exp-w (used to crash)

Percentage Accurate: 99.4% → 99.4%

Time: 21.2s

Alternatives: 8

Speedup: 1.0×

• could not determine a ground truth

  1. w = 2.00000000000000007e154
  2. l = -2

Specification

Math

\[\begin{array}{l} \\ e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (* (exp (- w)) (pow l (exp w))))

C

double code(double w, double l) {
	return exp(-w) * pow(l, exp(w));
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    code = exp(-w) * (l ** exp(w))
end function

Java

public static double code(double w, double l) {
	return Math.exp(-w) * Math.pow(l, Math.exp(w));
}

Python

def code(w, l):
	return math.exp(-w) * math.pow(l, math.exp(w))

Julia

function code(w, l)
	return Float64(exp(Float64(-w)) * (l ^ exp(w)))
end

MATLAB

function tmp = code(w, l)
	tmp = exp(-w) * (l ^ exp(w));
end

Wolfram

code[w_, l_] := N[(N[Exp[(-w)], $MachinePrecision] * N[Power[l, N[Exp[w], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 99.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (* (exp (- w)) (pow l (exp w))))

C

double code(double w, double l) {
	return exp(-w) * pow(l, exp(w));
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    code = exp(-w) * (l ** exp(w))
end function

Java

public static double code(double w, double l) {
	return Math.exp(-w) * Math.pow(l, Math.exp(w));
}

Python

def code(w, l):
	return math.exp(-w) * math.pow(l, math.exp(w))

Julia

function code(w, l)
	return Float64(exp(Float64(-w)) * (l ^ exp(w)))
end

MATLAB

function tmp = code(w, l)
	tmp = exp(-w) * (l ^ exp(w));
end

Wolfram

code[w_, l_] := N[(N[Exp[(-w)], $MachinePrecision] * N[Power[l, N[Exp[w], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (* (exp (- w)) (pow l (exp w))))

C

double code(double w, double l) {
	return exp(-w) * pow(l, exp(w));
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    code = exp(-w) * (l ** exp(w))
end function

Java

public static double code(double w, double l) {
	return Math.exp(-w) * Math.pow(l, Math.exp(w));
}

Python

def code(w, l):
	return math.exp(-w) * math.pow(l, math.exp(w))

Julia

function code(w, l)
	return Float64(exp(Float64(-w)) * (l ^ exp(w)))
end

MATLAB

function tmp = code(w, l)
	tmp = exp(-w) * (l ^ exp(w));
end

Wolfram

code[w_, l_] := N[(N[Exp[(-w)], $MachinePrecision] * N[Power[l, N[Exp[w], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.3%

   \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
2. Add Preprocessing

3. Final simplification 99.3%

   \[\leadsto e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
4. Add Preprocessing

Alternative 2: 99.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{{\ell}^{\left(e^{w}\right)}}{e^{w}} \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (/ (pow l (exp w)) (exp w)))

C

double code(double w, double l) {
	return pow(l, exp(w)) / exp(w);
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    code = (l ** exp(w)) / exp(w)
end function

Java

public static double code(double w, double l) {
	return Math.pow(l, Math.exp(w)) / Math.exp(w);
}

Python

def code(w, l):
	return math.pow(l, math.exp(w)) / math.exp(w)

Julia

function code(w, l)
	return Float64((l ^ exp(w)) / exp(w))
end

MATLAB

function tmp = code(w, l)
	tmp = (l ^ exp(w)) / exp(w);
end

Wolfram

code[w_, l_] := N[(N[Power[l, N[Exp[w], $MachinePrecision]], $MachinePrecision] / N[Exp[w], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.3%

   \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
2. Step-by-step derivation

   1. exp-neg 99.3%

      \[\leadsto \color{blue}{\frac{1}{e^{w}}} \cdot {\ell}^{\left(e^{w}\right)} \]

   2. associate-*l/ 99.3%

      \[\leadsto \color{blue}{\frac{1 \cdot {\ell}^{\left(e^{w}\right)}}{e^{w}}} \]

