2cos (problem 3.3.5)

Percentage Accurate: 52.2% → 99.6%

Time: 16.8s

Alternatives: 12

Speedup: 25.9×

Specification

\[\left(\left(-10000 \leq x \land x \leq 10000\right) \land 10^{-16} \cdot \left|x\right| < \varepsilon\right) \land \varepsilon < \left|x\right|\]

Math

\[\begin{array}{l} \\ \cos \left(x + \varepsilon\right) - \cos x \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (- (cos (+ x eps)) (cos x)))

C

double code(double x, double eps) {
	return cos((x + eps)) - cos(x);
}

Fortran

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

Java

public static double code(double x, double eps) {
	return Math.cos((x + eps)) - Math.cos(x);
}

Python

def code(x, eps):
	return math.cos((x + eps)) - math.cos(x)

Julia

function code(x, eps)
	return Float64(cos(Float64(x + eps)) - cos(x))
end

MATLAB

function tmp = code(x, eps)
	tmp = cos((x + eps)) - cos(x);
end

Wolfram

code[x_, eps_] := N[(N[Cos[N[(x + eps), $MachinePrecision]], $MachinePrecision] - N[Cos[x], $MachinePrecision]), $MachinePrecision]

Initial Program: 52.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \cos \left(x + \varepsilon\right) - \cos x \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (- (cos (+ x eps)) (cos x)))

C

double code(double x, double eps) {
	return cos((x + eps)) - cos(x);
}

Fortran

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

Java

public static double code(double x, double eps) {
	return Math.cos((x + eps)) - Math.cos(x);
}

Python

def code(x, eps):
	return math.cos((x + eps)) - math.cos(x)

Julia

function code(x, eps)
	return Float64(cos(Float64(x + eps)) - cos(x))
end

MATLAB

function tmp = code(x, eps)
	tmp = cos((x + eps)) - cos(x);
end

Wolfram

code[x_, eps_] := N[(N[Cos[N[(x + eps), $MachinePrecision]], $MachinePrecision] - N[Cos[x], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \left(\mathsf{fma}\left(\mathsf{fma}\left(\sin x, 0.16666666666666666, 0.041666666666666664 \cdot \left(\cos x \cdot \varepsilon\right)\right), \varepsilon, -0.5 \cdot \cos x\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (*
  (-
   (*
    (fma
     (fma (sin x) 0.16666666666666666 (* 0.041666666666666664 (* (cos x) eps)))
     eps
     (* -0.5 (cos x)))
    eps)
   (sin x))
  eps))

C

double code(double x, double eps) {
	return ((fma(fma(sin(x), 0.16666666666666666, (0.041666666666666664 * (cos(x) * eps))), eps, (-0.5 * cos(x))) * eps) - sin(x)) * eps;
}

Julia

function code(x, eps)
	return Float64(Float64(Float64(fma(fma(sin(x), 0.16666666666666666, Float64(0.041666666666666664 * Float64(cos(x) * eps))), eps, Float64(-0.5 * cos(x))) * eps) - sin(x)) * eps)
end

Wolfram

code[x_, eps_] := N[(N[(N[(N[(N[(N[Sin[x], $MachinePrecision] * 0.16666666666666666 + N[(0.041666666666666664 * N[(N[Cos[x], $MachinePrecision] * eps), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * eps + N[(-0.5 * N[Cos[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision] - N[Sin[x], $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

   \[\leadsto \color{blue}{\varepsilon \cdot \left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \varepsilon \cdot \left(\frac{1}{24} \cdot \left(\varepsilon \cdot \cos x\right) - \frac{-1}{6} \cdot \sin x\right)\right) - \sin x\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \varepsilon \cdot \left(\frac{1}{24} \cdot \left(\varepsilon \cdot \cos x\right) - \frac{-1}{6} \cdot \sin x\right)\right) - \sin x\right) \cdot \varepsilon} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \varepsilon \cdot \left(\frac{1}{24} \cdot \left(\varepsilon \cdot \cos x\right) - \frac{-1}{6} \cdot \sin x\right)\right) - \sin x\right) \cdot \varepsilon} \]

