Jmat.Real.lambertw, newton loop step

Percentage Accurate: 77.4% → 96.4%

Time: 12.0s

Alternatives: 7

Speedup: 313.0×

• could not determine a ground truth

  1. wj = -3.077818527174166e+33
  2. x = 3.375972273335887e+146

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := wj \cdot e^{wj}\\ wj - \frac{t\_0 - x}{e^{wj} + t\_0} \end{array} \end{array} \]

FPCore

(FPCore (wj x)
 :precision binary64
 (let* ((t_0 (* wj (exp wj)))) (- wj (/ (- t_0 x) (+ (exp wj) t_0)))))

C

double code(double wj, double x) {
	double t_0 = wj * exp(wj);
	return wj - ((t_0 - x) / (exp(wj) + t_0));
}

Fortran

real(8) function code(wj, x)
    real(8), intent (in) :: wj
    real(8), intent (in) :: x
    real(8) :: t_0
    t_0 = wj * exp(wj)
    code = wj - ((t_0 - x) / (exp(wj) + t_0))
end function

Java

public static double code(double wj, double x) {
	double t_0 = wj * Math.exp(wj);
	return wj - ((t_0 - x) / (Math.exp(wj) + t_0));
}

Python

def code(wj, x):
	t_0 = wj * math.exp(wj)
	return wj - ((t_0 - x) / (math.exp(wj) + t_0))

Julia

function code(wj, x)
	t_0 = Float64(wj * exp(wj))
	return Float64(wj - Float64(Float64(t_0 - x) / Float64(exp(wj) + t_0)))
end

MATLAB

function tmp = code(wj, x)
	t_0 = wj * exp(wj);
	tmp = wj - ((t_0 - x) / (exp(wj) + t_0));
end

Wolfram

code[wj_, x_] := Block[{t$95$0 = N[(wj * N[Exp[wj], $MachinePrecision]), $MachinePrecision]}, N[(wj - N[(N[(t$95$0 - x), $MachinePrecision] / N[(N[Exp[wj], $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Initial Program: 77.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := wj \cdot e^{wj}\\ wj - \frac{t\_0 - x}{e^{wj} + t\_0} \end{array} \end{array} \]

FPCore

(FPCore (wj x)
 :precision binary64
 (let* ((t_0 (* wj (exp wj)))) (- wj (/ (- t_0 x) (+ (exp wj) t_0)))))

C

double code(double wj, double x) {
	double t_0 = wj * exp(wj);
	return wj - ((t_0 - x) / (exp(wj) + t_0));
}

Fortran

real(8) function code(wj, x)
    real(8), intent (in) :: wj
    real(8), intent (in) :: x
    real(8) :: t_0
    t_0 = wj * exp(wj)
    code = wj - ((t_0 - x) / (exp(wj) + t_0))
end function

Java

public static double code(double wj, double x) {
	double t_0 = wj * Math.exp(wj);
	return wj - ((t_0 - x) / (Math.exp(wj) + t_0));
}

Python

def code(wj, x):
	t_0 = wj * math.exp(wj)
	return wj - ((t_0 - x) / (math.exp(wj) + t_0))

Julia

function code(wj, x)
	t_0 = Float64(wj * exp(wj))
	return Float64(wj - Float64(Float64(t_0 - x) / Float64(exp(wj) + t_0)))
end

MATLAB

function tmp = code(wj, x)
	t_0 = wj * exp(wj);
	tmp = wj - ((t_0 - x) / (exp(wj) + t_0));
end

Wolfram

code[wj_, x_] := Block[{t$95$0 = N[(wj * N[Exp[wj], $MachinePrecision]), $MachinePrecision]}, N[(wj - N[(N[(t$95$0 - x), $MachinePrecision] / N[(N[Exp[wj], $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Alternative 1: 96.4% accurate, 10.1× speedup

