ENA, Section 1.4, Exercise 1

Percentage Accurate: 94.5% → 99.5%

Time: 8.6s

Alternatives: 14

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: 99.5% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ {\left({\left(e^{80}\right)}^{x}\right)}^{\left(0.125 \cdot x\right)} \cdot \cos x \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* (pow (pow (exp 80.0) x) (* 0.125 x)) (cos x)))

C

double code(double x) {
	return pow(pow(exp(80.0), x), (0.125 * x)) * cos(x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((exp(80.0d0) ** x) ** (0.125d0 * x)) * cos(x)
end function

Java

public static double code(double x) {
	return Math.pow(Math.pow(Math.exp(80.0), x), (0.125 * x)) * Math.cos(x);
}

Python

def code(x):
	return math.pow(math.pow(math.exp(80.0), x), (0.125 * x)) * math.cos(x)

Julia

function code(x)
	return Float64(((exp(80.0) ^ x) ^ Float64(0.125 * x)) * cos(x))
end

MATLAB

function tmp = code(x)
	tmp = ((exp(80.0) ^ x) ^ (0.125 * x)) * cos(x);
end

Wolfram

code[x_] := N[(N[Power[N[Power[N[Exp[80.0], $MachinePrecision], x], $MachinePrecision], N[(0.125 * x), $MachinePrecision]], $MachinePrecision] * N[Cos[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. lift-exp.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. exp-prod N/A

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

   4. lift-*.f64 N/A

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

   5. pow-unpow N/A

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

   6. lower-pow.f64 N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-exp.f64 98.0

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

4. Applied rewrites 98.0%

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

   1. lift-pow.f64 N/A

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

   2. sqr-pow N/A

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

   3. pow-prod-down N/A

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

   4. lower-pow.f64 N/A

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

6. Applied rewrites 99.4%

   \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{20}\right)}^{x}\right)}^{\left(0.5 \cdot x\right)}} \]
7. Step-by-step derivation

   1. lift-pow.f64 N/A

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

   2. sqr-pow N/A

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

   3. pow-prod-down N/A

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

   4. lower-pow.f64 N/A

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

   5. lift-pow.f64 N/A

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

   6. lift-pow.f64 N/A

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

   7. pow-prod-down N/A

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

   8. lower-pow.f64 N/A

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

   9. lift-exp.f64 N/A

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

   10. lift-exp.f64 N/A

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

   11. prod-exp N/A

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

   12. lower-exp.f64 N/A

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

   13. metadata-eval N/A

       \[\leadsto \cos x \cdot {\left({\left(e^{\color{blue}{40}}\right)}^{x}\right)}^{\left(\frac{\frac{1}{2} \cdot x}{2}\right)} \]

   14. lift-*.f64 N/A

       \[\leadsto \cos x \cdot {\left({\left(e^{40}\right)}^{x}\right)}^{\left(\frac{\color{blue}{\frac{1}{2} \cdot x}}{2}\right)} \]

   15. *-commutative N/A

       \[\leadsto \cos x \cdot {\left({\left(e^{40}\right)}^{x}\right)}^{\left(\frac{\color{blue}{x \cdot \frac{1}{2}}}{2}\right)} \]

   16. associate-/l* N/A

       \[\leadsto \cos x \cdot {\left({\left(e^{40}\right)}^{x}\right)}^{\color{blue}{\left(x \cdot \frac{\frac{1}{2}}{2}\right)}} \]

   17. metadata-eval N/A

       \[\leadsto \cos x \cdot {\left({\left(e^{40}\right)}^{x}\right)}^{\left(x \cdot \color{blue}{\frac{1}{4}}\right)} \]

   18. metadata-eval N/A

       \[\leadsto \cos x \cdot {\left({\left(e^{40}\right)}^{x}\right)}^{\left(x \cdot \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{2}\right)}\right)} \]

   19. lower-*.f64 N/A

       \[\leadsto \cos x \cdot {\left({\left(e^{40}\right)}^{x}\right)}^{\color{blue}{\left(x \cdot \left(\frac{1}{2} \cdot \frac{1}{2}\right)\right)}} \]