5. Applied rewrites 99.6%

   \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(\sin x, 0.16666666666666666, \left(\cos x \cdot \varepsilon\right) \cdot 0.041666666666666664\right), \varepsilon, \cos x \cdot -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon} \]

6. Final simplification 99.6%

   \[\leadsto \left(\mathsf{fma}\left(\mathsf{fma}\left(\sin x, 0.16666666666666666, 0.041666666666666664 \cdot \left(\cos x \cdot \varepsilon\right)\right), \varepsilon, -0.5 \cdot \cos x\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon \]
7. Add Preprocessing

Alternative 2: 99.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\left(-0.5 \cdot \cos x\right) \cdot \varepsilon, \varepsilon, \left(\mathsf{fma}\left(0.16666666666666666 \cdot \varepsilon, \varepsilon, -1\right) \cdot \sin x\right) \cdot \varepsilon\right) \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (fma
  (* (* -0.5 (cos x)) eps)
  eps
  (* (* (fma (* 0.16666666666666666 eps) eps -1.0) (sin x)) eps)))

C

double code(double x, double eps) {
	return fma(((-0.5 * cos(x)) * eps), eps, ((fma((0.16666666666666666 * eps), eps, -1.0) * sin(x)) * eps));
}

Julia

function code(x, eps)
	return fma(Float64(Float64(-0.5 * cos(x)) * eps), eps, Float64(Float64(fma(Float64(0.16666666666666666 * eps), eps, -1.0) * sin(x)) * eps))
end

Wolfram

code[x_, eps_] := N[(N[(N[(-0.5 * N[Cos[x], $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision] * eps + N[(N[(N[(N[(0.16666666666666666 * eps), $MachinePrecision] * eps + -1.0), $MachinePrecision] * N[Sin[x], $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \frac{1}{6} \cdot \left(\varepsilon \cdot \sin x\right)\right) - \sin x\right) \cdot \varepsilon} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \frac{1}{6} \cdot \left(\varepsilon \cdot \sin x\right)\right) - \sin x\right) \cdot \varepsilon} \]

5. Applied rewrites 99.5%

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

7. Applied rewrites 99.5%

   \[\leadsto \mathsf{fma}\left(\left(-0.5 \cdot \cos x\right) \cdot \varepsilon, \color{blue}{\varepsilon}, \left(\mathsf{fma}\left(0.16666666666666666 \cdot \varepsilon, \varepsilon, -1\right) \cdot \sin x\right) \cdot \varepsilon\right) \]
8. Add Preprocessing

Alternative 3: 99.5% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\sin x, \mathsf{fma}\left(\varepsilon \cdot \varepsilon, 0.16666666666666666, -1\right), \left(-0.5 \cdot \cos x\right) \cdot \varepsilon\right) \cdot \varepsilon \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (*
  (fma
   (sin x)
   (fma (* eps eps) 0.16666666666666666 -1.0)
   (* (* -0.5 (cos x)) eps))
  eps))

C

double code(double x, double eps) {
	return fma(sin(x), fma((eps * eps), 0.16666666666666666, -1.0), ((-0.5 * cos(x)) * eps)) * eps;
}

Julia

function code(x, eps)
	return Float64(fma(sin(x), fma(Float64(eps * eps), 0.16666666666666666, -1.0), Float64(Float64(-0.5 * cos(x)) * eps)) * eps)
end

Wolfram

code[x_, eps_] := N[(N[(N[Sin[x], $MachinePrecision] * N[(N[(eps * eps), $MachinePrecision] * 0.16666666666666666 + -1.0), $MachinePrecision] + N[(N[(-0.5 * N[Cos[x], $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \frac{1}{6} \cdot \left(\varepsilon \cdot \sin x\right)\right) - \sin x\right) \cdot \varepsilon} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \frac{1}{6} \cdot \left(\varepsilon \cdot \sin x\right)\right) - \sin x\right) \cdot \varepsilon} \]

