exp-w (used to crash)

Percentage Accurate: 99.4% → 99.0%

Time: 17.8s

Alternatives: 12

Speedup: 1.0×

• could not determine a ground truth

  1. w = 6.212233466673114e-102
  2. l = -1.217692072177142e-165

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: 99.0% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\ell \leq 1:\\ \;\;\;\;{\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, w, 0.5\right), w, 1\right), w, 1\right)\right)} \cdot \left(1 - w\right)\\ \mathbf{else}:\\ \;\;\;\;{\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)} \cdot \mathsf{fma}\left(\mathsf{fma}\left(0.5, w, -1\right), w, 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= l 1.0)
   (*
    (pow l (fma (fma (fma 0.16666666666666666 w 0.5) w 1.0) w 1.0))
    (- 1.0 w))
   (* (pow l (fma (fma w 0.5 1.0) w 1.0)) (fma (fma 0.5 w -1.0) w 1.0))))

C

double code(double w, double l) {
	double tmp;
	if (l <= 1.0) {
		tmp = pow(l, fma(fma(fma(0.16666666666666666, w, 0.5), w, 1.0), w, 1.0)) * (1.0 - w);
	} else {
		tmp = pow(l, fma(fma(w, 0.5, 1.0), w, 1.0)) * fma(fma(0.5, w, -1.0), w, 1.0);
	}
	return tmp;
}

Julia

function code(w, l)
	tmp = 0.0
	if (l <= 1.0)
		tmp = Float64((l ^ fma(fma(fma(0.16666666666666666, w, 0.5), w, 1.0), w, 1.0)) * Float64(1.0 - w));
	else
		tmp = Float64((l ^ fma(fma(w, 0.5, 1.0), w, 1.0)) * fma(fma(0.5, w, -1.0), w, 1.0));
	end
	return tmp
end

Wolfram

code[w_, l_] := If[LessEqual[l, 1.0], N[(N[Power[l, N[(N[(N[(0.16666666666666666 * w + 0.5), $MachinePrecision] * w + 1.0), $MachinePrecision] * w + 1.0), $MachinePrecision]], $MachinePrecision] * N[(1.0 - w), $MachinePrecision]), $MachinePrecision], N[(N[Power[l, N[(N[(w * 0.5 + 1.0), $MachinePrecision] * w + 1.0), $MachinePrecision]], $MachinePrecision] * N[(N[(0.5 * w + -1.0), $MachinePrecision] * w + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if l < 1

   1. Initial program 99.7%

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

   3. Taylor expanded in w around 0

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

      1. neg-mul-1 N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 75.9

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

   5. Applied rewrites 75.9%

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

   6. Taylor expanded in w around 0

      \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(1 + w \cdot \left(1 + w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right)\right)\right)}} \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(w \cdot \left(1 + w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right)\right) + 1\right)}} \]

      2. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\color{blue}{\left(1 + w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right)\right) \cdot w} + 1\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(1 + w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right), w, 1\right)\right)}} \]

      4. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right) + 1}, w, 1\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\left(\frac{1}{2} + \frac{1}{6} \cdot w\right) \cdot w} + 1, w, 1\right)\right)} \]

      6. lower-fma.f64 N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{6} \cdot w, w, 1\right)}, w, 1\right)\right)} \]

      7. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{1}{6} \cdot w + \frac{1}{2}}, w, 1\right), w, 1\right)\right)} \]

      8. lower-fma.f64 99.5

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(0.16666666666666666, w, 0.5\right)}, w, 1\right), w, 1\right)\right)} \]

   8. Applied rewrites 99.5%

      \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, w, 0.5\right), w, 1\right), w, 1\right)\right)}} \]

   if 1 < l

   1. Initial program 97.6%

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

   3. Taylor expanded in w around 0

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

      1. neg-mul-1 N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 65.8

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

   5. Applied rewrites 65.8%

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

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(w \cdot \left(1 + \frac{1}{2} \cdot w\right) + 1\right)}} \]

      2. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\color{blue}{\left(1 + \frac{1}{2} \cdot w\right) \cdot w} + 1\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(1 + \frac{1}{2} \cdot w, w, 1\right)\right)}} \]

      4. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot w + 1}, w, 1\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{w \cdot \frac{1}{2}} + 1, w, 1\right)\right)} \]

      6. lower-fma.f64 96.0

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(w, 0.5, 1\right)}, w, 1\right)\right)} \]

   8. Applied rewrites 96.0%

      \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)}} \]

   9. Taylor expanded in w around 0

      \[\leadsto \color{blue}{\left(1 + w \cdot \left(\frac{1}{2} \cdot w - 1\right)\right)} \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]
   10. Step-by-step derivation

       1. +-commutative N/A

          \[\leadsto \color{blue}{\left(w \cdot \left(\frac{1}{2} \cdot w - 1\right) + 1\right)} \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]

       2. *-commutative N/A

          \[\leadsto \left(\color{blue}{\left(\frac{1}{2} \cdot w - 1\right) \cdot w} + 1\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]

       3. lower-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{2} \cdot w - 1, w, 1\right)} \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]

       4. sub-neg N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot w + \left(\mathsf{neg}\left(1\right)\right)}, w, 1\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]

       5. metadata-eval N/A

          \[\leadsto \mathsf{fma}\left(\frac{1}{2} \cdot w + \color{blue}{-1}, w, 1\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]

       6. lower-fma.f64 97.8

          \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(0.5, w, -1\right)}, w, 1\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)} \]

