ENA, Section 1.4, Exercise 1

Percentage Accurate: 94.5% → 99.4%

Time: 13.9s

Alternatives: 9

Speedup: 1.0×

Specification

\[1.99 \leq x \land x \leq 2.01\]

Math

\[\begin{array}{l} \\ \cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (exp (* 10.0 (* x x)))))

C

double code(double x) {
	return cos(x) * exp((10.0 * (x * x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * exp((10.0d0 * (x * x)))
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.exp((10.0 * (x * x)));
}

Python

def code(x):
	return math.cos(x) * math.exp((10.0 * (x * x)))

Julia

function code(x)
	return Float64(cos(x) * exp(Float64(10.0 * Float64(x * x))))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * exp((10.0 * (x * x)));
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Exp[N[(10.0 * N[(x * x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 94.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (exp (* 10.0 (* x x)))))

C

double code(double x) {
	return cos(x) * exp((10.0 * (x * x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * exp((10.0d0 * (x * x)))
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.exp((10.0 * (x * x)));
}

Python

def code(x):
	return math.cos(x) * math.exp((10.0 * (x * x)))

Julia

function code(x)
	return Float64(cos(x) * exp(Float64(10.0 * Float64(x * x))))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * exp((10.0 * (x * x)));
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Exp[N[(10.0 * N[(x * x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \cos x \cdot {\left({\left(e^{20}\right)}^{x}\right)}^{\left(x \cdot 0.5\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (pow (pow (exp 20.0) x) (* x 0.5))))

C

double code(double x) {
	return cos(x) * pow(pow(exp(20.0), x), (x * 0.5));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * ((exp(20.0d0) ** x) ** (x * 0.5d0))
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.pow(Math.pow(Math.exp(20.0), x), (x * 0.5));
}

Python

def code(x):
	return math.cos(x) * math.pow(math.pow(math.exp(20.0), x), (x * 0.5))

Julia

function code(x)
	return Float64(cos(x) * ((exp(20.0) ^ x) ^ Float64(x * 0.5)))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * ((exp(20.0) ^ x) ^ (x * 0.5));
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Power[N[Power[N[Exp[20.0], $MachinePrecision], x], $MachinePrecision], N[(x * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Step-by-step derivation

   1. associate-*r* 94.5%

      \[\leadsto \cos x \cdot e^{\color{blue}{\left(10 \cdot x\right) \cdot x}} \]

3. Simplified 94.5%

   \[\leadsto \color{blue}{\cos x \cdot e^{\left(10 \cdot x\right) \cdot x}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. log1p-expm1-u 94.5%

      \[\leadsto \color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\cos x\right)\right)} \cdot e^{\left(10 \cdot x\right) \cdot x} \]

   2. log1p-udef 94.5%

      \[\leadsto \color{blue}{\log \left(1 + \mathsf{expm1}\left(\cos x\right)\right)} \cdot e^{\left(10 \cdot x\right) \cdot x} \]

6. Applied egg-rr 94.5%

   \[\leadsto \color{blue}{\log \left(1 + \mathsf{expm1}\left(\cos x\right)\right)} \cdot e^{\left(10 \cdot x\right) \cdot x} \]
7. Step-by-step derivation

   1. exp-prod 94.9%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot \color{blue}{{\left(e^{10 \cdot x}\right)}^{x}} \]

   2. pow-exp 98.1%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot {\color{blue}{\left({\left(e^{10}\right)}^{x}\right)}}^{x} \]

   3. sqr-pow 98.0%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot \color{blue}{\left({\left({\left(e^{10}\right)}^{x}\right)}^{\left(\frac{x}{2}\right)} \cdot {\left({\left(e^{10}\right)}^{x}\right)}^{\left(\frac{x}{2}\right)}\right)} \]

   4. pow-prod-down 98.0%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot \color{blue}{{\left({\left(e^{10}\right)}^{x} \cdot {\left(e^{10}\right)}^{x}\right)}^{\left(\frac{x}{2}\right)}} \]

   5. pow-prod-down 98.1%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot {\color{blue}{\left({\left(e^{10} \cdot e^{10}\right)}^{x}\right)}}^{\left(\frac{x}{2}\right)} \]