Math

\[\begin{array}{l} \\ x + wj \cdot \left(\left(wj \cdot \left(\left(1 + \left(x \cdot 0.5 + wj \cdot \left(\left(-1 - \left(x + x \cdot 0.5\right)\right) - x\right)\right)\right) - x \cdot -2\right) - x\right) - x\right) \end{array} \]

FPCore

(FPCore (wj x)
 :precision binary64
 (+
  x
  (*
   wj
   (-
    (-
     (*
      wj
      (-
       (+ 1.0 (+ (* x 0.5) (* wj (- (- -1.0 (+ x (* x 0.5))) x))))
       (* x -2.0)))
     x)
    x))))

C

double code(double wj, double x) {
	return x + (wj * (((wj * ((1.0 + ((x * 0.5) + (wj * ((-1.0 - (x + (x * 0.5))) - x)))) - (x * -2.0))) - x) - x));
}

Fortran

real(8) function code(wj, x)
    real(8), intent (in) :: wj
    real(8), intent (in) :: x
    code = x + (wj * (((wj * ((1.0d0 + ((x * 0.5d0) + (wj * (((-1.0d0) - (x + (x * 0.5d0))) - x)))) - (x * (-2.0d0)))) - x) - x))
end function

Java

public static double code(double wj, double x) {
	return x + (wj * (((wj * ((1.0 + ((x * 0.5) + (wj * ((-1.0 - (x + (x * 0.5))) - x)))) - (x * -2.0))) - x) - x));
}

Python

def code(wj, x):
	return x + (wj * (((wj * ((1.0 + ((x * 0.5) + (wj * ((-1.0 - (x + (x * 0.5))) - x)))) - (x * -2.0))) - x) - x))

Julia

function code(wj, x)
	return Float64(x + Float64(wj * Float64(Float64(Float64(wj * Float64(Float64(1.0 + Float64(Float64(x * 0.5) + Float64(wj * Float64(Float64(-1.0 - Float64(x + Float64(x * 0.5))) - x)))) - Float64(x * -2.0))) - x) - x)))
end

MATLAB

function tmp = code(wj, x)
	tmp = x + (wj * (((wj * ((1.0 + ((x * 0.5) + (wj * ((-1.0 - (x + (x * 0.5))) - x)))) - (x * -2.0))) - x) - x));
end

Wolfram

code[wj_, x_] := N[(x + N[(wj * N[(N[(N[(wj * N[(N[(1.0 + N[(N[(x * 0.5), $MachinePrecision] + N[(wj * N[(N[(-1.0 - N[(x + N[(x * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - N[(x * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision] - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 74.4%

   \[wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}} \]
2. Step-by-step derivation

   1. distribute-rgt1-in 75.1%

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

   2. associate-/l/ 75.1%

      \[\leadsto wj - \color{blue}{\frac{\frac{wj \cdot e^{wj} - x}{e^{wj}}}{wj + 1}} \]

   3. div-sub 74.3%

      \[\leadsto wj - \frac{\color{blue}{\frac{wj \cdot e^{wj}}{e^{wj}} - \frac{x}{e^{wj}}}}{wj + 1} \]

   4. associate-/l* 74.3%

      \[\leadsto wj - \frac{\color{blue}{wj \cdot \frac{e^{wj}}{e^{wj}}} - \frac{x}{e^{wj}}}{wj + 1} \]

   5. *-inverses 75.5%

      \[\leadsto wj - \frac{wj \cdot \color{blue}{1} - \frac{x}{e^{wj}}}{wj + 1} \]

   6. *-rgt-identity 75.5%

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

3. Simplified 75.5%

   \[\leadsto \color{blue}{wj - \frac{wj - \frac{x}{e^{wj}}}{wj + 1}} \]
4. Add Preprocessing

5. Taylor expanded in wj around 0 73.8%

   \[\leadsto wj - \frac{wj - \color{blue}{\left(x + wj \cdot \left(-1 \cdot \left(wj \cdot \left(-1 \cdot x + 0.5 \cdot x\right)\right) - x\right)\right)}}{wj + 1} \]
6. Step-by-step derivation

