Asymptote C

Percentage Accurate: 53.5% → 99.8%

Time: 6.3s

Alternatives: 8

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \frac{x}{x + 1} - \frac{x + 1}{x - 1} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 53.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x}{x + 1} - \frac{x + 1}{x - 1} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{\mathsf{fma}\left(-3, x, -1\right)}{1 + x}}{x - 1} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (/ (fma -3.0 x -1.0) (+ 1.0 x)) (- x 1.0)))

C

double code(double x) {
	return (fma(-3.0, x, -1.0) / (1.0 + x)) / (x - 1.0);
}

Julia

function code(x)
	return Float64(Float64(fma(-3.0, x, -1.0) / Float64(1.0 + x)) / Float64(x - 1.0))
end

Wolfram

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

Derivation

1. Initial program 59.1%

   \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

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

   2. sub-neg N/A

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

   3. +-commutative N/A

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

   4. lift-/.f64 N/A

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

   5. distribute-neg-frac N/A

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

   6. lift-/.f64 N/A

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

   7. frac-add N/A

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

   8. *-commutative N/A

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

   9. lift-+.f64 N/A

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

   10. lift--.f64 N/A

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

   11. difference-of-sqr-1 N/A

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

   12. metadata-eval N/A

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

   13. lower-/.f64 N/A

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

4. Applied rewrites 57.5%

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

5. Taylor expanded in x around 0

   \[\leadsto \frac{\color{blue}{-3 \cdot x - 1}}{\mathsf{fma}\left(x, x, -1\right)} \]
6. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto \frac{\color{blue}{-3 \cdot x + \left(\mathsf{neg}\left(1\right)\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]

   2. metadata-eval N/A

      \[\leadsto \frac{-3 \cdot x + \color{blue}{-1}}{\mathsf{fma}\left(x, x, -1\right)} \]

   3. lower-fma.f64 77.7

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(-3, x, -1\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]

7. Applied rewrites 77.7%

   \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(-3, x, -1\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]
8. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(-3, x, -1\right)}{\mathsf{fma}\left(x, x, -1\right)}} \]

   2. lift-fma.f64 N/A

      \[\leadsto \frac{\mathsf{fma}\left(-3, x, -1\right)}{\color{blue}{x \cdot x + -1}} \]

   3. difference-of-sqr--1 N/A

      \[\leadsto \frac{\mathsf{fma}\left(-3, x, -1\right)}{\color{blue}{\left(x + 1\right) \cdot \left(x - 1\right)}} \]

   4. associate-/r* N/A

      \[\leadsto \color{blue}{\frac{\frac{\mathsf{fma}\left(-3, x, -1\right)}{x + 1}}{x - 1}} \]

   5. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{\frac{\mathsf{fma}\left(-3, x, -1\right)}{x + 1}}{x - 1}} \]

   6. lower-/.f64 N/A

      \[\leadsto \frac{\color{blue}{\frac{\mathsf{fma}\left(-3, x, -1\right)}{x + 1}}}{x - 1} \]

   7. +-commutative N/A

      \[\leadsto \frac{\frac{\mathsf{fma}\left(-3, x, -1\right)}{\color{blue}{1 + x}}}{x - 1} \]

   8. lower-+.f64 N/A

      \[\leadsto \frac{\frac{\mathsf{fma}\left(-3, x, -1\right)}{\color{blue}{1 + x}}}{x - 1} \]

   9. lift--.f64 99.5

      \[\leadsto \frac{\frac{\mathsf{fma}\left(-3, x, -1\right)}{1 + x}}{\color{blue}{x - 1}} \]

9. Applied rewrites 99.5%

   \[\leadsto \color{blue}{\frac{\frac{\mathsf{fma}\left(-3, x, -1\right)}{1 + x}}{x - 1}} \]
10. Add Preprocessing

