expax (section 3.5)

Percentage Accurate: 53.6% → 100.0%

Time: 6.0s

Alternatives: 7

Speedup: 18.2×

Specification

\[710 > a \cdot x\]

Math

\[\begin{array}{l} \\ e^{a \cdot x} - 1 \end{array} \]

FPCore

(FPCore (a x) :precision binary64 (- (exp (* a x)) 1.0))

C

double code(double a, double x) {
	return exp((a * x)) - 1.0;
}

Fortran

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

Java

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

Python

def code(a, x):
	return math.exp((a * x)) - 1.0

Julia

function code(a, x)
	return Float64(exp(Float64(a * x)) - 1.0)
end

MATLAB

function tmp = code(a, x)
	tmp = exp((a * x)) - 1.0;
end

Wolfram

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

Initial Program: 53.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ e^{a \cdot x} - 1 \end{array} \]

FPCore

(FPCore (a x) :precision binary64 (- (exp (* a x)) 1.0))

C

double code(double a, double x) {
	return exp((a * x)) - 1.0;
}

Fortran

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

Java

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

Python

def code(a, x):
	return math.exp((a * x)) - 1.0

Julia

function code(a, x)
	return Float64(exp(Float64(a * x)) - 1.0)
end

MATLAB

function tmp = code(a, x)
	tmp = exp((a * x)) - 1.0;
end

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{expm1}\left(x \cdot a\right) \end{array} \]

FPCore

(FPCore (a x) :precision binary64 (expm1 (* x a)))

C

double code(double a, double x) {
	return expm1((x * a));
}

Java

public static double code(double a, double x) {
	return Math.expm1((x * a));
}

Python

def code(a, x):
	return math.expm1((x * a))

Julia

function code(a, x)
	return expm1(Float64(x * a))
end

Wolfram

code[a_, x_] := N[(Exp[N[(x * a), $MachinePrecision]] - 1), $MachinePrecision]

Derivation

1. Initial program 55.1%

   \[e^{a \cdot x} - 1 \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

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

   2. lift-exp.f64 N/A

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

   3. lower-expm1.f64 100.0

      \[\leadsto \color{blue}{\mathsf{expm1}\left(a \cdot x\right)} \]

   4. lift-*.f64 N/A

      \[\leadsto \mathsf{expm1}\left(\color{blue}{a \cdot x}\right) \]

   5. *-commutative N/A

      \[\leadsto \mathsf{expm1}\left(\color{blue}{x \cdot a}\right) \]

   6. lower-*.f64 100.0

      \[\leadsto \mathsf{expm1}\left(\color{blue}{x \cdot a}\right) \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\mathsf{expm1}\left(x \cdot a\right)} \]
5. Add Preprocessing

Alternative 2: 99.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;e^{a \cdot x} - 1 \leq -1:\\ \;\;\;\;{\left(\mathsf{fma}\left(x \cdot \mathsf{fma}\left(x, a, -1\right), a, 1\right)\right)}^{-1} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664 \cdot x, a, 0.16666666666666666\right), x \cdot a, 0.5\right) \cdot x, a, 1\right) \cdot \left(x \cdot a\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (a x)
 :precision binary64
 (if (<= (- (exp (* a x)) 1.0) -1.0)
   (- (pow (fma (* x (fma x a -1.0)) a 1.0) -1.0) 1.0)
   (*
    (fma
     (*
      (fma (fma (* 0.041666666666666664 x) a 0.16666666666666666) (* x a) 0.5)
      x)
     a
     1.0)
    (* x a))))

C

double code(double a, double x) {
	double tmp;
	if ((exp((a * x)) - 1.0) <= -1.0) {
		tmp = pow(fma((x * fma(x, a, -1.0)), a, 1.0), -1.0) - 1.0;
	} else {
		tmp = fma((fma(fma((0.041666666666666664 * x), a, 0.16666666666666666), (x * a), 0.5) * x), a, 1.0) * (x * a);
	}
	return tmp;
}

Julia

function code(a, x)
	tmp = 0.0
	if (Float64(exp(Float64(a * x)) - 1.0) <= -1.0)
		tmp = Float64((fma(Float64(x * fma(x, a, -1.0)), a, 1.0) ^ -1.0) - 1.0);
	else
		tmp = Float64(fma(Float64(fma(fma(Float64(0.041666666666666664 * x), a, 0.16666666666666666), Float64(x * a), 0.5) * x), a, 1.0) * Float64(x * a));
	end
	return tmp
end