   6. prod-exp 98.9%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot {\left({\color{blue}{\left(e^{10 + 10}\right)}}^{x}\right)}^{\left(\frac{x}{2}\right)} \]

   7. metadata-eval 98.9%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot {\left({\left(e^{\color{blue}{20}}\right)}^{x}\right)}^{\left(\frac{x}{2}\right)} \]

   8. div-inv 98.9%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot {\left({\left(e^{20}\right)}^{x}\right)}^{\color{blue}{\left(x \cdot \frac{1}{2}\right)}} \]

   9. metadata-eval 98.9%

      \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot {\left({\left(e^{20}\right)}^{x}\right)}^{\left(x \cdot \color{blue}{0.5}\right)} \]

8. Applied egg-rr 98.9%

   \[\leadsto \log \left(1 + \mathsf{expm1}\left(\cos x\right)\right) \cdot \color{blue}{{\left({\left(e^{20}\right)}^{x}\right)}^{\left(x \cdot 0.5\right)}} \]

9. Taylor expanded in x around inf 99.4%

   \[\leadsto \color{blue}{\cos x} \cdot {\left({\left(e^{20}\right)}^{x}\right)}^{\left(x \cdot 0.5\right)} \]

10. Final simplification 99.4%

    \[\leadsto \cos x \cdot {\left({\left(e^{20}\right)}^{x}\right)}^{\left(x \cdot 0.5\right)} \]
11. Add Preprocessing

Alternative 2: 98.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \cos x \cdot {\left({\left(e^{5}\right)}^{x}\right)}^{\left(x \cdot 2\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (pow (pow (exp 5.0) x) (* x 2.0))))

C

double code(double x) {
	return cos(x) * pow(pow(exp(5.0), x), (x * 2.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * ((exp(5.0d0) ** x) ** (x * 2.0d0))
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.pow(Math.pow(Math.exp(5.0), x), (x * 2.0));
}

Python

def code(x):
	return math.cos(x) * math.pow(math.pow(math.exp(5.0), x), (x * 2.0))

Julia

function code(x)
	return Float64(cos(x) * ((exp(5.0) ^ x) ^ Float64(x * 2.0)))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * ((exp(5.0) ^ x) ^ (x * 2.0));
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Power[N[Power[N[Exp[5.0], $MachinePrecision], x], $MachinePrecision], N[(x * 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. pow-exp 95.1%

      \[\leadsto \cos x \cdot \color{blue}{{\left(e^{10}\right)}^{\left(x \cdot x\right)}} \]

   2. pow-unpow 98.0%

      \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{10}\right)}^{x}\right)}^{x}} \]

4. Applied egg-rr 98.0%

   \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{10}\right)}^{x}\right)}^{x}} \]
5. Step-by-step derivation

   1. add-sqr-sqrt 97.9%

      \[\leadsto \cos x \cdot {\color{blue}{\left(\sqrt{{\left(e^{10}\right)}^{x}} \cdot \sqrt{{\left(e^{10}\right)}^{x}}\right)}}^{x} \]

   2. sqrt-prod 98.0%

      \[\leadsto \cos x \cdot {\color{blue}{\left(\sqrt{{\left(e^{10}\right)}^{x} \cdot {\left(e^{10}\right)}^{x}}\right)}}^{x} \]

   3. pow-prod-down 98.1%

      \[\leadsto \cos x \cdot {\left(\sqrt{\color{blue}{{\left(e^{10} \cdot e^{10}\right)}^{x}}}\right)}^{x} \]

   4. prod-exp 99.1%

      \[\leadsto \cos x \cdot {\left(\sqrt{{\color{blue}{\left(e^{10 + 10}\right)}}^{x}}\right)}^{x} \]

   5. metadata-eval 99.1%

      \[\leadsto \cos x \cdot {\left(\sqrt{{\left(e^{\color{blue}{20}}\right)}^{x}}\right)}^{x} \]

6. Applied egg-rr 99.1%

   \[\leadsto \cos x \cdot {\color{blue}{\left(\sqrt{{\left(e^{20}\right)}^{x}}\right)}}^{x} \]
7. Step-by-step derivation