   1. associate-*r* 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\color{blue}{\left(-1 \cdot wj\right) \cdot \left(-1 \cdot x + 0.5 \cdot x\right)} - x\right)\right)}{wj + 1} \]

   2. neg-mul-1 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\color{blue}{\left(-wj\right)} \cdot \left(-1 \cdot x + 0.5 \cdot x\right) - x\right)\right)}{wj + 1} \]

   3. distribute-rgt-out 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\left(-wj\right) \cdot \color{blue}{\left(x \cdot \left(-1 + 0.5\right)\right)} - x\right)\right)}{wj + 1} \]

   4. metadata-eval 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\left(-wj\right) \cdot \left(x \cdot \color{blue}{-0.5}\right) - x\right)\right)}{wj + 1} \]

7. Simplified 73.8%

   \[\leadsto wj - \frac{wj - \color{blue}{\left(x + wj \cdot \left(\left(-wj\right) \cdot \left(x \cdot -0.5\right) - x\right)\right)}}{wj + 1} \]

8. Taylor expanded in wj around 0 97.4%

   \[\leadsto \color{blue}{x + wj \cdot \left(\left(-1 \cdot x + wj \cdot \left(\left(1 + \left(0.5 \cdot x + wj \cdot \left(-1 \cdot x - \left(1 + \left(x + 0.5 \cdot x\right)\right)\right)\right)\right) - -2 \cdot x\right)\right) - x\right)} \]

9. Final simplification 97.4%

   \[\leadsto x + wj \cdot \left(\left(wj \cdot \left(\left(1 + \left(x \cdot 0.5 + wj \cdot \left(\left(-1 - \left(x + x \cdot 0.5\right)\right) - x\right)\right)\right) - x \cdot -2\right) - x\right) - x\right) \]
10. Add Preprocessing

Alternative 2: 96.0% accurate, 20.9× speedup

Math

\[\begin{array}{l} \\ x + wj \cdot \left(wj \cdot \left(1 - x \cdot -2.5\right) + x \cdot -2\right) \end{array} \]

FPCore

(FPCore (wj x)
 :precision binary64
 (+ x (* wj (+ (* wj (- 1.0 (* x -2.5))) (* x -2.0)))))

C

double code(double wj, double x) {
	return x + (wj * ((wj * (1.0 - (x * -2.5))) + (x * -2.0)));
}

Fortran

real(8) function code(wj, x)
    real(8), intent (in) :: wj
    real(8), intent (in) :: x
    code = x + (wj * ((wj * (1.0d0 - (x * (-2.5d0)))) + (x * (-2.0d0))))
end function

Java

public static double code(double wj, double x) {
	return x + (wj * ((wj * (1.0 - (x * -2.5))) + (x * -2.0)));
}

Python

def code(wj, x):
	return x + (wj * ((wj * (1.0 - (x * -2.5))) + (x * -2.0)))

Julia

function code(wj, x)
	return Float64(x + Float64(wj * Float64(Float64(wj * Float64(1.0 - Float64(x * -2.5))) + Float64(x * -2.0))))
end

MATLAB

function tmp = code(wj, x)
	tmp = x + (wj * ((wj * (1.0 - (x * -2.5))) + (x * -2.0)));
end

Wolfram

code[wj_, x_] := N[(x + N[(wj * N[(N[(wj * N[(1.0 - N[(x * -2.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + N[(x * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 74.4%

   \[wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}} \]
2. Step-by-step derivation

   1. distribute-rgt1-in 75.1%

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

   2. associate-/l/ 75.1%

      \[\leadsto wj - \color{blue}{\frac{\frac{wj \cdot e^{wj} - x}{e^{wj}}}{wj + 1}} \]

