Asymptote A

Average Accuracy: 77.3% → 99.9%

Time: 6.0s

Precision: binary64

Cost: 576

Math

\[\frac{1}{x + 1} - \frac{1}{x - 1} \]

↓

\[\frac{\frac{2}{x + 1}}{1 - x} \]

FPCore

(FPCore (x) :precision binary64 (- (/ 1.0 (+ x 1.0)) (/ 1.0 (- x 1.0))))

↓

(FPCore (x) :precision binary64 (/ (/ 2.0 (+ x 1.0)) (- 1.0 x)))

C

double code(double x) {
	return (1.0 / (x + 1.0)) - (1.0 / (x - 1.0));
}

↓

double code(double x) {
	return (2.0 / (x + 1.0)) / (1.0 - x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 / (x + 1.0d0)) - (1.0d0 / (x - 1.0d0))
end function

↓

real(8) function code(x)
    real(8), intent (in) :: x
    code = (2.0d0 / (x + 1.0d0)) / (1.0d0 - x)
end function

Java

public static double code(double x) {
	return (1.0 / (x + 1.0)) - (1.0 / (x - 1.0));
}

↓

public static double code(double x) {
	return (2.0 / (x + 1.0)) / (1.0 - x);
}

Python

def code(x):
	return (1.0 / (x + 1.0)) - (1.0 / (x - 1.0))

↓

def code(x):
	return (2.0 / (x + 1.0)) / (1.0 - x)

Julia

function code(x)
	return Float64(Float64(1.0 / Float64(x + 1.0)) - Float64(1.0 / Float64(x - 1.0)))
end

↓

function code(x)
	return Float64(Float64(2.0 / Float64(x + 1.0)) / Float64(1.0 - x))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / (x + 1.0)) - (1.0 / (x - 1.0));
end

↓

function tmp = code(x)
	tmp = (2.0 / (x + 1.0)) / (1.0 - x);
end

Wolfram

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

↓

code[x_] := N[(N[(2.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 77.3%

   \[\frac{1}{x + 1} - \frac{1}{x - 1} \]

2. Applied egg-rr 78.4%

   \[\leadsto \color{blue}{\frac{\frac{x + \left(-2 - x\right)}{-1 - x}}{1 - x}} \]

3. Applied egg-rr 99.9%

   \[\leadsto \frac{\color{blue}{\left(2 + \left(x - x\right)\right) \cdot \frac{1}{x + 1}}}{1 - x} \]

4. Simplified 99.9%

   \[\leadsto \frac{\color{blue}{\frac{2}{x + 1}}}{1 - x} \]
   Proof

   [Start] 99.9	\[ \frac{\left(2 + \left(x - x\right)\right) \cdot \frac{1}{x + 1}}{1 - x} \]
   associate-*r/ [=>] 99.9	\[ \frac{\color{blue}{\frac{\left(2 + \left(x - x\right)\right) \cdot 1}{x + 1}}}{1 - x} \]
   +-commutative [=>] 99.9	\[ \frac{\frac{\color{blue}{\left(\left(x - x\right) + 2\right)} \cdot 1}{x + 1}}{1 - x} \]
   +-inverses [=>] 99.9	\[ \frac{\frac{\left(\color{blue}{0} + 2\right) \cdot 1}{x + 1}}{1 - x} \]
   metadata-eval [=>] 99.9	\[ \frac{\frac{\color{blue}{2} \cdot 1}{x + 1}}{1 - x} \]
   metadata-eval [=>] 99.9	\[ \frac{\frac{\color{blue}{2}}{x + 1}}{1 - x} \]

5. Final simplification 99.9%

   \[\leadsto \frac{\frac{2}{x + 1}}{1 - x} \]

Alternatives

Alternative 1
Accuracy	98.9%
Cost	841

\[\begin{array}{l} \mathbf{if}\;x \leq -1.55 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{\frac{-2}{x}}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{1}{x + 1} + \left(x + 1\right)\\ \end{array} \]

Alternative 2
Accuracy	98.4%
Cost	585

\[\begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{-2}{x \cdot x}\\ \mathbf{else}:\\ \;\;\;\;2 + x \cdot x\\ \end{array} \]

Alternative 3
Accuracy	98.9%
Cost	585

\[\begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 1\right):\\ \;\;\;\;\frac{\frac{-2}{x}}{x}\\ \mathbf{else}:\\ \;\;\;\;2 + x \cdot x\\ \end{array} \]

Alternative 4
Accuracy	99.4%
Cost	576

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

Alternative 5
Accuracy	99.9%
Cost	576

\[\frac{\frac{-2}{1 - x}}{-1 - x} \]

Alternative 6
Accuracy	10.7%
Cost	64

\[1 \]

Alternative 7
Accuracy	50.8%
Cost	64

\[2 \]

Reproduce

herbie shell --seed 2023129 
(FPCore (x)
  :name "Asymptote A"
  :precision binary64
  (- (/ 1.0 (+ x 1.0)) (/ 1.0 (- x 1.0))))