   20. metadata-eval 99.3

       \[\leadsto \cos x \cdot {\left({\left(e^{40}\right)}^{x}\right)}^{\left(x \cdot \color{blue}{0.25}\right)} \]

8. Applied rewrites 99.3%

   \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{40}\right)}^{x}\right)}^{\left(x \cdot 0.25\right)}} \]
9. Step-by-step derivation

   1. lift-pow.f64 N/A

      \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{40}\right)}^{x}\right)}^{\left(x \cdot \frac{1}{4}\right)}} \]

   2. sqr-pow N/A

      \[\leadsto \cos x \cdot \color{blue}{\left({\left({\left(e^{40}\right)}^{x}\right)}^{\left(\frac{x \cdot \frac{1}{4}}{2}\right)} \cdot {\left({\left(e^{40}\right)}^{x}\right)}^{\left(\frac{x \cdot \frac{1}{4}}{2}\right)}\right)} \]

   3. pow-prod-down N/A

      \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{40}\right)}^{x} \cdot {\left(e^{40}\right)}^{x}\right)}^{\left(\frac{x \cdot \frac{1}{4}}{2}\right)}} \]

   4. lower-pow.f64 N/A

      \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{40}\right)}^{x} \cdot {\left(e^{40}\right)}^{x}\right)}^{\left(\frac{x \cdot \frac{1}{4}}{2}\right)}} \]

10. Applied rewrites 99.4%

    \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{80}\right)}^{x}\right)}^{\left(x \cdot 0.125\right)}} \]

11. Final simplification 99.4%

    \[\leadsto {\left({\left(e^{80}\right)}^{x}\right)}^{\left(0.125 \cdot x\right)} \cdot \cos x \]
12. Add Preprocessing

Alternative 2: 99.4% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(N[Power[N[Power[N[Exp[20.0], $MachinePrecision], x], $MachinePrecision], N[(0.5 * x), $MachinePrecision]], $MachinePrecision] * N[Cos[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. lift-exp.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. exp-prod N/A

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

   4. lift-*.f64 N/A

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

   5. pow-unpow N/A

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

   6. lower-pow.f64 N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-exp.f64 98.0

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

4. Applied rewrites 98.0%

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

   1. lift-pow.f64 N/A

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

   2. sqr-pow N/A

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

   3. pow-prod-down N/A

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

   4. lower-pow.f64 N/A

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

6. Applied rewrites 99.4%

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

7. Final simplification 99.4%

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

Alternative 3: 98.4% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(N[Power[N[Power[N[Exp[5.0], $MachinePrecision], N[(2.0 * x), $MachinePrecision]], $MachinePrecision], x], $MachinePrecision] * N[Cos[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. lift-exp.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. exp-prod N/A

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

   4. lift-*.f64 N/A

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

   5. pow-unpow N/A

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

   6. lower-pow.f64 N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-exp.f64 98.0

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

4. Applied rewrites 98.0%

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

   1. lift-pow.f64 N/A

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

   2. lift-exp.f64 N/A

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

   3. pow-exp N/A

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

   4. metadata-eval N/A

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

   5. associate-*r* N/A

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

   6. lift-*.f64 N/A

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

   7. exp-prod N/A

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

   8. lower-pow.f64 N/A

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

   9. lower-exp.f64 98.3

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

6. Applied rewrites 98.3%

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

7. Final simplification 98.3%

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

Alternative 4: 98.0% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(N[Power[N[Power[N[Exp[10.0], $MachinePrecision], x], $MachinePrecision], x], $MachinePrecision] * N[Cos[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. lift-exp.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. exp-prod N/A

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

   4. lift-*.f64 N/A

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

   5. pow-unpow N/A

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

   6. lower-pow.f64 N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-exp.f64 98.0

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

4. Applied rewrites 98.0%

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

5. Final simplification 98.0%

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

Alternative 5: 96.7% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

   \[\leadsto \cos x \cdot \color{blue}{e^{10 \cdot {x}^{2}}} \]
4. Step-by-step derivation