   3. div-sub 74.3%

      \[\leadsto wj - \frac{\color{blue}{\frac{wj \cdot e^{wj}}{e^{wj}} - \frac{x}{e^{wj}}}}{wj + 1} \]

   4. associate-/l* 74.3%

      \[\leadsto wj - \frac{\color{blue}{wj \cdot \frac{e^{wj}}{e^{wj}}} - \frac{x}{e^{wj}}}{wj + 1} \]

   5. *-inverses 75.5%

      \[\leadsto wj - \frac{wj \cdot \color{blue}{1} - \frac{x}{e^{wj}}}{wj + 1} \]

   6. *-rgt-identity 75.5%

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

3. Simplified 75.5%

   \[\leadsto \color{blue}{wj - \frac{wj - \frac{x}{e^{wj}}}{wj + 1}} \]
4. Add Preprocessing

5. Taylor expanded in wj around 0 97.1%

   \[\leadsto \color{blue}{x + wj \cdot \left(wj \cdot \left(1 - \left(-4 \cdot x + 1.5 \cdot x\right)\right) - 2 \cdot x\right)} \]
6. Step-by-step derivation

   1. cancel-sign-sub-inv 97.1%

      \[\leadsto x + wj \cdot \color{blue}{\left(wj \cdot \left(1 - \left(-4 \cdot x + 1.5 \cdot x\right)\right) + \left(-2\right) \cdot x\right)} \]

   2. distribute-rgt-out 97.4%

      \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - \color{blue}{x \cdot \left(-4 + 1.5\right)}\right) + \left(-2\right) \cdot x\right) \]

   3. metadata-eval 97.4%

      \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - x \cdot \color{blue}{-2.5}\right) + \left(-2\right) \cdot x\right) \]

   4. metadata-eval 97.4%

      \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - x \cdot -2.5\right) + \color{blue}{-2} \cdot x\right) \]

   5. *-commutative 97.4%

      \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - x \cdot -2.5\right) + \color{blue}{x \cdot -2}\right) \]

7. Simplified 97.4%

   \[\leadsto \color{blue}{x + wj \cdot \left(wj \cdot \left(1 - x \cdot -2.5\right) + x \cdot -2\right)} \]

8. Final simplification 97.4%

   \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - x \cdot -2.5\right) + x \cdot -2\right) \]
9. Add Preprocessing

Alternative 3: 95.7% accurate, 34.8× speedup

Math

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

FPCore

(FPCore (wj x) :precision binary64 (+ x (* wj (* wj (- 1.0 wj)))))

C

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

Fortran

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

Java

public static double code(double wj, double x) {
	return x + (wj * (wj * (1.0 - wj)));
}

Python

def code(wj, x):
	return x + (wj * (wj * (1.0 - wj)))

Julia

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

MATLAB

function tmp = code(wj, x)
	tmp = x + (wj * (wj * (1.0 - wj)));
end

Wolfram

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

Derivation

1. Initial program 74.4%

   \[wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}} \]
2. Step-by-step derivation

   1. distribute-rgt1-in 75.1%

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

   2. associate-/l/ 75.1%

      \[\leadsto wj - \color{blue}{\frac{\frac{wj \cdot e^{wj} - x}{e^{wj}}}{wj + 1}} \]

   3. div-sub 74.3%

      \[\leadsto wj - \frac{\color{blue}{\frac{wj \cdot e^{wj}}{e^{wj}} - \frac{x}{e^{wj}}}}{wj + 1} \]

   4. associate-/l* 74.3%

      \[\leadsto wj - \frac{\color{blue}{wj \cdot \frac{e^{wj}}{e^{wj}}} - \frac{x}{e^{wj}}}{wj + 1} \]

   5. *-inverses 75.5%

      \[\leadsto wj - \frac{wj \cdot \color{blue}{1} - \frac{x}{e^{wj}}}{wj + 1} \]