   1. add-sqr-sqrt 98.4%

      \[\leadsto \cos x \cdot {\color{blue}{\left(\sqrt{\sqrt{{\left(e^{20}\right)}^{x}}} \cdot \sqrt{\sqrt{{\left(e^{20}\right)}^{x}}}\right)}}^{x} \]

   2. unpow-prod-down 98.4%

      \[\leadsto \cos x \cdot \color{blue}{\left({\left(\sqrt{\sqrt{{\left(e^{20}\right)}^{x}}}\right)}^{x} \cdot {\left(\sqrt{\sqrt{{\left(e^{20}\right)}^{x}}}\right)}^{x}\right)} \]

8. Applied egg-rr 98.1%

   \[\leadsto \cos x \cdot \color{blue}{\left({\left({\left(e^{5}\right)}^{x}\right)}^{x} \cdot {\left({\left(e^{5}\right)}^{x}\right)}^{x}\right)} \]
9. Step-by-step derivation

   1. pow-sqr 98.2%

      \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{5}\right)}^{x}\right)}^{\left(2 \cdot x\right)}} \]

   2. *-commutative 98.2%

      \[\leadsto \cos x \cdot {\left({\left(e^{5}\right)}^{x}\right)}^{\color{blue}{\left(x \cdot 2\right)}} \]

10. Simplified 98.2%

    \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{5}\right)}^{x}\right)}^{\left(x \cdot 2\right)}} \]

11. Final simplification 98.2%

    \[\leadsto \cos x \cdot {\left({\left(e^{5}\right)}^{x}\right)}^{\left(x \cdot 2\right)} \]
12. Add Preprocessing

Alternative 3: 98.4% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \cos x \cdot {\left({\left(e^{5}\right)}^{\left(x \cdot 2\right)}\right)}^{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (pow (pow (exp 5.0) (* x 2.0)) x)))

C

double code(double x) {
	return cos(x) * pow(pow(exp(5.0), (x * 2.0)), x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * ((exp(5.0d0) ** (x * 2.0d0)) ** x)
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.pow(Math.pow(Math.exp(5.0), (x * 2.0)), x);
}

Python

def code(x):
	return math.cos(x) * math.pow(math.pow(math.exp(5.0), (x * 2.0)), x)

Julia

function code(x)
	return Float64(cos(x) * ((exp(5.0) ^ Float64(x * 2.0)) ^ x))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * ((exp(5.0) ^ (x * 2.0)) ^ x);
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Power[N[Power[N[Exp[5.0], $MachinePrecision], N[(x * 2.0), $MachinePrecision]], $MachinePrecision], x], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. pow-exp 95.1%

      \[\leadsto \cos x \cdot \color{blue}{{\left(e^{10}\right)}^{\left(x \cdot x\right)}} \]

   2. pow-unpow 98.0%

      \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{10}\right)}^{x}\right)}^{x}} \]

4. Applied egg-rr 98.0%

   \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{10}\right)}^{x}\right)}^{x}} \]
5. Step-by-step derivation

   1. add-sqr-sqrt 97.9%

      \[\leadsto \cos x \cdot {\color{blue}{\left(\sqrt{{\left(e^{10}\right)}^{x}} \cdot \sqrt{{\left(e^{10}\right)}^{x}}\right)}}^{x} \]

   2. sqrt-prod 98.0%

      \[\leadsto \cos x \cdot {\color{blue}{\left(\sqrt{{\left(e^{10}\right)}^{x} \cdot {\left(e^{10}\right)}^{x}}\right)}}^{x} \]

   3. pow-prod-down 98.1%

      \[\leadsto \cos x \cdot {\left(\sqrt{\color{blue}{{\left(e^{10} \cdot e^{10}\right)}^{x}}}\right)}^{x} \]

   4. prod-exp 99.1%

      \[\leadsto \cos x \cdot {\left(\sqrt{{\color{blue}{\left(e^{10 + 10}\right)}}^{x}}\right)}^{x} \]

   5. metadata-eval 99.1%

      \[\leadsto \cos x \cdot {\left(\sqrt{{\left(e^{\color{blue}{20}}\right)}^{x}}\right)}^{x} \]

6. Applied egg-rr 99.1%

   \[\leadsto \cos x \cdot {\color{blue}{\left(\sqrt{{\left(e^{20}\right)}^{x}}\right)}}^{x} \]
7. Step-by-step derivation

