2cos (problem 3.3.5)

Percentage Accurate: 51.5% → 99.8%

Time: 17.4s

Alternatives: 13

Speedup: 25.9×

Specification

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

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 51.5% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.8% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sin \left(0.5 \cdot \varepsilon\right)\\ -2 \cdot \left(\mathsf{fma}\left(\cos \left(0.5 \cdot \varepsilon\right), \sin x, \cos x \cdot t\_0\right) \cdot t\_0\right) \end{array} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (let* ((t_0 (sin (* 0.5 eps))))
   (* -2.0 (* (fma (cos (* 0.5 eps)) (sin x) (* (cos x) t_0)) t_0))))

C

double code(double x, double eps) {
	double t_0 = sin((0.5 * eps));
	return -2.0 * (fma(cos((0.5 * eps)), sin(x), (cos(x) * t_0)) * t_0);
}

Julia

function code(x, eps)
	t_0 = sin(Float64(0.5 * eps))
	return Float64(-2.0 * Float64(fma(cos(Float64(0.5 * eps)), sin(x), Float64(cos(x) * t_0)) * t_0))
end

Wolfram

code[x_, eps_] := Block[{t$95$0 = N[Sin[N[(0.5 * eps), $MachinePrecision]], $MachinePrecision]}, N[(-2.0 * N[(N[(N[Cos[N[(0.5 * eps), $MachinePrecision]], $MachinePrecision] * N[Sin[x], $MachinePrecision] + N[(N[Cos[x], $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision] * t$95$0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 51.1%

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

   1. lift--.f64 N/A

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

   2. lift-cos.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. diff-cos N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

4. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\left(\sin \left(0.5 \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \sin \left(0.5 \cdot \left(0 + \varepsilon\right)\right)\right) \cdot -2} \]
5. Step-by-step derivation

   1. lift-sin.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-fma.f64 N/A

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

   4. distribute-rgt-in N/A

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

   5. *-commutative N/A

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

   6. sin-sum N/A

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

   7. lower-fma.f64 N/A

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

   8. lower-sin.f64 N/A

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

   9. *-commutative N/A

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

   10. associate-*r* N/A

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

   11. metadata-eval N/A

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

   12. lower-*.f64 N/A

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

   13. lower-cos.f64 N/A

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

   14. *-commutative N/A

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

   15. lower-*.f64 N/A

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

   16. +-lft-identity N/A

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

   17. lift-+.f64 N/A

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

   18. lift-*.f64 N/A

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

   19. lift-sin.f64 N/A

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

   20. lower-*.f64 N/A

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

6. Applied rewrites 99.8%

   \[\leadsto \left(\color{blue}{\mathsf{fma}\left(\sin \left(1 \cdot x\right), \cos \left(\varepsilon \cdot 0.5\right), \cos \left(1 \cdot x\right) \cdot \sin \left(\varepsilon \cdot 0.5\right)\right)} \cdot \sin \left(0.5 \cdot \left(0 + \varepsilon\right)\right)\right) \cdot -2 \]

7. Taylor expanded in eps around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. +-commutative N/A

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

   4. lower-fma.f64 N/A

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

   5. lower-cos.f64 N/A

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

   6. lower-*.f64 N/A

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

   7. lower-sin.f64 N/A

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

   8. *-commutative N/A

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

   9. lower-*.f64 N/A

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

   10. lower-sin.f64 N/A

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

   11. lower-*.f64 N/A

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

   12. lower-cos.f64 N/A

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

   13. lower-sin.f64 N/A

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

   14. lower-*.f64 99.8

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

9. Applied rewrites 99.8%

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

10. Final simplification 99.8%

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

Alternative 2: 99.6% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

   1. lift--.f64 N/A

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

   2. lift-cos.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. diff-cos N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

4. Applied rewrites 99.7%

   \[\leadsto \color{blue}{\left(\sin \left(0.5 \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \sin \left(0.5 \cdot \left(0 + \varepsilon\right)\right)\right) \cdot -2} \]
5. Step-by-step derivation

