2frac (problem 3.3.1)

Percentage Accurate: 77.5% → 99.9%

Time: 4.9s

Alternatives: 6

Speedup: 1.6×

Specification

Math

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

FPCore

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

C

double code(double x) {
	return (1.0 / (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)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 77.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

double code(double x) {
	return (1.0 / (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)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \frac{{\left(-1 - x\right)}^{-1}}{x} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

public static double code(double x) {
	return Math.pow((-1.0 - x), -1.0) / x;
}

Python

def code(x):
	return math.pow((-1.0 - x), -1.0) / x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 81.1%

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

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\frac{-\frac{\left(x - x\right) - 1}{-1 - x}}{x}} \]
5. Step-by-step derivation

   1. lift-neg.f64 N/A

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

   2. lift-/.f64 N/A

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

   3. distribute-neg-frac N/A

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

   4. lift--.f64 N/A

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

   5. lift--.f64 N/A

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

   6. +-inverses N/A

      \[\leadsto \frac{\frac{\mathsf{neg}\left(\left(\color{blue}{0} - 1\right)\right)}{-1 - x}}{x} \]

   7. metadata-eval N/A

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

   8. metadata-eval N/A

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

   9. lower-/.f64 99.9

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

6. Applied rewrites 99.9%

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

7. Final simplification 99.9%

   \[\leadsto \frac{{\left(-1 - x\right)}^{-1}}{x} \]
8. Add Preprocessing

Alternative 2: 97.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(x + 1\right)}^{-1} - {x}^{-1}\\ \mathbf{if}\;t\_0 \leq -200 \lor \neg \left(t\_0 \leq 0\right):\\ \;\;\;\;\left(1 - x\right) - {x}^{-1}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (pow (+ x 1.0) -1.0) (pow x -1.0))))
   (if (or (<= t_0 -200.0) (not (<= t_0 0.0)))
     (- (- 1.0 x) (pow x -1.0))
     (/ -1.0 (* x x)))))

C

double code(double x) {
	double t_0 = pow((x + 1.0), -1.0) - pow(x, -1.0);
	double tmp;
	if ((t_0 <= -200.0) || !(t_0 <= 0.0)) {
		tmp = (1.0 - x) - pow(x, -1.0);
	} else {
		tmp = -1.0 / (x * x);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((x + 1.0d0) ** (-1.0d0)) - (x ** (-1.0d0))
    if ((t_0 <= (-200.0d0)) .or. (.not. (t_0 <= 0.0d0))) then
        tmp = (1.0d0 - x) - (x ** (-1.0d0))
    else
        tmp = (-1.0d0) / (x * x)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = Math.pow((x + 1.0), -1.0) - Math.pow(x, -1.0);
	double tmp;
	if ((t_0 <= -200.0) || !(t_0 <= 0.0)) {
		tmp = (1.0 - x) - Math.pow(x, -1.0);
	} else {
		tmp = -1.0 / (x * x);
	}
	return tmp;
}

Python

def code(x):
	t_0 = math.pow((x + 1.0), -1.0) - math.pow(x, -1.0)
	tmp = 0
	if (t_0 <= -200.0) or not (t_0 <= 0.0):
		tmp = (1.0 - x) - math.pow(x, -1.0)
	else:
		tmp = -1.0 / (x * x)
	return tmp

Julia

function code(x)
	t_0 = Float64((Float64(x + 1.0) ^ -1.0) - (x ^ -1.0))
	tmp = 0.0
	if ((t_0 <= -200.0) || !(t_0 <= 0.0))
		tmp = Float64(Float64(1.0 - x) - (x ^ -1.0));
	else
		tmp = Float64(-1.0 / Float64(x * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = ((x + 1.0) ^ -1.0) - (x ^ -1.0);
	tmp = 0.0;
	if ((t_0 <= -200.0) || ~((t_0 <= 0.0)))
		tmp = (1.0 - x) - (x ^ -1.0);
	else
		tmp = -1.0 / (x * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Power[N[(x + 1.0), $MachinePrecision], -1.0], $MachinePrecision] - N[Power[x, -1.0], $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$0, -200.0], N[Not[LessEqual[t$95$0, 0.0]], $MachinePrecision]], N[(N[(1.0 - x), $MachinePrecision] - N[Power[x, -1.0], $MachinePrecision]), $MachinePrecision], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < -200 or 0.0 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x))

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 99.0

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

   5. Applied rewrites 99.0%

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

   if -200 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < 0.0

   1. Initial program 65.2%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{-1}{{x}^{2}}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