Wolfram

code[a_, x_] := If[LessEqual[N[(N[Exp[N[(a * x), $MachinePrecision]], $MachinePrecision] - 1.0), $MachinePrecision], -1.0], N[(N[Power[N[(N[(x * N[(x * a + -1.0), $MachinePrecision]), $MachinePrecision] * a + 1.0), $MachinePrecision], -1.0], $MachinePrecision] - 1.0), $MachinePrecision], N[(N[(N[(N[(N[(N[(0.041666666666666664 * x), $MachinePrecision] * a + 0.16666666666666666), $MachinePrecision] * N[(x * a), $MachinePrecision] + 0.5), $MachinePrecision] * x), $MachinePrecision] * a + 1.0), $MachinePrecision] * N[(x * a), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (exp.f64 (*.f64 a x)) #s(literal 1 binary64)) < -1

   1. Initial program 100.0%

      \[e^{a \cdot x} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 5.2

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

   5. Applied rewrites 5.2%

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

   7. Applied rewrites 5.2%

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

   8. Taylor expanded in a around 0

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

   10. Applied rewrites 98.0%

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

   if -1 < (-.f64 (exp.f64 (*.f64 a x)) #s(literal 1 binary64))

   1. Initial program 33.9%

      \[e^{a \cdot x} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

      \[\leadsto \color{blue}{a \cdot \left(x + a \cdot \left(\frac{1}{2} \cdot {x}^{2} + a \cdot \left(\frac{1}{24} \cdot \left(a \cdot {x}^{4}\right) + \frac{1}{6} \cdot {x}^{3}\right)\right)\right)} \]

   5. Applied rewrites 100.0%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664 \cdot x, a, 0.16666666666666666\right), x \cdot a, 0.5\right) \cdot x, a, 1\right) \cdot \left(x \cdot a\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;e^{a \cdot x} - 1 \leq -1:\\ \;\;\;\;{\left(\mathsf{fma}\left(x \cdot \mathsf{fma}\left(x, a, -1\right), a, 1\right)\right)}^{-1} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664 \cdot x, a, 0.16666666666666666\right), x \cdot a, 0.5\right) \cdot x, a, 1\right) \cdot \left(x \cdot a\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.0% accurate, 0.5× speedup

Math

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

FPCore

(FPCore (a x)
 :precision binary64
 (if (<= (- (exp (* a x)) 1.0) -1.0)
   (- (pow (fma (* x (fma x a -1.0)) a 1.0) -1.0) 1.0)
   (* a (fma (* (* (fma a (* x 0.16666666666666666) 0.5) a) x) x x))))

C

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

Julia

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

Wolfram

code[a_, x_] := If[LessEqual[N[(N[Exp[N[(a * x), $MachinePrecision]], $MachinePrecision] - 1.0), $MachinePrecision], -1.0], N[(N[Power[N[(N[(x * N[(x * a + -1.0), $MachinePrecision]), $MachinePrecision] * a + 1.0), $MachinePrecision], -1.0], $MachinePrecision] - 1.0), $MachinePrecision], N[(a * N[(N[(N[(N[(a * N[(x * 0.16666666666666666), $MachinePrecision] + 0.5), $MachinePrecision] * a), $MachinePrecision] * x), $MachinePrecision] * x + x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 (exp.f64 (*.f64 a x)) #s(literal 1 binary64)) < -1

   1. Initial program 100.0%

      \[e^{a \cdot x} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 5.2

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

   5. Applied rewrites 5.2%

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

   7. Applied rewrites 5.2%

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

   8. Taylor expanded in a around 0

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

   10. Applied rewrites 98.0%

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

   if -1 < (-.f64 (exp.f64 (*.f64 a x)) #s(literal 1 binary64))

   1. Initial program 33.9%

      \[e^{a \cdot x} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

      \[\leadsto \color{blue}{a \cdot \left(x + a \cdot \left(\frac{1}{6} \cdot \left(a \cdot {x}^{3}\right) + \frac{1}{2} \cdot {x}^{2}\right)\right)} \]

   5. Applied rewrites 99.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666 \cdot x, a, 0.5\right) \cdot a, x, 1\right) \cdot \left(x \cdot a\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 99.8%

      \[\leadsto \mathsf{fma}\left(\left(\mathsf{fma}\left(0.16666666666666666 \cdot x, a, 0.5\right) \cdot a\right) \cdot x, \color{blue}{x \cdot a}, x \cdot a\right) \]
   8. Step-by-step derivation