   1. unpow2 N/A

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

   2. associate-*r* N/A

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

   3. exp-prod N/A

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

   4. lower-pow.f64 N/A

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

   5. *-commutative N/A

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

   6. exp-prod N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-exp.f64 96.7

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

5. Applied rewrites 96.7%

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

6. Final simplification 96.7%

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

Alternative 6: 95.2% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ {\left(e^{10}\right)}^{\left(x \cdot x\right)} \cdot \frac{1}{\frac{1}{\cos x}} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* (pow (exp 10.0) (* x x)) (/ 1.0 (/ 1.0 (cos x)))))

C

double code(double x) {
	return pow(exp(10.0), (x * x)) * (1.0 / (1.0 / cos(x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (exp(10.0d0) ** (x * x)) * (1.0d0 / (1.0d0 / cos(x)))
end function

Java

public static double code(double x) {
	return Math.pow(Math.exp(10.0), (x * x)) * (1.0 / (1.0 / Math.cos(x)));
}

Python

def code(x):
	return math.pow(math.exp(10.0), (x * x)) * (1.0 / (1.0 / math.cos(x)))

Julia

function code(x)
	return Float64((exp(10.0) ^ Float64(x * x)) * Float64(1.0 / Float64(1.0 / cos(x))))
end

MATLAB

function tmp = code(x)
	tmp = (exp(10.0) ^ (x * x)) * (1.0 / (1.0 / cos(x)));
end

Wolfram

code[x_] := N[(N[Power[N[Exp[10.0], $MachinePrecision], N[(x * x), $MachinePrecision]], $MachinePrecision] * N[(1.0 / N[(1.0 / N[Cos[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. Step-by-step derivation

   1. lift-exp.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. exp-prod N/A

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

   4. lower-pow.f64 N/A

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

   5. lower-exp.f64 95.2

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

4. Applied rewrites 95.2%

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

   1. lift-cos.f64 N/A

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

   2. *-rgt-identity N/A

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

   3. metadata-eval N/A

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

   4. associate-/l* N/A

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

   5. *-commutative N/A

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

   6. associate-/l* N/A

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

   7. cos-2 N/A

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

   8. lower--.f64 N/A

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

   9. sqr-cos-a N/A

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

   10. lower-+.f64 N/A

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

   11. associate-/l* N/A

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

   12. *-commutative N/A

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

   13. associate-/l* N/A

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

   14. metadata-eval N/A

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

   15. *-rgt-identity N/A

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

   16. lift-cos.f64 N/A

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

   17. lower-*.f64 N/A

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

   18. sqr-sin-a N/A

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

   19. lower--.f64 N/A

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

   20. associate-/l* N/A

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

   21. *-commutative N/A

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

   22. associate-/l* N/A

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

   23. metadata-eval N/A

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

   24. *-rgt-identity N/A

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

6. Applied rewrites 95.2%

   \[\leadsto \color{blue}{\left(\left(0.5 + 0.5 \cdot \cos x\right) - \left(0.5 - 0.5 \cdot \cos x\right)\right)} \cdot {\left(e^{10}\right)}^{\left(x \cdot x\right)} \]
7. Step-by-step derivation

   1. lift--.f64 N/A

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

   2. flip-- N/A

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

   3. clear-num N/A

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

   4. lower-/.f64 N/A

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

   5. clear-num N/A

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

   6. flip-- N/A

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

   7. lift--.f64 N/A

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

   8. lower-/.f64 95.2

      \[\leadsto \frac{1}{\color{blue}{\frac{1}{\left(0.5 + 0.5 \cdot \cos x\right) - \left(0.5 - 0.5 \cdot \cos x\right)}}} \cdot {\left(e^{10}\right)}^{\left(x \cdot x\right)} \]

8. Applied rewrites 95.3%

   \[\leadsto \color{blue}{\frac{1}{\frac{1}{\mathsf{fma}\left(0.5, \cos x, \mathsf{fma}\left(0.5, \cos x, 0\right)\right)}}} \cdot {\left(e^{10}\right)}^{\left(x \cdot x\right)} \]