Alternative 2: 99.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0:\\ \;\;\;\;\frac{\frac{-1}{x} - 3}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-3, x, -1\right)}{\mathsf{fma}\left(x, x, -1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (- (/ x (+ 1.0 x)) (/ (+ 1.0 x) (- x 1.0))) 0.0)
   (/ (- (/ -1.0 x) 3.0) x)
   (/ (fma -3.0 x -1.0) (fma x x -1.0))))

C

double code(double x) {
	double tmp;
	if (((x / (1.0 + x)) - ((1.0 + x) / (x - 1.0))) <= 0.0) {
		tmp = ((-1.0 / x) - 3.0) / x;
	} else {
		tmp = fma(-3.0, x, -1.0) / fma(x, x, -1.0);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(x / Float64(1.0 + x)) - Float64(Float64(1.0 + x) / Float64(x - 1.0))) <= 0.0)
		tmp = Float64(Float64(Float64(-1.0 / x) - 3.0) / x);
	else
		tmp = Float64(fma(-3.0, x, -1.0) / fma(x, x, -1.0));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision] - N[(N[(1.0 + x), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], N[(N[(N[(-1.0 / x), $MachinePrecision] - 3.0), $MachinePrecision] / x), $MachinePrecision], N[(N[(-3.0 * x + -1.0), $MachinePrecision] / N[(x * x + -1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64)))) < 0.0

   1. Initial program 8.1%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{-1 \cdot \frac{3 + \frac{1}{x}}{x}} \]
   4. Step-by-step derivation

      1. associate-*r/ N/A

         \[\leadsto \color{blue}{\frac{-1 \cdot \left(3 + \frac{1}{x}\right)}{x}} \]

      2. lower-/.f64 N/A

         \[\leadsto \color{blue}{\frac{-1 \cdot \left(3 + \frac{1}{x}\right)}{x}} \]

      3. neg-mul-1 N/A

         \[\leadsto \frac{\color{blue}{\mathsf{neg}\left(\left(3 + \frac{1}{x}\right)\right)}}{x} \]

      4. +-commutative N/A

         \[\leadsto \frac{\mathsf{neg}\left(\color{blue}{\left(\frac{1}{x} + 3\right)}\right)}{x} \]

      5. distribute-neg-in N/A

         \[\leadsto \frac{\color{blue}{\left(\mathsf{neg}\left(\frac{1}{x}\right)\right) + \left(\mathsf{neg}\left(3\right)\right)}}{x} \]

      6. sub-neg N/A

         \[\leadsto \frac{\color{blue}{\left(\mathsf{neg}\left(\frac{1}{x}\right)\right) - 3}}{x} \]

      7. lower--.f64 N/A

         \[\leadsto \frac{\color{blue}{\left(\mathsf{neg}\left(\frac{1}{x}\right)\right) - 3}}{x} \]

      8. distribute-neg-frac N/A

         \[\leadsto \frac{\color{blue}{\frac{\mathsf{neg}\left(1\right)}{x}} - 3}{x} \]

      9. metadata-eval N/A

         \[\leadsto \frac{\frac{\color{blue}{-1}}{x} - 3}{x} \]

      10. lower-/.f64 98.6

          \[\leadsto \frac{\color{blue}{\frac{-1}{x}} - 3}{x} \]

   5. Applied rewrites 98.6%

      \[\leadsto \color{blue}{\frac{\frac{-1}{x} - 3}{x}} \]

   if 0.0 < (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64))))

   1. Initial program 98.8%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

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

      2. sub-neg N/A

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

      3. +-commutative N/A

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

      4. lift-/.f64 N/A

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

      5. distribute-neg-frac N/A

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

      6. lift-/.f64 N/A

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

      7. frac-add N/A

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

      8. *-commutative N/A

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

      9. lift-+.f64 N/A

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

      10. lift--.f64 N/A

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

      11. difference-of-sqr-1 N/A

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

      12. metadata-eval N/A

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

      13. lower-/.f64 N/A

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

   4. Applied rewrites 98.8%

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

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{-3 \cdot x - 1}}{\mathsf{fma}\left(x, x, -1\right)} \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \frac{\color{blue}{-3 \cdot x + \left(\mathsf{neg}\left(1\right)\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]

