subtraction fraction

Average Accuracy: 100.0% → 100.0%

Time: 6.0s

Precision: binary64

Cost: 448

Math

\[\frac{-\left(f + n\right)}{f - n} \]

↓

\[\frac{f + n}{n - f} \]

FPCore

(FPCore (f n) :precision binary64 (/ (- (+ f n)) (- f n)))

↓

(FPCore (f n) :precision binary64 (/ (+ f n) (- n f)))

C

double code(double f, double n) {
	return -(f + n) / (f - n);
}

↓

double code(double f, double n) {
	return (f + n) / (n - f);
}

Fortran

real(8) function code(f, n)
    real(8), intent (in) :: f
    real(8), intent (in) :: n
    code = -(f + n) / (f - n)
end function

↓

real(8) function code(f, n)
    real(8), intent (in) :: f
    real(8), intent (in) :: n
    code = (f + n) / (n - f)
end function

Java

public static double code(double f, double n) {
	return -(f + n) / (f - n);
}

↓

public static double code(double f, double n) {
	return (f + n) / (n - f);
}

Python

def code(f, n):
	return -(f + n) / (f - n)

↓

def code(f, n):
	return (f + n) / (n - f)

Julia

function code(f, n)
	return Float64(Float64(-Float64(f + n)) / Float64(f - n))
end

↓

function code(f, n)
	return Float64(Float64(f + n) / Float64(n - f))
end

MATLAB

function tmp = code(f, n)
	tmp = -(f + n) / (f - n);
end

↓

function tmp = code(f, n)
	tmp = (f + n) / (n - f);
end

Wolfram

code[f_, n_] := N[((-N[(f + n), $MachinePrecision]) / N[(f - n), $MachinePrecision]), $MachinePrecision]

↓

code[f_, n_] := N[(N[(f + n), $MachinePrecision] / N[(n - f), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[\frac{-\left(f + n\right)}{f - n} \]

2. Simplified 100.0%

   \[\leadsto \color{blue}{\frac{f + n}{n - f}} \]
   Proof

   [Start] 100.0	\[ \frac{-\left(f + n\right)}{f - n} \]
   sub-neg [=>] 100.0	\[ \frac{-\left(f + n\right)}{\color{blue}{f + \left(-n\right)}} \]
   +-commutative [=>] 100.0	\[ \frac{-\left(f + n\right)}{\color{blue}{\left(-n\right) + f}} \]
   neg-sub0 [=>] 100.0	\[ \frac{-\left(f + n\right)}{\color{blue}{\left(0 - n\right)} + f} \]
   associate-+l- [=>] 100.0	\[ \frac{-\left(f + n\right)}{\color{blue}{0 - \left(n - f\right)}} \]
   sub0-neg [=>] 100.0	\[ \frac{-\left(f + n\right)}{\color{blue}{-\left(n - f\right)}} \]
   neg-mul-1 [=>] 100.0	\[ \frac{-\left(f + n\right)}{\color{blue}{-1 \cdot \left(n - f\right)}} \]
   associate-/r* [=>] 100.0	\[ \color{blue}{\frac{\frac{-\left(f + n\right)}{-1}}{n - f}} \]
   neg-mul-1 [=>] 100.0	\[ \frac{\frac{\color{blue}{-1 \cdot \left(f + n\right)}}{-1}}{n - f} \]
   *-commutative [=>] 100.0	\[ \frac{\frac{\color{blue}{\left(f + n\right) \cdot -1}}{-1}}{n - f} \]
   associate-/l* [=>] 100.0	\[ \frac{\color{blue}{\frac{f + n}{\frac{-1}{-1}}}}{n - f} \]
   metadata-eval [=>] 100.0	\[ \frac{\frac{f + n}{\color{blue}{1}}}{n - f} \]
   /-rgt-identity [=>] 100.0	\[ \frac{\color{blue}{f + n}}{n - f} \]

3. Final simplification 100.0%

   \[\leadsto \frac{f + n}{n - f} \]

Alternatives

Alternative 1
Accuracy	73.2%
Cost	976

\[\begin{array}{l} \mathbf{if}\;f \leq -5 \cdot 10^{-13}:\\ \;\;\;\;-1\\ \mathbf{elif}\;f \leq -1.36 \cdot 10^{-42}:\\ \;\;\;\;1\\ \mathbf{elif}\;f \leq -5.2 \cdot 10^{-101}:\\ \;\;\;\;-1\\ \mathbf{elif}\;f \leq 2.2 \cdot 10^{+20}:\\ \;\;\;\;1 + 2 \cdot \frac{f}{n}\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 2
Accuracy	73.6%
Cost	976

\[\begin{array}{l} t_0 := -2 \cdot \frac{n}{f} + -1\\ \mathbf{if}\;f \leq -7.5:\\ \;\;\;\;t_0\\ \mathbf{elif}\;f \leq -9 \cdot 10^{-47}:\\ \;\;\;\;1\\ \mathbf{elif}\;f \leq -5.2 \cdot 10^{-101}:\\ \;\;\;\;-1\\ \mathbf{elif}\;f \leq 2:\\ \;\;\;\;1 + 2 \cdot \frac{f}{n}\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 3
Accuracy	72.5%
Cost	592

\[\begin{array}{l} \mathbf{if}\;f \leq -5 \cdot 10^{-14}:\\ \;\;\;\;-1\\ \mathbf{elif}\;f \leq -6 \cdot 10^{-47}:\\ \;\;\;\;1\\ \mathbf{elif}\;f \leq -5.2 \cdot 10^{-101}:\\ \;\;\;\;-1\\ \mathbf{elif}\;f \leq 0.05:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-1\\ \end{array} \]

Alternative 4
Accuracy	48.6%
Cost	64

\[-1 \]

Reproduce

herbie shell --seed 2023125 
(FPCore (f n)
  :name "subtraction fraction"
  :precision binary64
  (/ (- (+ f n)) (- f n)))