2sin (example 3.3)

Percentage Accurate: 62.3% → 99.7%

Time: 12.7s

Alternatives: 10

Speedup: 12.2×

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: 62.3% 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: 99.7% accurate, 0.9× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 62.4%

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

3. Taylor expanded in eps around 0

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

   1. lower-*.f64 N/A

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

   2. +-commutative N/A

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

   3. distribute-lft-in N/A

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

   4. associate-+l+ N/A

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

   5. lower-fma.f64 N/A

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

   6. lower-*.f64 N/A

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

   7. lower-sin.f64 N/A

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

   8. associate-*r* N/A

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

   9. associate-*r* N/A

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

   10. distribute-lft1-in N/A

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

   11. lower-*.f64 N/A

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

   12. lower-fma.f64 N/A

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

   13. *-commutative N/A

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

   14. lower-*.f64 N/A

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

   15. lower-cos.f64 100.0

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

5. Applied rewrites 100.0%

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

Alternative 2: 99.7% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ 2 \cdot \left(\left(\varepsilon \cdot \mathsf{fma}\left(\varepsilon, \varepsilon \cdot -0.020833333333333332, 0.5\right)\right) \cdot \cos \left(\mathsf{fma}\left(0.5, \varepsilon, x\right)\right)\right) \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (*
  2.0
  (*
   (* eps (fma eps (* eps -0.020833333333333332) 0.5))
   (cos (fma 0.5 eps x)))))

C

double code(double x, double eps) {
	return 2.0 * ((eps * fma(eps, (eps * -0.020833333333333332), 0.5)) * cos(fma(0.5, eps, x)));
}

Julia

function code(x, eps)
	return Float64(2.0 * Float64(Float64(eps * fma(eps, Float64(eps * -0.020833333333333332), 0.5)) * cos(fma(0.5, eps, x))))
end

Wolfram

code[x_, eps_] := N[(2.0 * N[(N[(eps * N[(eps * N[(eps * -0.020833333333333332), $MachinePrecision] + 0.5), $MachinePrecision]), $MachinePrecision] * N[Cos[N[(0.5 * eps + x), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 62.4%

   \[\sin \left(x + \varepsilon\right) - \sin x \]
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. *-commutative N/A

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

   6. lower-*.f64 N/A

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

4. Applied rewrites 99.9%

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

5. Taylor expanded in eps around 0

   \[\leadsto \left(\color{blue}{\left(\varepsilon \cdot \left(\frac{1}{2} + \frac{-1}{48} \cdot {\varepsilon}^{2}\right)\right)} \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot \frac{1}{2}\right)\right) \cdot 2 \]
6. Step-by-step derivation

   1. lower-*.f64 N/A

      \[\leadsto \left(\color{blue}{\left(\varepsilon \cdot \left(\frac{1}{2} + \frac{-1}{48} \cdot {\varepsilon}^{2}\right)\right)} \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot \frac{1}{2}\right)\right) \cdot 2 \]

   2. +-commutative N/A

      \[\leadsto \left(\left(\varepsilon \cdot \color{blue}{\left(\frac{-1}{48} \cdot {\varepsilon}^{2} + \frac{1}{2}\right)}\right) \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot \frac{1}{2}\right)\right) \cdot 2 \]

   3. *-commutative N/A

      \[\leadsto \left(\left(\varepsilon \cdot \left(\color{blue}{{\varepsilon}^{2} \cdot \frac{-1}{48}} + \frac{1}{2}\right)\right) \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot \frac{1}{2}\right)\right) \cdot 2 \]

   4. unpow2 N/A

      \[\leadsto \left(\left(\varepsilon \cdot \left(\color{blue}{\left(\varepsilon \cdot \varepsilon\right)} \cdot \frac{-1}{48} + \frac{1}{2}\right)\right) \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot \frac{1}{2}\right)\right) \cdot 2 \]

   5. associate-*l* N/A

      \[\leadsto \left(\left(\varepsilon \cdot \left(\color{blue}{\varepsilon \cdot \left(\varepsilon \cdot \frac{-1}{48}\right)} + \frac{1}{2}\right)\right) \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot \frac{1}{2}\right)\right) \cdot 2 \]

   6. lower-fma.f64 N/A

      \[\leadsto \left(\left(\varepsilon \cdot \color{blue}{\mathsf{fma}\left(\varepsilon, \varepsilon \cdot \frac{-1}{48}, \frac{1}{2}\right)}\right) \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot \frac{1}{2}\right)\right) \cdot 2 \]

   7. lower-*.f64 99.9

      \[\leadsto \left(\left(\varepsilon \cdot \mathsf{fma}\left(\varepsilon, \color{blue}{\varepsilon \cdot -0.020833333333333332}, 0.5\right)\right) \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot 0.5\right)\right) \cdot 2 \]