   6. *-rgt-identity 75.5%

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

3. Simplified 75.5%

   \[\leadsto \color{blue}{wj - \frac{wj - \frac{x}{e^{wj}}}{wj + 1}} \]
4. Add Preprocessing

5. Taylor expanded in wj around 0 73.8%

   \[\leadsto wj - \frac{wj - \color{blue}{\left(x + wj \cdot \left(-1 \cdot \left(wj \cdot \left(-1 \cdot x + 0.5 \cdot x\right)\right) - x\right)\right)}}{wj + 1} \]
6. Step-by-step derivation

   1. associate-*r* 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\color{blue}{\left(-1 \cdot wj\right) \cdot \left(-1 \cdot x + 0.5 \cdot x\right)} - x\right)\right)}{wj + 1} \]

   2. neg-mul-1 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\color{blue}{\left(-wj\right)} \cdot \left(-1 \cdot x + 0.5 \cdot x\right) - x\right)\right)}{wj + 1} \]

   3. distribute-rgt-out 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\left(-wj\right) \cdot \color{blue}{\left(x \cdot \left(-1 + 0.5\right)\right)} - x\right)\right)}{wj + 1} \]

   4. metadata-eval 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\left(-wj\right) \cdot \left(x \cdot \color{blue}{-0.5}\right) - x\right)\right)}{wj + 1} \]

7. Simplified 73.8%

   \[\leadsto wj - \frac{wj - \color{blue}{\left(x + wj \cdot \left(\left(-wj\right) \cdot \left(x \cdot -0.5\right) - x\right)\right)}}{wj + 1} \]

8. Taylor expanded in wj around 0 97.4%

   \[\leadsto \color{blue}{x + wj \cdot \left(\left(-1 \cdot x + wj \cdot \left(\left(1 + \left(0.5 \cdot x + wj \cdot \left(-1 \cdot x - \left(1 + \left(x + 0.5 \cdot x\right)\right)\right)\right)\right) - -2 \cdot x\right)\right) - x\right)} \]

9. Taylor expanded in x around 0 97.1%

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

    1. neg-mul-1 97.1%

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

    2. sub-neg 97.1%

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

11. Simplified 97.1%

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

12. Taylor expanded in x around 0 96.3%

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

13. Final simplification 96.3%

    \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - wj\right)\right) \]
14. Add Preprocessing

Alternative 4: 95.8% accurate, 34.8× speedup

Math

\[\begin{array}{l} \\ x + wj \cdot \left(wj + x \cdot -2\right) \end{array} \]

FPCore

(FPCore (wj x) :precision binary64 (+ x (* wj (+ wj (* x -2.0)))))

C

double code(double wj, double x) {
	return x + (wj * (wj + (x * -2.0)));
}

Fortran

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

Java

public static double code(double wj, double x) {
	return x + (wj * (wj + (x * -2.0)));
}

Python

def code(wj, x):
	return x + (wj * (wj + (x * -2.0)))

Julia

function code(wj, x)
	return Float64(x + Float64(wj * Float64(wj + Float64(x * -2.0))))
end

MATLAB

function tmp = code(wj, x)
	tmp = x + (wj * (wj + (x * -2.0)));
end

Wolfram

code[wj_, x_] := N[(x + N[(wj * N[(wj + N[(x * -2.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 74.4%

   \[wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}} \]
2. Step-by-step derivation

   1. distribute-rgt1-in 75.1%

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

   2. associate-/l/ 75.1%

      \[\leadsto wj - \color{blue}{\frac{\frac{wj \cdot e^{wj} - x}{e^{wj}}}{wj + 1}} \]

   3. div-sub 74.3%

      \[\leadsto wj - \frac{\color{blue}{\frac{wj \cdot e^{wj}}{e^{wj}} - \frac{x}{e^{wj}}}}{wj + 1} \]

   4. associate-/l* 74.3%

      \[\leadsto wj - \frac{\color{blue}{wj \cdot \frac{e^{wj}}{e^{wj}}} - \frac{x}{e^{wj}}}{wj + 1} \]