   3. *-lft-identity 99.3%

      \[\leadsto \frac{\color{blue}{{\ell}^{\left(e^{w}\right)}}}{e^{w}} \]

3. Simplified 99.3%

   \[\leadsto \color{blue}{\frac{{\ell}^{\left(e^{w}\right)}}{e^{w}}} \]
4. Add Preprocessing

5. Final simplification 99.3%

   \[\leadsto \frac{{\ell}^{\left(e^{w}\right)}}{e^{w}} \]
6. Add Preprocessing

Alternative 3: 84.3% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;w \leq -0.68:\\ \;\;\;\;\mathsf{expm1}\left(-w\right)\\ \mathbf{else}:\\ \;\;\;\;\ell\\ \end{array} \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (if (<= w -0.68) (expm1 (- w)) l))

C

double code(double w, double l) {
	double tmp;
	if (w <= -0.68) {
		tmp = expm1(-w);
	} else {
		tmp = l;
	}
	return tmp;
}

Java

public static double code(double w, double l) {
	double tmp;
	if (w <= -0.68) {
		tmp = Math.expm1(-w);
	} else {
		tmp = l;
	}
	return tmp;
}

Python

def code(w, l):
	tmp = 0
	if w <= -0.68:
		tmp = math.expm1(-w)
	else:
		tmp = l
	return tmp

Julia

function code(w, l)
	tmp = 0.0
	if (w <= -0.68)
		tmp = expm1(Float64(-w));
	else
		tmp = l;
	end
	return tmp
end

Wolfram

code[w_, l_] := If[LessEqual[w, -0.68], N[(Exp[(-w)] - 1), $MachinePrecision], l]

Derivation

1. Split input into 2 regimes

2. if w < -0.680000000000000049

   1. Initial program 100.0%

      \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
   2. Step-by-step derivation

      1. exp-neg 100.0%

         \[\leadsto \color{blue}{\frac{1}{e^{w}}} \cdot {\ell}^{\left(e^{w}\right)} \]

      2. associate-*l/ 100.0%

         \[\leadsto \color{blue}{\frac{1 \cdot {\ell}^{\left(e^{w}\right)}}{e^{w}}} \]

      3. *-lft-identity 100.0%

         \[\leadsto \frac{\color{blue}{{\ell}^{\left(e^{w}\right)}}}{e^{w}} \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{{\ell}^{\left(e^{w}\right)}}{e^{w}}} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. *-un-lft-identity 100.0%

         \[\leadsto \frac{{\color{blue}{\left(1 \cdot \ell\right)}}^{\left(e^{w}\right)}}{e^{w}} \]

      2. add-sqr-sqrt 0.0%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}\right)}}{e^{w}} \]

      3. sqrt-unprod 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w \cdot w}}}\right)}}{e^{w}} \]

      4. sqr-neg 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\sqrt{\color{blue}{\left(-w\right) \cdot \left(-w\right)}}}\right)}}{e^{w}} \]

      5. sqrt-unprod 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}\right)}}{e^{w}} \]

      6. add-sqr-sqrt 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{-w}}\right)}}{e^{w}} \]

      7. add-sqr-sqrt 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w}} \cdot \sqrt{e^{-w}}\right)}}}{e^{w}} \]

      8. sqrt-unprod 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w} \cdot e^{-w}}\right)}}}{e^{w}} \]

      9. exp-neg 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{\frac{1}{e^{w}}} \cdot e^{-w}}\right)}}{e^{w}} \]

      10. inv-pow 42.2%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{-1}} \cdot e^{-w}}\right)}}{e^{w}} \]

      11. add-sqr-sqrt 42.2%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}}\right)}}{e^{w}} \]

      12. sqrt-unprod 42.2%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{\left(-w\right) \cdot \left(-w\right)}}}}\right)}}{e^{w}} \]

      13. sqr-neg 42.2%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\sqrt{\color{blue}{w \cdot w}}}}\right)}}{e^{w}} \]

      14. sqrt-unprod 0.0%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}}\right)}}{e^{w}} \]

      15. add-sqr-sqrt 0.3%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{w}}}\right)}}{e^{w}} \]