   11. Applied rewrites 97.8%

       \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, w, -1\right), w, 1\right)} \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\ell \leq 1:\\ \;\;\;\;{\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, w, 0.5\right), w, 1\right), w, 1\right)\right)} \cdot \left(1 - w\right)\\ \mathbf{else}:\\ \;\;\;\;{\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)} \cdot \mathsf{fma}\left(\mathsf{fma}\left(0.5, w, -1\right), w, 1\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 19.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;{\ell}^{\left(e^{w}\right)} \cdot e^{-w} \leq 2 \cdot 10^{-157}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;1 - w\\ \end{array} \end{array} \]

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= (* (pow l (exp w)) (exp (- w))) 2e-157) 0.0 (- 1.0 w)))

C

double code(double w, double l) {
	double tmp;
	if ((pow(l, exp(w)) * exp(-w)) <= 2e-157) {
		tmp = 0.0;
	} else {
		tmp = 1.0 - w;
	}
	return tmp;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    real(8) :: tmp
    if (((l ** exp(w)) * exp(-w)) <= 2d-157) then
        tmp = 0.0d0
    else
        tmp = 1.0d0 - w
    end if
    code = tmp
end function

Java

public static double code(double w, double l) {
	double tmp;
	if ((Math.pow(l, Math.exp(w)) * Math.exp(-w)) <= 2e-157) {
		tmp = 0.0;
	} else {
		tmp = 1.0 - w;
	}
	return tmp;
}

Python

def code(w, l):
	tmp = 0
	if (math.pow(l, math.exp(w)) * math.exp(-w)) <= 2e-157:
		tmp = 0.0
	else:
		tmp = 1.0 - w
	return tmp

Julia

function code(w, l)
	tmp = 0.0
	if (Float64((l ^ exp(w)) * exp(Float64(-w))) <= 2e-157)
		tmp = 0.0;
	else
		tmp = Float64(1.0 - w);
	end
	return tmp
end

MATLAB

function tmp_2 = code(w, l)
	tmp = 0.0;
	if (((l ^ exp(w)) * exp(-w)) <= 2e-157)
		tmp = 0.0;
	else
		tmp = 1.0 - w;
	end
	tmp_2 = tmp;
end

Wolfram

code[w_, l_] := If[LessEqual[N[(N[Power[l, N[Exp[w], $MachinePrecision]], $MachinePrecision] * N[Exp[(-w)], $MachinePrecision]), $MachinePrecision], 2e-157], 0.0, N[(1.0 - w), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 (neg.f64 w)) (pow.f64 l (exp.f64 w))) < 1.99999999999999989e-157

   1. Initial program 99.7%

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

   4. Applied rewrites 60.0%

      \[\leadsto \color{blue}{0} \]

   if 1.99999999999999989e-157 < (*.f64 (exp.f64 (neg.f64 w)) (pow.f64 l (exp.f64 w)))

   1. Initial program 98.4%

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

      1. lift-pow.f64 N/A

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

      2. sqr-pow N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left({\ell}^{\left(\frac{e^{w}}{2}\right)} \cdot {\ell}^{\left(\frac{e^{w}}{2}\right)}\right)} \]

      3. pow-prod-up N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{{\ell}^{\left(\frac{e^{w}}{2} + \frac{e^{w}}{2}\right)}} \]

      4. flip-+ N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(\frac{\frac{e^{w}}{2} \cdot \frac{e^{w}}{2} - \frac{e^{w}}{2} \cdot \frac{e^{w}}{2}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)}} \]

      5. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      6. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 - 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      7. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 \cdot 0} - 0}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      8. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - \color{blue}{0 \cdot 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      9. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0}}\right)} \]

      10. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0 + 0}}\right)} \]

      11. flip-- N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(0 - 0\right)}} \]

      12. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{0}} \]

      13. metadata-eval 42.2

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

   4. Applied rewrites 42.2%

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

   5. Taylor expanded in w around 0

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

      1. neg-mul-1 N/A

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

      2. unsub-neg N/A

         \[\leadsto \color{blue}{1 - w} \]

      3. lower--.f64 5.4

         \[\leadsto \color{blue}{1 - w} \]

   7. Applied rewrites 5.4%

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

4. Final simplification 20.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;{\ell}^{\left(e^{w}\right)} \cdot e^{-w} \leq 2 \cdot 10^{-157}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;1 - w\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 19.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;{\ell}^{\left(e^{w}\right)} \cdot e^{-w} \leq 1.12 \cdot 10^{-154}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \end{array} \]

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= (* (pow l (exp w)) (exp (- w))) 1.12e-154) 0.0 1.0))

C

double code(double w, double l) {
	double tmp;
	if ((pow(l, exp(w)) * exp(-w)) <= 1.12e-154) {
		tmp = 0.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    real(8) :: tmp
    if (((l ** exp(w)) * exp(-w)) <= 1.12d-154) then
        tmp = 0.0d0
    else
        tmp = 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double w, double l) {
	double tmp;
	if ((Math.pow(l, Math.exp(w)) * Math.exp(-w)) <= 1.12e-154) {
		tmp = 0.0;
	} else {
		tmp = 1.0;
	}
	return tmp;
}

Python

def code(w, l):
	tmp = 0
	if (math.pow(l, math.exp(w)) * math.exp(-w)) <= 1.12e-154:
		tmp = 0.0
	else:
		tmp = 1.0
	return tmp

Julia

function code(w, l)
	tmp = 0.0
	if (Float64((l ^ exp(w)) * exp(Float64(-w))) <= 1.12e-154)
		tmp = 0.0;
	else
		tmp = 1.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(w, l)
	tmp = 0.0;
	if (((l ^ exp(w)) * exp(-w)) <= 1.12e-154)
		tmp = 0.0;
	else
		tmp = 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w_, l_] := If[LessEqual[N[(N[Power[l, N[Exp[w], $MachinePrecision]], $MachinePrecision] * N[Exp[(-w)], $MachinePrecision]), $MachinePrecision], 1.12e-154], 0.0, 1.0]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (exp.f64 (neg.f64 w)) (pow.f64 l (exp.f64 w))) < 1.12e-154