   5. *-inverses 75.5%

      \[\leadsto wj - \frac{wj \cdot \color{blue}{1} - \frac{x}{e^{wj}}}{wj + 1} \]

   6. *-rgt-identity 75.5%

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

3. Simplified 75.5%

   \[\leadsto \color{blue}{wj - \frac{wj - \frac{x}{e^{wj}}}{wj + 1}} \]
4. Add Preprocessing

5. Taylor expanded in wj around 0 97.1%

   \[\leadsto \color{blue}{x + wj \cdot \left(wj \cdot \left(1 - \left(-4 \cdot x + 1.5 \cdot x\right)\right) - 2 \cdot x\right)} \]
6. Step-by-step derivation

   1. cancel-sign-sub-inv 97.1%

      \[\leadsto x + wj \cdot \color{blue}{\left(wj \cdot \left(1 - \left(-4 \cdot x + 1.5 \cdot x\right)\right) + \left(-2\right) \cdot x\right)} \]

   2. distribute-rgt-out 97.4%

      \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - \color{blue}{x \cdot \left(-4 + 1.5\right)}\right) + \left(-2\right) \cdot x\right) \]

   3. metadata-eval 97.4%

      \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - x \cdot \color{blue}{-2.5}\right) + \left(-2\right) \cdot x\right) \]

   4. metadata-eval 97.4%

      \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - x \cdot -2.5\right) + \color{blue}{-2} \cdot x\right) \]

   5. *-commutative 97.4%

      \[\leadsto x + wj \cdot \left(wj \cdot \left(1 - x \cdot -2.5\right) + \color{blue}{x \cdot -2}\right) \]

7. Simplified 97.4%

   \[\leadsto \color{blue}{x + wj \cdot \left(wj \cdot \left(1 - x \cdot -2.5\right) + x \cdot -2\right)} \]

8. Taylor expanded in x around 0 97.1%

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

9. Final simplification 97.1%

   \[\leadsto x + wj \cdot \left(wj + x \cdot -2\right) \]
10. Add Preprocessing

Alternative 5: 84.1% accurate, 44.7× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(1 + wj \cdot -2\right) \end{array} \]

FPCore

(FPCore (wj x) :precision binary64 (* x (+ 1.0 (* wj -2.0))))

C

double code(double wj, double x) {
	return x * (1.0 + (wj * -2.0));
}

Fortran

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

Java

public static double code(double wj, double x) {
	return x * (1.0 + (wj * -2.0));
}

Python

def code(wj, x):
	return x * (1.0 + (wj * -2.0))

Julia

function code(wj, x)
	return Float64(x * Float64(1.0 + Float64(wj * -2.0)))
end

MATLAB

function tmp = code(wj, x)
	tmp = x * (1.0 + (wj * -2.0));
end

Wolfram

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

Derivation

1. Initial program 74.4%

   \[wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}} \]
2. Step-by-step derivation

   1. distribute-rgt1-in 75.1%

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

   2. associate-/l/ 75.1%

      \[\leadsto wj - \color{blue}{\frac{\frac{wj \cdot e^{wj} - x}{e^{wj}}}{wj + 1}} \]

   3. div-sub 74.3%

      \[\leadsto wj - \frac{\color{blue}{\frac{wj \cdot e^{wj}}{e^{wj}} - \frac{x}{e^{wj}}}}{wj + 1} \]

   4. associate-/l* 74.3%

      \[\leadsto wj - \frac{\color{blue}{wj \cdot \frac{e^{wj}}{e^{wj}}} - \frac{x}{e^{wj}}}{wj + 1} \]

   5. *-inverses 75.5%

      \[\leadsto wj - \frac{wj \cdot \color{blue}{1} - \frac{x}{e^{wj}}}{wj + 1} \]

