exp2 (problem 3.3.7)

Percentage Accurate: 53.5% → 99.2%

Time: 11.7s

Alternatives: 7

Speedup: 34.8×

Specification

\[\left|x\right| \leq 710\]

Math

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

FPCore

(FPCore (x) :precision binary64 (+ (- (exp x) 2.0) (exp (- x))))

C

double code(double x) {
	return (exp(x) - 2.0) + exp(-x);
}

Fortran

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

Java

public static double code(double x) {
	return (Math.exp(x) - 2.0) + Math.exp(-x);
}

Python

def code(x):
	return (math.exp(x) - 2.0) + math.exp(-x)

Julia

function code(x)
	return Float64(Float64(exp(x) - 2.0) + exp(Float64(-x)))
end

MATLAB

function tmp = code(x)
	tmp = (exp(x) - 2.0) + exp(-x);
end

Wolfram

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

Initial Program: 53.5% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (+ (- (exp x) 2.0) (exp (- x))))

C

double code(double x) {
	return (exp(x) - 2.0) + exp(-x);
}

Fortran

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

Java

public static double code(double x) {
	return (Math.exp(x) - 2.0) + Math.exp(-x);
}

Python

def code(x):
	return (math.exp(x) - 2.0) + math.exp(-x)

Julia

function code(x)
	return Float64(Float64(exp(x) - 2.0) + exp(Float64(-x)))
end

MATLAB

function tmp = code(x)
	tmp = (exp(x) - 2.0) + exp(-x);
end

Wolfram

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

Alternative 1: 99.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x \cdot x, \left(x \cdot x\right) \cdot 0.08333333333333333, \mathsf{fma}\left(x, x, \mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right) \cdot {x}^{6}\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (fma
  (* x x)
  (* (* x x) 0.08333333333333333)
  (fma
   x
   x
   (* (fma (* x x) 4.96031746031746e-5 0.002777777777777778) (pow x 6.0)))))

C

double code(double x) {
	return fma((x * x), ((x * x) * 0.08333333333333333), fma(x, x, (fma((x * x), 4.96031746031746e-5, 0.002777777777777778) * pow(x, 6.0))));
}

Julia

function code(x)
	return fma(Float64(x * x), Float64(Float64(x * x) * 0.08333333333333333), fma(x, x, Float64(fma(Float64(x * x), 4.96031746031746e-5, 0.002777777777777778) * (x ^ 6.0))))
end

Wolfram

code[x_] := N[(N[(x * x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * 0.08333333333333333), $MachinePrecision] + N[(x * x + N[(N[(N[(x * x), $MachinePrecision] * 4.96031746031746e-5 + 0.002777777777777778), $MachinePrecision] * N[Power[x, 6.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 55.4%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto {x}^{2} \cdot \left(1 + \color{blue}{\left({x}^{2} \cdot \frac{1}{12} + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)}\right) \]

   2. *-commutative N/A

      \[\leadsto {x}^{2} \cdot \left(1 + \left(\color{blue}{\frac{1}{12} \cdot {x}^{2}} + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)\right) \]

   3. associate-+r+ N/A

      \[\leadsto {x}^{2} \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]

   4. distribute-lft-in N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]

   5. +-commutative N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right) + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)} \]

   6. *-commutative N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right) \cdot {x}^{2}} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   7. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left({x}^{2} \cdot {x}^{2}\right) \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)} \cdot {x}^{2} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   8. *-commutative N/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right) \cdot \left({x}^{2} \cdot {x}^{2}\right)\right)} \cdot {x}^{2} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   9. associate-*l* N/A

      \[\leadsto \color{blue}{\left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right) \cdot \left(\left({x}^{2} \cdot {x}^{2}\right) \cdot {x}^{2}\right)} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   10. *-commutative N/A

       \[\leadsto \color{blue}{\left(\left({x}^{2} \cdot {x}^{2}\right) \cdot {x}^{2}\right) \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

5. Applied rewrites 98.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{6}, \mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), \mathsf{fma}\left({x}^{4}, 0.08333333333333333, x \cdot x\right)\right)} \]
6. Step-by-step derivation

7. Applied rewrites 98.6%

   \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\left(x \cdot x\right) \cdot 0.08333333333333333}, \mathsf{fma}\left(x, x, \mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right) \cdot {x}^{6}\right)\right) \]
8. Add Preprocessing