   1. Initial program 99.7%

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

   4. Applied rewrites 60.0%

      \[\leadsto \color{blue}{0} \]

   if 1.12e-154 < (*.f64 (exp.f64 (neg.f64 w)) (pow.f64 l (exp.f64 w)))

   1. Initial program 98.4%

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

      1. lift-pow.f64 N/A

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

      2. sqr-pow N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left({\ell}^{\left(\frac{e^{w}}{2}\right)} \cdot {\ell}^{\left(\frac{e^{w}}{2}\right)}\right)} \]

      3. pow-prod-up N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{{\ell}^{\left(\frac{e^{w}}{2} + \frac{e^{w}}{2}\right)}} \]

      4. flip-+ N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(\frac{\frac{e^{w}}{2} \cdot \frac{e^{w}}{2} - \frac{e^{w}}{2} \cdot \frac{e^{w}}{2}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)}} \]

      5. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      6. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 - 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      7. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 \cdot 0} - 0}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      8. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - \color{blue}{0 \cdot 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      9. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0}}\right)} \]

      10. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0 + 0}}\right)} \]

      11. flip-- N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(0 - 0\right)}} \]

      12. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{0}} \]

      13. metadata-eval 42.2

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

   4. Applied rewrites 42.2%

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

   5. Taylor expanded in w around 0

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

   7. Applied rewrites 4.7%

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

4. Final simplification 19.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;{\ell}^{\left(e^{w}\right)} \cdot e^{-w} \leq 1.12 \cdot 10^{-154}:\\ \;\;\;\;0\\ \mathbf{else}:\\ \;\;\;\;1\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.4% accurate, 1.0× speedup

Math

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

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(Float64(-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 98.7%

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

3. Final simplification 98.7%

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

Alternative 5: 98.7% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\ell \leq 1:\\ \;\;\;\;{\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, w, 0.5\right), w, 1\right), w, 1\right)\right)} \cdot \left(1 - w\right)\\ \mathbf{else}:\\ \;\;\;\;1 \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= l 1.0)
   (*
    (pow l (fma (fma (fma 0.16666666666666666 w 0.5) w 1.0) w 1.0))
    (- 1.0 w))
   (* 1.0 (pow l (fma (fma w 0.5 1.0) w 1.0)))))

C

double code(double w, double l) {
	double tmp;
	if (l <= 1.0) {
		tmp = pow(l, fma(fma(fma(0.16666666666666666, w, 0.5), w, 1.0), w, 1.0)) * (1.0 - w);
	} else {
		tmp = 1.0 * pow(l, fma(fma(w, 0.5, 1.0), w, 1.0));
	}
	return tmp;
}

Julia

function code(w, l)
	tmp = 0.0
	if (l <= 1.0)
		tmp = Float64((l ^ fma(fma(fma(0.16666666666666666, w, 0.5), w, 1.0), w, 1.0)) * Float64(1.0 - w));
	else
		tmp = Float64(1.0 * (l ^ fma(fma(w, 0.5, 1.0), w, 1.0)));
	end
	return tmp
end

Wolfram

code[w_, l_] := If[LessEqual[l, 1.0], N[(N[Power[l, N[(N[(N[(0.16666666666666666 * w + 0.5), $MachinePrecision] * w + 1.0), $MachinePrecision] * w + 1.0), $MachinePrecision]], $MachinePrecision] * N[(1.0 - w), $MachinePrecision]), $MachinePrecision], N[(1.0 * N[Power[l, N[(N[(w * 0.5 + 1.0), $MachinePrecision] * w + 1.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if l < 1

   1. Initial program 99.7%

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

   3. Taylor expanded in w around 0

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

      1. neg-mul-1 N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 75.9

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

   5. Applied rewrites 75.9%

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

   6. Taylor expanded in w around 0

      \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(1 + w \cdot \left(1 + w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right)\right)\right)}} \]
   7. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(w \cdot \left(1 + w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right)\right) + 1\right)}} \]

      2. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\color{blue}{\left(1 + w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right)\right) \cdot w} + 1\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(1 + w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right), w, 1\right)\right)}} \]

      4. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{w \cdot \left(\frac{1}{2} + \frac{1}{6} \cdot w\right) + 1}, w, 1\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\left(\frac{1}{2} + \frac{1}{6} \cdot w\right) \cdot w} + 1, w, 1\right)\right)} \]

      6. lower-fma.f64 N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{6} \cdot w, w, 1\right)}, w, 1\right)\right)} \]

      7. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{1}{6} \cdot w + \frac{1}{2}}, w, 1\right), w, 1\right)\right)} \]

      8. lower-fma.f64 99.5

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(0.16666666666666666, w, 0.5\right)}, w, 1\right), w, 1\right)\right)} \]

   8. Applied rewrites 99.5%

      \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, w, 0.5\right), w, 1\right), w, 1\right)\right)}} \]

   if 1 < l

   1. Initial program 97.6%

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

   3. Taylor expanded in w around 0

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

      1. neg-mul-1 N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 65.8

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

   5. Applied rewrites 65.8%

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

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(w \cdot \left(1 + \frac{1}{2} \cdot w\right) + 1\right)}} \]

      2. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\color{blue}{\left(1 + \frac{1}{2} \cdot w\right) \cdot w} + 1\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(1 + \frac{1}{2} \cdot w, w, 1\right)\right)}} \]