   1. lift-sin.f64 N/A

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

   2. lift-*.f64 N/A

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

   3. lift-fma.f64 N/A

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

   4. distribute-rgt-in N/A

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

   5. *-commutative N/A

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

   6. sin-sum N/A

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

   7. lower-fma.f64 N/A

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

   8. lower-sin.f64 N/A

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

   9. *-commutative N/A

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

   10. associate-*r* N/A

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

   11. metadata-eval N/A

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

   12. lower-*.f64 N/A

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

   13. lower-cos.f64 N/A

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

   14. *-commutative N/A

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

   15. lower-*.f64 N/A

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

   16. +-lft-identity N/A

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

   17. lift-+.f64 N/A

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

   18. lift-*.f64 N/A

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

   19. lift-sin.f64 N/A

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

   20. lower-*.f64 N/A

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

6. Applied rewrites 99.8%

   \[\leadsto \left(\color{blue}{\mathsf{fma}\left(\sin \left(1 \cdot x\right), \cos \left(\varepsilon \cdot 0.5\right), \cos \left(1 \cdot x\right) \cdot \sin \left(\varepsilon \cdot 0.5\right)\right)} \cdot \sin \left(0.5 \cdot \left(0 + \varepsilon\right)\right)\right) \cdot -2 \]

7. Taylor expanded in eps around inf

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. +-commutative N/A

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

   4. lower-fma.f64 N/A

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

   5. lower-cos.f64 N/A

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

   6. lower-*.f64 N/A

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

   7. lower-sin.f64 N/A

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

   8. *-commutative N/A

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

   9. lower-*.f64 N/A

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

   10. lower-sin.f64 N/A

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

   11. lower-*.f64 N/A

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

   12. lower-cos.f64 N/A

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

   13. lower-sin.f64 N/A

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

   14. lower-*.f64 99.8

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

9. Applied rewrites 99.8%

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

10. Taylor expanded in eps around 0

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

12. Applied rewrites 99.7%

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

13. Final simplification 99.7%

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

Alternative 3: 99.5% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ \left(\left(\mathsf{fma}\left(\mathsf{fma}\left(0.00026041666666666666, \varepsilon \cdot \varepsilon, -0.020833333333333332\right), \varepsilon \cdot \varepsilon, 0.5\right) \cdot \varepsilon\right) \cdot \sin \left(\mathsf{fma}\left(2, x, \varepsilon\right) \cdot 0.5\right)\right) \cdot -2 \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (*
  (*
   (*
    (fma
     (fma 0.00026041666666666666 (* eps eps) -0.020833333333333332)
     (* eps eps)
     0.5)
    eps)
   (sin (* (fma 2.0 x eps) 0.5)))
  -2.0))

C

double code(double x, double eps) {
	return ((fma(fma(0.00026041666666666666, (eps * eps), -0.020833333333333332), (eps * eps), 0.5) * eps) * sin((fma(2.0, x, eps) * 0.5))) * -2.0;
}

Julia

function code(x, eps)
	return Float64(Float64(Float64(fma(fma(0.00026041666666666666, Float64(eps * eps), -0.020833333333333332), Float64(eps * eps), 0.5) * eps) * sin(Float64(fma(2.0, x, eps) * 0.5))) * -2.0)
end

Wolfram

code[x_, eps_] := N[(N[(N[(N[(N[(0.00026041666666666666 * N[(eps * eps), $MachinePrecision] + -0.020833333333333332), $MachinePrecision] * N[(eps * eps), $MachinePrecision] + 0.5), $MachinePrecision] * eps), $MachinePrecision] * N[Sin[N[(N[(2.0 * x + eps), $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * -2.0), $MachinePrecision]

Derivation

1. Initial program 51.1%

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

   1. lift--.f64 N/A

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

   2. lift-cos.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. diff-cos N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

4. Applied rewrites 99.7%

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

5. Taylor expanded in eps around 0

   \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \color{blue}{\left(\varepsilon \cdot \left(\frac{1}{2} + {\varepsilon}^{2} \cdot \left(\frac{1}{3840} \cdot {\varepsilon}^{2} - \frac{1}{48}\right)\right)\right)}\right) \cdot -2 \]
6. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \color{blue}{\left(\left(\frac{1}{2} + {\varepsilon}^{2} \cdot \left(\frac{1}{3840} \cdot {\varepsilon}^{2} - \frac{1}{48}\right)\right) \cdot \varepsilon\right)}\right) \cdot -2 \]

   2. lower-*.f64 N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \color{blue}{\left(\left(\frac{1}{2} + {\varepsilon}^{2} \cdot \left(\frac{1}{3840} \cdot {\varepsilon}^{2} - \frac{1}{48}\right)\right) \cdot \varepsilon\right)}\right) \cdot -2 \]

