2sin (example 3.3)

Percentage Accurate: 62.7% → 99.5%

Time: 11.3s

Alternatives: 10

Speedup: 34.5×

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

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 62.7% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.5% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \cos \left(\mathsf{fma}\left(0.5, \varepsilon, x\right)\right) \cdot \left(2 \cdot \left(\varepsilon \cdot 0.5\right)\right) \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (* (cos (fma 0.5 eps x)) (* 2.0 (* eps 0.5))))

C

double code(double x, double eps) {
	return cos(fma(0.5, eps, x)) * (2.0 * (eps * 0.5));
}

Julia

function code(x, eps)
	return Float64(cos(fma(0.5, eps, x)) * Float64(2.0 * Float64(eps * 0.5)))
end

Wolfram

code[x_, eps_] := N[(N[Cos[N[(0.5 * eps + x), $MachinePrecision]], $MachinePrecision] * N[(2.0 * N[(eps * 0.5), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 63.4%

   \[\sin \left(x + \varepsilon\right) - \sin x \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f64 N/A

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

   2. lift-sin.f64 N/A

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

   3. lift-sin.f64 N/A

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

   4. diff-sin N/A

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

   5. associate-*r* N/A

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

   6. lower-*.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. lower-sin.f64 N/A

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

   10. clear-num N/A

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

   11. associate-/r/ N/A

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

   12. metadata-eval N/A

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

   13. lower-*.f64 N/A

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

   14. lift-+.f64 N/A

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

   15. +-commutative N/A

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

   16. associate--l+ N/A

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

   17. +-inverses N/A

       \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \left(\varepsilon + \color{blue}{0}\right)\right) \cdot 2\right) \cdot \cos \left(\frac{\left(x + \varepsilon\right) + x}{2}\right) \]

   18. +-commutative N/A

       \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \color{blue}{\left(0 + \varepsilon\right)}\right) \cdot 2\right) \cdot \cos \left(\frac{\left(x + \varepsilon\right) + x}{2}\right) \]

   19. lower-+.f64 N/A

       \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \color{blue}{\left(0 + \varepsilon\right)}\right) \cdot 2\right) \cdot \cos \left(\frac{\left(x + \varepsilon\right) + x}{2}\right) \]

   20. frac-2neg N/A

       \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \left(0 + \varepsilon\right)\right) \cdot 2\right) \cdot \cos \color{blue}{\left(\frac{\mathsf{neg}\left(\left(\left(x + \varepsilon\right) + x\right)\right)}{\mathsf{neg}\left(2\right)}\right)} \]

   21. distribute-frac-neg N/A

       \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \left(0 + \varepsilon\right)\right) \cdot 2\right) \cdot \cos \color{blue}{\left(\mathsf{neg}\left(\frac{\left(x + \varepsilon\right) + x}{\mathsf{neg}\left(2\right)}\right)\right)} \]

4. Applied rewrites 100.0%

   \[\leadsto \color{blue}{\left(\sin \left(0.5 \cdot \left(0 + \varepsilon\right)\right) \cdot 2\right) \cdot \cos \left(\frac{\mathsf{fma}\left(2, x, \varepsilon\right)}{-2}\right)} \]

5. Taylor expanded in eps around 0

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

   1. lower-*.f64 100.0

      \[\leadsto \left(\color{blue}{\left(0.5 \cdot \varepsilon\right)} \cdot 2\right) \cdot \cos \left(\frac{\mathsf{fma}\left(2, x, \varepsilon\right)}{-2}\right) \]

7. Applied rewrites 100.0%

   \[\leadsto \left(\color{blue}{\left(0.5 \cdot \varepsilon\right)} \cdot 2\right) \cdot \cos \left(\frac{\mathsf{fma}\left(2, x, \varepsilon\right)}{-2}\right) \]

8. Taylor expanded in eps around 0

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

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. lower-fma.f64 N/A

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

   4. mul-1-neg N/A

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

   5. lower-neg.f64 100.0

      \[\leadsto \left(\left(0.5 \cdot \varepsilon\right) \cdot 2\right) \cdot \cos \left(\mathsf{fma}\left(\varepsilon, -0.5, \color{blue}{-x}\right)\right) \]

10. Applied rewrites 100.0%

    \[\leadsto \left(\left(0.5 \cdot \varepsilon\right) \cdot 2\right) \cdot \cos \color{blue}{\left(\mathsf{fma}\left(\varepsilon, -0.5, -x\right)\right)} \]

11. Taylor expanded in eps around inf

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

    1. distribute-lft-in N/A

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

    2. associate-*r* N/A

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

    3. metadata-eval N/A

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

    4. *-rgt-identity N/A

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

    5. rgt-mult-inverse N/A

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

    6. associate-*r/ N/A

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

    7. *-rgt-identity N/A

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

    8. associate-/l* N/A

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

    9. associate-*l/ N/A

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

    10. neg-mul-1 N/A

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

    11. metadata-eval N/A

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

    12. distribute-lft-neg-in N/A

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

    13. distribute-neg-in N/A

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

    14. distribute-rgt-in N/A

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

    15. cos-neg N/A

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

    16. lower-cos.f64 N/A

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

    17. distribute-rgt-in N/A

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

    18. associate-*l/ N/A

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

    19. associate-/l* N/A

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

    20. *-rgt-identity N/A

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

    21. associate-*r/ N/A

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

    22. rgt-mult-inverse N/A

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

    23. *-rgt-identity N/A

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

    24. lower-fma.f64 100.0

        \[\leadsto \left(\left(0.5 \cdot \varepsilon\right) \cdot 2\right) \cdot \cos \color{blue}{\left(\mathsf{fma}\left(0.5, \varepsilon, x\right)\right)} \]