      4. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot w + 1}, w, 1\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{w \cdot \frac{1}{2}} + 1, w, 1\right)\right)} \]

      6. lower-fma.f64 96.0

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(w, 0.5, 1\right)}, w, 1\right)\right)} \]

   8. Applied rewrites 96.0%

      \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)}} \]

   9. Taylor expanded in w around 0

      \[\leadsto 1 \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 96.8%

       \[\leadsto 1 \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\ell \leq 1:\\ \;\;\;\;{\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666, w, 0.5\right), w, 1\right), w, 1\right)\right)} \cdot \left(1 - w\right)\\ \mathbf{else}:\\ \;\;\;\;1 \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 98.6% accurate, 2.5× speedup

Math

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

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= l 1.0)
   (* (pow l (+ 1.0 w)) (- 1.0 w))
   (* 1.0 (pow l (fma (fma w 0.5 1.0) w 1.0)))))

C

double code(double w, double l) {
	double tmp;
	if (l <= 1.0) {
		tmp = pow(l, (1.0 + w)) * (1.0 - w);
	} else {
		tmp = 1.0 * pow(l, fma(fma(w, 0.5, 1.0), w, 1.0));
	}
	return tmp;
}

Julia

function code(w, l)
	tmp = 0.0
	if (l <= 1.0)
		tmp = Float64((l ^ Float64(1.0 + w)) * Float64(1.0 - w));
	else
		tmp = Float64(1.0 * (l ^ fma(fma(w, 0.5, 1.0), w, 1.0)));
	end
	return tmp
end

Wolfram

code[w_, l_] := If[LessEqual[l, 1.0], N[(N[Power[l, N[(1.0 + w), $MachinePrecision]], $MachinePrecision] * N[(1.0 - w), $MachinePrecision]), $MachinePrecision], N[(1.0 * N[Power[l, N[(N[(w * 0.5 + 1.0), $MachinePrecision] * w + 1.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if l < 1

   1. Initial program 99.7%

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

   3. Taylor expanded in w around 0

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

      1. neg-mul-1 N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 75.9

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

   5. Applied rewrites 75.9%

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

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

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

      2. lower-+.f64 99.4

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

   8. Applied rewrites 99.4%

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

   if 1 < l

   1. Initial program 97.6%

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

   3. Taylor expanded in w around 0

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

      1. neg-mul-1 N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 65.8

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

   5. Applied rewrites 65.8%

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

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(w \cdot \left(1 + \frac{1}{2} \cdot w\right) + 1\right)}} \]

      2. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\color{blue}{\left(1 + \frac{1}{2} \cdot w\right) \cdot w} + 1\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(1 + \frac{1}{2} \cdot w, w, 1\right)\right)}} \]

      4. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot w + 1}, w, 1\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{w \cdot \frac{1}{2}} + 1, w, 1\right)\right)} \]

      6. lower-fma.f64 96.0

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(w, 0.5, 1\right)}, w, 1\right)\right)} \]

   8. Applied rewrites 96.0%

      \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)}} \]

   9. Taylor expanded in w around 0

      \[\leadsto 1 \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 96.8%

       \[\leadsto 1 \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.2%

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

Alternative 7: 98.5% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;w \leq -1:\\ \;\;\;\;e^{-w}\\ \mathbf{else}:\\ \;\;\;\;{\ell}^{\left(1 + w\right)} \cdot 1\\ \end{array} \end{array} \]

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= w -1.0) (exp (- w)) (* (pow l (+ 1.0 w)) 1.0)))

C

double code(double w, double l) {
	double tmp;
	if (w <= -1.0) {
		tmp = exp(-w);
	} else {
		tmp = pow(l, (1.0 + w)) * 1.0;
	}
	return tmp;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    real(8) :: tmp
    if (w <= (-1.0d0)) then
        tmp = exp(-w)
    else
        tmp = (l ** (1.0d0 + w)) * 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double w, double l) {
	double tmp;
	if (w <= -1.0) {
		tmp = Math.exp(-w);
	} else {
		tmp = Math.pow(l, (1.0 + w)) * 1.0;
	}
	return tmp;
}

Python

def code(w, l):
	tmp = 0
	if w <= -1.0:
		tmp = math.exp(-w)
	else:
		tmp = math.pow(l, (1.0 + w)) * 1.0
	return tmp

Julia

function code(w, l)
	tmp = 0.0
	if (w <= -1.0)
		tmp = exp(Float64(-w));
	else
		tmp = Float64((l ^ Float64(1.0 + w)) * 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(w, l)
	tmp = 0.0;
	if (w <= -1.0)
		tmp = exp(-w);
	else
		tmp = (l ^ (1.0 + w)) * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w_, l_] := If[LessEqual[w, -1.0], N[Exp[(-w)], $MachinePrecision], N[(N[Power[l, N[(1.0 + w), $MachinePrecision]], $MachinePrecision] * 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if w < -1

   1. Initial program 99.9%

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

      1. lift-pow.f64 N/A

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

      2. sqr-pow N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left({\ell}^{\left(\frac{e^{w}}{2}\right)} \cdot {\ell}^{\left(\frac{e^{w}}{2}\right)}\right)} \]

      3. pow-prod-up N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{{\ell}^{\left(\frac{e^{w}}{2} + \frac{e^{w}}{2}\right)}} \]

      4. flip-+ N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(\frac{\frac{e^{w}}{2} \cdot \frac{e^{w}}{2} - \frac{e^{w}}{2} \cdot \frac{e^{w}}{2}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)}} \]

      5. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      6. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 - 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      7. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 \cdot 0} - 0}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      8. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - \color{blue}{0 \cdot 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      9. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0}}\right)} \]

      10. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0 + 0}}\right)} \]

