ENA, Section 1.4, Exercise 1

Percentage Accurate: 94.5% → 99.2%

Time: 9.9s

Alternatives: 17

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: 99.2% accurate, 0.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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-pow.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. pow2 N/A

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

   4. pow-to-exp N/A

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

   5. exp-lft-sqr N/A

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

   6. pow-unpow N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-pow.f64 N/A

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

   9. lower-exp.f64 N/A

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

   10. lower-log.f64 N/A

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

   11. lower-exp.f64 N/A

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

   12. lower-log.f64 98.0

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

6. Applied rewrites 98.0%

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

   1. lift-pow.f64 N/A

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

   2. lift-exp.f64 N/A

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

   3. lift-log.f64 N/A

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

   4. rem-exp-log N/A

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

   5. sqr-pow N/A

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

   6. pow-prod-down N/A

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

   7. lift-exp.f64 N/A

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

   8. lift-exp.f64 N/A

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

   9. prod-exp N/A

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

   10. metadata-eval N/A

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

   11. lift-exp.f64 N/A

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

   12. lower-pow.f64 N/A

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

   13. div-inv N/A

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

   14. metadata-eval N/A

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

   15. lower-*.f64 99.3

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

8. Applied rewrites 99.3%

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

Alternative 2: 97.9% accurate, 0.4× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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-pow.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. pow2 N/A

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

   4. pow-to-exp N/A

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

   5. exp-lft-sqr N/A

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

   6. pow-unpow N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-pow.f64 N/A

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

   9. lower-exp.f64 N/A

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

   10. lower-log.f64 N/A

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

   11. lower-exp.f64 N/A

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

   12. lower-log.f64 98.0

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

6. Applied rewrites 98.0%

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

   1. lift-exp.f64 N/A

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

   2. lift-log.f64 N/A

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

   3. rem-exp-log 98.0

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

8. Applied rewrites 98.0%

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

Alternative 3: 97.7% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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-pow.f64 N/A

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

   2. lift-exp.f64 N/A

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

   3. pow-exp N/A

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

   4. *-commutative N/A

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

   5. pow-exp N/A

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

   6. lift-*.f64 N/A

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

   7. sqr-pow N/A

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

   8. pow-prod-down N/A

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

   9. exp-prod N/A

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

   10. lift-exp.f64 N/A

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

   11. exp-prod N/A

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

   12. lift-exp.f64 N/A

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

   13. pow-prod-down N/A

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

   14. pow-unpow N/A

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

   15. *-commutative N/A

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

   16. pow-unpow N/A

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

   17. lower-pow.f64 N/A

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

6. Applied rewrites 97.6%

   \[\leadsto \cos x \cdot \color{blue}{{\left({\left(e^{x + x}\right)}^{5}\right)}^{x}} \]
7. Add Preprocessing

Alternative 4: 95.3% accurate, 0.7× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 94.5%

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

4. Applied rewrites 95.2%

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

Alternative 5: 95.3% 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 94.5%

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

Alternative 6: 94.5% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (* (cos x) (sqrt (exp (* 20.0 (* x x))))))

C

double code(double x) {
	return cos(x) * sqrt(exp((20.0 * (x * x))));
}

Fortran

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

Java

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

Python

def code(x):
	return math.cos(x) * math.sqrt(math.exp((20.0 * (x * x))))

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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-pow.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. pow2 N/A

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

   4. pow-to-exp N/A

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

   5. exp-lft-sqr N/A

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

   6. pow-unpow N/A

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

   7. lower-pow.f64 N/A

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

   8. lower-pow.f64 N/A

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

   9. lower-exp.f64 N/A

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

   10. lower-log.f64 N/A

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

   11. lower-exp.f64 N/A

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

   12. lower-log.f64 98.0

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

6. Applied rewrites 98.0%

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

8. Applied rewrites 94.6%

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

9. Final simplification 94.6%

   \[\leadsto \cos x \cdot \sqrt{e^{20 \cdot \left(x \cdot x\right)}} \]
10. 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 94.5%

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

Alternative 8: 27.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(x \cdot \left(x \cdot x\right)\right)\\ \left(1 + \left(x \cdot \left(x \cdot t\_0\right)\right) \cdot \left(-0.001388888888888889 + \left(\frac{0.041666666666666664}{x \cdot x} + \frac{-0.5}{t\_0}\right)\right)\right) \cdot e^{x \cdot \left(x \cdot 10\right)} \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (let* ((t_0 (* x (* x (* x x)))))
   (*
    (+
     1.0
     (*
      (* x (* x t_0))
      (+
       -0.001388888888888889
       (+ (/ 0.041666666666666664 (* x x)) (/ -0.5 t_0)))))
    (exp (* x (* x 10.0))))))