5. Applied rewrites 99.5%

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

6. Final simplification 99.5%

   \[\leadsto \mathsf{fma}\left(\sin x, \mathsf{fma}\left(\varepsilon \cdot \varepsilon, 0.16666666666666666, -1\right), \left(-0.5 \cdot \cos x\right) \cdot \varepsilon\right) \cdot \varepsilon \]
7. Add Preprocessing

Alternative 4: 99.4% accurate, 0.9× speedup

Math

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

FPCore

(FPCore (x eps)
 :precision binary64
 (fma (* (* -0.5 (cos x)) eps) eps (* (- eps) (sin x))))

C

double code(double x, double eps) {
	return fma(((-0.5 * cos(x)) * eps), eps, (-eps * sin(x)));
}

Julia

function code(x, eps)
	return fma(Float64(Float64(-0.5 * cos(x)) * eps), eps, Float64(Float64(-eps) * sin(x)))
end

Wolfram

code[x_, eps_] := N[(N[(N[(-0.5 * N[Cos[x], $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision] * eps + N[((-eps) * N[Sin[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.3

       \[\leadsto \left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \color{blue}{\sin x}\right) \cdot \varepsilon \]

5. Applied rewrites 99.3%

   \[\leadsto \color{blue}{\left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon} \]
6. Step-by-step derivation

7. Applied rewrites 99.4%

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

8. Final simplification 99.4%

   \[\leadsto \mathsf{fma}\left(\left(-0.5 \cdot \cos x\right) \cdot \varepsilon, \varepsilon, \left(-\varepsilon\right) \cdot \sin x\right) \]
9. Add Preprocessing

Alternative 5: 99.4% accurate, 0.9× speedup

Math

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

FPCore

(FPCore (x eps)
 :precision binary64
 (* (- (* (* -0.5 (cos x)) eps) (sin x)) eps))

C

double code(double x, double eps) {
	return (((-0.5 * cos(x)) * eps) - sin(x)) * eps;
}

Fortran

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

Java

public static double code(double x, double eps) {
	return (((-0.5 * Math.cos(x)) * eps) - Math.sin(x)) * eps;
}

Python

def code(x, eps):
	return (((-0.5 * math.cos(x)) * eps) - math.sin(x)) * eps

Julia

function code(x, eps)
	return Float64(Float64(Float64(Float64(-0.5 * cos(x)) * eps) - sin(x)) * eps)
end

MATLAB

function tmp = code(x, eps)
	tmp = (((-0.5 * cos(x)) * eps) - sin(x)) * eps;
end

Wolfram

code[x_, eps_] := N[(N[(N[(N[(-0.5 * N[Cos[x], $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision] - N[Sin[x], $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.3

       \[\leadsto \left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \color{blue}{\sin x}\right) \cdot \varepsilon \]

5. Applied rewrites 99.3%

   \[\leadsto \color{blue}{\left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon} \]

6. Final simplification 99.3%

   \[\leadsto \left(\left(-0.5 \cdot \cos x\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon \]
7. Add Preprocessing

Alternative 6: 99.0% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \left(\mathsf{fma}\left(\varepsilon \cdot \varepsilon, 0.041666666666666664, -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (* (- (* (fma (* eps eps) 0.041666666666666664 -0.5) eps) (sin x)) eps))

C

double code(double x, double eps) {
	return ((fma((eps * eps), 0.041666666666666664, -0.5) * eps) - sin(x)) * eps;
}

Julia

function code(x, eps)
	return Float64(Float64(Float64(fma(Float64(eps * eps), 0.041666666666666664, -0.5) * eps) - sin(x)) * eps)
end