      11. flip-- N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(0 - 0\right)}} \]

      12. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{0}} \]

      13. metadata-eval 98.7

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

   4. Applied rewrites 98.7%

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

      1. lift-*.f64 N/A

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

      2. lift-exp.f64 N/A

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

      3. lift-neg.f64 N/A

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

      4. *-rgt-identity N/A

         \[\leadsto \color{blue}{e^{\mathsf{neg}\left(w\right)}} \]

      5. lift-neg.f64 N/A

         \[\leadsto e^{\color{blue}{\mathsf{neg}\left(w\right)}} \]

      6. lift-exp.f64 98.7

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

   6. Applied rewrites 98.7%

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

   if -1 < w

   1. Initial program 98.3%

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

   3. Taylor expanded in w around 0

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

      1. neg-mul-1 N/A

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

      2. unsub-neg N/A

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

      3. lower--.f64 97.7

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

   5. Applied rewrites 97.7%

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

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(w \cdot \left(1 + \frac{1}{2} \cdot w\right) + 1\right)}} \]

      2. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\color{blue}{\left(1 + \frac{1}{2} \cdot w\right) \cdot w} + 1\right)} \]

      3. lower-fma.f64 N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(1 + \frac{1}{2} \cdot w, w, 1\right)\right)}} \]

      4. +-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot w + 1}, w, 1\right)\right)} \]

      5. *-commutative N/A

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{w \cdot \frac{1}{2}} + 1, w, 1\right)\right)} \]

      6. lower-fma.f64 97.6

         \[\leadsto \left(1 - w\right) \cdot {\ell}^{\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(w, 0.5, 1\right)}, w, 1\right)\right)} \]

   8. Applied rewrites 97.6%

      \[\leadsto \left(1 - w\right) \cdot {\ell}^{\color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)}} \]

   9. Taylor expanded in w around 0

      \[\leadsto 1 \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, \frac{1}{2}, 1\right), w, 1\right)\right)} \]
   10. Step-by-step derivation

   11. Applied rewrites 98.0%

       \[\leadsto 1 \cdot {\ell}^{\left(\mathsf{fma}\left(\mathsf{fma}\left(w, 0.5, 1\right), w, 1\right)\right)} \]

   12. Taylor expanded in w around 0

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

       1. lower-+.f64 97.9

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

   14. Applied rewrites 97.9%

       \[\leadsto 1 \cdot {\ell}^{\color{blue}{\left(1 + w\right)}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;w \leq -1:\\ \;\;\;\;e^{-w}\\ \mathbf{else}:\\ \;\;\;\;{\ell}^{\left(1 + w\right)} \cdot 1\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 97.5% accurate, 2.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;w \leq -0.7:\\ \;\;\;\;e^{-w}\\ \mathbf{elif}\;w \leq 22500:\\ \;\;\;\;1 \cdot \ell\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= w -0.7) (exp (- w)) (if (<= w 22500.0) (* 1.0 l) 0.0)))

C

double code(double w, double l) {
	double tmp;
	if (w <= -0.7) {
		tmp = exp(-w);
	} else if (w <= 22500.0) {
		tmp = 1.0 * l;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    real(8) :: tmp
    if (w <= (-0.7d0)) then
        tmp = exp(-w)
    else if (w <= 22500.0d0) then
        tmp = 1.0d0 * l
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

public static double code(double w, double l) {
	double tmp;
	if (w <= -0.7) {
		tmp = Math.exp(-w);
	} else if (w <= 22500.0) {
		tmp = 1.0 * l;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

def code(w, l):
	tmp = 0
	if w <= -0.7:
		tmp = math.exp(-w)
	elif w <= 22500.0:
		tmp = 1.0 * l
	else:
		tmp = 0.0
	return tmp

Julia

function code(w, l)
	tmp = 0.0
	if (w <= -0.7)
		tmp = exp(Float64(-w));
	elseif (w <= 22500.0)
		tmp = Float64(1.0 * l);
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(w, l)
	tmp = 0.0;
	if (w <= -0.7)
		tmp = exp(-w);
	elseif (w <= 22500.0)
		tmp = 1.0 * l;
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w_, l_] := If[LessEqual[w, -0.7], N[Exp[(-w)], $MachinePrecision], If[LessEqual[w, 22500.0], N[(1.0 * l), $MachinePrecision], 0.0]]

Derivation

1. Split input into 3 regimes

2. if w < -0.69999999999999996

   1. Initial program 99.9%

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

      1. lift-pow.f64 N/A

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

      2. sqr-pow N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left({\ell}^{\left(\frac{e^{w}}{2}\right)} \cdot {\ell}^{\left(\frac{e^{w}}{2}\right)}\right)} \]

      3. pow-prod-up N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{{\ell}^{\left(\frac{e^{w}}{2} + \frac{e^{w}}{2}\right)}} \]

      4. flip-+ N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(\frac{\frac{e^{w}}{2} \cdot \frac{e^{w}}{2} - \frac{e^{w}}{2} \cdot \frac{e^{w}}{2}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)}} \]

      5. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      6. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 - 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      7. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 \cdot 0} - 0}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      8. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - \color{blue}{0 \cdot 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      9. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0}}\right)} \]

      10. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0 + 0}}\right)} \]

      11. flip-- N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(0 - 0\right)}} \]

      12. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{0}} \]

      13. metadata-eval 98.7

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

   4. Applied rewrites 98.7%

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

      1. lift-*.f64 N/A

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

      2. lift-exp.f64 N/A

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

      3. lift-neg.f64 N/A

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

      4. *-rgt-identity N/A

         \[\leadsto \color{blue}{e^{\mathsf{neg}\left(w\right)}} \]