C

double code(double x) {
	double t_0 = x * (x * (x * x));
	return (1.0 + ((x * (x * t_0)) * (-0.001388888888888889 + ((0.041666666666666664 / (x * x)) + (-0.5 / t_0))))) * exp((x * (x * 10.0)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: t_0
    t_0 = x * (x * (x * x))
    code = (1.0d0 + ((x * (x * t_0)) * ((-0.001388888888888889d0) + ((0.041666666666666664d0 / (x * x)) + ((-0.5d0) / t_0))))) * exp((x * (x * 10.0d0)))
end function

Java

public static double code(double x) {
	double t_0 = x * (x * (x * x));
	return (1.0 + ((x * (x * t_0)) * (-0.001388888888888889 + ((0.041666666666666664 / (x * x)) + (-0.5 / t_0))))) * Math.exp((x * (x * 10.0)));
}

Python

def code(x):
	t_0 = x * (x * (x * x))
	return (1.0 + ((x * (x * t_0)) * (-0.001388888888888889 + ((0.041666666666666664 / (x * x)) + (-0.5 / t_0))))) * math.exp((x * (x * 10.0)))

Julia

function code(x)
	t_0 = Float64(x * Float64(x * Float64(x * x)))
	return Float64(Float64(1.0 + Float64(Float64(x * Float64(x * t_0)) * Float64(-0.001388888888888889 + Float64(Float64(0.041666666666666664 / Float64(x * x)) + Float64(-0.5 / t_0))))) * exp(Float64(x * Float64(x * 10.0))))
end

MATLAB

function tmp = code(x)
	t_0 = x * (x * (x * x));
	tmp = (1.0 + ((x * (x * t_0)) * (-0.001388888888888889 + ((0.041666666666666664 / (x * x)) + (-0.5 / t_0))))) * exp((x * (x * 10.0)));
end

Wolfram

code[x_] := Block[{t$95$0 = N[(x * N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, N[(N[(1.0 + N[(N[(x * N[(x * t$95$0), $MachinePrecision]), $MachinePrecision] * N[(-0.001388888888888889 + N[(N[(0.041666666666666664 / N[(x * x), $MachinePrecision]), $MachinePrecision] + N[(-0.5 / t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Exp[N[(x * N[(x * 10.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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. *-commutative N/A

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

   6. lower-*.f64 94.4

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

4. Applied rewrites 94.4%

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

5. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. lower-fma.f64 N/A

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

   3. unpow2 N/A

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

   4. lower-*.f64 N/A

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

   5. sub-neg N/A

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

   6. metadata-eval N/A

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

   7. lower-fma.f64 N/A

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

   8. unpow2 N/A

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

   9. lower-*.f64 N/A

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

   10. +-commutative N/A

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

   11. *-commutative N/A

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

   12. lower-fma.f64 N/A

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

   13. unpow2 N/A

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

   14. lower-*.f64 27.5

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

7. Applied rewrites 27.5%

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

8. Taylor expanded in x around inf

   \[\leadsto \left({x}^{6} \cdot \color{blue}{\left(\left(\frac{1}{24} \cdot \frac{1}{{x}^{2}} + \frac{1}{{x}^{6}}\right) - \left(\frac{1}{720} + \frac{\frac{1}{2}}{{x}^{4}}\right)\right)}\right) \cdot e^{\left(x \cdot 10\right) \cdot x} \]

10. Applied rewrites 27.5%

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

11. Final simplification 27.5%

    \[\leadsto \left(1 + \left(x \cdot \left(x \cdot \left(x \cdot \left(x \cdot \left(x \cdot x\right)\right)\right)\right)\right) \cdot \left(-0.001388888888888889 + \left(\frac{0.041666666666666664}{x \cdot x} + \frac{-0.5}{x \cdot \left(x \cdot \left(x \cdot x\right)\right)}\right)\right)\right) \cdot e^{x \cdot \left(x \cdot 10\right)} \]
12. Add Preprocessing

Alternative 9: 27.5% accurate, 1.2× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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. *-commutative N/A

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

   6. lower-*.f64 94.4

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

4. Applied rewrites 94.4%

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

5. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. lower-fma.f64 N/A

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

   3. unpow2 N/A

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

   4. lower-*.f64 N/A

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

   5. sub-neg N/A

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

   6. metadata-eval N/A

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

   7. lower-fma.f64 N/A

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

   8. unpow2 N/A

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

   9. lower-*.f64 N/A

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

   10. +-commutative N/A

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

   11. *-commutative N/A

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

   12. lower-fma.f64 N/A

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

   13. unpow2 N/A

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

   14. lower-*.f64 27.5

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

7. Applied rewrites 27.5%

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

9. Applied rewrites 27.5%

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

10. Final simplification 27.5%

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

Alternative 10: 27.5% accurate, 1.4× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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. *-commutative N/A