   3. +-commutative N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\color{blue}{\left({\varepsilon}^{2} \cdot \left(\frac{1}{3840} \cdot {\varepsilon}^{2} - \frac{1}{48}\right) + \frac{1}{2}\right)} \cdot \varepsilon\right)\right) \cdot -2 \]

   4. *-commutative N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\left(\color{blue}{\left(\frac{1}{3840} \cdot {\varepsilon}^{2} - \frac{1}{48}\right) \cdot {\varepsilon}^{2}} + \frac{1}{2}\right) \cdot \varepsilon\right)\right) \cdot -2 \]

   5. lower-fma.f64 N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\color{blue}{\mathsf{fma}\left(\frac{1}{3840} \cdot {\varepsilon}^{2} - \frac{1}{48}, {\varepsilon}^{2}, \frac{1}{2}\right)} \cdot \varepsilon\right)\right) \cdot -2 \]

   6. sub-neg N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\mathsf{fma}\left(\color{blue}{\frac{1}{3840} \cdot {\varepsilon}^{2} + \left(\mathsf{neg}\left(\frac{1}{48}\right)\right)}, {\varepsilon}^{2}, \frac{1}{2}\right) \cdot \varepsilon\right)\right) \cdot -2 \]

   7. metadata-eval N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\mathsf{fma}\left(\frac{1}{3840} \cdot {\varepsilon}^{2} + \color{blue}{\frac{-1}{48}}, {\varepsilon}^{2}, \frac{1}{2}\right) \cdot \varepsilon\right)\right) \cdot -2 \]

   8. lower-fma.f64 N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{1}{3840}, {\varepsilon}^{2}, \frac{-1}{48}\right)}, {\varepsilon}^{2}, \frac{1}{2}\right) \cdot \varepsilon\right)\right) \cdot -2 \]

   9. unpow2 N/A

      \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{3840}, \color{blue}{\varepsilon \cdot \varepsilon}, \frac{-1}{48}\right), {\varepsilon}^{2}, \frac{1}{2}\right) \cdot \varepsilon\right)\right) \cdot -2 \]

   10. lower-*.f64 N/A

       \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{3840}, \color{blue}{\varepsilon \cdot \varepsilon}, \frac{-1}{48}\right), {\varepsilon}^{2}, \frac{1}{2}\right) \cdot \varepsilon\right)\right) \cdot -2 \]

   11. unpow2 N/A

       \[\leadsto \left(\sin \left(\frac{1}{2} \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{1}{3840}, \varepsilon \cdot \varepsilon, \frac{-1}{48}\right), \color{blue}{\varepsilon \cdot \varepsilon}, \frac{1}{2}\right) \cdot \varepsilon\right)\right) \cdot -2 \]

   12. lower-*.f64 99.7

       \[\leadsto \left(\sin \left(0.5 \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \left(\mathsf{fma}\left(\mathsf{fma}\left(0.00026041666666666666, \varepsilon \cdot \varepsilon, -0.020833333333333332\right), \color{blue}{\varepsilon \cdot \varepsilon}, 0.5\right) \cdot \varepsilon\right)\right) \cdot -2 \]

7. Applied rewrites 99.7%

   \[\leadsto \left(\sin \left(0.5 \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \color{blue}{\left(\mathsf{fma}\left(\mathsf{fma}\left(0.00026041666666666666, \varepsilon \cdot \varepsilon, -0.020833333333333332\right), \varepsilon \cdot \varepsilon, 0.5\right) \cdot \varepsilon\right)}\right) \cdot -2 \]

8. Final simplification 99.7%

   \[\leadsto \left(\left(\mathsf{fma}\left(\mathsf{fma}\left(0.00026041666666666666, \varepsilon \cdot \varepsilon, -0.020833333333333332\right), \varepsilon \cdot \varepsilon, 0.5\right) \cdot \varepsilon\right) \cdot \sin \left(\mathsf{fma}\left(2, x, \varepsilon\right) \cdot 0.5\right)\right) \cdot -2 \]
9. Add Preprocessing

Alternative 4: 99.5% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

   1. lift--.f64 N/A

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

   2. lift-cos.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. diff-cos N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

