expax (section 3.5)

Percentage Accurate: 54.6% → 100.0%

Time: 5.5s

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: 54.6% 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

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

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]

Derivation

1. Initial program 53.3%

   \[e^{a \cdot x} - 1 \]
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. Final simplification 100.0%

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

Alternative 2: 99.1% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot x \leq -1000:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(a, x, -1\right) \cdot x, a, 1\right)} - 1\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664 \cdot x, a, 0.16666666666666666\right), a \cdot x, 0.5\right) \cdot x, a, 1\right) \cdot a\right) \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (a x)
 :precision binary64
 (if (<= (* a x) -1000.0)
   (- (/ 1.0 (fma (* (fma a x -1.0) x) a 1.0)) 1.0)
   (*
    (*
     (fma
      (*
       (fma (fma (* 0.041666666666666664 x) a 0.16666666666666666) (* a x) 0.5)
       x)
      a
      1.0)
     a)
    x)))

C

double code(double a, double x) {
	double tmp;
	if ((a * x) <= -1000.0) {
		tmp = (1.0 / fma((fma(a, x, -1.0) * x), a, 1.0)) - 1.0;
	} else {
		tmp = (fma((fma(fma((0.041666666666666664 * x), a, 0.16666666666666666), (a * x), 0.5) * x), a, 1.0) * a) * x;
	}
	return tmp;
}

Julia

function code(a, x)
	tmp = 0.0
	if (Float64(a * x) <= -1000.0)
		tmp = Float64(Float64(1.0 / fma(Float64(fma(a, x, -1.0) * x), a, 1.0)) - 1.0);
	else
		tmp = Float64(Float64(fma(Float64(fma(fma(Float64(0.041666666666666664 * x), a, 0.16666666666666666), Float64(a * x), 0.5) * x), a, 1.0) * a) * x);
	end
	return tmp
end

Wolfram

code[a_, x_] := If[LessEqual[N[(a * x), $MachinePrecision], -1000.0], N[(N[(1.0 / N[(N[(N[(a * x + -1.0), $MachinePrecision] * x), $MachinePrecision] * a + 1.0), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision], N[(N[(N[(N[(N[(N[(N[(0.041666666666666664 * x), $MachinePrecision] * a + 0.16666666666666666), $MachinePrecision] * N[(a * x), $MachinePrecision] + 0.5), $MachinePrecision] * x), $MachinePrecision] * a + 1.0), $MachinePrecision] * a), $MachinePrecision] * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 a x) < -1e3

   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 4.9

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

   5. Applied rewrites 4.9%

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

   7. Applied rewrites 4.9%

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

   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 99.5%

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

   if -1e3 < (*.f64 a x)

   1. Initial program 26.7%

      \[e^{a \cdot x} - 1 \]
   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 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664 \cdot x, a, 0.16666666666666666\right), x \cdot a, 0.5\right) \cdot x, a, 1\right) \cdot \left(x \cdot a\right)} \]
   6. Step-by-step derivation

   7. Applied rewrites 99.7%

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

4. Final simplification 99.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot x \leq -1000:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(a, x, -1\right) \cdot x, a, 1\right)} - 1\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664 \cdot x, a, 0.16666666666666666\right), a \cdot x, 0.5\right) \cdot x, a, 1\right) \cdot a\right) \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 99.0% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot x \leq -1000:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(a, x, -1\right) \cdot x, a, 1\right)} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666 \cdot x, a, 0.5\right) \cdot a, x, 1\right) \cdot \left(a \cdot x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (a x)
 :precision binary64
 (if (<= (* a x) -1000.0)
   (- (/ 1.0 (fma (* (fma a x -1.0) x) a 1.0)) 1.0)
   (* (fma (* (fma (* 0.16666666666666666 x) a 0.5) a) x 1.0) (* a x))))

C

double code(double a, double x) {
	double tmp;
	if ((a * x) <= -1000.0) {
		tmp = (1.0 / fma((fma(a, x, -1.0) * x), a, 1.0)) - 1.0;
	} else {
		tmp = fma((fma((0.16666666666666666 * x), a, 0.5) * a), x, 1.0) * (a * x);
	}
	return tmp;
}

Julia

function code(a, x)
	tmp = 0.0
	if (Float64(a * x) <= -1000.0)
		tmp = Float64(Float64(1.0 / fma(Float64(fma(a, x, -1.0) * x), a, 1.0)) - 1.0);
	else
		tmp = Float64(fma(Float64(fma(Float64(0.16666666666666666 * x), a, 0.5) * a), x, 1.0) * Float64(a * x));
	end
	return tmp
end

Wolfram

code[a_, x_] := If[LessEqual[N[(a * x), $MachinePrecision], -1000.0], N[(N[(1.0 / N[(N[(N[(a * x + -1.0), $MachinePrecision] * x), $MachinePrecision] * a + 1.0), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision], N[(N[(N[(N[(N[(0.16666666666666666 * x), $MachinePrecision] * a + 0.5), $MachinePrecision] * a), $MachinePrecision] * x + 1.0), $MachinePrecision] * N[(a * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 a x) < -1e3

   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 4.9

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

   5. Applied rewrites 4.9%

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

   7. Applied rewrites 4.9%

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

   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 99.5%

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

   if -1e3 < (*.f64 a x)

   1. Initial program 26.7%

      \[e^{a \cdot x} - 1 \]
   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 99.5%