      16. pow1 0.3%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot \color{blue}{{\left(e^{w}\right)}^{1}}}\right)}}{e^{w}} \]

      17. pow-prod-up 97.4%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{\left(-1 + 1\right)}}}\right)}}{e^{w}} \]

      18. metadata-eval 97.4%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{\color{blue}{0}}}\right)}}{e^{w}} \]

      19. metadata-eval 97.4%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{1}}\right)}}{e^{w}} \]

      20. metadata-eval 97.4%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{1}}}{e^{w}} \]

   6. Applied egg-rr 97.4%

      \[\leadsto \frac{\color{blue}{\ell \cdot 1}}{e^{w}} \]
   7. Step-by-step derivation

      1. expm1-log1p-u 97.4%

         \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{\ell \cdot 1}{e^{w}}\right)\right)} \]

      2. expm1-undefine 97.4%

         \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{\ell \cdot 1}{e^{w}}\right)} - 1} \]

      3. *-rgt-identity 97.4%

         \[\leadsto e^{\mathsf{log1p}\left(\frac{\color{blue}{\ell}}{e^{w}}\right)} - 1 \]

   8. Applied egg-rr 97.4%

      \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(\frac{\ell}{e^{w}}\right)} - 1} \]
   9. Step-by-step derivation

      1. expm1-define 97.4%

         \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{\ell}{e^{w}}\right)\right)} \]

   10. Simplified 97.4%

       \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\frac{\ell}{e^{w}}\right)\right)} \]

   11. Taylor expanded in l around inf 97.4%

       \[\leadsto \mathsf{expm1}\left(\color{blue}{\log \left(\frac{1}{e^{w}}\right) + -1 \cdot \log \left(\frac{1}{\ell}\right)}\right) \]
   12. Step-by-step derivation

       1. +-commutative 97.4%

          \[\leadsto \mathsf{expm1}\left(\color{blue}{-1 \cdot \log \left(\frac{1}{\ell}\right) + \log \left(\frac{1}{e^{w}}\right)}\right) \]

       2. rec-exp 97.4%

          \[\leadsto \mathsf{expm1}\left(-1 \cdot \log \left(\frac{1}{\ell}\right) + \log \color{blue}{\left(e^{-w}\right)}\right) \]

       3. rem-log-exp 97.4%

          \[\leadsto \mathsf{expm1}\left(-1 \cdot \log \left(\frac{1}{\ell}\right) + \color{blue}{\left(-w\right)}\right) \]

       4. unsub-neg 97.4%

          \[\leadsto \mathsf{expm1}\left(\color{blue}{-1 \cdot \log \left(\frac{1}{\ell}\right) - w}\right) \]

       5. mul-1-neg 97.4%

          \[\leadsto \mathsf{expm1}\left(\color{blue}{\left(-\log \left(\frac{1}{\ell}\right)\right)} - w\right) \]

       6. log-rec 97.4%

          \[\leadsto \mathsf{expm1}\left(\left(-\color{blue}{\left(-\log \ell\right)}\right) - w\right) \]

       7. remove-double-neg 97.4%

          \[\leadsto \mathsf{expm1}\left(\color{blue}{\log \ell} - w\right) \]

   13. Simplified 97.4%

       \[\leadsto \mathsf{expm1}\left(\color{blue}{\log \ell - w}\right) \]

   14. Taylor expanded in w around inf 100.0%

       \[\leadsto \mathsf{expm1}\left(\color{blue}{-1 \cdot w}\right) \]
   15. Step-by-step derivation

       1. neg-mul-1 100.0%

          \[\leadsto \mathsf{expm1}\left(\color{blue}{-w}\right) \]

   16. Simplified 100.0%

       \[\leadsto \mathsf{expm1}\left(\color{blue}{-w}\right) \]

   if -0.680000000000000049 < w

   1. Initial program 99.0%

      \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in w around 0 79.2%

      \[\leadsto \color{blue}{\ell} \]
3. Recombined 2 regimes into one program.