   1. sqrt-pow1 99.2%

      \[\leadsto \cos x \cdot {\color{blue}{\left({\left(e^{20}\right)}^{\left(\frac{x}{2}\right)}\right)}}^{x} \]

   2. add-sqr-sqrt 98.0%

      \[\leadsto \cos x \cdot {\left({\color{blue}{\left(\sqrt{e^{20}} \cdot \sqrt{e^{20}}\right)}}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   3. unpow-prod-down 97.9%

      \[\leadsto \cos x \cdot {\color{blue}{\left({\left(\sqrt{e^{20}}\right)}^{\left(\frac{x}{2}\right)} \cdot {\left(\sqrt{e^{20}}\right)}^{\left(\frac{x}{2}\right)}\right)}}^{x} \]

   4. metadata-eval 97.9%

      \[\leadsto \cos x \cdot {\left({\left(\sqrt{e^{\color{blue}{10 + 10}}}\right)}^{\left(\frac{x}{2}\right)} \cdot {\left(\sqrt{e^{20}}\right)}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   5. prod-exp 97.9%

      \[\leadsto \cos x \cdot {\left({\left(\sqrt{\color{blue}{e^{10} \cdot e^{10}}}\right)}^{\left(\frac{x}{2}\right)} \cdot {\left(\sqrt{e^{20}}\right)}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   6. sqrt-unprod 98.5%

      \[\leadsto \cos x \cdot {\left({\color{blue}{\left(\sqrt{e^{10}} \cdot \sqrt{e^{10}}\right)}}^{\left(\frac{x}{2}\right)} \cdot {\left(\sqrt{e^{20}}\right)}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   7. add-sqr-sqrt 97.9%

      \[\leadsto \cos x \cdot {\left({\color{blue}{\left(e^{10}\right)}}^{\left(\frac{x}{2}\right)} \cdot {\left(\sqrt{e^{20}}\right)}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   8. sqrt-pow2 98.9%

      \[\leadsto \cos x \cdot {\left(\color{blue}{{\left(\sqrt{e^{10}}\right)}^{x}} \cdot {\left(\sqrt{e^{20}}\right)}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   9. metadata-eval 98.9%

      \[\leadsto \cos x \cdot {\left({\left(\sqrt{e^{10}}\right)}^{x} \cdot {\left(\sqrt{e^{\color{blue}{10 + 10}}}\right)}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   10. prod-exp 98.9%

       \[\leadsto \cos x \cdot {\left({\left(\sqrt{e^{10}}\right)}^{x} \cdot {\left(\sqrt{\color{blue}{e^{10} \cdot e^{10}}}\right)}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   11. sqrt-unprod 97.8%

       \[\leadsto \cos x \cdot {\left({\left(\sqrt{e^{10}}\right)}^{x} \cdot {\color{blue}{\left(\sqrt{e^{10}} \cdot \sqrt{e^{10}}\right)}}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   12. add-sqr-sqrt 98.9%

       \[\leadsto \cos x \cdot {\left({\left(\sqrt{e^{10}}\right)}^{x} \cdot {\color{blue}{\left(e^{10}\right)}}^{\left(\frac{x}{2}\right)}\right)}^{x} \]

   13. sqrt-pow2 98.2%

       \[\leadsto \cos x \cdot {\left({\left(\sqrt{e^{10}}\right)}^{x} \cdot \color{blue}{{\left(\sqrt{e^{10}}\right)}^{x}}\right)}^{x} \]

   14. unpow2 98.2%

       \[\leadsto \cos x \cdot {\color{blue}{\left({\left({\left(\sqrt{e^{10}}\right)}^{x}\right)}^{2}\right)}}^{x} \]

   15. pow-unpow 98.4%

       \[\leadsto \cos x \cdot {\color{blue}{\left({\left(\sqrt{e^{10}}\right)}^{\left(x \cdot 2\right)}\right)}}^{x} \]

   16. pow1/2 98.4%

       \[\leadsto \cos x \cdot {\left({\color{blue}{\left({\left(e^{10}\right)}^{0.5}\right)}}^{\left(x \cdot 2\right)}\right)}^{x} \]

   17. pow-exp 98.4%

       \[\leadsto \cos x \cdot {\left({\color{blue}{\left(e^{10 \cdot 0.5}\right)}}^{\left(x \cdot 2\right)}\right)}^{x} \]