   6. *-rgt-identity 75.5%

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

3. Simplified 75.5%

   \[\leadsto \color{blue}{wj - \frac{wj - \frac{x}{e^{wj}}}{wj + 1}} \]
4. Add Preprocessing

5. Taylor expanded in wj around 0 73.8%

   \[\leadsto wj - \frac{wj - \color{blue}{\left(x + wj \cdot \left(-1 \cdot \left(wj \cdot \left(-1 \cdot x + 0.5 \cdot x\right)\right) - x\right)\right)}}{wj + 1} \]
6. Step-by-step derivation

   1. associate-*r* 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\color{blue}{\left(-1 \cdot wj\right) \cdot \left(-1 \cdot x + 0.5 \cdot x\right)} - x\right)\right)}{wj + 1} \]

   2. neg-mul-1 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\color{blue}{\left(-wj\right)} \cdot \left(-1 \cdot x + 0.5 \cdot x\right) - x\right)\right)}{wj + 1} \]

   3. distribute-rgt-out 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\left(-wj\right) \cdot \color{blue}{\left(x \cdot \left(-1 + 0.5\right)\right)} - x\right)\right)}{wj + 1} \]

   4. metadata-eval 73.8%

      \[\leadsto wj - \frac{wj - \left(x + wj \cdot \left(\left(-wj\right) \cdot \left(x \cdot \color{blue}{-0.5}\right) - x\right)\right)}{wj + 1} \]

7. Simplified 73.8%

   \[\leadsto wj - \frac{wj - \color{blue}{\left(x + wj \cdot \left(\left(-wj\right) \cdot \left(x \cdot -0.5\right) - x\right)\right)}}{wj + 1} \]

8. Taylor expanded in wj around 0 97.4%

   \[\leadsto \color{blue}{x + wj \cdot \left(\left(-1 \cdot x + wj \cdot \left(\left(1 + \left(0.5 \cdot x + wj \cdot \left(-1 \cdot x - \left(1 + \left(x + 0.5 \cdot x\right)\right)\right)\right)\right) - -2 \cdot x\right)\right) - x\right)} \]

9. Taylor expanded in x around 0 97.1%

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

    1. neg-mul-1 97.1%

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

    2. sub-neg 97.1%

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

11. Simplified 97.1%

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

12. Taylor expanded in x around inf 84.5%

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

13. Final simplification 84.5%

    \[\leadsto x \cdot \left(1 + wj \cdot -2\right) \]
14. Add Preprocessing

Alternative 6: 4.4% accurate, 313.0× speedup

Math

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

FPCore

(FPCore (wj x) :precision binary64 wj)

C

double code(double wj, double x) {
	return wj;
}

Fortran

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

Java

public static double code(double wj, double x) {
	return wj;
}

Python

def code(wj, x):
	return wj

Julia

function code(wj, x)
	return wj
end

MATLAB

function tmp = code(wj, x)
	tmp = wj;
end

Wolfram

code[wj_, x_] := wj

Derivation

1. Initial program 74.4%

   \[wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}} \]
2. Step-by-step derivation

   1. distribute-rgt1-in 75.1%

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

   2. associate-/l/ 75.1%

      \[\leadsto wj - \color{blue}{\frac{\frac{wj \cdot e^{wj} - x}{e^{wj}}}{wj + 1}} \]

   3. div-sub 74.3%

      \[\leadsto wj - \frac{\color{blue}{\frac{wj \cdot e^{wj}}{e^{wj}} - \frac{x}{e^{wj}}}}{wj + 1} \]

   4. associate-/l* 74.3%

      \[\leadsto wj - \frac{\color{blue}{wj \cdot \frac{e^{wj}}{e^{wj}}} - \frac{x}{e^{wj}}}{wj + 1} \]

   5. *-inverses 75.5%

      \[\leadsto wj - \frac{wj \cdot \color{blue}{1} - \frac{x}{e^{wj}}}{wj + 1} \]