7. Applied rewrites 99.9%

   \[\leadsto \left(\color{blue}{\left(\varepsilon \cdot \mathsf{fma}\left(\varepsilon, \varepsilon \cdot -0.020833333333333332, 0.5\right)\right)} \cdot \cos \left(\mathsf{fma}\left(x, 2, \varepsilon\right) \cdot 0.5\right)\right) \cdot 2 \]

8. Taylor expanded in x around 0

   \[\leadsto \left(\left(\varepsilon \cdot \mathsf{fma}\left(\varepsilon, \varepsilon \cdot \frac{-1}{48}, \frac{1}{2}\right)\right) \cdot \cos \color{blue}{\left(x + \frac{1}{2} \cdot \varepsilon\right)}\right) \cdot 2 \]
9. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \left(\left(\varepsilon \cdot \mathsf{fma}\left(\varepsilon, \varepsilon \cdot \frac{-1}{48}, \frac{1}{2}\right)\right) \cdot \cos \color{blue}{\left(\frac{1}{2} \cdot \varepsilon + x\right)}\right) \cdot 2 \]

   2. lower-fma.f64 99.9

      \[\leadsto \left(\left(\varepsilon \cdot \mathsf{fma}\left(\varepsilon, \varepsilon \cdot -0.020833333333333332, 0.5\right)\right) \cdot \cos \color{blue}{\left(\mathsf{fma}\left(0.5, \varepsilon, x\right)\right)}\right) \cdot 2 \]

10. Applied rewrites 99.9%

    \[\leadsto \left(\left(\varepsilon \cdot \mathsf{fma}\left(\varepsilon, \varepsilon \cdot -0.020833333333333332, 0.5\right)\right) \cdot \cos \color{blue}{\left(\mathsf{fma}\left(0.5, \varepsilon, x\right)\right)}\right) \cdot 2 \]

11. Final simplification 99.9%

    \[\leadsto 2 \cdot \left(\left(\varepsilon \cdot \mathsf{fma}\left(\varepsilon, \varepsilon \cdot -0.020833333333333332, 0.5\right)\right) \cdot \cos \left(\mathsf{fma}\left(0.5, \varepsilon, x\right)\right)\right) \]
12. Add Preprocessing

Alternative 3: 99.5% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 62.4%

   \[\sin \left(x + \varepsilon\right) - \sin x \]
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. *-commutative N/A

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

   6. lower-*.f64 N/A

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

4. Applied rewrites 99.9%

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

5. Taylor expanded in eps around 0

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

   1. lower-*.f64 99.7

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

7. Applied rewrites 99.7%

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

8. Final simplification 99.7%

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

Alternative 4: 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 62.4%

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

3. Taylor expanded in eps around 0

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

   1. lower-*.f64 N/A

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

   2. lower-cos.f64 99.1

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

5. Applied rewrites 99.1%

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

Alternative 5: 98.3% accurate, 4.9× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\varepsilon, \mathsf{fma}\left(\varepsilon, \varepsilon \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -0.16666666666666666\right), -0.5 \cdot \left(x \cdot \left(x + \varepsilon\right)\right)\right), \varepsilon\right) \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (fma
  eps
  (fma
   eps
   (* eps (fma x (* x 0.08333333333333333) -0.16666666666666666))
   (* -0.5 (* x (+ x eps))))
  eps))

C

double code(double x, double eps) {
	return fma(eps, fma(eps, (eps * fma(x, (x * 0.08333333333333333), -0.16666666666666666)), (-0.5 * (x * (x + eps)))), eps);
}

Julia

function code(x, eps)
	return fma(eps, fma(eps, Float64(eps * fma(x, Float64(x * 0.08333333333333333), -0.16666666666666666)), Float64(-0.5 * Float64(x * Float64(x + eps)))), eps)
end

Wolfram

code[x_, eps_] := N[(eps * N[(eps * N[(eps * N[(x * N[(x * 0.08333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision]), $MachinePrecision] + N[(-0.5 * N[(x * N[(x + eps), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] + eps), $MachinePrecision]

Derivation

1. Initial program 62.4%

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

3. Taylor expanded in x around 0

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

   1. sub-neg N/A

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

   2. metadata-eval N/A

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

   3. distribute-lft-in N/A

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

   4. *-commutative N/A

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

   5. associate-+r+ N/A

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

   6. mul-1-neg N/A

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

   7. unsub-neg N/A

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

   8. lower--.f64 N/A

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

5. Applied rewrites 61.7%

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

6. Taylor expanded in eps around 0

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

8. Applied rewrites 98.3%

   \[\leadsto \mathsf{fma}\left(\varepsilon, \color{blue}{\mathsf{fma}\left(\varepsilon, \varepsilon \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -0.16666666666666666\right), -0.5 \cdot \left(x \cdot \left(\varepsilon + x\right)\right)\right)}, \varepsilon\right) \]

9. Final simplification 98.3%

   \[\leadsto \mathsf{fma}\left(\varepsilon, \mathsf{fma}\left(\varepsilon, \varepsilon \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -0.16666666666666666\right), -0.5 \cdot \left(x \cdot \left(x + \varepsilon\right)\right)\right), \varepsilon\right) \]
10. Add Preprocessing