Alternative 2: 99.2% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left({x}^{3}, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x\right) \cdot x \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  (fma
   (pow x 3.0)
   (fma
    (fma (* x x) 4.96031746031746e-5 0.002777777777777778)
    (* x x)
    0.08333333333333333)
   x)
  x))

C

double code(double x) {
	return fma(pow(x, 3.0), fma(fma((x * x), 4.96031746031746e-5, 0.002777777777777778), (x * x), 0.08333333333333333), x) * x;
}

Julia

function code(x)
	return Float64(fma((x ^ 3.0), fma(fma(Float64(x * x), 4.96031746031746e-5, 0.002777777777777778), Float64(x * x), 0.08333333333333333), x) * x)
end

Wolfram

code[x_] := N[(N[(N[Power[x, 3.0], $MachinePrecision] * N[(N[(N[(x * x), $MachinePrecision] * 4.96031746031746e-5 + 0.002777777777777778), $MachinePrecision] * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] + x), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 55.4%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto {x}^{2} \cdot \left(1 + \color{blue}{\left({x}^{2} \cdot \frac{1}{12} + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)}\right) \]

   2. *-commutative N/A

      \[\leadsto {x}^{2} \cdot \left(1 + \left(\color{blue}{\frac{1}{12} \cdot {x}^{2}} + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)\right) \]

   3. associate-+r+ N/A

      \[\leadsto {x}^{2} \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]

   4. distribute-lft-in N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]

   5. +-commutative N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right) + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)} \]

   6. *-commutative N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right) \cdot {x}^{2}} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   7. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left({x}^{2} \cdot {x}^{2}\right) \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)} \cdot {x}^{2} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   8. *-commutative N/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right) \cdot \left({x}^{2} \cdot {x}^{2}\right)\right)} \cdot {x}^{2} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   9. associate-*l* N/A

      \[\leadsto \color{blue}{\left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right) \cdot \left(\left({x}^{2} \cdot {x}^{2}\right) \cdot {x}^{2}\right)} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   10. *-commutative N/A

       \[\leadsto \color{blue}{\left(\left({x}^{2} \cdot {x}^{2}\right) \cdot {x}^{2}\right) \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

5. Applied rewrites 98.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{6}, \mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), \mathsf{fma}\left({x}^{4}, 0.08333333333333333, x \cdot x\right)\right)} \]
6. Step-by-step derivation

7. Applied rewrites 98.6%

   \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\left(x \cdot x\right) \cdot 0.08333333333333333}, \mathsf{fma}\left(x, x, \mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right) \cdot {x}^{6}\right)\right) \]
8. Step-by-step derivation

9. Applied rewrites 98.6%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.08333333333333333 \cdot x, x, 1\right), \color{blue}{x \cdot x}, {x}^{6} \cdot \mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right)\right) \]

10. Taylor expanded in x around 0

    \[\leadsto {x}^{2} \cdot \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
11. Step-by-step derivation

12. Applied rewrites 98.6%

    \[\leadsto \mathsf{fma}\left({x}^{3}, \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right), x \cdot x, 0.08333333333333333\right), x\right) \cdot \color{blue}{x} \]
13. Add Preprocessing

Alternative 3: 99.1% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, x, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot {x}^{4}\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (fma
  x
  x
  (* (fma 0.002777777777777778 (* x x) 0.08333333333333333) (pow x 4.0))))

C

double code(double x) {
	return fma(x, x, (fma(0.002777777777777778, (x * x), 0.08333333333333333) * pow(x, 4.0)));
}

Julia

function code(x)
	return fma(x, x, Float64(fma(0.002777777777777778, Float64(x * x), 0.08333333333333333) * (x ^ 4.0)))
end

Wolfram

code[x_] := N[(x * x + N[(N[(0.002777777777777778 * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * N[Power[x, 4.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 55.4%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto {x}^{2} \cdot \left(1 + \color{blue}{\left({x}^{2} \cdot \frac{1}{12} + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)}\right) \]

   2. *-commutative N/A

      \[\leadsto {x}^{2} \cdot \left(1 + \left(\color{blue}{\frac{1}{12} \cdot {x}^{2}} + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)\right) \]

   3. associate-+r+ N/A

      \[\leadsto {x}^{2} \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]

   4. distribute-lft-in N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]

   5. +-commutative N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right) + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)} \]