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

   6. lower-*.f64 94.4

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

4. Applied rewrites 94.4%

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

5. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. lower-fma.f64 N/A

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

   3. unpow2 N/A

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

   4. lower-*.f64 N/A

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

   5. sub-neg N/A

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

   6. metadata-eval N/A

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

   7. lower-fma.f64 N/A

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

   8. unpow2 N/A

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

   9. lower-*.f64 N/A

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

   10. +-commutative N/A

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

   11. *-commutative N/A

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

   12. lower-fma.f64 N/A

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

   13. unpow2 N/A

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

   14. lower-*.f64 27.5

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

7. Applied rewrites 27.5%

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

8. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. lower-fma.f64 N/A

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

   3. unpow2 N/A

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

   4. lower-*.f64 N/A

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

   5. sub-neg N/A

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

   6. metadata-eval N/A

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

   7. lower-fma.f64 N/A

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

   8. unpow2 N/A

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

   9. lower-*.f64 N/A

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

   10. +-commutative N/A

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

   11. *-commutative N/A

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

   12. unpow2 N/A

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

   13. associate-*l* N/A

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

   14. lower-fma.f64 N/A

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

   15. lower-*.f64 27.5

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

10. Applied rewrites 27.5%

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

11. Final simplification 27.5%

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

Alternative 11: 21.3% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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. *-commutative N/A

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

   6. lower-*.f64 94.4

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

4. Applied rewrites 94.4%

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

5. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. unpow2 N/A

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

   3. associate-*l* N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 N/A

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

   8. sub-neg N/A

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

   9. unpow2 N/A

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

   10. associate-*r* N/A

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

   11. *-commutative N/A

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

   12. metadata-eval N/A

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

   13. lower-fma.f64 N/A

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

   14. *-commutative N/A

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

   15. lower-*.f64 21.3

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

7. Applied rewrites 21.3%

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

8. Final simplification 21.3%

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

Alternative 12: 18.2% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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. unpow2 N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 18.2

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

5. Applied rewrites 18.2%

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

6. Final simplification 18.2%

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

Alternative 13: 10.3% accurate, 4.3× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, x \cdot -0.5, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x, x \cdot 166.66666666666666, 50\right), 10\right), 1\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  (fma x (* x -0.5) 1.0)
  (fma (* x x) (fma x (* x (fma x (* x 166.66666666666666) 50.0)) 10.0) 1.0)))

C

double code(double x) {
	return fma(x, (x * -0.5), 1.0) * fma((x * x), fma(x, (x * fma(x, (x * 166.66666666666666), 50.0)), 10.0), 1.0);
}

Julia

function code(x)
	return Float64(fma(x, Float64(x * -0.5), 1.0) * fma(Float64(x * x), fma(x, Float64(x * fma(x, Float64(x * 166.66666666666666), 50.0)), 10.0), 1.0))
end

Wolfram

code[x_] := N[(N[(x * N[(x * -0.5), $MachinePrecision] + 1.0), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * N[(x * N[(x * N[(x * N[(x * 166.66666666666666), $MachinePrecision] + 50.0), $MachinePrecision]), $MachinePrecision] + 10.0), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.5%

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

4. Applied rewrites 95.2%

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

5. Taylor expanded in x around 0

   \[\leadsto \cos x \cdot \color{blue}{1} \]
6. Step-by-step derivation

7. Applied rewrites 9.6%

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

8. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. unpow2 N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 9.7

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

10. Applied rewrites 9.7%

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

11. Taylor expanded in x around 0

    \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \color{blue}{\left(1 + {x}^{2} \cdot \left(10 + {x}^{2} \cdot \left(50 + \frac{500}{3} \cdot {x}^{2}\right)\right)\right)} \]

13. Applied rewrites 10.3%

    \[\leadsto \mathsf{fma}\left(x, x \cdot -0.5, 1\right) \cdot \color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x, x \cdot 166.66666666666666, 50\right), 10\right), 1\right)} \]
14. Add Preprocessing

Alternative 14: 10.1% accurate, 5.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, x \cdot -0.5, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot 50, 10\right), 1\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* (fma x (* x -0.5) 1.0) (fma (* x x) (fma x (* x 50.0) 10.0) 1.0)))

C

double code(double x) {
	return fma(x, (x * -0.5), 1.0) * fma((x * x), fma(x, (x * 50.0), 10.0), 1.0);
}

Julia

function code(x)
	return Float64(fma(x, Float64(x * -0.5), 1.0) * fma(Float64(x * x), fma(x, Float64(x * 50.0), 10.0), 1.0))
end