Wolfram

code[x_, eps_] := N[(N[(N[(N[(N[(eps * eps), $MachinePrecision] * 0.041666666666666664 + -0.5), $MachinePrecision] * eps), $MachinePrecision] - N[Sin[x], $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

   \[\leadsto \color{blue}{\varepsilon \cdot \left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \varepsilon \cdot \left(\frac{1}{24} \cdot \left(\varepsilon \cdot \cos x\right) - \frac{-1}{6} \cdot \sin x\right)\right) - \sin x\right)} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \varepsilon \cdot \left(\frac{1}{24} \cdot \left(\varepsilon \cdot \cos x\right) - \frac{-1}{6} \cdot \sin x\right)\right) - \sin x\right) \cdot \varepsilon} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{2} \cdot \cos x + \varepsilon \cdot \left(\frac{1}{24} \cdot \left(\varepsilon \cdot \cos x\right) - \frac{-1}{6} \cdot \sin x\right)\right) - \sin x\right) \cdot \varepsilon} \]

5. Applied rewrites 99.6%

   \[\leadsto \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(\sin x, 0.16666666666666666, \left(\cos x \cdot \varepsilon\right) \cdot 0.041666666666666664\right), \varepsilon, \cos x \cdot -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon} \]

6. Taylor expanded in x around 0

   \[\leadsto \left(\left(\frac{1}{24} \cdot {\varepsilon}^{2} - \frac{1}{2}\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon \]
7. Step-by-step derivation

8. Applied rewrites 98.8%

   \[\leadsto \left(\mathsf{fma}\left(\varepsilon \cdot \varepsilon, 0.041666666666666664, -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon \]
9. Add Preprocessing

Alternative 7: 98.5% accurate, 4.5× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.25, \varepsilon \cdot \varepsilon, \left(\varepsilon \cdot x\right) \cdot 0.16666666666666666\right), x, -\varepsilon\right), x, \left(\varepsilon \cdot \varepsilon\right) \cdot -0.5\right) \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (fma
  (fma (fma 0.25 (* eps eps) (* (* eps x) 0.16666666666666666)) x (- eps))
  x
  (* (* eps eps) -0.5)))

C

double code(double x, double eps) {
	return fma(fma(fma(0.25, (eps * eps), ((eps * x) * 0.16666666666666666)), x, -eps), x, ((eps * eps) * -0.5));
}

Julia

function code(x, eps)
	return fma(fma(fma(0.25, Float64(eps * eps), Float64(Float64(eps * x) * 0.16666666666666666)), x, Float64(-eps)), x, Float64(Float64(eps * eps) * -0.5))
end

Wolfram

code[x_, eps_] := N[(N[(N[(0.25 * N[(eps * eps), $MachinePrecision] + N[(N[(eps * x), $MachinePrecision] * 0.16666666666666666), $MachinePrecision]), $MachinePrecision] * x + (-eps)), $MachinePrecision] * x + N[(N[(eps * eps), $MachinePrecision] * -0.5), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.3

       \[\leadsto \left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \color{blue}{\sin x}\right) \cdot \varepsilon \]

5. Applied rewrites 99.3%

   \[\leadsto \color{blue}{\left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon} \]

6. Taylor expanded in x around 0

   \[\leadsto \frac{-1}{2} \cdot {\varepsilon}^{2} + \color{blue}{x \cdot \left(-1 \cdot \varepsilon + x \cdot \left(\frac{1}{6} \cdot \left(\varepsilon \cdot x\right) + \frac{1}{4} \cdot {\varepsilon}^{2}\right)\right)} \]
7. Step-by-step derivation

8. Applied rewrites 97.9%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.25, \varepsilon \cdot \varepsilon, \left(\varepsilon \cdot x\right) \cdot 0.16666666666666666\right), x, -\varepsilon\right), \color{blue}{x}, -0.5 \cdot \left(\varepsilon \cdot \varepsilon\right)\right) \]

9. Final simplification 97.9%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.25, \varepsilon \cdot \varepsilon, \left(\varepsilon \cdot x\right) \cdot 0.16666666666666666\right), x, -\varepsilon\right), x, \left(\varepsilon \cdot \varepsilon\right) \cdot -0.5\right) \]
10. Add Preprocessing