4. Final simplification 84.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;w \leq -0.68:\\ \;\;\;\;\mathsf{expm1}\left(-w\right)\\ \mathbf{else}:\\ \;\;\;\;\ell\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 97.6% accurate, 2.9× speedup

Math

\[\begin{array}{l} \\ e^{-w} \cdot \ell \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (* (exp (- w)) l))

C

double code(double w, double l) {
	return exp(-w) * l;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    code = exp(-w) * l
end function

Java

public static double code(double w, double l) {
	return Math.exp(-w) * l;
}

Python

def code(w, l):
	return math.exp(-w) * l

Julia

function code(w, l)
	return Float64(exp(Float64(-w)) * l)
end

MATLAB

function tmp = code(w, l)
	tmp = exp(-w) * l;
end

Wolfram

code[w_, l_] := N[(N[Exp[(-w)], $MachinePrecision] * l), $MachinePrecision]

Derivation

1. Initial program 99.3%

   \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
2. Step-by-step derivation

   1. exp-neg 99.3%

      \[\leadsto \color{blue}{\frac{1}{e^{w}}} \cdot {\ell}^{\left(e^{w}\right)} \]

   2. associate-*l/ 99.3%

      \[\leadsto \color{blue}{\frac{1 \cdot {\ell}^{\left(e^{w}\right)}}{e^{w}}} \]

   3. *-lft-identity 99.3%

      \[\leadsto \frac{\color{blue}{{\ell}^{\left(e^{w}\right)}}}{e^{w}} \]

3. Simplified 99.3%

   \[\leadsto \color{blue}{\frac{{\ell}^{\left(e^{w}\right)}}{e^{w}}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. *-un-lft-identity 99.3%

      \[\leadsto \frac{{\color{blue}{\left(1 \cdot \ell\right)}}^{\left(e^{w}\right)}}{e^{w}} \]

   2. add-sqr-sqrt 44.7%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}\right)}}{e^{w}} \]

   3. sqrt-unprod 83.2%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w \cdot w}}}\right)}}{e^{w}} \]

   4. sqr-neg 83.2%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\sqrt{\color{blue}{\left(-w\right) \cdot \left(-w\right)}}}\right)}}{e^{w}} \]

   5. sqrt-unprod 38.6%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}\right)}}{e^{w}} \]

   6. add-sqr-sqrt 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{-w}}\right)}}{e^{w}} \]

   7. add-sqr-sqrt 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w}} \cdot \sqrt{e^{-w}}\right)}}}{e^{w}} \]

   8. sqrt-unprod 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w} \cdot e^{-w}}\right)}}}{e^{w}} \]

   9. exp-neg 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{\frac{1}{e^{w}}} \cdot e^{-w}}\right)}}{e^{w}} \]

   10. inv-pow 82.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{-1}} \cdot e^{-w}}\right)}}{e^{w}} \]

   11. add-sqr-sqrt 38.6%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}}\right)}}{e^{w}} \]

   12. sqrt-unprod 68.4%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{\left(-w\right) \cdot \left(-w\right)}}}}\right)}}{e^{w}} \]

   13. sqr-neg 68.4%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\sqrt{\color{blue}{w \cdot w}}}}\right)}}{e^{w}} \]

   14. sqrt-unprod 29.9%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}}\right)}}{e^{w}} \]

   15. add-sqr-sqrt 57.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{w}}}\right)}}{e^{w}} \]

   16. pow1 57.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot \color{blue}{{\left(e^{w}\right)}^{1}}}\right)}}{e^{w}} \]

   17. pow-prod-up 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{\left(-1 + 1\right)}}}\right)}}{e^{w}} \]

   18. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{\color{blue}{0}}}\right)}}{e^{w}} \]

   19. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{1}}\right)}}{e^{w}} \]

   20. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{1}}}{e^{w}} \]

6. Applied egg-rr 97.0%

   \[\leadsto \frac{\color{blue}{\ell \cdot 1}}{e^{w}} \]
7. Step-by-step derivation

   1. associate-/l* 97.0%

      \[\leadsto \color{blue}{\ell \cdot \frac{1}{e^{w}}} \]

   2. *-commutative 97.0%

      \[\leadsto \color{blue}{\frac{1}{e^{w}} \cdot \ell} \]