Wolfram

code[x_] := N[(N[(x * N[(x * -0.5), $MachinePrecision] + 1.0), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * N[(x * N[(x * 50.0), $MachinePrecision] + 10.0), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 94.5%

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

4. Applied rewrites 95.2%

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

5. Taylor expanded in x around 0

   \[\leadsto \cos x \cdot \color{blue}{1} \]
6. Step-by-step derivation

7. Applied rewrites 9.6%

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

8. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. unpow2 N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 9.7

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

10. Applied rewrites 9.7%

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

11. Taylor expanded in x around 0

    \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \color{blue}{\left(1 + {x}^{2} \cdot \left(10 + 50 \cdot {x}^{2}\right)\right)} \]
12. Step-by-step derivation

    1. +-commutative N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \color{blue}{\left({x}^{2} \cdot \left(10 + 50 \cdot {x}^{2}\right) + 1\right)} \]

    2. lower-fma.f64 N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \color{blue}{\mathsf{fma}\left({x}^{2}, 10 + 50 \cdot {x}^{2}, 1\right)} \]

    3. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, 10 + 50 \cdot {x}^{2}, 1\right) \]

    4. lower-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, 10 + 50 \cdot {x}^{2}, 1\right) \]

    5. +-commutative N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{50 \cdot {x}^{2} + 10}, 1\right) \]

    6. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot 50} + 10, 1\right) \]

    7. unpow2 N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{\left(x \cdot x\right)} \cdot 50 + 10, 1\right) \]

    8. associate-*r* N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{x \cdot \left(x \cdot 50\right)} + 10, 1\right) \]

    9. *-commutative N/A

       \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, x \cdot \color{blue}{\left(50 \cdot x\right)} + 10, 1\right) \]

    10. lower-fma.f64 N/A

        \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left(x, 50 \cdot x, 10\right)}, 1\right) \]

    11. *-commutative N/A

        \[\leadsto \mathsf{fma}\left(x, x \cdot \frac{-1}{2}, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, \color{blue}{x \cdot 50}, 10\right), 1\right) \]

    12. lower-*.f64 10.1

        \[\leadsto \mathsf{fma}\left(x, x \cdot -0.5, 1\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, \color{blue}{x \cdot 50}, 10\right), 1\right) \]

13. Applied rewrites 10.1%

    \[\leadsto \mathsf{fma}\left(x, x \cdot -0.5, 1\right) \cdot \color{blue}{\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot 50, 10\right), 1\right)} \]
14. Add Preprocessing

Alternative 15: 9.9% accurate, 7.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 94.5%

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

4. Applied rewrites 95.2%

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

5. Taylor expanded in x around 0

   \[\leadsto \cos x \cdot \color{blue}{1} \]
6. Step-by-step derivation

7. Applied rewrites 9.6%

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

8. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. unpow2 N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 9.7

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

10. Applied rewrites 9.7%

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

11. Taylor expanded in x around 0

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

    1. +-commutative N/A

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

    2. unpow2 N/A

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

    3. associate-*r* N/A

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

    4. *-commutative N/A

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

    5. lower-fma.f64 N/A

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

    6. *-commutative N/A

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

    7. lower-*.f64 9.9

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

13. Applied rewrites 9.9%

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

Alternative 16: 9.7% accurate, 13.5× speedup

Math

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

FPCore

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

C

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

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * (x * (-0.5d0))) * 1.0d0
end function

Java

public static double code(double x) {
	return (x * (x * -0.5)) * 1.0;
}

Python

def code(x):
	return (x * (x * -0.5)) * 1.0

Julia

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

MATLAB

function tmp = code(x)
	tmp = (x * (x * -0.5)) * 1.0;
end

Wolfram

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

Derivation

1. Initial program 94.5%

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

4. Applied rewrites 95.2%

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

5. Taylor expanded in x around 0

   \[\leadsto \cos x \cdot \color{blue}{1} \]
6. Step-by-step derivation

7. Applied rewrites 9.6%

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

8. Taylor expanded in x around 0

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

   1. +-commutative N/A

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

   2. unpow2 N/A

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

   3. associate-*r* N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. *-commutative N/A

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

   7. lower-*.f64 9.7

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

10. Applied rewrites 9.7%

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

11. Taylor expanded in x around inf

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

13. Applied rewrites 9.7%

    \[\leadsto \left(x \cdot \color{blue}{\left(x \cdot -0.5\right)}\right) \cdot 1 \]
14. Add Preprocessing

Alternative 17: 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 94.5%

   \[\cos x \cdot e^{10 \cdot \left(x \cdot x\right)} \]
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 2024219 
(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)))))