Alternative 8: 98.4% accurate, 6.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.25, \varepsilon, 0.16666666666666666 \cdot x\right), x, -1\right), x, -0.5 \cdot \varepsilon\right) \cdot \varepsilon \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (*
  (fma (fma (fma 0.25 eps (* 0.16666666666666666 x)) x -1.0) x (* -0.5 eps))
  eps))

C

double code(double x, double eps) {
	return fma(fma(fma(0.25, eps, (0.16666666666666666 * x)), x, -1.0), x, (-0.5 * eps)) * eps;
}

Julia

function code(x, eps)
	return Float64(fma(fma(fma(0.25, eps, Float64(0.16666666666666666 * x)), x, -1.0), x, Float64(-0.5 * eps)) * eps)
end

Wolfram

code[x_, eps_] := N[(N[(N[(N[(0.25 * eps + N[(0.16666666666666666 * x), $MachinePrecision]), $MachinePrecision] * x + -1.0), $MachinePrecision] * x + N[(-0.5 * eps), $MachinePrecision]), $MachinePrecision] * eps), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.3

       \[\leadsto \left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \color{blue}{\sin x}\right) \cdot \varepsilon \]

5. Applied rewrites 99.3%

   \[\leadsto \color{blue}{\left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon} \]

6. Taylor expanded in x around 0

   \[\leadsto \left(\frac{-1}{2} \cdot \varepsilon + x \cdot \left(x \cdot \left(\frac{1}{6} \cdot x + \frac{1}{4} \cdot \varepsilon\right) - 1\right)\right) \cdot \varepsilon \]
7. Step-by-step derivation

8. Applied rewrites 97.7%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.25, \varepsilon, 0.16666666666666666 \cdot x\right), x, -1\right), x, -0.5 \cdot \varepsilon\right) \cdot \varepsilon \]
9. Add Preprocessing

Alternative 9: 98.1% accurate, 10.9× speedup

Math

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

FPCore

(FPCore (x eps) :precision binary64 (fma (- x) eps (* (* eps eps) -0.5)))

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.3

       \[\leadsto \left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \color{blue}{\sin x}\right) \cdot \varepsilon \]

5. Applied rewrites 99.3%

   \[\leadsto \color{blue}{\left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon} \]

6. Taylor expanded in x around 0

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

8. Applied rewrites 97.4%

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

9. Final simplification 97.4%

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

Alternative 10: 97.9% accurate, 14.8× speedup

Math

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

FPCore

(FPCore (x eps) :precision binary64 (* (fma -0.5 eps (- x)) eps))

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.3

       \[\leadsto \left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \color{blue}{\sin x}\right) \cdot \varepsilon \]

5. Applied rewrites 99.3%

   \[\leadsto \color{blue}{\left(\left(\cos x \cdot -0.5\right) \cdot \varepsilon - \sin x\right) \cdot \varepsilon} \]

6. Taylor expanded in x around 0

   \[\leadsto \left(-1 \cdot x + \frac{-1}{2} \cdot \varepsilon\right) \cdot \varepsilon \]
7. Step-by-step derivation

8. Applied rewrites 97.2%

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

Alternative 11: 78.8% accurate, 25.9× speedup

Math

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

FPCore

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

C

double code(double x, double eps) {
	return -x * eps;
}

Fortran

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

Java

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

Python

def code(x, eps):
	return -x * eps

Julia

function code(x, eps)
	return Float64(Float64(-x) * eps)
end

MATLAB

function tmp = code(x, eps)
	tmp = -x * eps;
end

Wolfram

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

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in eps around 0

   \[\leadsto \color{blue}{-1 \cdot \left(\varepsilon \cdot \sin x\right)} \]
4. Step-by-step derivation

   1. associate-*r* N/A

      \[\leadsto \color{blue}{\left(-1 \cdot \varepsilon\right) \cdot \sin x} \]