   3. rec-exp 97.0%

      \[\leadsto \color{blue}{e^{-w}} \cdot \ell \]

8. Applied egg-rr 97.0%

   \[\leadsto \color{blue}{e^{-w} \cdot \ell} \]

9. Final simplification 97.0%

   \[\leadsto e^{-w} \cdot \ell \]
10. Add Preprocessing

Alternative 5: 97.6% accurate, 3.0× speedup

Math

\[\begin{array}{l} \\ \frac{\ell}{e^{w}} \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (/ l (exp w)))

C

double code(double w, double l) {
	return l / exp(w);
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    code = l / exp(w)
end function

Java

public static double code(double w, double l) {
	return l / Math.exp(w);
}

Python

def code(w, l):
	return l / math.exp(w)

Julia

function code(w, l)
	return Float64(l / exp(w))
end

MATLAB

function tmp = code(w, l)
	tmp = l / exp(w);
end

Wolfram

code[w_, l_] := N[(l / N[Exp[w], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.3%

   \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
2. Step-by-step derivation

   1. exp-neg 99.3%

      \[\leadsto \color{blue}{\frac{1}{e^{w}}} \cdot {\ell}^{\left(e^{w}\right)} \]

   2. associate-*l/ 99.3%

      \[\leadsto \color{blue}{\frac{1 \cdot {\ell}^{\left(e^{w}\right)}}{e^{w}}} \]

   3. *-lft-identity 99.3%

      \[\leadsto \frac{\color{blue}{{\ell}^{\left(e^{w}\right)}}}{e^{w}} \]

3. Simplified 99.3%

   \[\leadsto \color{blue}{\frac{{\ell}^{\left(e^{w}\right)}}{e^{w}}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. *-un-lft-identity 99.3%

      \[\leadsto \frac{{\color{blue}{\left(1 \cdot \ell\right)}}^{\left(e^{w}\right)}}{e^{w}} \]

   2. add-sqr-sqrt 44.7%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}\right)}}{e^{w}} \]

   3. sqrt-unprod 83.2%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w \cdot w}}}\right)}}{e^{w}} \]

   4. sqr-neg 83.2%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\sqrt{\color{blue}{\left(-w\right) \cdot \left(-w\right)}}}\right)}}{e^{w}} \]

   5. sqrt-unprod 38.6%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}\right)}}{e^{w}} \]

   6. add-sqr-sqrt 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{-w}}\right)}}{e^{w}} \]

   7. add-sqr-sqrt 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w}} \cdot \sqrt{e^{-w}}\right)}}}{e^{w}} \]

   8. sqrt-unprod 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w} \cdot e^{-w}}\right)}}}{e^{w}} \]

   9. exp-neg 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{\frac{1}{e^{w}}} \cdot e^{-w}}\right)}}{e^{w}} \]

   10. inv-pow 82.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{-1}} \cdot e^{-w}}\right)}}{e^{w}} \]

   11. add-sqr-sqrt 38.6%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}}\right)}}{e^{w}} \]

   12. sqrt-unprod 68.4%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{\left(-w\right) \cdot \left(-w\right)}}}}\right)}}{e^{w}} \]

   13. sqr-neg 68.4%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\sqrt{\color{blue}{w \cdot w}}}}\right)}}{e^{w}} \]

   14. sqrt-unprod 29.9%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}}\right)}}{e^{w}} \]

   15. add-sqr-sqrt 57.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{w}}}\right)}}{e^{w}} \]

   16. pow1 57.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot \color{blue}{{\left(e^{w}\right)}^{1}}}\right)}}{e^{w}} \]

   17. pow-prod-up 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{\left(-1 + 1\right)}}}\right)}}{e^{w}} \]

   18. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{\color{blue}{0}}}\right)}}{e^{w}} \]

   19. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{1}}\right)}}{e^{w}} \]

   20. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{1}}}{e^{w}} \]

6. Applied egg-rr 97.0%

   \[\leadsto \frac{\color{blue}{\ell \cdot 1}}{e^{w}} \]