   6. *-rgt-identity 75.5%

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

3. Simplified 75.5%

   \[\leadsto \color{blue}{wj - \frac{wj - \frac{x}{e^{wj}}}{wj + 1}} \]
4. Add Preprocessing

5. Taylor expanded in wj around inf 4.3%

   \[\leadsto \color{blue}{wj} \]

6. Final simplification 4.3%

   \[\leadsto wj \]
7. Add Preprocessing

Alternative 7: 83.6% accurate, 313.0× speedup

Math

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

FPCore

(FPCore (wj x) :precision binary64 x)

C

double code(double wj, double x) {
	return x;
}

Fortran

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

Java

public static double code(double wj, double x) {
	return x;
}

Python

def code(wj, x):
	return x

Julia

function code(wj, x)
	return x
end

MATLAB

function tmp = code(wj, x)
	tmp = x;
end

Wolfram

code[wj_, x_] := x

Derivation

1. Initial program 74.4%

   \[wj - \frac{wj \cdot e^{wj} - x}{e^{wj} + wj \cdot e^{wj}} \]
2. Step-by-step derivation

   1. distribute-rgt1-in 75.1%

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

   2. associate-/l/ 75.1%

      \[\leadsto wj - \color{blue}{\frac{\frac{wj \cdot e^{wj} - x}{e^{wj}}}{wj + 1}} \]

   3. div-sub 74.3%

      \[\leadsto wj - \frac{\color{blue}{\frac{wj \cdot e^{wj}}{e^{wj}} - \frac{x}{e^{wj}}}}{wj + 1} \]

   4. associate-/l* 74.3%

      \[\leadsto wj - \frac{\color{blue}{wj \cdot \frac{e^{wj}}{e^{wj}}} - \frac{x}{e^{wj}}}{wj + 1} \]

   5. *-inverses 75.5%

      \[\leadsto wj - \frac{wj \cdot \color{blue}{1} - \frac{x}{e^{wj}}}{wj + 1} \]

   6. *-rgt-identity 75.5%

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

3. Simplified 75.5%

   \[\leadsto \color{blue}{wj - \frac{wj - \frac{x}{e^{wj}}}{wj + 1}} \]
4. Add Preprocessing

5. Taylor expanded in wj around 0 83.7%

   \[\leadsto \color{blue}{x} \]

6. Final simplification 83.7%

   \[\leadsto x \]
7. Add Preprocessing

Developer target: 78.4% accurate, 1.5× speedup

Math

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

FPCore

(FPCore (wj x)
 :precision binary64
 (- wj (- (/ wj (+ wj 1.0)) (/ x (+ (exp wj) (* wj (exp wj)))))))

C

double code(double wj, double x) {
	return wj - ((wj / (wj + 1.0)) - (x / (exp(wj) + (wj * exp(wj)))));
}

Fortran

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

Java

public static double code(double wj, double x) {
	return wj - ((wj / (wj + 1.0)) - (x / (Math.exp(wj) + (wj * Math.exp(wj)))));
}

Python

def code(wj, x):
	return wj - ((wj / (wj + 1.0)) - (x / (math.exp(wj) + (wj * math.exp(wj)))))

Julia

function code(wj, x)
	return Float64(wj - Float64(Float64(wj / Float64(wj + 1.0)) - Float64(x / Float64(exp(wj) + Float64(wj * exp(wj))))))
end

MATLAB

function tmp = code(wj, x)
	tmp = wj - ((wj / (wj + 1.0)) - (x / (exp(wj) + (wj * exp(wj)))));
end

Wolfram

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

Reproduce

herbie shell --seed 2024077 
(FPCore (wj x)
  :name "Jmat.Real.lambertw, newton loop step"
  :precision binary64

  :alt
  (- wj (- (/ wj (+ wj 1.0)) (/ x (+ (exp wj) (* wj (exp wj))))))

  (- wj (/ (- (* wj (exp wj)) x) (+ (exp wj) (* wj (exp wj))))))