9. Taylor expanded in x around inf

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

    1. lower-/.f64 N/A

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

    2. lower-cos.f64 95.3

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

11. Applied rewrites 95.3%

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

12. Final simplification 95.3%

    \[\leadsto {\left(e^{10}\right)}^{\left(x \cdot x\right)} \cdot \frac{1}{\frac{1}{\cos x}} \]
13. Add Preprocessing

Alternative 7: 95.2% accurate, 0.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(N[Power[N[Exp[10.0], $MachinePrecision], N[(x * x), $MachinePrecision]], $MachinePrecision] * N[Cos[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. lift-exp.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. exp-prod N/A

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

   4. lower-pow.f64 N/A

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

   5. lower-exp.f64 95.2

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

4. Applied rewrites 95.2%

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

5. Final simplification 95.2%

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

Alternative 8: 94.4% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_] := N[(N[Exp[N[(N[(10.0 * x), $MachinePrecision] * x), $MachinePrecision]], $MachinePrecision] * N[Cos[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. lift-*.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. associate-*r* N/A

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

   4. lower-*.f64 N/A

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

   5. lower-*.f64 94.6

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

4. Applied rewrites 94.6%

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

5. Final simplification 94.6%

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

Alternative 9: 94.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

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

Alternative 10: 27.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.001388888888888889, x \cdot x, 0.041666666666666664\right), x \cdot x, -0.5\right), x \cdot x, 1\right) \cdot e^{\left(x \cdot x\right) \cdot 10} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  (fma
   (fma (fma -0.001388888888888889 (* x x) 0.041666666666666664) (* x x) -0.5)
   (* x x)
   1.0)
  (exp (* (* x x) 10.0))))

C

double code(double x) {
	return fma(fma(fma(-0.001388888888888889, (x * x), 0.041666666666666664), (x * x), -0.5), (x * x), 1.0) * exp(((x * x) * 10.0));
}

Julia

function code(x)
	return Float64(fma(fma(fma(-0.001388888888888889, Float64(x * x), 0.041666666666666664), Float64(x * x), -0.5), Float64(x * x), 1.0) * exp(Float64(Float64(x * x) * 10.0)))
end

Wolfram

code[x_] := N[(N[(N[(N[(-0.001388888888888889 * N[(x * x), $MachinePrecision] + 0.041666666666666664), $MachinePrecision] * N[(x * x), $MachinePrecision] + -0.5), $MachinePrecision] * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * N[Exp[N[(N[(x * x), $MachinePrecision] * 10.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) - \frac{1}{2}\right)\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) - \frac{1}{2}\right) + 1\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   2. *-commutative N/A

      \[\leadsto \left(\color{blue}{\left({x}^{2} \cdot \left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) - \frac{1}{2}\right) \cdot {x}^{2}} + 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   3. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left({x}^{2} \cdot \left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) - \frac{1}{2}, {x}^{2}, 1\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   4. sub-neg N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   5. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left({x}^{2} \cdot \left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) + \color{blue}{\frac{-1}{2}}, {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   6. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) \cdot {x}^{2}} + \frac{-1}{2}, {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   7. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}, {x}^{2}, \frac{-1}{2}\right)}, {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   8. +-commutative N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{-1}{720} \cdot {x}^{2} + \frac{1}{24}}, {x}^{2}, \frac{-1}{2}\right), {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   9. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{-1}{720}, {x}^{2}, \frac{1}{24}\right)}, {x}^{2}, \frac{-1}{2}\right), {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   10. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{720}, \color{blue}{x \cdot x}, \frac{1}{24}\right), {x}^{2}, \frac{-1}{2}\right), {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   11. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{720}, \color{blue}{x \cdot x}, \frac{1}{24}\right), {x}^{2}, \frac{-1}{2}\right), {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   12. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{720}, x \cdot x, \frac{1}{24}\right), \color{blue}{x \cdot x}, \frac{-1}{2}\right), {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   13. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{720}, x \cdot x, \frac{1}{24}\right), \color{blue}{x \cdot x}, \frac{-1}{2}\right), {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   14. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{-1}{720}, x \cdot x, \frac{1}{24}\right), x \cdot x, \frac{-1}{2}\right), \color{blue}{x \cdot x}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   15. lower-*.f64 27.5