7. Taylor expanded in l around 0 97.0%

   \[\leadsto \color{blue}{\frac{\ell}{e^{w}}} \]

8. Final simplification 97.0%

   \[\leadsto \frac{\ell}{e^{w}} \]
9. Add Preprocessing

Alternative 6: 64.5% accurate, 33.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;w \leq -0.56:\\ \;\;\;\;w \cdot \left(-\ell\right)\\ \mathbf{else}:\\ \;\;\;\;\ell\\ \end{array} \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (if (<= w -0.56) (* w (- l)) l))

C

double code(double w, double l) {
	double tmp;
	if (w <= -0.56) {
		tmp = w * -l;
	} else {
		tmp = l;
	}
	return tmp;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    real(8) :: tmp
    if (w <= (-0.56d0)) then
        tmp = w * -l
    else
        tmp = l
    end if
    code = tmp
end function

Java

public static double code(double w, double l) {
	double tmp;
	if (w <= -0.56) {
		tmp = w * -l;
	} else {
		tmp = l;
	}
	return tmp;
}

Python

def code(w, l):
	tmp = 0
	if w <= -0.56:
		tmp = w * -l
	else:
		tmp = l
	return tmp

Julia

function code(w, l)
	tmp = 0.0
	if (w <= -0.56)
		tmp = Float64(w * Float64(-l));
	else
		tmp = l;
	end
	return tmp
end

MATLAB

function tmp_2 = code(w, l)
	tmp = 0.0;
	if (w <= -0.56)
		tmp = w * -l;
	else
		tmp = l;
	end
	tmp_2 = tmp;
end

Wolfram

code[w_, l_] := If[LessEqual[w, -0.56], N[(w * (-l)), $MachinePrecision], l]

Derivation

1. Split input into 2 regimes

2. if w < -0.56000000000000005

   1. Initial program 100.0%

      \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
   2. Step-by-step derivation

      1. exp-neg 100.0%

         \[\leadsto \color{blue}{\frac{1}{e^{w}}} \cdot {\ell}^{\left(e^{w}\right)} \]

      2. associate-*l/ 100.0%

         \[\leadsto \color{blue}{\frac{1 \cdot {\ell}^{\left(e^{w}\right)}}{e^{w}}} \]

      3. *-lft-identity 100.0%

         \[\leadsto \frac{\color{blue}{{\ell}^{\left(e^{w}\right)}}}{e^{w}} \]

   3. Simplified 100.0%

      \[\leadsto \color{blue}{\frac{{\ell}^{\left(e^{w}\right)}}{e^{w}}} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. *-un-lft-identity 100.0%

         \[\leadsto \frac{{\color{blue}{\left(1 \cdot \ell\right)}}^{\left(e^{w}\right)}}{e^{w}} \]

      2. add-sqr-sqrt 0.0%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}\right)}}{e^{w}} \]

      3. sqrt-unprod 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w \cdot w}}}\right)}}{e^{w}} \]

      4. sqr-neg 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\sqrt{\color{blue}{\left(-w\right) \cdot \left(-w\right)}}}\right)}}{e^{w}} \]

      5. sqrt-unprod 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}\right)}}{e^{w}} \]

      6. add-sqr-sqrt 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{-w}}\right)}}{e^{w}} \]

      7. add-sqr-sqrt 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w}} \cdot \sqrt{e^{-w}}\right)}}}{e^{w}} \]

      8. sqrt-unprod 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w} \cdot e^{-w}}\right)}}}{e^{w}} \]

      9. exp-neg 42.2%

         \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{\frac{1}{e^{w}}} \cdot e^{-w}}\right)}}{e^{w}} \]

      10. inv-pow 42.2%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{-1}} \cdot e^{-w}}\right)}}{e^{w}} \]

      11. add-sqr-sqrt 42.2%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}}\right)}}{e^{w}} \]

      12. sqrt-unprod 42.2%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{\left(-w\right) \cdot \left(-w\right)}}}}\right)}}{e^{w}} \]

      13. sqr-neg 42.2%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\sqrt{\color{blue}{w \cdot w}}}}\right)}}{e^{w}} \]