   9. Applied rewrites 99.8%

      \[\leadsto a \cdot \color{blue}{\mathsf{fma}\left(\left(\mathsf{fma}\left(a, x \cdot 0.16666666666666666, 0.5\right) \cdot a\right) \cdot x, x, x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.2%

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

Alternative 4: 98.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot x \leq -50:\\ \;\;\;\;{\left(\mathsf{fma}\left(x \cdot \mathsf{fma}\left(x, a, -1\right), a, 1\right)\right)}^{-1} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\left(a \cdot x\right) \cdot a, 0.5, a\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (a x)
 :precision binary64
 (if (<= (* a x) -50.0)
   (- (pow (fma (* x (fma x a -1.0)) a 1.0) -1.0) 1.0)
   (* (fma (* (* a x) a) 0.5 a) x)))

C

double code(double a, double x) {
	double tmp;
	if ((a * x) <= -50.0) {
		tmp = pow(fma((x * fma(x, a, -1.0)), a, 1.0), -1.0) - 1.0;
	} else {
		tmp = fma(((a * x) * a), 0.5, a) * x;
	}
	return tmp;
}

Julia

function code(a, x)
	tmp = 0.0
	if (Float64(a * x) <= -50.0)
		tmp = Float64((fma(Float64(x * fma(x, a, -1.0)), a, 1.0) ^ -1.0) - 1.0);
	else
		tmp = Float64(fma(Float64(Float64(a * x) * a), 0.5, a) * x);
	end
	return tmp
end

Wolfram

code[a_, x_] := If[LessEqual[N[(a * x), $MachinePrecision], -50.0], N[(N[Power[N[(N[(x * N[(x * a + -1.0), $MachinePrecision]), $MachinePrecision] * a + 1.0), $MachinePrecision], -1.0], $MachinePrecision] - 1.0), $MachinePrecision], N[(N[(N[(N[(a * x), $MachinePrecision] * a), $MachinePrecision] * 0.5 + a), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 a x) < -50

   1. Initial program 100.0%

      \[e^{a \cdot x} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 5.2

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

   5. Applied rewrites 5.2%

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

   7. Applied rewrites 5.2%

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

   8. Taylor expanded in a around 0

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

   10. Applied rewrites 98.0%

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

   if -50 < (*.f64 a x)

   1. Initial program 33.9%

      \[e^{a \cdot x} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

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

      1. distribute-lft-in N/A

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

      2. associate-*r* N/A

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

      3. unpow2 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. associate-*r* N/A

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

      7. *-commutative N/A

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

      8. distribute-rgt1-in N/A

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

      9. *-commutative N/A

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

      10. associate-*r* N/A

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

      11. distribute-rgt1-in N/A

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

      12. associate-*r* N/A

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

      13. associate-*l* N/A

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

      14. unpow2 N/A

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

      15. associate-*r* N/A

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

      16. *-commutative N/A

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

      17. distribute-lft-in N/A

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

      18. *-commutative N/A

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

      19. lower-*.f64 N/A

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

   5. Applied rewrites 92.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left(a \cdot a\right) \cdot x, 0.5, a\right) \cdot x} \]
   6. Step-by-step derivation

   7. Applied rewrites 99.4%

      \[\leadsto \mathsf{fma}\left(\left(a \cdot x\right) \cdot a, 0.5, a\right) \cdot x \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot x \leq -50:\\ \;\;\;\;{\left(\mathsf{fma}\left(x \cdot \mathsf{fma}\left(x, a, -1\right), a, 1\right)\right)}^{-1} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\left(a \cdot x\right) \cdot a, 0.5, a\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot x \leq -50:\\ \;\;\;\;{\left(1 - x \cdot a\right)}^{-1} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\left(a \cdot x\right) \cdot a, 0.5, a\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (a x)
 :precision binary64
 (if (<= (* a x) -50.0)
   (- (pow (- 1.0 (* x a)) -1.0) 1.0)
   (* (fma (* (* a x) a) 0.5 a) x)))

C

double code(double a, double x) {
	double tmp;
	if ((a * x) <= -50.0) {
		tmp = pow((1.0 - (x * a)), -1.0) - 1.0;
	} else {
		tmp = fma(((a * x) * a), 0.5, a) * x;
	}
	return tmp;
}