      5. lift-neg.f64 N/A

         \[\leadsto e^{\color{blue}{\mathsf{neg}\left(w\right)}} \]

      6. lift-exp.f64 98.7

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

   6. Applied rewrites 98.7%

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

   if -0.69999999999999996 < w < 22500

   1. Initial program 97.8%

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

   3. Taylor expanded in w around 0

      \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\ell + w \cdot \left(\ell \cdot \log \ell + \ell \cdot \left(w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\ell \cdot \log \ell + \ell \cdot \left(w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right) \cdot w}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\ell \cdot \left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right)} \cdot w\right) \]

      3. associate-*l* N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\ell \cdot \left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w\right)}\right) \]

      4. *-commutative N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w\right) \cdot \ell}\right) \]

      5. distribute-rgt1-in N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w + 1\right) \cdot \ell\right)} \]

      6. lower-*.f64 N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w + 1\right) \cdot \ell\right)} \]

   5. Applied rewrites 97.3%

      \[\leadsto e^{-w} \cdot \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.5 \cdot w, 1 + \log \ell, 1\right) \cdot \log \ell, w, 1\right) \cdot \ell\right)} \]

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

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

      2. metadata-eval N/A

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

      3. cancel-sign-sub-inv N/A

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

      4. *-commutative N/A

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

      5. distribute-rgt-out-- N/A

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

      6. *-commutative N/A

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

      7. associate-*l* N/A

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

      8. distribute-rgt1-in N/A

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

      9. +-commutative N/A

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

      10. lower-*.f64 N/A

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

      11. +-commutative N/A

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

      12. *-commutative N/A

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

      13. lower-fma.f64 N/A

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

      14. lower--.f64 N/A

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

      15. lower-log.f64 96.9

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

   8. Applied rewrites 96.9%

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

   9. Taylor expanded in w around 0

      \[\leadsto 1 \cdot \ell \]
   10. Step-by-step derivation

   11. Applied rewrites 95.2%

       \[\leadsto 1 \cdot \ell \]

   if 22500 < w

   1. Initial program 100.0%

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

   4. Applied rewrites 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 88.8% accurate, 12.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;w \leq -1.3 \cdot 10^{+59}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, w, 0.5\right), w, -1\right), w, 1\right)\\ \mathbf{elif}\;w \leq 22500:\\ \;\;\;\;1 \cdot \ell\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= w -1.3e+59)
   (fma (fma (fma -0.16666666666666666 w 0.5) w -1.0) w 1.0)
   (if (<= w 22500.0) (* 1.0 l) 0.0)))

C

double code(double w, double l) {
	double tmp;
	if (w <= -1.3e+59) {
		tmp = fma(fma(fma(-0.16666666666666666, w, 0.5), w, -1.0), w, 1.0);
	} else if (w <= 22500.0) {
		tmp = 1.0 * l;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Julia

function code(w, l)
	tmp = 0.0
	if (w <= -1.3e+59)
		tmp = fma(fma(fma(-0.16666666666666666, w, 0.5), w, -1.0), w, 1.0);
	elseif (w <= 22500.0)
		tmp = Float64(1.0 * l);
	else
		tmp = 0.0;
	end
	return tmp
end

Wolfram

code[w_, l_] := If[LessEqual[w, -1.3e+59], N[(N[(N[(-0.16666666666666666 * w + 0.5), $MachinePrecision] * w + -1.0), $MachinePrecision] * w + 1.0), $MachinePrecision], If[LessEqual[w, 22500.0], N[(1.0 * l), $MachinePrecision], 0.0]]

Derivation

1. Split input into 3 regimes

2. if w < -1.3e59

   1. Initial program 100.0%

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

      1. lift-pow.f64 N/A

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

      2. sqr-pow N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left({\ell}^{\left(\frac{e^{w}}{2}\right)} \cdot {\ell}^{\left(\frac{e^{w}}{2}\right)}\right)} \]

      3. pow-prod-up N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{{\ell}^{\left(\frac{e^{w}}{2} + \frac{e^{w}}{2}\right)}} \]

      4. flip-+ N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(\frac{\frac{e^{w}}{2} \cdot \frac{e^{w}}{2} - \frac{e^{w}}{2} \cdot \frac{e^{w}}{2}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)}} \]

      5. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      6. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 - 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      7. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 \cdot 0} - 0}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      8. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - \color{blue}{0 \cdot 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      9. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0}}\right)} \]

      10. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0 + 0}}\right)} \]

      11. flip-- N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(0 - 0\right)}} \]

      12. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{0}} \]

      13. metadata-eval 100.0

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

   4. Applied rewrites 100.0%

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

   5. Taylor expanded in w around 0

      \[\leadsto \color{blue}{1 + w \cdot \left(w \cdot \left(\frac{1}{2} + \frac{-1}{6} \cdot w\right) - 1\right)} \]
   6. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto \color{blue}{w \cdot \left(w \cdot \left(\frac{1}{2} + \frac{-1}{6} \cdot w\right) - 1\right) + 1} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{\left(w \cdot \left(\frac{1}{2} + \frac{-1}{6} \cdot w\right) - 1\right) \cdot w} + 1 \]

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(w \cdot \left(\frac{1}{2} + \frac{-1}{6} \cdot w\right) - 1, w, 1\right)} \]

   7. Applied rewrites 76.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.16666666666666666, w, 0.5\right), w, -1\right), w, 1\right)} \]

   if -1.3e59 < w < 22500

   1. Initial program 98.0%

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

   3. Taylor expanded in w around 0

      \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\ell + w \cdot \left(\ell \cdot \log \ell + \ell \cdot \left(w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\ell \cdot \log \ell + \ell \cdot \left(w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right) \cdot w}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\ell \cdot \left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right)} \cdot w\right) \]