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

      2. associate-/r* N/A

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

      3. metadata-eval N/A

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

      4. distribute-neg-frac N/A

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

      5. lower-/.f64 N/A

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

      6. distribute-neg-frac N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 96.8

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

   5. Applied rewrites 96.8%

      \[\leadsto \color{blue}{\frac{\frac{-1}{x}}{x}} \]
   6. Step-by-step derivation

   7. Applied rewrites 96.4%

      \[\leadsto \frac{-1}{\color{blue}{x \cdot x}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;{\left(x + 1\right)}^{-1} - {x}^{-1} \leq -200 \lor \neg \left({\left(x + 1\right)}^{-1} - {x}^{-1} \leq 0\right):\\ \;\;\;\;\left(1 - x\right) - {x}^{-1}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 97.7% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := {\left(x + 1\right)}^{-1} - {x}^{-1}\\ \mathbf{if}\;t\_0 \leq -200 \lor \neg \left(t\_0 \leq 0\right):\\ \;\;\;\;1 - {x}^{-1}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (- (pow (+ x 1.0) -1.0) (pow x -1.0))))
   (if (or (<= t_0 -200.0) (not (<= t_0 0.0)))
     (- 1.0 (pow x -1.0))
     (/ -1.0 (* x x)))))

C

double code(double x) {
	double t_0 = pow((x + 1.0), -1.0) - pow(x, -1.0);
	double tmp;
	if ((t_0 <= -200.0) || !(t_0 <= 0.0)) {
		tmp = 1.0 - pow(x, -1.0);
	} else {
		tmp = -1.0 / (x * x);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    real(8) :: tmp
    t_0 = ((x + 1.0d0) ** (-1.0d0)) - (x ** (-1.0d0))
    if ((t_0 <= (-200.0d0)) .or. (.not. (t_0 <= 0.0d0))) then
        tmp = 1.0d0 - (x ** (-1.0d0))
    else
        tmp = (-1.0d0) / (x * x)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double t_0 = Math.pow((x + 1.0), -1.0) - Math.pow(x, -1.0);
	double tmp;
	if ((t_0 <= -200.0) || !(t_0 <= 0.0)) {
		tmp = 1.0 - Math.pow(x, -1.0);
	} else {
		tmp = -1.0 / (x * x);
	}
	return tmp;
}

Python

def code(x):
	t_0 = math.pow((x + 1.0), -1.0) - math.pow(x, -1.0)
	tmp = 0
	if (t_0 <= -200.0) or not (t_0 <= 0.0):
		tmp = 1.0 - math.pow(x, -1.0)
	else:
		tmp = -1.0 / (x * x)
	return tmp

Julia

function code(x)
	t_0 = Float64((Float64(x + 1.0) ^ -1.0) - (x ^ -1.0))
	tmp = 0.0
	if ((t_0 <= -200.0) || !(t_0 <= 0.0))
		tmp = Float64(1.0 - (x ^ -1.0));
	else
		tmp = Float64(-1.0 / Float64(x * x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	t_0 = ((x + 1.0) ^ -1.0) - (x ^ -1.0);
	tmp = 0.0;
	if ((t_0 <= -200.0) || ~((t_0 <= 0.0)))
		tmp = 1.0 - (x ^ -1.0);
	else
		tmp = -1.0 / (x * x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := Block[{t$95$0 = N[(N[Power[N[(x + 1.0), $MachinePrecision], -1.0], $MachinePrecision] - N[Power[x, -1.0], $MachinePrecision]), $MachinePrecision]}, If[Or[LessEqual[t$95$0, -200.0], N[Not[LessEqual[t$95$0, 0.0]], $MachinePrecision]], N[(1.0 - N[Power[x, -1.0], $MachinePrecision]), $MachinePrecision], N[(-1.0 / N[(x * x), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < -200 or 0.0 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x))

   1. Initial program 100.0%

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

   3. Taylor expanded in x around 0

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

   5. Applied rewrites 98.4%

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

   if -200 < (-.f64 (/.f64 #s(literal 1 binary64) (+.f64 x #s(literal 1 binary64))) (/.f64 #s(literal 1 binary64) x)) < 0.0

   1. Initial program 65.2%

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

   3. Taylor expanded in x around inf

      \[\leadsto \color{blue}{\frac{-1}{{x}^{2}}} \]
   4. Step-by-step derivation

      1. unpow2 N/A

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

      2. associate-/r* N/A

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

      3. metadata-eval N/A

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

      4. distribute-neg-frac N/A

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

      5. lower-/.f64 N/A

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

      6. distribute-neg-frac N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 96.8