   18. metadata-eval 98.4%

       \[\leadsto \cos x \cdot {\left({\left(e^{\color{blue}{5}}\right)}^{\left(x \cdot 2\right)}\right)}^{x} \]

8. Applied egg-rr 98.4%

   \[\leadsto \cos x \cdot {\color{blue}{\left({\left(e^{5}\right)}^{\left(x \cdot 2\right)}\right)}}^{x} \]

9. Final simplification 98.4%

   \[\leadsto \cos x \cdot {\left({\left(e^{5}\right)}^{\left(x \cdot 2\right)}\right)}^{x} \]
10. Add Preprocessing

Alternative 4: 95.2% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \cos x \cdot {\left(e^{{x}^{2}}\right)}^{10} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (pow (exp (pow x 2.0)) 10.0)))

C

double code(double x) {
	return cos(x) * pow(exp(pow(x, 2.0)), 10.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * (exp((x ** 2.0d0)) ** 10.0d0)
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.pow(Math.exp(Math.pow(x, 2.0)), 10.0);
}

Python

def code(x):
	return math.cos(x) * math.pow(math.exp(math.pow(x, 2.0)), 10.0)

Julia

function code(x)
	return Float64(cos(x) * (exp((x ^ 2.0)) ^ 10.0))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * (exp((x ^ 2.0)) ^ 10.0);
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Power[N[Exp[N[Power[x, 2.0], $MachinePrecision]], $MachinePrecision], 10.0], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. *-commutative 94.6%

      \[\leadsto \cos x \cdot e^{\color{blue}{\left(x \cdot x\right) \cdot 10}} \]

   2. exp-prod 95.2%

      \[\leadsto \cos x \cdot \color{blue}{{\left(e^{x \cdot x}\right)}^{10}} \]

   3. pow2 95.2%

      \[\leadsto \cos x \cdot {\left(e^{\color{blue}{{x}^{2}}}\right)}^{10} \]

4. Applied egg-rr 95.2%

   \[\leadsto \cos x \cdot \color{blue}{{\left(e^{{x}^{2}}\right)}^{10}} \]

5. Final simplification 95.2%

   \[\leadsto \cos x \cdot {\left(e^{{x}^{2}}\right)}^{10} \]
6. Add Preprocessing

Alternative 5: 98.0% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \cos x \cdot {\left({\left(e^{10}\right)}^{x}\right)}^{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (pow (pow (exp 10.0) x) x)))

C

double code(double x) {
	return cos(x) * pow(pow(exp(10.0), x), x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * ((exp(10.0d0) ** x) ** x)
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.pow(Math.pow(Math.exp(10.0), x), x);
}

Python

def code(x):
	return math.cos(x) * math.pow(math.pow(math.exp(10.0), x), x)

Julia

function code(x)
	return Float64(cos(x) * ((exp(10.0) ^ x) ^ x))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * ((exp(10.0) ^ x) ^ x);
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Power[N[Power[N[Exp[10.0], $MachinePrecision], x], $MachinePrecision], x], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. pow-exp 95.1%

      \[\leadsto \cos x \cdot \color{blue}{{\left(e^{10}\right)}^{\left(x \cdot x\right)}} \]

   2. pow-unpow 98.0%

      \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{10}\right)}^{x}\right)}^{x}} \]

4. Applied egg-rr 98.0%

   \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{10}\right)}^{x}\right)}^{x}} \]

5. Final simplification 98.0%

   \[\leadsto \cos x \cdot {\left({\left(e^{10}\right)}^{x}\right)}^{x} \]
6. Add Preprocessing

Alternative 6: 95.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \cos x \cdot {\left(e^{10}\right)}^{\left(x \cdot x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (pow (exp 10.0) (* x x))))

C

double code(double x) {
	return cos(x) * pow(exp(10.0), (x * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * (exp(10.0d0) ** (x * x))
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.pow(Math.exp(10.0), (x * x));
}

Python

def code(x):
	return math.cos(x) * math.pow(math.exp(10.0), (x * x))

Julia

function code(x)
	return Float64(cos(x) * (exp(10.0) ^ Float64(x * x)))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * (exp(10.0) ^ (x * x));
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Power[N[Exp[10.0], $MachinePrecision], N[(x * x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Step-by-step derivation