      2. metadata-eval N/A

         \[\leadsto \frac{-3 \cdot x + \color{blue}{-1}}{\mathsf{fma}\left(x, x, -1\right)} \]

      3. lower-fma.f64 100.0

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(-3, x, -1\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]

   7. Applied rewrites 100.0%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(-3, x, -1\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0:\\ \;\;\;\;\frac{\frac{-1}{x} - 3}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-3, x, -1\right)}{\mathsf{fma}\left(x, x, -1\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0:\\ \;\;\;\;\frac{-3}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-3, x, -1\right)}{\mathsf{fma}\left(x, x, -1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (- (/ x (+ 1.0 x)) (/ (+ 1.0 x) (- x 1.0))) 0.0)
   (/ -3.0 x)
   (/ (fma -3.0 x -1.0) (fma x x -1.0))))

C

double code(double x) {
	double tmp;
	if (((x / (1.0 + x)) - ((1.0 + x) / (x - 1.0))) <= 0.0) {
		tmp = -3.0 / x;
	} else {
		tmp = fma(-3.0, x, -1.0) / fma(x, x, -1.0);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(x / Float64(1.0 + x)) - Float64(Float64(1.0 + x) / Float64(x - 1.0))) <= 0.0)
		tmp = Float64(-3.0 / x);
	else
		tmp = Float64(fma(-3.0, x, -1.0) / fma(x, x, -1.0));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision] - N[(N[(1.0 + x), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.0], N[(-3.0 / x), $MachinePrecision], N[(N[(-3.0 * x + -1.0), $MachinePrecision] / N[(x * x + -1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64)))) < 0.0

   1. Initial program 8.1%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{-3}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 98.4

         \[\leadsto \color{blue}{\frac{-3}{x}} \]

   5. Applied rewrites 98.4%

      \[\leadsto \color{blue}{\frac{-3}{x}} \]

   if 0.0 < (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64))))

   1. Initial program 98.8%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

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

      2. sub-neg N/A

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

      3. +-commutative N/A

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

      4. lift-/.f64 N/A

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

      5. distribute-neg-frac N/A

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

      6. lift-/.f64 N/A

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

      7. frac-add N/A

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

      8. *-commutative N/A

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

      9. lift-+.f64 N/A

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

      10. lift--.f64 N/A

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

      11. difference-of-sqr-1 N/A

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

      12. metadata-eval N/A

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

      13. lower-/.f64 N/A

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

   4. Applied rewrites 98.8%

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

   5. Taylor expanded in x around 0

      \[\leadsto \frac{\color{blue}{-3 \cdot x - 1}}{\mathsf{fma}\left(x, x, -1\right)} \]
   6. Step-by-step derivation

      1. sub-neg N/A

         \[\leadsto \frac{\color{blue}{-3 \cdot x + \left(\mathsf{neg}\left(1\right)\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]

      2. metadata-eval N/A

         \[\leadsto \frac{-3 \cdot x + \color{blue}{-1}}{\mathsf{fma}\left(x, x, -1\right)} \]

      3. lower-fma.f64 100.0

         \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(-3, x, -1\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]

   7. Applied rewrites 100.0%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(-3, x, -1\right)}}{\mathsf{fma}\left(x, x, -1\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0:\\ \;\;\;\;\frac{-3}{x}\\ \mathbf{else}:\\ \;\;\;\;\frac{\mathsf{fma}\left(-3, x, -1\right)}{\mathsf{fma}\left(x, x, -1\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0.5:\\ \;\;\;\;\frac{-3}{x}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x, \mathsf{fma}\left(\mathsf{fma}\left(x, x, 1\right), 3, x\right), 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (- (/ x (+ 1.0 x)) (/ (+ 1.0 x) (- x 1.0))) 0.5)
   (/ -3.0 x)
   (fma x (fma (fma x x 1.0) 3.0 x) 1.0)))