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.001388888888888889, x \cdot x, 0.041666666666666664\right), x \cdot x, -0.5\right), \color{blue}{x \cdot x}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

5. Applied rewrites 27.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.001388888888888889, x \cdot x, 0.041666666666666664\right), x \cdot x, -0.5\right), x \cdot x, 1\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]

6. Final simplification 27.5%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.001388888888888889, x \cdot x, 0.041666666666666664\right), x \cdot x, -0.5\right), x \cdot x, 1\right) \cdot e^{\left(x \cdot x\right) \cdot 10} \]
7. Add Preprocessing

Alternative 11: 21.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x \cdot x, -0.5\right), x \cdot x, 1\right) \cdot e^{\left(x \cdot x\right) \cdot 10} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  (fma (fma 0.041666666666666664 (* x x) -0.5) (* x x) 1.0)
  (exp (* (* x x) 10.0))))

C

double code(double x) {
	return fma(fma(0.041666666666666664, (x * x), -0.5), (x * x), 1.0) * exp(((x * x) * 10.0));
}

Julia

function code(x)
	return Float64(fma(fma(0.041666666666666664, Float64(x * x), -0.5), Float64(x * x), 1.0) * exp(Float64(Float64(x * x) * 10.0)))
end

Wolfram

code[x_] := N[(N[(N[(0.041666666666666664 * N[(x * x), $MachinePrecision] + -0.5), $MachinePrecision] * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * N[Exp[N[(N[(x * x), $MachinePrecision] * 10.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{1}{24} \cdot {x}^{2} - \frac{1}{2}\right)\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{1}{24} \cdot {x}^{2} - \frac{1}{2}\right) + 1\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   2. *-commutative N/A

      \[\leadsto \left(\color{blue}{\left(\frac{1}{24} \cdot {x}^{2} - \frac{1}{2}\right) \cdot {x}^{2}} + 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   3. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{24} \cdot {x}^{2} - \frac{1}{2}, {x}^{2}, 1\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   4. sub-neg N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{1}{24} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)}, {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   5. metadata-eval N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{24} \cdot {x}^{2} + \color{blue}{\frac{-1}{2}}, {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   6. lower-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{1}{24}, {x}^{2}, \frac{-1}{2}\right)}, {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   7. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24}, \color{blue}{x \cdot x}, \frac{-1}{2}\right), {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   8. lower-*.f64 N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24}, \color{blue}{x \cdot x}, \frac{-1}{2}\right), {x}^{2}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   9. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{24}, x \cdot x, \frac{-1}{2}\right), \color{blue}{x \cdot x}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   10. lower-*.f64 21.3

       \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x \cdot x, -0.5\right), \color{blue}{x \cdot x}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

5. Applied rewrites 21.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x \cdot x, -0.5\right), x \cdot x, 1\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]

6. Final simplification 21.3%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(0.041666666666666664, x \cdot x, -0.5\right), x \cdot x, 1\right) \cdot e^{\left(x \cdot x\right) \cdot 10} \]
7. Add Preprocessing

Alternative 12: 18.2% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(-0.5, x \cdot x, 1\right) \cdot e^{\left(x \cdot x\right) \cdot 10} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* (fma -0.5 (* x x) 1.0) (exp (* (* x x) 10.0))))

C

double code(double x) {
	return fma(-0.5, (x * x), 1.0) * exp(((x * x) * 10.0));
}

Julia

function code(x)
	return Float64(fma(-0.5, Float64(x * x), 1.0) * exp(Float64(Float64(x * x) * 10.0)))
end

Wolfram

code[x_] := N[(N[(-0.5 * N[(x * x), $MachinePrecision] + 1.0), $MachinePrecision] * N[Exp[N[(N[(x * x), $MachinePrecision] * 10.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.6%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\left(1 + \frac{-1}{2} \cdot {x}^{2}\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

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

   2. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{2}, {x}^{2}, 1\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   3. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\frac{-1}{2}, \color{blue}{x \cdot x}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

