2sin (example 3.3)

Percentage Accurate: 62.4% → 99.9%

Time: 10.8s

Alternatives: 5

Speedup: 205.0×

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: 62.4% 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: 99.9% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Java

public static double code(double x, double eps) {
	return Math.cbrt(Math.pow(Math.cos((x - (-0.5 * eps))), 3.0)) * (2.0 * Math.sin((eps * 0.5)));
}

Julia

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

Wolfram

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

Derivation

1. Initial program 61.1%

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

   1. diff-sin 61.1%

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

   2. div-inv 61.1%

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

   3. associate--l+ 61.1%

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

   4. metadata-eval 61.1%

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

   5. div-inv 61.1%

      \[\leadsto 2 \cdot \left(\sin \left(\left(x + \left(\varepsilon - x\right)\right) \cdot 0.5\right) \cdot \cos \color{blue}{\left(\left(\left(x + \varepsilon\right) + x\right) \cdot \frac{1}{2}\right)}\right) \]

   6. +-commutative 61.1%

      \[\leadsto 2 \cdot \left(\sin \left(\left(x + \left(\varepsilon - x\right)\right) \cdot 0.5\right) \cdot \cos \left(\color{blue}{\left(x + \left(x + \varepsilon\right)\right)} \cdot \frac{1}{2}\right)\right) \]

   7. metadata-eval 61.1%

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

4. Applied egg-rr 61.1%

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

   1. associate-*r* 61.1%

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

   2. *-commutative 61.1%

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

   3. *-commutative 61.1%

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

   4. associate-+r+ 61.1%

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

   5. +-commutative 61.1%

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

   6. *-rgt-identity 61.1%

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

   7. *-rgt-identity 61.1%

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

   8. distribute-lft-out 61.1%

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

   9. metadata-eval 61.1%

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

   10. +-commutative 61.1%

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

   11. associate-+l- 100.0%

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

   12. +-inverses 100.0%

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

   13. --rgt-identity 100.0%

       \[\leadsto \cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right) \cdot \left(2 \cdot \sin \left(\color{blue}{\varepsilon} \cdot 0.5\right)\right) \]

6. Simplified 100.0%

   \[\leadsto \color{blue}{\cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right) \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right)} \]
7. Step-by-step derivation

   1. add-cbrt-cube 100.0%

      \[\leadsto \color{blue}{\sqrt[3]{\left(\cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right) \cdot \cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right)\right) \cdot \cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right)}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   2. pow3 100.0%

      \[\leadsto \sqrt[3]{\color{blue}{{\cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right)}^{3}}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   3. distribute-lft-in 100.0%

      \[\leadsto \sqrt[3]{{\cos \color{blue}{\left(0.5 \cdot \varepsilon + 0.5 \cdot \left(x \cdot 2\right)\right)}}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   4. *-commutative 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\color{blue}{\varepsilon \cdot 0.5} + 0.5 \cdot \left(x \cdot 2\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   5. fma-define 100.0%

      \[\leadsto \sqrt[3]{{\cos \color{blue}{\left(\mathsf{fma}\left(\varepsilon, 0.5, 0.5 \cdot \left(x \cdot 2\right)\right)\right)}}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   6. *-commutative 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, 0.5 \cdot \color{blue}{\left(2 \cdot x\right)}\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   7. associate-*r* 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, \color{blue}{\left(0.5 \cdot 2\right) \cdot x}\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   8. metadata-eval 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, \color{blue}{1} \cdot x\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   9. *-un-lft-identity 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, \color{blue}{x}\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

8. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, x\right)\right)}^{3}}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

9. Taylor expanded in eps around -inf 100.0%

   \[\leadsto \sqrt[3]{\color{blue}{{\cos \left(x - -0.5 \cdot \varepsilon\right)}^{3}}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

10. Final simplification 100.0%

    \[\leadsto \sqrt[3]{{\cos \left(x - -0.5 \cdot \varepsilon\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]
11. Add Preprocessing

Alternative 2: 99.9% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 61.1%

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

   1. diff-sin 61.1%

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

   2. div-inv 61.1%

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

   3. associate--l+ 61.1%

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

   4. metadata-eval 61.1%

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

   5. div-inv 61.1%

      \[\leadsto 2 \cdot \left(\sin \left(\left(x + \left(\varepsilon - x\right)\right) \cdot 0.5\right) \cdot \cos \color{blue}{\left(\left(\left(x + \varepsilon\right) + x\right) \cdot \frac{1}{2}\right)}\right) \]

   6. +-commutative 61.1%

      \[\leadsto 2 \cdot \left(\sin \left(\left(x + \left(\varepsilon - x\right)\right) \cdot 0.5\right) \cdot \cos \left(\color{blue}{\left(x + \left(x + \varepsilon\right)\right)} \cdot \frac{1}{2}\right)\right) \]