4. Applied rewrites 99.7%

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

5. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. +-commutative N/A

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

   4. *-commutative N/A

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

   5. lower-fma.f64 N/A

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

   6. unpow2 N/A

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

   7. lower-*.f64 99.5

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

7. Applied rewrites 99.5%

   \[\leadsto \left(\sin \left(0.5 \cdot \mathsf{fma}\left(2, x, \varepsilon\right)\right) \cdot \color{blue}{\left(\mathsf{fma}\left(\varepsilon \cdot \varepsilon, -0.020833333333333332, 0.5\right) \cdot \varepsilon\right)}\right) \cdot -2 \]
8. Step-by-step derivation

   1. lift-*.f64 N/A

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

   2. lift-fma.f64 N/A

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

   3. +-commutative N/A

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

   4. distribute-lft-in N/A

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

   5. *-commutative N/A

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

   6. associate-*r* N/A

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

   7. metadata-eval N/A

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

   8. *-lft-identity N/A

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

   9. lower-fma.f64 99.5

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

9. Applied rewrites 99.5%

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

10. Final simplification 99.5%

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

Alternative 5: 99.3% accurate, 1.6× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

   1. lift--.f64 N/A

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

   2. lift-cos.f64 N/A

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

   3. lift-cos.f64 N/A

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

   4. diff-cos N/A

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

   5. *-commutative N/A

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

   6. lower-*.f64 N/A

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

4. Applied rewrites 99.7%

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

5. Taylor expanded in eps around 0

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

   1. lower-*.f64 99.0

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

7. Applied rewrites 99.0%

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

8. Final simplification 99.0%

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

Alternative 6: 98.9% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

3. Taylor expanded in x around 0

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

   1. lower--.f64 N/A

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

   2. lower-cos.f64 49.5

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

5. Applied rewrites 49.5%

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

6. Taylor expanded in eps around 0

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

8. Applied rewrites 99.6%

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

9. Taylor expanded in x around 0

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

11. Applied rewrites 98.5%

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

Alternative 7: 98.9% accurate, 1.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.1

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

5. Applied rewrites 99.1%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 98.5%

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

Alternative 8: 98.4% accurate, 4.5× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.1

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

5. Applied rewrites 99.1%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 97.1%

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

9. Taylor expanded in x around 0

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

11. Applied rewrites 97.7%

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

12. Final simplification 97.7%

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

Alternative 9: 98.3% accurate, 6.1× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.1

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

5. Applied rewrites 99.1%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 97.6%

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

Alternative 10: 97.8% accurate, 8.3× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.1

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

5. Applied rewrites 99.1%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 97.1%

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

Alternative 11: 97.8% accurate, 14.8× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

3. Taylor expanded in eps around 0

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

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

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

   4. associate-*r* N/A

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

   5. *-commutative N/A

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

   6. lower--.f64 N/A

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

   7. *-commutative N/A

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

   8. lower-*.f64 N/A

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

   9. *-commutative N/A

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

   10. lower-*.f64 N/A

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

   11. lower-cos.f64 N/A

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

   12. lower-sin.f64 99.1

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

5. Applied rewrites 99.1%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 97.1%

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

Alternative 12: 78.6% accurate, 25.9× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

3. Taylor expanded in eps around 0

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

   1. associate-*r* N/A

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

   2. lower-*.f64 N/A

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

   3. mul-1-neg N/A

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

   4. lower-neg.f64 N/A

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

   5. lower-sin.f64 79.0

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

5. Applied rewrites 79.0%

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

6. Taylor expanded in x around 0

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

8. Applied rewrites 77.8%

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

9. Final simplification 77.8%

   \[\leadsto x \cdot \left(-\varepsilon\right) \]
10. Add Preprocessing

Alternative 13: 50.3% accurate, 51.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 51.1%

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

3. Taylor expanded in x around 0

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

   1. lower--.f64 N/A

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

   2. lower-cos.f64 49.5

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

5. Applied rewrites 49.5%

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

6. Taylor expanded in eps around 0

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

8. Applied rewrites 49.4%

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

Developer Target 1: 98.8% accurate, 0.5× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Reproduce

herbie shell --seed 2024272 
(FPCore (x eps)
  :name "2cos (problem 3.3.5)"
  :precision binary64
  :pre (and (and (and (<= -10000.0 x) (<= x 10000.0)) (< (* 1e-16 (fabs x)) eps)) (< eps (fabs x)))

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

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