C

double code(double x) {
	double tmp;
	if (((x / (1.0 + x)) - ((1.0 + x) / (x - 1.0))) <= 0.5) {
		tmp = -3.0 / x;
	} else {
		tmp = fma(x, fma(fma(x, x, 1.0), 3.0, x), 1.0);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(x / Float64(1.0 + x)) - Float64(Float64(1.0 + x) / Float64(x - 1.0))) <= 0.5)
		tmp = Float64(-3.0 / x);
	else
		tmp = fma(x, fma(fma(x, x, 1.0), 3.0, x), 1.0);
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision] - N[(N[(1.0 + x), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.5], N[(-3.0 / x), $MachinePrecision], N[(x * N[(N[(x * x + 1.0), $MachinePrecision] * 3.0 + x), $MachinePrecision] + 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64)))) < 0.5

   1. Initial program 9.8%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{-3}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 97.3

         \[\leadsto \color{blue}{\frac{-3}{x}} \]

   5. Applied rewrites 97.3%

      \[\leadsto \color{blue}{\frac{-3}{x}} \]

   if 0.5 < (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64))))

   1. Initial program 100.0%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1 + x \cdot \left(3 + x \cdot \left(1 + 3 \cdot x\right)\right)} \]
   4. Step-by-step derivation

      1. distribute-lft-in N/A

         \[\leadsto 1 + \color{blue}{\left(x \cdot 3 + x \cdot \left(x \cdot \left(1 + 3 \cdot x\right)\right)\right)} \]

      2. *-commutative N/A

         \[\leadsto 1 + \left(\color{blue}{3 \cdot x} + x \cdot \left(x \cdot \left(1 + 3 \cdot x\right)\right)\right) \]

      3. associate-+r+ N/A

         \[\leadsto \color{blue}{\left(1 + 3 \cdot x\right) + x \cdot \left(x \cdot \left(1 + 3 \cdot x\right)\right)} \]

      4. associate-*r* N/A

         \[\leadsto \left(1 + 3 \cdot x\right) + \color{blue}{\left(x \cdot x\right) \cdot \left(1 + 3 \cdot x\right)} \]

      5. unpow2 N/A

         \[\leadsto \left(1 + 3 \cdot x\right) + \color{blue}{{x}^{2}} \cdot \left(1 + 3 \cdot x\right) \]

      6. distribute-rgt1-in N/A

         \[\leadsto \color{blue}{\left({x}^{2} + 1\right) \cdot \left(1 + 3 \cdot x\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left({x}^{2} + 1\right) \cdot \left(1 + 3 \cdot x\right)} \]

      8. unpow2 N/A

         \[\leadsto \left(\color{blue}{x \cdot x} + 1\right) \cdot \left(1 + 3 \cdot x\right) \]

      9. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, 1\right)} \cdot \left(1 + 3 \cdot x\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(x, x, 1\right) \cdot \color{blue}{\left(3 \cdot x + 1\right)} \]

      11. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(x, x, 1\right) \cdot \left(\color{blue}{x \cdot 3} + 1\right) \]

      12. lower-fma.f64 98.6

          \[\leadsto \mathsf{fma}\left(x, x, 1\right) \cdot \color{blue}{\mathsf{fma}\left(x, 3, 1\right)} \]

   5. Applied rewrites 98.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, 1\right) \cdot \mathsf{fma}\left(x, 3, 1\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 98.6%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x, x, 1\right), \color{blue}{3 \cdot x}, \mathsf{fma}\left(x, x, 1\right)\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 98.6%

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x, x, 1\right), 3, x\right)}, 1\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0.5:\\ \;\;\;\;\frac{-3}{x}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x, \mathsf{fma}\left(\mathsf{fma}\left(x, x, 1\right), 3, x\right), 1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.7% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0.5:\\ \;\;\;\;\frac{-3}{x}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x, 3, 1\right) \cdot \mathsf{fma}\left(x, x, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (- (/ x (+ 1.0 x)) (/ (+ 1.0 x) (- x 1.0))) 0.5)
   (/ -3.0 x)
   (* (fma x 3.0 1.0) (fma x x 1.0))))