      3. associate-*l* N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\ell \cdot \left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w\right)}\right) \]

      4. *-commutative N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w\right) \cdot \ell}\right) \]

      5. distribute-rgt1-in N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w + 1\right) \cdot \ell\right)} \]

      6. lower-*.f64 N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w + 1\right) \cdot \ell\right)} \]

   5. Applied rewrites 97.0%

      \[\leadsto e^{-w} \cdot \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.5 \cdot w, 1 + \log \ell, 1\right) \cdot \log \ell, w, 1\right) \cdot \ell\right)} \]

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

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

      2. metadata-eval N/A

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

      3. cancel-sign-sub-inv N/A

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

      4. *-commutative N/A

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

      5. distribute-rgt-out-- N/A

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

      6. *-commutative N/A

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

      7. associate-*l* N/A

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

      8. distribute-rgt1-in N/A

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

      9. +-commutative N/A

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

      10. lower-*.f64 N/A

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

      11. +-commutative N/A

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

      12. *-commutative N/A

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

      13. lower-fma.f64 N/A

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

      14. lower--.f64 N/A

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

      15. lower-log.f64 86.7

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

   8. Applied rewrites 86.7%

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

   9. Taylor expanded in w around 0

      \[\leadsto 1 \cdot \ell \]
   10. Step-by-step derivation

   11. Applied rewrites 85.7%

       \[\leadsto 1 \cdot \ell \]

   if 22500 < w

   1. Initial program 100.0%

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

   4. Applied rewrites 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 84.2% accurate, 16.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;w \leq -2.5 \cdot 10^{+59}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(0.5, w, -1\right), w, 1\right)\\ \mathbf{elif}\;w \leq 22500:\\ \;\;\;\;1 \cdot \ell\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (w l)
 :precision binary64
 (if (<= w -2.5e+59)
   (fma (fma 0.5 w -1.0) w 1.0)
   (if (<= w 22500.0) (* 1.0 l) 0.0)))

C

double code(double w, double l) {
	double tmp;
	if (w <= -2.5e+59) {
		tmp = fma(fma(0.5, w, -1.0), w, 1.0);
	} else if (w <= 22500.0) {
		tmp = 1.0 * l;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Julia

function code(w, l)
	tmp = 0.0
	if (w <= -2.5e+59)
		tmp = fma(fma(0.5, w, -1.0), w, 1.0);
	elseif (w <= 22500.0)
		tmp = Float64(1.0 * l);
	else
		tmp = 0.0;
	end
	return tmp
end

Wolfram

code[w_, l_] := If[LessEqual[w, -2.5e+59], N[(N[(0.5 * w + -1.0), $MachinePrecision] * w + 1.0), $MachinePrecision], If[LessEqual[w, 22500.0], N[(1.0 * l), $MachinePrecision], 0.0]]

Derivation

1. Split input into 3 regimes

2. if w < -2.4999999999999999e59

   1. Initial program 100.0%

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

      1. lift-pow.f64 N/A

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

      2. sqr-pow N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left({\ell}^{\left(\frac{e^{w}}{2}\right)} \cdot {\ell}^{\left(\frac{e^{w}}{2}\right)}\right)} \]

      3. pow-prod-up N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{{\ell}^{\left(\frac{e^{w}}{2} + \frac{e^{w}}{2}\right)}} \]

      4. flip-+ N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(\frac{\frac{e^{w}}{2} \cdot \frac{e^{w}}{2} - \frac{e^{w}}{2} \cdot \frac{e^{w}}{2}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)}} \]

      5. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      6. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 - 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      7. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{\color{blue}{0 \cdot 0} - 0}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      8. metadata-eval N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - \color{blue}{0 \cdot 0}}{\frac{e^{w}}{2} - \frac{e^{w}}{2}}\right)} \]

      9. +-inverses N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0}}\right)} \]

      10. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\left(\frac{0 \cdot 0 - 0 \cdot 0}{\color{blue}{0 + 0}}\right)} \]

      11. flip-- N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{\left(0 - 0\right)}} \]

      12. metadata-eval N/A

          \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot {\ell}^{\color{blue}{0}} \]

      13. metadata-eval 100.0

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

   4. Applied rewrites 100.0%

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

   5. Taylor expanded in w around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{2} \cdot w - 1, w, 1\right)} \]

      4. sub-neg N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{1}{2} \cdot w + \left(\mathsf{neg}\left(1\right)\right)}, w, 1\right) \]

      5. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\frac{1}{2} \cdot w + \color{blue}{-1}, w, 1\right) \]

      6. lower-fma.f64 56.3

         \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(0.5, w, -1\right)}, w, 1\right) \]

   7. Applied rewrites 56.3%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.5, w, -1\right), w, 1\right)} \]

   if -2.4999999999999999e59 < w < 22500

   1. Initial program 98.0%

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

   3. Taylor expanded in w around 0

      \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\ell + w \cdot \left(\ell \cdot \log \ell + \ell \cdot \left(w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\ell \cdot \log \ell + \ell \cdot \left(w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right) \cdot w}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\ell \cdot \left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right)} \cdot w\right) \]

      3. associate-*l* N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\ell \cdot \left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w\right)}\right) \]

      4. *-commutative N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w\right) \cdot \ell}\right) \]

      5. distribute-rgt1-in N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w + 1\right) \cdot \ell\right)} \]

      6. lower-*.f64 N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w + 1\right) \cdot \ell\right)} \]

   5. Applied rewrites 97.0%

      \[\leadsto e^{-w} \cdot \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.5 \cdot w, 1 + \log \ell, 1\right) \cdot \log \ell, w, 1\right) \cdot \ell\right)} \]

