Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, D

Average Accuracy: 99.8% → 99.9%

Time: 5.5s

Precision: binary64

Cost: 6848

• 24.9% of points produce a very large (infinite) output. You may want to add a precondition.

Math

\[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

↓

\[x \cdot \mathsf{fma}\left(x, 9, -12\right) + 3 \]

FPCore

(FPCore (x) :precision binary64 (* 3.0 (+ (- (* (* x 3.0) x) (* x 4.0)) 1.0)))

↓

(FPCore (x) :precision binary64 (+ (* x (fma x 9.0 -12.0)) 3.0))

C

double code(double x) {
	return 3.0 * ((((x * 3.0) * x) - (x * 4.0)) + 1.0);
}

↓

double code(double x) {
	return (x * fma(x, 9.0, -12.0)) + 3.0;
}

Julia

function code(x)
	return Float64(3.0 * Float64(Float64(Float64(Float64(x * 3.0) * x) - Float64(x * 4.0)) + 1.0))
end

↓

function code(x)
	return Float64(Float64(x * fma(x, 9.0, -12.0)) + 3.0)
end

Wolfram

code[x_] := N[(3.0 * N[(N[(N[(N[(x * 3.0), $MachinePrecision] * x), $MachinePrecision] - N[(x * 4.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

↓

code[x_] := N[(N[(x * N[(x * 9.0 + -12.0), $MachinePrecision]), $MachinePrecision] + 3.0), $MachinePrecision]

Target

Original	99.8%
Target	99.9%
Herbie	99.9%

\[3 + \left(\left(9 \cdot x\right) \cdot x - 12 \cdot x\right) \]

Derivation

1. Initial program 99.8%

   \[3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]

2. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, -12\right), 3\right)} \]
   Proof

   [Start] 99.8	\[ 3 \cdot \left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \]
   *-commutative [=>] 99.8	\[ \color{blue}{\left(\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) + 1\right) \cdot 3} \]
   distribute-lft1-in [<=] 99.9	\[ \color{blue}{\left(\left(x \cdot 3\right) \cdot x - x \cdot 4\right) \cdot 3 + 3} \]
   *-commutative [=>] 99.9	\[ \left(\color{blue}{x \cdot \left(x \cdot 3\right)} - x \cdot 4\right) \cdot 3 + 3 \]
   distribute-lft-out-- [=>] 99.9	\[ \color{blue}{\left(x \cdot \left(x \cdot 3 - 4\right)\right)} \cdot 3 + 3 \]
   associate-*l* [=>] 99.9	\[ \color{blue}{x \cdot \left(\left(x \cdot 3 - 4\right) \cdot 3\right)} + 3 \]
   fma-def [=>] 99.9	\[ \color{blue}{\mathsf{fma}\left(x, \left(x \cdot 3 - 4\right) \cdot 3, 3\right)} \]
   *-commutative [=>] 99.9	\[ \mathsf{fma}\left(x, \color{blue}{3 \cdot \left(x \cdot 3 - 4\right)}, 3\right) \]
   sub-neg [=>] 99.9	\[ \mathsf{fma}\left(x, 3 \cdot \color{blue}{\left(x \cdot 3 + \left(-4\right)\right)}, 3\right) \]
   distribute-lft-in [=>] 99.9	\[ \mathsf{fma}\left(x, \color{blue}{3 \cdot \left(x \cdot 3\right) + 3 \cdot \left(-4\right)}, 3\right) \]
   *-commutative [<=] 99.9	\[ \mathsf{fma}\left(x, \color{blue}{\left(x \cdot 3\right) \cdot 3} + 3 \cdot \left(-4\right), 3\right) \]
   associate-*l* [=>] 99.9	\[ \mathsf{fma}\left(x, \color{blue}{x \cdot \left(3 \cdot 3\right)} + 3 \cdot \left(-4\right), 3\right) \]
   fma-def [=>] 99.9	\[ \mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(x, 3 \cdot 3, 3 \cdot \left(-4\right)\right)}, 3\right) \]
   metadata-eval [=>] 99.9	\[ \mathsf{fma}\left(x, \mathsf{fma}\left(x, \color{blue}{9}, 3 \cdot \left(-4\right)\right), 3\right) \]
   metadata-eval [=>] 99.9	\[ \mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, 3 \cdot \color{blue}{-4}\right), 3\right) \]
   metadata-eval [=>] 99.9	\[ \mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, \color{blue}{-12}\right), 3\right) \]

3. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{x \cdot \mathsf{fma}\left(x, 9, -12\right) + 3} \]
   Proof

   [Start] 99.9	\[ \mathsf{fma}\left(x, \mathsf{fma}\left(x, 9, -12\right), 3\right) \]
   fma-udef [=>] 99.9	\[ \color{blue}{x \cdot \mathsf{fma}\left(x, 9, -12\right) + 3} \]

4. Final simplification 99.9%

   \[\leadsto x \cdot \mathsf{fma}\left(x, 9, -12\right) + 3 \]

Alternatives

Alternative 1
Accuracy	99.8%
Cost	832

\[3 \cdot \left(\left(x \cdot \left(x \cdot 3\right) - x \cdot 4\right) + 1\right) \]

Alternative 2
Accuracy	98.9%
Cost	713

\[\begin{array}{l} \mathbf{if}\;x \leq -0.58 \lor \neg \left(x \leq 0.58\right):\\ \;\;\;\;x \cdot \left(-12 + x \cdot 9\right)\\ \mathbf{else}:\\ \;\;\;\;3 + x \cdot -12\\ \end{array} \]

Alternative 3
Accuracy	99.9%
Cost	704

\[3 + \left(x \cdot -12 + x \cdot \left(x \cdot 9\right)\right) \]

Alternative 4
Accuracy	97.7%
Cost	585

\[\begin{array}{l} \mathbf{if}\;x \leq -0.58 \lor \neg \left(x \leq 0.2\right):\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;3\\ \end{array} \]

Alternative 5
Accuracy	97.7%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x \leq -0.58:\\ \;\;\;\;x \cdot \left(x \cdot 9\right)\\ \mathbf{elif}\;x \leq 0.2:\\ \;\;\;\;3\\ \mathbf{else}:\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \end{array} \]

Alternative 6
Accuracy	98.3%
Cost	584

\[\begin{array}{l} \mathbf{if}\;x \leq -1.55:\\ \;\;\;\;x \cdot \left(x \cdot 9\right)\\ \mathbf{elif}\;x \leq 1:\\ \;\;\;\;3 + x \cdot -12\\ \mathbf{else}:\\ \;\;\;\;9 \cdot \left(x \cdot x\right)\\ \end{array} \]

Alternative 7
Accuracy	50.6%
Cost	64

\[3 \]

Reproduce

herbie shell --seed 2023160 
(FPCore (x)
  :name "Diagrams.Tangent:$catParam from diagrams-lib-1.3.0.3, D"
  :precision binary64

  :herbie-target
  (+ 3.0 (- (* (* 9.0 x) x) (* 12.0 x)))

  (* 3.0 (+ (- (* (* x 3.0) x) (* x 4.0)) 1.0)))