Numeric.Integration.TanhSinh:everywhere from integration-0.2.1

Average Accuracy: 68.3% → 74.1%

Time: 6.2s

Precision: binary64

Cost: 6985

Math

\[x \cdot \left(1 + y \cdot y\right) \]

↓

\[\begin{array}{l} \mathbf{if}\;y \leq -8 \cdot 10^{+59} \lor \neg \left(y \leq 13200000000\right):\\ \;\;\;\;y \cdot \left(y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x, y \cdot y, x\right)\\ \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (+ 1.0 (* y y))))

↓

(FPCore (x y)
 :precision binary64
 (if (or (<= y -8e+59) (not (<= y 13200000000.0)))
   (* y (* y x))
   (fma x (* y y) x)))

C

double code(double x, double y) {
	return x * (1.0 + (y * y));
}

↓

double code(double x, double y) {
	double tmp;
	if ((y <= -8e+59) || !(y <= 13200000000.0)) {
		tmp = y * (y * x);
	} else {
		tmp = fma(x, (y * y), x);
	}
	return tmp;
}

Julia

function code(x, y)
	return Float64(x * Float64(1.0 + Float64(y * y)))
end

↓

function code(x, y)
	tmp = 0.0
	if ((y <= -8e+59) || !(y <= 13200000000.0))
		tmp = Float64(y * Float64(y * x));
	else
		tmp = fma(x, Float64(y * y), x);
	end
	return tmp
end

Wolfram

code[x_, y_] := N[(x * N[(1.0 + N[(y * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_] := If[Or[LessEqual[y, -8e+59], N[Not[LessEqual[y, 13200000000.0]], $MachinePrecision]], N[(y * N[(y * x), $MachinePrecision]), $MachinePrecision], N[(x * N[(y * y), $MachinePrecision] + x), $MachinePrecision]]

Target

Original	68.3%
Target	74.1%
Herbie	74.1%

\[x + \left(x \cdot y\right) \cdot y \]

Derivation

1. Split input into 2 regimes

2. if y < -7.99999999999999977e59 or 1.32e10 < y

   1. Initial program 28.5%

      \[x \cdot \left(1 + y \cdot y\right) \]

   2. Taylor expanded in y around inf 28.5%

      \[\leadsto \color{blue}{{y}^{2} \cdot x} \]

   3. Simplified 42.5%

      \[\leadsto \color{blue}{y \cdot \left(y \cdot x\right)} \]
      Step-by-step derivation

      [Start] 28.5	\[ {y}^{2} \cdot x \]
      unpow2 [=>] 28.5	\[ \color{blue}{\left(y \cdot y\right)} \cdot x \]
      associate-*r* [<=] 42.5	\[ \color{blue}{y \cdot \left(y \cdot x\right)} \]

   if -7.99999999999999977e59 < y < 1.32e10

   1. Initial program 100.0%

      \[x \cdot \left(1 + y \cdot y\right) \]

   2. Simplified 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, y \cdot y, x\right)} \]
      Step-by-step derivation

      [Start] 100.0	\[ x \cdot \left(1 + y \cdot y\right) \]
      +-commutative [=>] 100.0	\[ x \cdot \color{blue}{\left(y \cdot y + 1\right)} \]
      distribute-lft-in [=>] 100.0	\[ \color{blue}{x \cdot \left(y \cdot y\right) + x \cdot 1} \]
      *-rgt-identity [=>] 100.0	\[ x \cdot \left(y \cdot y\right) + \color{blue}{x} \]
      fma-def [=>] 100.0	\[ \color{blue}{\mathsf{fma}\left(x, y \cdot y, x\right)} \]

3. Recombined 2 regimes into one program.

4. Final simplification 75.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -8 \cdot 10^{+59} \lor \neg \left(y \leq 13200000000\right):\\ \;\;\;\;y \cdot \left(y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x, y \cdot y, x\right)\\ \end{array} \]

Alternatives

Alternative 1
Accuracy	74.1%
Cost	713

\[\begin{array}{l} \mathbf{if}\;y \leq -1.15 \cdot 10^{+60} \lor \neg \left(y \leq 20000000000\right):\\ \;\;\;\;y \cdot \left(y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(y \cdot y + 1\right)\\ \end{array} \]

Alternative 2
Accuracy	74.1%
Cost	713

\[\begin{array}{l} \mathbf{if}\;y \leq -1.1 \cdot 10^{+60} \lor \neg \left(y \leq 122000000\right):\\ \;\;\;\;y \cdot \left(y \cdot x\right)\\ \mathbf{else}:\\ \;\;\;\;x + x \cdot \left(y \cdot y\right)\\ \end{array} \]

Alternative 3
Accuracy	73.1%
Cost	580

\[\begin{array}{l} \mathbf{if}\;y \cdot y \leq 0.001:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(y \cdot x\right)\\ \end{array} \]

Alternative 4
Accuracy	50.8%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023157 
(FPCore (x y)
  :name "Numeric.Integration.TanhSinh:everywhere from integration-0.2.1"
  :precision binary64

  :herbie-target
  (+ x (* (* x y) y))

  (* x (+ 1.0 (* y y))))