C

double code(double x) {
	double tmp;
	if (((x / (1.0 + x)) - ((1.0 + x) / (x - 1.0))) <= 0.5) {
		tmp = -3.0 / x;
	} else {
		tmp = fma(x, 3.0, 1.0) * fma(x, x, 1.0);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(x / Float64(1.0 + x)) - Float64(Float64(1.0 + x) / Float64(x - 1.0))) <= 0.5)
		tmp = Float64(-3.0 / x);
	else
		tmp = Float64(fma(x, 3.0, 1.0) * fma(x, x, 1.0));
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision] - N[(N[(1.0 + x), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.5], N[(-3.0 / x), $MachinePrecision], N[(N[(x * 3.0 + 1.0), $MachinePrecision] * N[(x * x + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64)))) < 0.5

   1. Initial program 9.8%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{-3}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 97.3

         \[\leadsto \color{blue}{\frac{-3}{x}} \]

   5. Applied rewrites 97.3%

      \[\leadsto \color{blue}{\frac{-3}{x}} \]

   if 0.5 < (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64))))

   1. Initial program 100.0%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1 + x \cdot \left(3 + x \cdot \left(1 + 3 \cdot x\right)\right)} \]
   4. Step-by-step derivation

      1. distribute-lft-in N/A

         \[\leadsto 1 + \color{blue}{\left(x \cdot 3 + x \cdot \left(x \cdot \left(1 + 3 \cdot x\right)\right)\right)} \]

      2. *-commutative N/A

         \[\leadsto 1 + \left(\color{blue}{3 \cdot x} + x \cdot \left(x \cdot \left(1 + 3 \cdot x\right)\right)\right) \]

      3. associate-+r+ N/A

         \[\leadsto \color{blue}{\left(1 + 3 \cdot x\right) + x \cdot \left(x \cdot \left(1 + 3 \cdot x\right)\right)} \]

      4. associate-*r* N/A

         \[\leadsto \left(1 + 3 \cdot x\right) + \color{blue}{\left(x \cdot x\right) \cdot \left(1 + 3 \cdot x\right)} \]

      5. unpow2 N/A

         \[\leadsto \left(1 + 3 \cdot x\right) + \color{blue}{{x}^{2}} \cdot \left(1 + 3 \cdot x\right) \]

      6. distribute-rgt1-in N/A

         \[\leadsto \color{blue}{\left({x}^{2} + 1\right) \cdot \left(1 + 3 \cdot x\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left({x}^{2} + 1\right) \cdot \left(1 + 3 \cdot x\right)} \]

      8. unpow2 N/A

         \[\leadsto \left(\color{blue}{x \cdot x} + 1\right) \cdot \left(1 + 3 \cdot x\right) \]

      9. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, 1\right)} \cdot \left(1 + 3 \cdot x\right) \]

      10. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(x, x, 1\right) \cdot \color{blue}{\left(3 \cdot x + 1\right)} \]

      11. *-commutative N/A

          \[\leadsto \mathsf{fma}\left(x, x, 1\right) \cdot \left(\color{blue}{x \cdot 3} + 1\right) \]

      12. lower-fma.f64 98.6

          \[\leadsto \mathsf{fma}\left(x, x, 1\right) \cdot \color{blue}{\mathsf{fma}\left(x, 3, 1\right)} \]

   5. Applied rewrites 98.6%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, 1\right) \cdot \mathsf{fma}\left(x, 3, 1\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.0%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0.5:\\ \;\;\;\;\frac{-3}{x}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(x, 3, 1\right) \cdot \mathsf{fma}\left(x, x, 1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 98.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0.5:\\ \;\;\;\;\frac{-3}{x}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(3 + x, x, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (- (/ x (+ 1.0 x)) (/ (+ 1.0 x) (- x 1.0))) 0.5)
   (/ -3.0 x)
   (fma (+ 3.0 x) x 1.0)))