   6. *-commutative N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right) \cdot {x}^{2}} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   7. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left({x}^{2} \cdot {x}^{2}\right) \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)} \cdot {x}^{2} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   8. *-commutative N/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right) \cdot \left({x}^{2} \cdot {x}^{2}\right)\right)} \cdot {x}^{2} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   9. associate-*l* N/A

      \[\leadsto \color{blue}{\left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right) \cdot \left(\left({x}^{2} \cdot {x}^{2}\right) \cdot {x}^{2}\right)} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   10. *-commutative N/A

       \[\leadsto \color{blue}{\left(\left({x}^{2} \cdot {x}^{2}\right) \cdot {x}^{2}\right) \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

5. Applied rewrites 98.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{6}, \mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), \mathsf{fma}\left({x}^{4}, 0.08333333333333333, x \cdot x\right)\right)} \]
6. Step-by-step derivation

7. Applied rewrites 98.6%

   \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\left(x \cdot x\right) \cdot 0.08333333333333333}, \mathsf{fma}\left(x, x, \mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right) \cdot {x}^{6}\right)\right) \]

8. Taylor expanded in x around 0

   \[\leadsto {x}^{2} \cdot \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)} \]
9. Step-by-step derivation

10. Applied rewrites 98.4%

    \[\leadsto \mathsf{fma}\left({x}^{3}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right), x\right) \cdot \color{blue}{x} \]
11. Step-by-step derivation

12. Applied rewrites 98.5%

    \[\leadsto \mathsf{fma}\left(x, x, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot {x}^{4}\right) \]

13. Final simplification 98.5%

    \[\leadsto \mathsf{fma}\left(x, x, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot {x}^{4}\right) \]
14. Add Preprocessing

Alternative 4: 99.1% accurate, 6.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), x, x\right) \cdot x \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  (fma (* (fma 0.002777777777777778 (* x x) 0.08333333333333333) (* x x)) x x)
  x))

C

double code(double x) {
	return fma((fma(0.002777777777777778, (x * x), 0.08333333333333333) * (x * x)), x, x) * x;
}

Julia

function code(x)
	return Float64(fma(Float64(fma(0.002777777777777778, Float64(x * x), 0.08333333333333333) * Float64(x * x)), x, x) * x)
end

Wolfram

code[x_] := N[(N[(N[(N[(0.002777777777777778 * N[(x * x), $MachinePrecision] + 0.08333333333333333), $MachinePrecision] * N[(x * x), $MachinePrecision]), $MachinePrecision] * x + x), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 55.4%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{12} + {x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]
4. Step-by-step derivation

   1. distribute-lft-in N/A

      \[\leadsto {x}^{2} \cdot \left(1 + \color{blue}{\left({x}^{2} \cdot \frac{1}{12} + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)}\right) \]

   2. *-commutative N/A

      \[\leadsto {x}^{2} \cdot \left(1 + \left(\color{blue}{\frac{1}{12} \cdot {x}^{2}} + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)\right) \]

   3. associate-+r+ N/A

      \[\leadsto {x}^{2} \cdot \color{blue}{\left(\left(1 + \frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]

   4. distribute-lft-in N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right)} \]

   5. +-commutative N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right) + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)} \]

   6. *-commutative N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)\right) \cdot {x}^{2}} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   7. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left({x}^{2} \cdot {x}^{2}\right) \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)\right)} \cdot {x}^{2} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   8. *-commutative N/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right) \cdot \left({x}^{2} \cdot {x}^{2}\right)\right)} \cdot {x}^{2} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   9. associate-*l* N/A

      \[\leadsto \color{blue}{\left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right) \cdot \left(\left({x}^{2} \cdot {x}^{2}\right) \cdot {x}^{2}\right)} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

   10. *-commutative N/A

       \[\leadsto \color{blue}{\left(\left({x}^{2} \cdot {x}^{2}\right) \cdot {x}^{2}\right) \cdot \left(\frac{1}{360} + \frac{1}{20160} \cdot {x}^{2}\right)} + {x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right) \]

5. Applied rewrites 98.6%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{6}, \mathsf{fma}\left(4.96031746031746 \cdot 10^{-5}, x \cdot x, 0.002777777777777778\right), \mathsf{fma}\left({x}^{4}, 0.08333333333333333, x \cdot x\right)\right)} \]
6. Step-by-step derivation