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

   5. Applied rewrites 96.8%

      \[\leadsto \color{blue}{\frac{\frac{-1}{x}}{x}} \]
   6. Step-by-step derivation

   7. Applied rewrites 96.4%

      \[\leadsto \frac{-1}{\color{blue}{x \cdot x}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;{\left(x + 1\right)}^{-1} - {x}^{-1} \leq -200 \lor \neg \left({\left(x + 1\right)}^{-1} - {x}^{-1} \leq 0\right):\\ \;\;\;\;1 - {x}^{-1}\\ \mathbf{else}:\\ \;\;\;\;\frac{-1}{x \cdot x}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.5% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 81.1%

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

4. Applied rewrites 99.9%

   \[\leadsto \color{blue}{\frac{-\frac{\left(x - x\right) - 1}{-1 - x}}{x}} \]
5. Step-by-step derivation

   1. lift-/.f64 N/A

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

   2. lift-neg.f64 N/A

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

   3. distribute-frac-neg N/A

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

   4. neg-mul-1 N/A

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

   5. div-inv N/A

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

   6. lift-/.f64 N/A

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

   7. lift-/.f64 N/A

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

   8. lift--.f64 N/A

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

   9. lift--.f64 N/A

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

   10. +-inverses N/A

       \[\leadsto -1 \cdot \left(\frac{\color{blue}{0} - 1}{-1 - x} \cdot \frac{1}{x}\right) \]

   11. metadata-eval N/A

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

   12. metadata-eval N/A

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

   13. lift--.f64 N/A

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

   14. sub-neg N/A

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

   15. +-commutative N/A

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

   16. metadata-eval N/A

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

   17. distribute-neg-in N/A

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

   18. frac-2neg N/A

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

6. Applied rewrites 99.6%

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

Alternative 5: 51.6% accurate, 2.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return -1.0 / x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 81.1%

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

3. Taylor expanded in x around 0

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

   1. lower-/.f64 48.2

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

5. Applied rewrites 48.2%

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

6. Final simplification 48.2%

   \[\leadsto \frac{-1}{x} \]
7. Add Preprocessing

Alternative 6: 2.9% accurate, 7.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return 1.0 - x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 81.1%

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

4. Applied rewrites 99.9%

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

5. Taylor expanded in x around 0

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

   1. div-sub N/A

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

   2. sub-neg N/A

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

   3. *-commutative N/A

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

   4. associate-/l* N/A

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

   5. *-inverses N/A

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

   6. *-rgt-identity N/A

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

   7. +-commutative N/A

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

   8. associate-+l+ N/A

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

   9. sub-neg N/A

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

   10. metadata-eval N/A

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

   11. distribute-rgt-in N/A

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

   12. sqr-neg N/A

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

   13. mul-1-neg N/A

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

   14. mul-1-neg N/A

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

   15. distribute-lft1-in N/A

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

   16. +-commutative N/A

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

   17. +-commutative N/A

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

   18. neg-sub0 N/A

       \[\leadsto \left(1 + -1 \cdot x\right) \cdot \left(-1 \cdot x\right) + \left(\color{blue}{\left(0 - \frac{1}{x}\right)} + 1\right) \]

   19. associate-+l- N/A

       \[\leadsto \left(1 + -1 \cdot x\right) \cdot \left(-1 \cdot x\right) + \color{blue}{\left(0 - \left(\frac{1}{x} - 1\right)\right)} \]

   20. neg-sub0 N/A

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

   21. mul-1-neg N/A

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

   22. *-commutative N/A

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

7. Applied rewrites 46.4%

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

8. Taylor expanded in x around inf

   \[\leadsto {x}^{2} \cdot \color{blue}{\left(\left(1 + \frac{1}{{x}^{2}}\right) - \frac{1}{x}\right)} \]
9. Step-by-step derivation

10. Applied rewrites 2.5%

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

11. Taylor expanded in x around 0

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

13. Applied rewrites 3.1%

    \[\leadsto 1 - x \]
14. Add Preprocessing

Developer Target 1: 99.5% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \frac{1}{x \cdot \left(-1 - x\right)} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024322 
(FPCore (x)
  :name "2frac (problem 3.3.1)"
  :precision binary64

  :alt
  (! :herbie-platform default (/ 1 (* x (- -1 x))))

  (- (/ 1.0 (+ x 1.0)) (/ 1.0 x)))