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

4. Final simplification 99.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot x \leq -1000:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(a, x, -1\right) \cdot x, a, 1\right)} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666 \cdot x, a, 0.5\right) \cdot a, x, 1\right) \cdot \left(a \cdot x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.7% accurate, 2.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot x \leq -1000:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(a, x, -1\right) \cdot x, a, 1\right)} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.5 \cdot a, x, 1\right) \cdot \left(a \cdot x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (a x)
 :precision binary64
 (if (<= (* a x) -1000.0)
   (- (/ 1.0 (fma (* (fma a x -1.0) x) a 1.0)) 1.0)
   (* (fma (* 0.5 a) x 1.0) (* a x))))

C

double code(double a, double x) {
	double tmp;
	if ((a * x) <= -1000.0) {
		tmp = (1.0 / fma((fma(a, x, -1.0) * x), a, 1.0)) - 1.0;
	} else {
		tmp = fma((0.5 * a), x, 1.0) * (a * x);
	}
	return tmp;
}

Julia

function code(a, x)
	tmp = 0.0
	if (Float64(a * x) <= -1000.0)
		tmp = Float64(Float64(1.0 / fma(Float64(fma(a, x, -1.0) * x), a, 1.0)) - 1.0);
	else
		tmp = Float64(fma(Float64(0.5 * a), x, 1.0) * Float64(a * x));
	end
	return tmp
end

Wolfram

code[a_, x_] := If[LessEqual[N[(a * x), $MachinePrecision], -1000.0], N[(N[(1.0 / N[(N[(N[(a * x + -1.0), $MachinePrecision] * x), $MachinePrecision] * a + 1.0), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision], N[(N[(N[(0.5 * a), $MachinePrecision] * x + 1.0), $MachinePrecision] * N[(a * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 a x) < -1e3

   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 4.9

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

   5. Applied rewrites 4.9%

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

   7. Applied rewrites 4.9%

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

   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 99.5%

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

   if -1e3 < (*.f64 a x)

   1. Initial program 26.7%

      \[e^{a \cdot x} - 1 \]
   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 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666 \cdot x, a, 0.5\right) \cdot a, x, 1\right) \cdot \left(x \cdot a\right)} \]

   6. Taylor expanded in a around 0

      \[\leadsto \mathsf{fma}\left(\frac{1}{2} \cdot a, x, 1\right) \cdot \left(x \cdot a\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 99.2%

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

4. Final simplification 99.3%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot x \leq -1000:\\ \;\;\;\;\frac{1}{\mathsf{fma}\left(\mathsf{fma}\left(a, x, -1\right) \cdot x, a, 1\right)} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.5 \cdot a, x, 1\right) \cdot \left(a \cdot x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 98.4% accurate, 3.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;a \cdot x \leq -1000:\\ \;\;\;\;\frac{1}{1 - a \cdot x} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.5 \cdot a, x, 1\right) \cdot \left(a \cdot x\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (a x)
 :precision binary64
 (if (<= (* a x) -1000.0)
   (- (/ 1.0 (- 1.0 (* a x))) 1.0)
   (* (fma (* 0.5 a) x 1.0) (* a x))))

C

double code(double a, double x) {
	double tmp;
	if ((a * x) <= -1000.0) {
		tmp = (1.0 / (1.0 - (a * x))) - 1.0;
	} else {
		tmp = fma((0.5 * a), x, 1.0) * (a * x);
	}
	return tmp;
}

Julia

function code(a, x)
	tmp = 0.0
	if (Float64(a * x) <= -1000.0)
		tmp = Float64(Float64(1.0 / Float64(1.0 - Float64(a * x))) - 1.0);
	else
		tmp = Float64(fma(Float64(0.5 * a), x, 1.0) * Float64(a * x));
	end
	return tmp
end

Wolfram

code[a_, x_] := If[LessEqual[N[(a * x), $MachinePrecision], -1000.0], N[(N[(1.0 / N[(1.0 - N[(a * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 1.0), $MachinePrecision], N[(N[(N[(0.5 * a), $MachinePrecision] * x + 1.0), $MachinePrecision] * N[(a * x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 a x) < -1e3

   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 4.9

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

   5. Applied rewrites 4.9%

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

   7. Applied rewrites 4.9%

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

   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 98.0%

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

   if -1e3 < (*.f64 a x)

   1. Initial program 26.7%

      \[e^{a \cdot x} - 1 \]
   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 99.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.16666666666666666 \cdot x, a, 0.5\right) \cdot a, x, 1\right) \cdot \left(x \cdot a\right)} \]

   6. Taylor expanded in a around 0

      \[\leadsto \mathsf{fma}\left(\frac{1}{2} \cdot a, x, 1\right) \cdot \left(x \cdot a\right) \]
   7. Step-by-step derivation

   8. Applied rewrites 99.2%

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

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;a \cdot x \leq -1000:\\ \;\;\;\;\frac{1}{1 - a \cdot x} - 1\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0.5 \cdot a, x, 1\right) \cdot \left(a \cdot x\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 6: 66.8% accurate, 18.2× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 53.3%

   \[e^{a \cdot x} - 1 \]
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 64.2

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

5. Applied rewrites 64.2%

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

6. Final simplification 64.2%

   \[\leadsto a \cdot x \]
7. Add Preprocessing

Alternative 7: 20.1% 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 53.3%

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

3. Taylor expanded in a around 0

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

5. Applied rewrites 16.2%

   \[\leadsto \color{blue}{1} - 1 \]
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 2024304 
(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))