      14. sqrt-unprod 0.0%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}}\right)}}{e^{w}} \]

      15. add-sqr-sqrt 0.3%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{w}}}\right)}}{e^{w}} \]

      16. pow1 0.3%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot \color{blue}{{\left(e^{w}\right)}^{1}}}\right)}}{e^{w}} \]

      17. pow-prod-up 97.4%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{\left(-1 + 1\right)}}}\right)}}{e^{w}} \]

      18. metadata-eval 97.4%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{\color{blue}{0}}}\right)}}{e^{w}} \]

      19. metadata-eval 97.4%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{1}}\right)}}{e^{w}} \]

      20. metadata-eval 97.4%

          \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{1}}}{e^{w}} \]

   6. Applied egg-rr 97.4%

      \[\leadsto \frac{\color{blue}{\ell \cdot 1}}{e^{w}} \]

   7. Taylor expanded in w around 0 23.8%

      \[\leadsto \color{blue}{\ell + -1 \cdot \left(\ell \cdot w\right)} \]
   8. Step-by-step derivation

      1. mul-1-neg 23.8%

         \[\leadsto \ell + \color{blue}{\left(-\ell \cdot w\right)} \]

      2. unsub-neg 23.8%

         \[\leadsto \color{blue}{\ell - \ell \cdot w} \]

   9. Simplified 23.8%

      \[\leadsto \color{blue}{\ell - \ell \cdot w} \]

   10. Taylor expanded in w around inf 23.8%

       \[\leadsto \color{blue}{-1 \cdot \left(\ell \cdot w\right)} \]
   11. Step-by-step derivation

       1. associate-*r* 23.8%

          \[\leadsto \color{blue}{\left(-1 \cdot \ell\right) \cdot w} \]

       2. neg-mul-1 23.8%

          \[\leadsto \color{blue}{\left(-\ell\right)} \cdot w \]

   12. Simplified 23.8%

       \[\leadsto \color{blue}{\left(-\ell\right) \cdot w} \]

   if -0.56000000000000005 < w

   1. Initial program 99.0%

      \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in w around 0 79.2%

      \[\leadsto \color{blue}{\ell} \]
3. Recombined 2 regimes into one program.

4. Final simplification 64.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;w \leq -0.56:\\ \;\;\;\;w \cdot \left(-\ell\right)\\ \mathbf{else}:\\ \;\;\;\;\ell\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 64.2% accurate, 61.0× speedup

Math

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

FPCore

(FPCore (w l) :precision binary64 (* l (- 1.0 w)))

C

double code(double w, double l) {
	return l * (1.0 - w);
}

Fortran

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

Java

public static double code(double w, double l) {
	return l * (1.0 - w);
}

Python

def code(w, l):
	return l * (1.0 - w)

Julia

function code(w, l)
	return Float64(l * Float64(1.0 - w))
end

MATLAB

function tmp = code(w, l)
	tmp = l * (1.0 - w);
end

Wolfram

code[w_, l_] := N[(l * N[(1.0 - w), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.3%

   \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
2. Step-by-step derivation

   1. exp-neg 99.3%

      \[\leadsto \color{blue}{\frac{1}{e^{w}}} \cdot {\ell}^{\left(e^{w}\right)} \]

   2. associate-*l/ 99.3%

      \[\leadsto \color{blue}{\frac{1 \cdot {\ell}^{\left(e^{w}\right)}}{e^{w}}} \]

   3. *-lft-identity 99.3%

      \[\leadsto \frac{\color{blue}{{\ell}^{\left(e^{w}\right)}}}{e^{w}} \]

3. Simplified 99.3%

   \[\leadsto \color{blue}{\frac{{\ell}^{\left(e^{w}\right)}}{e^{w}}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. *-un-lft-identity 99.3%

      \[\leadsto \frac{{\color{blue}{\left(1 \cdot \ell\right)}}^{\left(e^{w}\right)}}{e^{w}} \]

   2. add-sqr-sqrt 44.7%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}\right)}}{e^{w}} \]

   3. sqrt-unprod 83.2%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{w \cdot w}}}\right)}}{e^{w}} \]

