{+110}\right):\\ \;\;\;\;-0.5 \cdot \left(z \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t + 0.125 \cdot x\\ \end{array} \]

Alternative 4
Accuracy	86.9%
Cost	713

\[\begin{array}{l} \mathbf{if}\;x \leq -4 \cdot 10^{+42} \lor \neg \left(x \leq 2.7 \cdot 10^{+26}\right):\\ \;\;\;\;t + 0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t + -0.5 \cdot \left(z \cdot y\right)\\ \end{array} \]

Alternative 5
Accuracy	100.0%
Cost	704

\[t + \left(0.125 \cdot x - z \cdot \frac{y}{2}\right) \]

Alternative 6
Accuracy	56.5%
Cost	456

\[\begin{array}{l} \mathbf{if}\;t \leq -1.65 \cdot 10^{+16}:\\ \;\;\;\;t\\ \mathbf{elif}\;t \leq 3.1 \cdot 10^{+85}:\\ \;\;\;\;0.125 \cdot x\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]

Alternative 7
Accuracy	36.9%
Cost	64

\[t \]

Reproduce

herbie shell --seed 2023138 
(FPCore (x y z t)
  :name "Diagrams.Solve.Polynomial:quartForm  from diagrams-solve-0.1, B"
  :precision binary64

  :herbie-target
  (- (+ (/ x 8.0) t) (* (/ z 2.0) y))

  (+ (- (* (/ 1.0 8.0) x) (/ (* y z) 2.0)) t))