   1. exp-prod 95.1%

      \[\leadsto \cos x \cdot \color{blue}{{\left(e^{10}\right)}^{\left(x \cdot x\right)}} \]

3. Simplified 95.1%

   \[\leadsto \color{blue}{\cos x \cdot {\left(e^{10}\right)}^{\left(x \cdot x\right)}} \]
4. Add Preprocessing

5. Final simplification 95.1%

   \[\leadsto \cos x \cdot {\left(e^{10}\right)}^{\left(x \cdot x\right)} \]
6. Add Preprocessing

Alternative 7: 94.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (cos x) (exp (* 10.0 (* x x)))))

C

double code(double x) {
	return cos(x) * exp((10.0 * (x * x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = cos(x) * exp((10.0d0 * (x * x)))
end function

Java

public static double code(double x) {
	return Math.cos(x) * Math.exp((10.0 * (x * x)));
}

Python

def code(x):
	return math.cos(x) * math.exp((10.0 * (x * x)))

Julia

function code(x)
	return Float64(cos(x) * exp(Float64(10.0 * Float64(x * x))))
end

MATLAB

function tmp = code(x)
	tmp = cos(x) * exp((10.0 * (x * x)));
end

Wolfram

code[x_] := N[(N[Cos[x], $MachinePrecision] * N[Exp[N[(10.0 * N[(x * x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Add Preprocessing

3. Final simplification 94.6%

   \[\leadsto \cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
4. Add Preprocessing

Alternative 8: 9.7% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ {x}^{2} \cdot -0.5 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (pow x 2.0) -0.5))

C

double code(double x) {
	return pow(x, 2.0) * -0.5;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x ** 2.0d0) * (-0.5d0)
end function

Java

public static double code(double x) {
	return Math.pow(x, 2.0) * -0.5;
}

Python

def code(x):
	return math.pow(x, 2.0) * -0.5

Julia

function code(x)
	return Float64((x ^ 2.0) * -0.5)
end

MATLAB

function tmp = code(x)
	tmp = (x ^ 2.0) * -0.5;
end

Wolfram

code[x_] := N[(N[Power[x, 2.0], $MachinePrecision] * -0.5), $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 9.6%

   \[\leadsto \cos x \cdot \color{blue}{1} \]

4. Taylor expanded in x around 0 9.7%

   \[\leadsto \color{blue}{\left(1 + -0.5 \cdot {x}^{2}\right)} \cdot 1 \]
5. Step-by-step derivation

   1. *-commutative 9.7%

      \[\leadsto \left(1 + \color{blue}{{x}^{2} \cdot -0.5}\right) \cdot 1 \]

6. Simplified 9.7%

   \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot -0.5\right)} \cdot 1 \]

7. Taylor expanded in x around inf 9.7%

   \[\leadsto \color{blue}{\left(-0.5 \cdot {x}^{2}\right)} \cdot 1 \]
8. Step-by-step derivation

   1. *-commutative 9.7%

      \[\leadsto \color{blue}{\left({x}^{2} \cdot -0.5\right)} \cdot 1 \]

9. Simplified 9.7%

   \[\leadsto \color{blue}{\left({x}^{2} \cdot -0.5\right)} \cdot 1 \]

10. Final simplification 9.7%

    \[\leadsto {x}^{2} \cdot -0.5 \]
11. Add Preprocessing

Alternative 9: 9.6% accurate, 2.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (cos x))

C

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

Fortran

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

Java

public static double code(double x) {
	return Math.cos(x);
}

Python

def code(x):
	return math.cos(x)

Julia

function code(x)
	return cos(x)
end

MATLAB

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

Wolfram

code[x_] := N[Cos[x], $MachinePrecision]

Derivation

1. Initial program 94.6%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 9.6%

   \[\leadsto \cos x \cdot \color{blue}{1} \]

4. Final simplification 9.6%

   \[\leadsto \cos x \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024024 
(FPCore (x)
  :name "ENA, Section 1.4, Exercise 1"
  :precision binary64
  :pre (and (<= 1.99 x) (<= x 2.01))
  (* (cos x) (exp (* 10.0 (* x x)))))