   4. sqr-neg 83.2%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\sqrt{\color{blue}{\left(-w\right) \cdot \left(-w\right)}}}\right)}}{e^{w}} \]

   5. sqrt-unprod 38.6%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}\right)}}{e^{w}} \]

   6. add-sqr-sqrt 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(e^{\color{blue}{-w}}\right)}}{e^{w}} \]

   7. add-sqr-sqrt 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w}} \cdot \sqrt{e^{-w}}\right)}}}{e^{w}} \]

   8. sqrt-unprod 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{\left(\sqrt{e^{-w} \cdot e^{-w}}\right)}}}{e^{w}} \]

   9. exp-neg 82.1%

      \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{\frac{1}{e^{w}}} \cdot e^{-w}}\right)}}{e^{w}} \]

   10. inv-pow 82.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{-1}} \cdot e^{-w}}\right)}}{e^{w}} \]

   11. add-sqr-sqrt 38.6%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{-w} \cdot \sqrt{-w}}}}\right)}}{e^{w}} \]

   12. sqrt-unprod 68.4%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{\left(-w\right) \cdot \left(-w\right)}}}}\right)}}{e^{w}} \]

   13. sqr-neg 68.4%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\sqrt{\color{blue}{w \cdot w}}}}\right)}}{e^{w}} \]

   14. sqrt-unprod 29.9%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{\sqrt{w} \cdot \sqrt{w}}}}\right)}}{e^{w}} \]

   15. add-sqr-sqrt 57.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot e^{\color{blue}{w}}}\right)}}{e^{w}} \]

   16. pow1 57.1%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{-1} \cdot \color{blue}{{\left(e^{w}\right)}^{1}}}\right)}}{e^{w}} \]

   17. pow-prod-up 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{{\left(e^{w}\right)}^{\left(-1 + 1\right)}}}\right)}}{e^{w}} \]

   18. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{{\left(e^{w}\right)}^{\color{blue}{0}}}\right)}}{e^{w}} \]

   19. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\left(\sqrt{\color{blue}{1}}\right)}}{e^{w}} \]

   20. metadata-eval 97.0%

       \[\leadsto \frac{{\left(1 \cdot \ell\right)}^{\color{blue}{1}}}{e^{w}} \]

6. Applied egg-rr 97.0%

   \[\leadsto \frac{\color{blue}{\ell \cdot 1}}{e^{w}} \]

7. Taylor expanded in w around 0 64.0%

   \[\leadsto \color{blue}{\ell + -1 \cdot \left(\ell \cdot w\right)} \]
8. Step-by-step derivation

   1. mul-1-neg 64.0%

      \[\leadsto \ell + \color{blue}{\left(-\ell \cdot w\right)} \]

   2. unsub-neg 64.0%

      \[\leadsto \color{blue}{\ell - \ell \cdot w} \]

9. Simplified 64.0%

   \[\leadsto \color{blue}{\ell - \ell \cdot w} \]

10. Taylor expanded in l around 0 64.0%

    \[\leadsto \color{blue}{\ell \cdot \left(1 - w\right)} \]

11. Final simplification 64.0%

    \[\leadsto \ell \cdot \left(1 - w\right) \]
12. Add Preprocessing

Alternative 8: 57.8% accurate, 305.0× speedup

Math

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

FPCore

(FPCore (w l) :precision binary64 l)

C

double code(double w, double l) {
	return l;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    code = l
end function

Java

public static double code(double w, double l) {
	return l;
}

Python

def code(w, l):
	return l

Julia

function code(w, l)
	return l
end

MATLAB

function tmp = code(w, l)
	tmp = l;
end

Wolfram

code[w_, l_] := l

Derivation

1. Initial program 99.3%

   \[e^{-w} \cdot {\ell}^{\left(e^{w}\right)} \]
2. Add Preprocessing

3. Taylor expanded in w around 0 58.9%

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

4. Final simplification 58.9%

   \[\leadsto \ell \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024050 
(FPCore (w l)
  :name "exp-w (used to crash)"
  :precision binary64
  (* (exp (- w)) (pow l (exp w))))