7. Applied rewrites 98.6%

   \[\leadsto \mathsf{fma}\left(x \cdot x, \color{blue}{\left(x \cdot x\right) \cdot 0.08333333333333333}, \mathsf{fma}\left(x, x, \mathsf{fma}\left(x \cdot x, 4.96031746031746 \cdot 10^{-5}, 0.002777777777777778\right) \cdot {x}^{6}\right)\right) \]

8. Taylor expanded in x around 0

   \[\leadsto {x}^{2} \cdot \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{1}{12} + \frac{1}{360} \cdot {x}^{2}\right)\right)} \]
9. Step-by-step derivation

10. Applied rewrites 98.4%

    \[\leadsto \mathsf{fma}\left({x}^{3}, \mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right), x\right) \cdot \color{blue}{x} \]
11. Step-by-step derivation

12. Applied rewrites 98.4%

    \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), x, x\right) \cdot x \]

13. Final simplification 98.4%

    \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.002777777777777778, x \cdot x, 0.08333333333333333\right) \cdot \left(x \cdot x\right), x, x\right) \cdot x \]
14. Add Preprocessing

Alternative 5: 98.9% accurate, 7.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, x, \left(x \cdot x\right) \cdot \left(0.08333333333333333 \cdot \left(x \cdot x\right)\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (fma x x (* (* x x) (* 0.08333333333333333 (* x x)))))

C

double code(double x) {
	return fma(x, x, ((x * x) * (0.08333333333333333 * (x * x))));
}

Julia

function code(x)
	return fma(x, x, Float64(Float64(x * x) * Float64(0.08333333333333333 * Float64(x * x))))
end

Wolfram

code[x_] := N[(x * x + N[(N[(x * x), $MachinePrecision] * N[(0.08333333333333333 * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 55.4%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto {x}^{2} \cdot \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \]

   2. distribute-lft-in N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot 1} \]

   3. *-commutative N/A

      \[\leadsto {x}^{2} \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + {x}^{2} \cdot 1 \]

   4. associate-*r* N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot {x}^{2}\right) \cdot \frac{1}{12}} + {x}^{2} \cdot 1 \]

   5. *-rgt-identity N/A

      \[\leadsto \left({x}^{2} \cdot {x}^{2}\right) \cdot \frac{1}{12} + \color{blue}{{x}^{2}} \]

   6. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot {x}^{2}, \frac{1}{12}, {x}^{2}\right)} \]

   7. pow-sqr N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{\left(2 \cdot 2\right)}}, \frac{1}{12}, {x}^{2}\right) \]

   8. lower-pow.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{\left(2 \cdot 2\right)}}, \frac{1}{12}, {x}^{2}\right) \]

   9. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left({x}^{\color{blue}{4}}, \frac{1}{12}, {x}^{2}\right) \]

   10. unpow2 N/A

       \[\leadsto \mathsf{fma}\left({x}^{4}, \frac{1}{12}, \color{blue}{x \cdot x}\right) \]

   11. lower-*.f64 98.2

       \[\leadsto \mathsf{fma}\left({x}^{4}, 0.08333333333333333, \color{blue}{x \cdot x}\right) \]

5. Applied rewrites 98.2%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, 0.08333333333333333, x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 98.2%

   \[\leadsto \mathsf{fma}\left(x, \color{blue}{x}, 0.08333333333333333 \cdot {x}^{4}\right) \]
8. Step-by-step derivation

9. Applied rewrites 98.2%

   \[\leadsto \mathsf{fma}\left(x, x, \left(x \cdot x\right) \cdot \left(0.08333333333333333 \cdot \left(x \cdot x\right)\right)\right) \]
10. Add Preprocessing

Alternative 6: 98.9% accurate, 9.5× speedup

Math

\[\begin{array}{l} \\ \left(\mathsf{fma}\left(x \cdot 0.08333333333333333, x, 1\right) \cdot x\right) \cdot x \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* (* (fma (* x 0.08333333333333333) x 1.0) x) x))

C

double code(double x) {
	return (fma((x * 0.08333333333333333), x, 1.0) * x) * x;
}

Julia

function code(x)
	return Float64(Float64(fma(Float64(x * 0.08333333333333333), x, 1.0) * x) * x)
end

Wolfram

code[x_] := N[(N[(N[(N[(x * 0.08333333333333333), $MachinePrecision] * x + 1.0), $MachinePrecision] * x), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 55.4%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2} \cdot \left(1 + \frac{1}{12} \cdot {x}^{2}\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto {x}^{2} \cdot \color{blue}{\left(\frac{1}{12} \cdot {x}^{2} + 1\right)} \]