Julia

function code(a, x)
	tmp = 0.0
	if (Float64(a * x) <= -50.0)
		tmp = Float64((Float64(1.0 - Float64(x * a)) ^ -1.0) - 1.0);
	else
		tmp = Float64(fma(Float64(Float64(a * x) * a), 0.5, a) * x);
	end
	return tmp
end

Wolfram

code[a_, x_] := If[LessEqual[N[(a * x), $MachinePrecision], -50.0], N[(N[Power[N[(1.0 - N[(x * a), $MachinePrecision]), $MachinePrecision], -1.0], $MachinePrecision] - 1.0), $MachinePrecision], N[(N[(N[(N[(a * x), $MachinePrecision] * a), $MachinePrecision] * 0.5 + a), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 a x) < -50

   1. Initial program 100.0%

      \[e^{a \cdot x} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

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

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. lower-fma.f64 5.2

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

   5. Applied rewrites 5.2%

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

   7. Applied rewrites 5.2%

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

   8. Taylor expanded in a around 0

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

   10. Applied rewrites 96.0%

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

   if -50 < (*.f64 a x)

   1. Initial program 33.9%

      \[e^{a \cdot x} - 1 \]
   2. Add Preprocessing

   3. Taylor expanded in a around 0

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

      1. distribute-lft-in N/A

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

      2. associate-*r* N/A

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

      3. unpow2 N/A

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

      4. associate-*r* N/A

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

      5. *-commutative N/A

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

      6. associate-*r* N/A

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

      7. *-commutative N/A

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

      8. distribute-rgt1-in N/A

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

      9. *-commutative N/A

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

      10. associate-*r* N/A

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

      11. distribute-rgt1-in N/A

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

      12. associate-*r* N/A

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

      13. associate-*l* N/A

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

      14. unpow2 N/A

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

      15. associate-*r* N/A

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

      16. *-commutative N/A

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

      17. distribute-lft-in N/A

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

      18. *-commutative N/A

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

      19. lower-*.f64 N/A

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

   5. Applied rewrites 92.2%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left(a \cdot a\right) \cdot x, 0.5, a\right) \cdot x} \]
   6. Step-by-step derivation

   7. Applied rewrites 99.4%

      \[\leadsto \mathsf{fma}\left(\left(a \cdot x\right) \cdot a, 0.5, a\right) \cdot x \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot x \leq -50:\\ \;\;\;\;{\left(1 - x \cdot a\right)}^{-1} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\left(a \cdot x\right) \cdot a, 0.5, a\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 67.1% accurate, 18.2× speedup

Math

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

FPCore

(FPCore (a x) :precision binary64 (* x a))

C

double code(double a, double x) {
	return x * a;
}

Fortran

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

Java

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

Python

def code(a, x):
	return x * a

Julia

function code(a, x)
	return Float64(x * a)
end

MATLAB

function tmp = code(a, x)
	tmp = x * a;
end

Wolfram

code[a_, x_] := N[(x * a), $MachinePrecision]

Derivation

1. Initial program 55.1%

   \[e^{a \cdot x} - 1 \]
2. Add Preprocessing

3. Taylor expanded in a around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 68.2

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

5. Applied rewrites 68.2%

   \[\leadsto \color{blue}{x \cdot a} \]
6. Add Preprocessing

Alternative 7: 19.4% accurate, 27.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(a, x):
	return 1.0 - 1.0

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 55.1%

   \[e^{a \cdot x} - 1 \]
2. Add Preprocessing

3. Taylor expanded in a around 0

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

5. Applied rewrites 22.0%

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

Developer Target 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{expm1}\left(a \cdot x\right) \end{array} \]

FPCore

(FPCore (a x) :precision binary64 (expm1 (* a x)))

C

double code(double a, double x) {
	return expm1((a * x));
}

Java

public static double code(double a, double x) {
	return Math.expm1((a * x));
}

Python

def code(a, x):
	return math.expm1((a * x))

Julia

function code(a, x)
	return expm1(Float64(a * x))
end

Wolfram

code[a_, x_] := N[(Exp[N[(a * x), $MachinePrecision]] - 1), $MachinePrecision]

Reproduce

herbie shell --seed 2024324 
(FPCore (a x)
  :name "expax (section 3.5)"
  :precision binary64
  :pre (> 710.0 (* a x))

  :alt
  (! :herbie-platform default (expm1 (* a x)))

  (- (exp (* a x)) 1.0))