13. Applied rewrites 100.0%

    \[\leadsto \left(\left(0.5 \cdot \varepsilon\right) \cdot 2\right) \cdot \color{blue}{\cos \left(\mathsf{fma}\left(0.5, \varepsilon, x\right)\right)} \]

14. Final simplification 100.0%

    \[\leadsto \cos \left(\mathsf{fma}\left(0.5, \varepsilon, x\right)\right) \cdot \left(2 \cdot \left(\varepsilon \cdot 0.5\right)\right) \]
15. Add Preprocessing

Alternative 2: 99.1% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. *-commutative N/A

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

   3. associate-*r* N/A

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

   4. lower-*.f64 N/A

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

   5. +-commutative N/A

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

   6. lower-fma.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. lower-sin.f64 N/A

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

   10. lower-cos.f64 100.0

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

5. Applied rewrites 100.0%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 99.8%

   \[\leadsto \mathsf{fma}\left(-0.5 \cdot x, \varepsilon, \cos x\right) \cdot \varepsilon \]
9. Step-by-step derivation

10. Applied rewrites 99.8%

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

Alternative 3: 99.1% accurate, 1.8× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. *-commutative N/A

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

   3. associate-*r* N/A

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

   4. lower-*.f64 N/A

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

   5. +-commutative N/A

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

   6. lower-fma.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. lower-sin.f64 N/A

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

   10. lower-cos.f64 100.0

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

5. Applied rewrites 100.0%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 99.8%

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

Alternative 4: 99.1% accurate, 2.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-cos.f64 99.7

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

5. Applied rewrites 99.7%

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

Alternative 5: 98.8% accurate, 4.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. *-commutative N/A

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

   3. associate-*r* N/A

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

   4. lower-*.f64 N/A

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

   5. +-commutative N/A

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

   6. lower-fma.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. lower-sin.f64 N/A

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

   10. lower-cos.f64 100.0

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

5. Applied rewrites 100.0%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 99.8%

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

9. Taylor expanded in x around 0

   \[\leadsto \mathsf{fma}\left(\frac{-1}{2} \cdot x, \varepsilon, 1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) - \frac{1}{2}\right)\right) \cdot \varepsilon \]
10. Step-by-step derivation

11. Applied rewrites 99.5%

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

Alternative 6: 98.6% accurate, 5.3× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-cos.f64 99.7

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

5. Applied rewrites 99.7%

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

6. Taylor expanded in x around 0

   \[\leadsto \left(1 + {x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{24} + \frac{-1}{720} \cdot {x}^{2}\right) - \frac{1}{2}\right)\right) \cdot \varepsilon \]
7. Step-by-step derivation

8. Applied rewrites 99.5%

   \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(-0.001388888888888889, x \cdot x, 0.041666666666666664\right), x \cdot x, -0.5\right), x \cdot x, 1\right) \cdot \varepsilon \]
9. Add Preprocessing

Alternative 7: 98.6% accurate, 7.4× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-cos.f64 99.7

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

5. Applied rewrites 99.7%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 99.3%

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

Alternative 8: 98.4% accurate, 12.2× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-cos.f64 99.7

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

5. Applied rewrites 99.7%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 99.0%

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

9. Taylor expanded in x around 0

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

11. Applied rewrites 99.0%

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

Alternative 9: 98.4% accurate, 12.2× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. lower-cos.f64 99.7

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

5. Applied rewrites 99.7%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 99.0%

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

Alternative 10: 97.9% accurate, 34.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 63.4%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. *-commutative N/A

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

   3. associate-*r* N/A

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

   4. lower-*.f64 N/A

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

   5. +-commutative N/A

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

   6. lower-fma.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. lower-sin.f64 N/A

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

   10. lower-cos.f64 100.0

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

5. Applied rewrites 100.0%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 98.4%

   \[\leadsto 1 \cdot \varepsilon \]
9. Add Preprocessing

Developer Target 1: 99.9% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \left(\cos \left(0.5 \cdot \left(\varepsilon - -2 \cdot x\right)\right) \cdot \sin \left(0.5 \cdot \varepsilon\right)\right) \cdot 2 \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (* (* (cos (* 0.5 (- eps (* -2.0 x)))) (sin (* 0.5 eps))) 2.0))

C

double code(double x, double eps) {
	return (cos((0.5 * (eps - (-2.0 * x)))) * sin((0.5 * eps))) * 2.0;
}

Fortran

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

Java

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

Python

def code(x, eps):
	return (math.cos((0.5 * (eps - (-2.0 * x)))) * math.sin((0.5 * eps))) * 2.0

Julia

function code(x, eps)
	return Float64(Float64(cos(Float64(0.5 * Float64(eps - Float64(-2.0 * x)))) * sin(Float64(0.5 * eps))) * 2.0)
end

MATLAB

function tmp = code(x, eps)
	tmp = (cos((0.5 * (eps - (-2.0 * x)))) * sin((0.5 * eps))) * 2.0;
end

Wolfram

code[x_, eps_] := N[(N[(N[Cos[N[(0.5 * N[(eps - N[(-2.0 * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * N[Sin[N[(0.5 * eps), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]

Reproduce

herbie shell --seed 2024242 
(FPCore (x eps)
  :name "2sin (example 3.3)"
  :precision binary64
  :pre (and (and (and (<= -10000.0 x) (<= x 10000.0)) (< (* 1e-16 (fabs x)) eps)) (< eps (fabs x)))

  :alt
  (! :herbie-platform default (* (cos (* 1/2 (- eps (* -2 x)))) (sin (* 1/2 eps)) 2))

  (- (sin (+ x eps)) (sin x)))