C

double code(double x) {
	double tmp;
	if (((x / (1.0 + x)) - ((1.0 + x) / (x - 1.0))) <= 0.5) {
		tmp = -3.0 / x;
	} else {
		tmp = fma((3.0 + x), x, 1.0);
	}
	return tmp;
}

Julia

function code(x)
	tmp = 0.0
	if (Float64(Float64(x / Float64(1.0 + x)) - Float64(Float64(1.0 + x) / Float64(x - 1.0))) <= 0.5)
		tmp = Float64(-3.0 / x);
	else
		tmp = fma(Float64(3.0 + x), x, 1.0);
	end
	return tmp
end

Wolfram

code[x_] := If[LessEqual[N[(N[(x / N[(1.0 + x), $MachinePrecision]), $MachinePrecision] - N[(N[(1.0 + x), $MachinePrecision] / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.5], N[(-3.0 / x), $MachinePrecision], N[(N[(3.0 + x), $MachinePrecision] * x + 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64)))) < 0.5

   1. Initial program 9.8%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{-3}{x}} \]
   4. Step-by-step derivation

      1. lower-/.f64 97.3

         \[\leadsto \color{blue}{\frac{-3}{x}} \]

   5. Applied rewrites 97.3%

      \[\leadsto \color{blue}{\frac{-3}{x}} \]

   if 0.5 < (-.f64 (/.f64 x (+.f64 x #s(literal 1 binary64))) (/.f64 (+.f64 x #s(literal 1 binary64)) (-.f64 x #s(literal 1 binary64))))

   1. Initial program 100.0%

      \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{1 + x \cdot \left(3 + x\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{x \cdot \left(3 + x\right) + 1} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\left(3 + x\right) \cdot x} + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(3 + x, x, 1\right)} \]

      4. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{x + 3}, x, 1\right) \]

      5. lower-+.f64 98.3

         \[\leadsto \mathsf{fma}\left(\color{blue}{x + 3}, x, 1\right) \]

   5. Applied rewrites 98.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x + 3, x, 1\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\frac{x}{1 + x} - \frac{1 + x}{x - 1} \leq 0.5:\\ \;\;\;\;\frac{-3}{x}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(3 + x, x, 1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 50.0% accurate, 3.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(3 + x, x, 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (fma (+ 3.0 x) x 1.0))

C

double code(double x) {
	return fma((3.0 + x), x, 1.0);
}

Julia

function code(x)
	return fma(Float64(3.0 + x), x, 1.0)
end

Wolfram

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

Derivation

1. Initial program 59.1%

   \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1 + x \cdot \left(3 + x\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \color{blue}{x \cdot \left(3 + x\right) + 1} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{\left(3 + x\right) \cdot x} + 1 \]

   3. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(3 + x, x, 1\right)} \]

   4. +-commutative N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{x + 3}, x, 1\right) \]

   5. lower-+.f64 54.7

      \[\leadsto \mathsf{fma}\left(\color{blue}{x + 3}, x, 1\right) \]

5. Applied rewrites 54.7%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x + 3, x, 1\right)} \]

6. Final simplification 54.7%

   \[\leadsto \mathsf{fma}\left(3 + x, x, 1\right) \]
7. Add Preprocessing

Alternative 8: 50.1% accurate, 35.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x) :precision binary64 1.0)

C

double code(double x) {
	return 1.0;
}

Fortran

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

Java

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

Python

def code(x):
	return 1.0

Julia

function code(x)
	return 1.0
end

MATLAB

function tmp = code(x)
	tmp = 1.0;
end

Wolfram

code[x_] := 1.0

Derivation

1. Initial program 59.1%

   \[\frac{x}{x + 1} - \frac{x + 1}{x - 1} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{1} \]
4. Step-by-step derivation

5. Applied rewrites 54.3%

   \[\leadsto \color{blue}{1} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024270 
(FPCore (x)
  :name "Asymptote C"
  :precision binary64
  (- (/ x (+ x 1.0)) (/ (+ x 1.0) (- x 1.0))))