   2. lower-*.f64 N/A

      \[\leadsto \color{blue}{\left(-1 \cdot \varepsilon\right) \cdot \sin x} \]

   3. mul-1-neg N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\varepsilon\right)\right)} \cdot \sin x \]

   4. lower-neg.f64 N/A

      \[\leadsto \color{blue}{\left(-\varepsilon\right)} \cdot \sin x \]

   5. lower-sin.f64 80.8

      \[\leadsto \left(-\varepsilon\right) \cdot \color{blue}{\sin x} \]

5. Applied rewrites 80.8%

   \[\leadsto \color{blue}{\left(-\varepsilon\right) \cdot \sin x} \]

6. Taylor expanded in x around 0

   \[\leadsto -1 \cdot \color{blue}{\left(\varepsilon \cdot x\right)} \]
7. Step-by-step derivation

8. Applied rewrites 79.5%

   \[\leadsto \left(-x\right) \cdot \color{blue}{\varepsilon} \]
9. Add Preprocessing

Alternative 12: 50.9% accurate, 51.8× speedup

Math

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

FPCore

(FPCore (x eps) :precision binary64 (- 1.0 1.0))

C

double code(double x, double eps) {
	return 1.0 - 1.0;
}

Fortran

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

Java

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

Python

def code(x, eps):
	return 1.0 - 1.0

Julia

function code(x, eps)
	return Float64(1.0 - 1.0)
end

MATLAB

function tmp = code(x, eps)
	tmp = 1.0 - 1.0;
end

Wolfram

code[x_, eps_] := N[(1.0 - 1.0), $MachinePrecision]

Derivation

1. Initial program 52.1%

   \[\cos \left(x + \varepsilon\right) - \cos x \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. lower--.f64 N/A

      \[\leadsto \color{blue}{\cos \varepsilon - 1} \]

   2. lower-cos.f64 50.7

      \[\leadsto \color{blue}{\cos \varepsilon} - 1 \]

5. Applied rewrites 50.7%

   \[\leadsto \color{blue}{\cos \varepsilon - 1} \]

6. Taylor expanded in eps around 0

   \[\leadsto 1 - 1 \]
7. Step-by-step derivation

8. Applied rewrites 50.6%

   \[\leadsto 1 - 1 \]
9. Add Preprocessing

Developer Target 1: 98.8% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ {\left(\sqrt[3]{\left(-2 \cdot \sin \left(0.5 \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right)\right) \cdot \sin \left(0.5 \cdot \varepsilon\right)}\right)}^{3} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (pow (cbrt (* (* -2.0 (sin (* 0.5 (fma 2.0 x eps)))) (sin (* 0.5 eps)))) 3.0))

C

double code(double x, double eps) {
	return pow(cbrt(((-2.0 * sin((0.5 * fma(2.0, x, eps)))) * sin((0.5 * eps)))), 3.0);
}

Julia

function code(x, eps)
	return cbrt(Float64(Float64(-2.0 * sin(Float64(0.5 * fma(2.0, x, eps)))) * sin(Float64(0.5 * eps)))) ^ 3.0
end

Wolfram

code[x_, eps_] := N[Power[N[Power[N[(N[(-2.0 * N[Sin[N[(0.5 * N[(2.0 * x + eps), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[Sin[N[(0.5 * eps), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], 1/3], $MachinePrecision], 3.0], $MachinePrecision]

Reproduce

herbie shell --seed 2024298 
(FPCore (x eps)
  :name "2cos (problem 3.3.5)"
  :precision binary64
  :pre (and (and (and (<= -10000.0 x) (<= x 10000.0)) (< (* 1e-16 (fabs x)) eps)) (< eps (fabs x)))

  :alt
  (! :herbie-platform default (pow (cbrt (* -2 (sin (* 1/2 (fma 2 x eps))) (sin (* 1/2 eps)))) 3))

  (- (cos (+ x eps)) (cos x)))