   7. metadata-eval 61.1%

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

4. Applied egg-rr 61.1%

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

   1. associate-*r* 61.1%

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

   2. *-commutative 61.1%

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

   3. *-commutative 61.1%

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

   4. associate-+r+ 61.1%

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

   5. +-commutative 61.1%

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

   6. *-rgt-identity 61.1%

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

   7. *-rgt-identity 61.1%

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

   8. distribute-lft-out 61.1%

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

   9. metadata-eval 61.1%

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

   10. +-commutative 61.1%

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

   11. associate-+l- 100.0%

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

   12. +-inverses 100.0%

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

   13. --rgt-identity 100.0%

       \[\leadsto \cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right) \cdot \left(2 \cdot \sin \left(\color{blue}{\varepsilon} \cdot 0.5\right)\right) \]

6. Simplified 100.0%

   \[\leadsto \color{blue}{\cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right) \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right)} \]
7. Step-by-step derivation

   1. add-cbrt-cube 100.0%

      \[\leadsto \color{blue}{\sqrt[3]{\left(\cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right) \cdot \cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right)\right) \cdot \cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right)}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   2. pow3 100.0%

      \[\leadsto \sqrt[3]{\color{blue}{{\cos \left(0.5 \cdot \left(\varepsilon + x \cdot 2\right)\right)}^{3}}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   3. distribute-lft-in 100.0%

      \[\leadsto \sqrt[3]{{\cos \color{blue}{\left(0.5 \cdot \varepsilon + 0.5 \cdot \left(x \cdot 2\right)\right)}}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   4. *-commutative 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\color{blue}{\varepsilon \cdot 0.5} + 0.5 \cdot \left(x \cdot 2\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   5. fma-define 100.0%

      \[\leadsto \sqrt[3]{{\cos \color{blue}{\left(\mathsf{fma}\left(\varepsilon, 0.5, 0.5 \cdot \left(x \cdot 2\right)\right)\right)}}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   6. *-commutative 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, 0.5 \cdot \color{blue}{\left(2 \cdot x\right)}\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   7. associate-*r* 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, \color{blue}{\left(0.5 \cdot 2\right) \cdot x}\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   8. metadata-eval 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, \color{blue}{1} \cdot x\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

   9. *-un-lft-identity 100.0%

      \[\leadsto \sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, \color{blue}{x}\right)\right)}^{3}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

8. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{\sqrt[3]{{\cos \left(\mathsf{fma}\left(\varepsilon, 0.5, x\right)\right)}^{3}}} \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]

9. Taylor expanded in eps around -inf 100.0%

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

10. Final simplification 100.0%

    \[\leadsto \cos \left(x - -0.5 \cdot \varepsilon\right) \cdot \left(2 \cdot \sin \left(\varepsilon \cdot 0.5\right)\right) \]
11. Add Preprocessing

Alternative 3: 99.0% accurate, 2.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 61.1%

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

3. Taylor expanded in eps around 0 99.7%

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

4. Final simplification 99.7%

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

Alternative 4: 97.7% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \sin \varepsilon \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

function code(x, eps)
	return sin(eps)
end

MATLAB

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

Wolfram

code[x_, eps_] := N[Sin[eps], $MachinePrecision]

Derivation

1. Initial program 61.1%

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

3. Taylor expanded in x around 0 98.9%

   \[\leadsto \color{blue}{\sin \varepsilon} \]

4. Final simplification 98.9%

   \[\leadsto \sin \varepsilon \]
5. Add Preprocessing

Alternative 5: 97.7% accurate, 205.0× speedup

Math

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

FPCore

(FPCore (x eps) :precision binary64 eps)

C

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

Fortran

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

Java

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

Python

def code(x, eps):
	return eps

Julia

function code(x, eps)
	return eps
end

MATLAB

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

Wolfram

code[x_, eps_] := eps

Derivation

1. Initial program 61.1%

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

3. Taylor expanded in eps around 0 99.7%

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

4. Taylor expanded in x around 0 98.9%

   \[\leadsto \color{blue}{\varepsilon} \]

5. Final simplification 98.9%

   \[\leadsto \varepsilon \]
6. Add Preprocessing

Developer target: 99.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x eps)
 :precision binary64
 (* (* 2.0 (cos (+ x (/ eps 2.0)))) (sin (/ eps 2.0))))

C

double code(double x, double eps) {
	return (2.0 * cos((x + (eps / 2.0)))) * sin((eps / 2.0));
}

Fortran

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

Java

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

Python

def code(x, eps):
	return (2.0 * math.cos((x + (eps / 2.0)))) * math.sin((eps / 2.0))

Julia

function code(x, eps)
	return Float64(Float64(2.0 * cos(Float64(x + Float64(eps / 2.0)))) * sin(Float64(eps / 2.0)))
end

MATLAB

function tmp = code(x, eps)
	tmp = (2.0 * cos((x + (eps / 2.0)))) * sin((eps / 2.0));
end

Wolfram

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

Reproduce

herbie shell --seed 2024046 
(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
  (* (* 2.0 (cos (+ x (/ eps 2.0)))) (sin (/ eps 2.0)))

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