   2. distribute-lft-in N/A

      \[\leadsto \color{blue}{{x}^{2} \cdot \left(\frac{1}{12} \cdot {x}^{2}\right) + {x}^{2} \cdot 1} \]

   3. *-commutative N/A

      \[\leadsto {x}^{2} \cdot \color{blue}{\left({x}^{2} \cdot \frac{1}{12}\right)} + {x}^{2} \cdot 1 \]

   4. associate-*r* N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot {x}^{2}\right) \cdot \frac{1}{12}} + {x}^{2} \cdot 1 \]

   5. *-rgt-identity N/A

      \[\leadsto \left({x}^{2} \cdot {x}^{2}\right) \cdot \frac{1}{12} + \color{blue}{{x}^{2}} \]

   6. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot {x}^{2}, \frac{1}{12}, {x}^{2}\right)} \]

   7. pow-sqr N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{\left(2 \cdot 2\right)}}, \frac{1}{12}, {x}^{2}\right) \]

   8. lower-pow.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{\left(2 \cdot 2\right)}}, \frac{1}{12}, {x}^{2}\right) \]

   9. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left({x}^{\color{blue}{4}}, \frac{1}{12}, {x}^{2}\right) \]

   10. unpow2 N/A

       \[\leadsto \mathsf{fma}\left({x}^{4}, \frac{1}{12}, \color{blue}{x \cdot x}\right) \]

   11. lower-*.f64 98.2

       \[\leadsto \mathsf{fma}\left({x}^{4}, 0.08333333333333333, \color{blue}{x \cdot x}\right) \]

5. Applied rewrites 98.2%

   \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{4}, 0.08333333333333333, x \cdot x\right)} \]
6. Step-by-step derivation

7. Applied rewrites 98.2%

   \[\leadsto \mathsf{fma}\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right), 0.08333333333333333, x \cdot x\right) \]
8. Step-by-step derivation

9. Applied rewrites 98.2%

   \[\leadsto \mathsf{fma}\left(0.08333333333333333 \cdot x, x, 1\right) \cdot \color{blue}{\left(x \cdot x\right)} \]
10. Step-by-step derivation

11. Applied rewrites 98.2%

    \[\leadsto \left(\mathsf{fma}\left(x \cdot 0.08333333333333333, x, 1\right) \cdot x\right) \cdot \color{blue}{x} \]
12. Add Preprocessing

Alternative 7: 98.4% accurate, 34.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

def code(x):
	return x * x

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 55.4%

   \[\left(e^{x} - 2\right) + e^{-x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{2}} \]
4. Step-by-step derivation

   1. unpow2 N/A

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

   2. lower-*.f64 97.5

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

5. Applied rewrites 97.5%

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

Developer Target 1: 99.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sinh \left(\frac{x}{2}\right)\\ 4 \cdot \left(t\_0 \cdot t\_0\right) \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (sinh (/ x 2.0)))) (* 4.0 (* t_0 t_0))))

C

double code(double x) {
	double t_0 = sinh((x / 2.0));
	return 4.0 * (t_0 * t_0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    t_0 = sinh((x / 2.0d0))
    code = 4.0d0 * (t_0 * t_0)
end function

Java

public static double code(double x) {
	double t_0 = Math.sinh((x / 2.0));
	return 4.0 * (t_0 * t_0);
}

Python

def code(x):
	t_0 = math.sinh((x / 2.0))
	return 4.0 * (t_0 * t_0)

Julia

function code(x)
	t_0 = sinh(Float64(x / 2.0))
	return Float64(4.0 * Float64(t_0 * t_0))
end

MATLAB

function tmp = code(x)
	t_0 = sinh((x / 2.0));
	tmp = 4.0 * (t_0 * t_0);
end

Wolfram

code[x_] := Block[{t$95$0 = N[Sinh[N[(x / 2.0), $MachinePrecision]], $MachinePrecision]}, N[(4.0 * N[(t$95$0 * t$95$0), $MachinePrecision]), $MachinePrecision]]

Reproduce

herbie shell --seed 2024324 
(FPCore (x)
  :name "exp2 (problem 3.3.7)"
  :precision binary64
  :pre (<= (fabs x) 710.0)

  :alt
  (! :herbie-platform default (* 4 (* (sinh (/ x 2)) (sinh (/ x 2)))))

  (+ (- (exp x) 2.0) (exp (- x))))