   4. lower-*.f64 18.2

      \[\leadsto \mathsf{fma}\left(-0.5, \color{blue}{x \cdot x}, 1\right) \cdot e^{10 \cdot \left(x \cdot x\right)} \]

5. Applied rewrites 18.2%

   \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5, x \cdot x, 1\right)} \cdot e^{10 \cdot \left(x \cdot x\right)} \]

6. Final simplification 18.2%

   \[\leadsto \mathsf{fma}\left(-0.5, x \cdot x, 1\right) \cdot e^{\left(x \cdot x\right) \cdot 10} \]
7. Add Preprocessing

Alternative 13: 9.9% accurate, 8.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x \cdot x, 10, 1\right) \cdot \left(-0.5 \cdot \left(x \cdot x\right)\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (fma (* x x) 10.0 1.0) (* -0.5 (* x x))))

C

double code(double x) {
	return fma((x * x), 10.0, 1.0) * (-0.5 * (x * x));
}

Julia

function code(x)
	return Float64(fma(Float64(x * x), 10.0, 1.0) * Float64(-0.5 * Float64(x * x)))
end

Wolfram

code[x_] := N[(N[(N[(x * x), $MachinePrecision] * 10.0 + 1.0), $MachinePrecision] * N[(-0.5 * N[(x * x), $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. lift-exp.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. exp-prod N/A

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

   4. lift-*.f64 N/A

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

   5. pow-unpow N/A

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

   6. lower-pow.f64 N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-exp.f64 98.0

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

4. Applied rewrites 98.0%

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

5. Taylor expanded in x around 0

   \[\leadsto \cos x \cdot \color{blue}{\left(1 + 10 \cdot {x}^{2}\right)} \]
6. Step-by-step derivation

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

      \[\leadsto \cos x \cdot \color{blue}{\mathsf{fma}\left({x}^{2}, 10, 1\right)} \]

   4. unpow2 N/A

      \[\leadsto \cos x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, 10, 1\right) \]

   5. lower-*.f64 9.8

      \[\leadsto \cos x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, 10, 1\right) \]

7. Applied rewrites 9.8%

   \[\leadsto \cos x \cdot \color{blue}{\mathsf{fma}\left(x \cdot x, 10, 1\right)} \]

8. Taylor expanded in x around 0

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

   1. +-commutative N/A

      \[\leadsto \color{blue}{\left(\frac{-1}{2} \cdot {x}^{2} + 1\right)} \cdot \mathsf{fma}\left(x \cdot x, 10, 1\right) \]

   2. lower-fma.f64 N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{-1}{2}, {x}^{2}, 1\right)} \cdot \mathsf{fma}\left(x \cdot x, 10, 1\right) \]

   3. unpow2 N/A

      \[\leadsto \mathsf{fma}\left(\frac{-1}{2}, \color{blue}{x \cdot x}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, 10, 1\right) \]

   4. lower-*.f64 9.9

      \[\leadsto \mathsf{fma}\left(-0.5, \color{blue}{x \cdot x}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, 10, 1\right) \]

10. Applied rewrites 9.9%

    \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5, x \cdot x, 1\right)} \cdot \mathsf{fma}\left(x \cdot x, 10, 1\right) \]

11. Taylor expanded in x around inf

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

13. Applied rewrites 9.9%

    \[\leadsto \left(-0.5 \cdot \color{blue}{\left(x \cdot x\right)}\right) \cdot \mathsf{fma}\left(x \cdot x, 10, 1\right) \]

14. Final simplification 9.9%

    \[\leadsto \mathsf{fma}\left(x \cdot x, 10, 1\right) \cdot \left(-0.5 \cdot \left(x \cdot x\right)\right) \]
15. Add Preprocessing

Alternative 14: 1.5% accurate, 216.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 1.0)

C

double code(double x) {
	return 1.0;
}

Fortran

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

Java

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

Python

def code(x):
	return 1.0

Julia

function code(x)
	return 1.0
end

MATLAB

function tmp = code(x)
	tmp = 1.0;
end

Wolfram

code[x_] := 1.0

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

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

5. Applied rewrites 1.5%

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

Reproduce

herbie shell --seed 2024325 
(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)))))