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

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

      2. metadata-eval N/A

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

      3. cancel-sign-sub-inv N/A

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

      4. *-commutative N/A

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

      5. distribute-rgt-out-- N/A

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

      6. *-commutative N/A

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

      7. associate-*l* N/A

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

      8. distribute-rgt1-in N/A

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

      9. +-commutative N/A

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

      10. lower-*.f64 N/A

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

      11. +-commutative N/A

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

      12. *-commutative N/A

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

      13. lower-fma.f64 N/A

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

      14. lower--.f64 N/A

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

      15. lower-log.f64 86.7

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

   8. Applied rewrites 86.7%

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

   9. Taylor expanded in w around 0

      \[\leadsto 1 \cdot \ell \]
   10. Step-by-step derivation

   11. Applied rewrites 85.7%

       \[\leadsto 1 \cdot \ell \]

   if 22500 < w

   1. Initial program 100.0%

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

   4. Applied rewrites 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 11: 70.5% accurate, 25.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;w \leq 22500:\\ \;\;\;\;1 \cdot \ell\\ \mathbf{else}:\\ \;\;\;\;0\\ \end{array} \end{array} \]

FPCore

(FPCore (w l) :precision binary64 (if (<= w 22500.0) (* 1.0 l) 0.0))

C

double code(double w, double l) {
	double tmp;
	if (w <= 22500.0) {
		tmp = 1.0 * l;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    real(8) :: tmp
    if (w <= 22500.0d0) then
        tmp = 1.0d0 * l
    else
        tmp = 0.0d0
    end if
    code = tmp
end function

Java

public static double code(double w, double l) {
	double tmp;
	if (w <= 22500.0) {
		tmp = 1.0 * l;
	} else {
		tmp = 0.0;
	}
	return tmp;
}

Python

def code(w, l):
	tmp = 0
	if w <= 22500.0:
		tmp = 1.0 * l
	else:
		tmp = 0.0
	return tmp

Julia

function code(w, l)
	tmp = 0.0
	if (w <= 22500.0)
		tmp = Float64(1.0 * l);
	else
		tmp = 0.0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(w, l)
	tmp = 0.0;
	if (w <= 22500.0)
		tmp = 1.0 * l;
	else
		tmp = 0.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w_, l_] := If[LessEqual[w, 22500.0], N[(1.0 * l), $MachinePrecision], 0.0]

Derivation

1. Split input into 2 regimes

2. if w < 22500

   1. Initial program 98.5%

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

   3. Taylor expanded in w around 0

      \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\ell + w \cdot \left(\ell \cdot \log \ell + \ell \cdot \left(w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\ell \cdot \log \ell + \ell \cdot \left(w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right) \cdot w}\right) \]

      2. distribute-lft-out N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\ell \cdot \left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right)\right)} \cdot w\right) \]

      3. associate-*l* N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\ell \cdot \left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w\right)}\right) \]

      4. *-commutative N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \left(\ell + \color{blue}{\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w\right) \cdot \ell}\right) \]

      5. distribute-rgt1-in N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w + 1\right) \cdot \ell\right)} \]

      6. lower-*.f64 N/A

         \[\leadsto e^{\mathsf{neg}\left(w\right)} \cdot \color{blue}{\left(\left(\left(\log \ell + w \cdot \left(\frac{1}{2} \cdot \log \ell + \frac{1}{2} \cdot {\log \ell}^{2}\right)\right) \cdot w + 1\right) \cdot \ell\right)} \]

   5. Applied rewrites 97.8%

      \[\leadsto e^{-w} \cdot \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.5 \cdot w, 1 + \log \ell, 1\right) \cdot \log \ell, w, 1\right) \cdot \ell\right)} \]

   6. Taylor expanded in w around 0

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

      1. +-commutative N/A

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

      2. metadata-eval N/A

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

      3. cancel-sign-sub-inv N/A

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

      4. *-commutative N/A

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

      5. distribute-rgt-out-- N/A

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

      6. *-commutative N/A

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

      7. associate-*l* N/A

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

      8. distribute-rgt1-in N/A

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

      9. +-commutative N/A

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

      10. lower-*.f64 N/A

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

      11. +-commutative N/A

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

      12. *-commutative N/A

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

      13. lower-fma.f64 N/A

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

      14. lower--.f64 N/A

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

      15. lower-log.f64 65.2

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

   8. Applied rewrites 65.2%

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

   9. Taylor expanded in w around 0

      \[\leadsto 1 \cdot \ell \]
   10. Step-by-step derivation

   11. Applied rewrites 64.4%

       \[\leadsto 1 \cdot \ell \]

   if 22500 < w

   1. Initial program 100.0%

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

   4. Applied rewrites 100.0%

      \[\leadsto \color{blue}{0} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 12: 17.3% accurate, 309.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (w l) :precision binary64 0.0)

C

double code(double w, double l) {
	return 0.0;
}

Fortran

real(8) function code(w, l)
    real(8), intent (in) :: w
    real(8), intent (in) :: l
    code = 0.0d0
end function

Java

public static double code(double w, double l) {
	return 0.0;
}

Python

def code(w, l):
	return 0.0

Julia

function code(w, l)
	return 0.0
end

MATLAB

function tmp = code(w, l)
	tmp = 0.0;
end

Wolfram

code[w_, l_] := 0.0

Derivation

1. Initial program 98.7%

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

4. Applied rewrites 18.1%

   \[\leadsto \color{blue}{0} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024236 
(FPCore (w l)
  :name "exp-w (used to crash)"
  :precision binary64
  (* (exp (- w)) (pow l (exp w))))