Alternative 6: 98.3% accurate, 6.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 62.4%

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

3. Taylor expanded in x around 0

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

   1. sub-neg N/A

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

   2. metadata-eval N/A

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

   3. distribute-lft-in N/A

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

   4. *-commutative N/A

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

   5. associate-+r+ N/A

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

   6. mul-1-neg N/A

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

   7. unsub-neg N/A

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

   8. lower--.f64 N/A

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

5. Applied rewrites 61.7%

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

6. Taylor expanded in eps around 0

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

8. Applied rewrites 98.3%

   \[\leadsto \mathsf{fma}\left(\varepsilon, \color{blue}{\mathsf{fma}\left(\varepsilon, \varepsilon \cdot \mathsf{fma}\left(x, x \cdot 0.08333333333333333, -0.16666666666666666\right), -0.5 \cdot \left(x \cdot \left(\varepsilon + x\right)\right)\right)}, \varepsilon\right) \]

9. Taylor expanded in x around 0

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

11. Applied rewrites 98.3%

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

12. Final simplification 98.3%

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

Alternative 7: 98.3% accurate, 10.4× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 62.4%

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

3. Taylor expanded in x around 0

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

   1. sub-neg N/A

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

   2. metadata-eval N/A

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

   3. distribute-lft-in N/A

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

   4. *-commutative N/A

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

   5. associate-+r+ N/A

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

   6. mul-1-neg N/A

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

   7. unsub-neg N/A

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

   8. lower--.f64 N/A

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

5. Applied rewrites 61.7%

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

6. Taylor expanded in eps around 0

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

8. Applied rewrites 98.3%

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

9. Final simplification 98.3%

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

Alternative 8: 98.2% accurate, 12.2× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 62.4%

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

3. Taylor expanded in x around 0

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

   1. sub-neg N/A

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

   2. metadata-eval N/A

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

   3. distribute-lft-in N/A

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

   4. *-commutative N/A

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

   5. associate-+r+ N/A

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

   6. mul-1-neg N/A

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

   7. unsub-neg N/A

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

   8. lower--.f64 N/A

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

5. Applied rewrites 61.7%

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

6. Taylor expanded in eps around 0

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

8. Applied rewrites 98.2%

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

Alternative 9: 97.7% accurate, 12.2× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 62.4%

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

3. Taylor expanded in x around 0

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

   1. sub-neg N/A

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

   2. metadata-eval N/A

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

   3. distribute-lft-in N/A

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

   4. *-commutative N/A

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

   5. associate-+r+ N/A

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

   6. mul-1-neg N/A

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

   7. unsub-neg N/A

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

   8. lower--.f64 N/A

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

5. Applied rewrites 61.7%

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

6. Taylor expanded in eps around 0

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

8. Applied rewrites 98.3%

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

9. Taylor expanded in x around 0

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

11. Applied rewrites 97.2%

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

12. Final simplification 97.2%

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

Alternative 10: 61.3% accurate, 29.6× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 62.4%

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

3. Taylor expanded in x around 0

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

   1. sub-neg N/A

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

   2. metadata-eval N/A

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

   3. distribute-lft-in N/A

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

   4. *-commutative N/A

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

   5. associate-+r+ N/A

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

   6. mul-1-neg N/A

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

   7. unsub-neg N/A

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

   8. lower--.f64 N/A

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

5. Applied rewrites 61.7%

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

6. Taylor expanded in eps around 0

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

8. Applied rewrites 61.6%

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

9. Taylor expanded in x around 0

   \[\leadsto \left(\varepsilon + x\right) - x \]
10. Step-by-step derivation

11. Applied rewrites 61.4%

    \[\leadsto \left(\varepsilon + x\right) - x \]

12. Final simplification 61.4%

    \[\leadsto \left(x + \varepsilon\right) - x \]
13. Add Preprocessing

Developer Target 1: 99.9% accurate, 0.9× 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]

Developer Target 2: 99.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \sin x \cdot \left(\cos \varepsilon - 1\right) + \cos x \cdot \sin \varepsilon \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (+ (* (sin x) (- (cos eps) 1.0)) (* (cos x) (sin eps))))

C

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

Fortran

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

Java

public static double code(double x, double eps) {
	return (Math.sin(x) * (Math.cos(eps) - 1.0)) + (Math.cos(x) * Math.sin(eps));
}

Python

def code(x, eps):
	return (math.sin(x) * (math.cos(eps) - 1.0)) + (math.cos(x) * math.sin(eps))

Julia

function code(x, eps)
	return Float64(Float64(sin(x) * Float64(cos(eps) - 1.0)) + Float64(cos(x) * sin(eps)))
end

MATLAB

function tmp = code(x, eps)
	tmp = (sin(x) * (cos(eps) - 1.0)) + (cos(x) * sin(eps));
end

Wolfram

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

Developer Target 3: 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 2024231 
(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 (* 2 (cos (+ x (/ eps 2))) (sin (/ eps 2))))

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

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

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