bug500 (missed optimization)

Percentage Accurate: 69.2% → 99.0%

Time: 7.2s

Alternatives: 8

Speedup: 6.5×

Specification

\[-1000 < x \land x < 1000\]

Math

\[\begin{array}{l} \\ \sin x - x \end{array} \]

FPCore

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

C

double code(double x) {
	return sin(x) - x;
}

Fortran

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

Java

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

Python

def code(x):
	return math.sin(x) - x

Julia

function code(x)
	return Float64(sin(x) - x)
end

MATLAB

function tmp = code(x)
	tmp = sin(x) - x;
end

Wolfram

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

Initial Program: 69.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sin x - x \end{array} \]

FPCore

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

C

double code(double x) {
	return sin(x) - x;
}

Fortran

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

Java

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

Python

def code(x):
	return math.sin(x) - x

Julia

function code(x)
	return Float64(sin(x) - x)
end

MATLAB

function tmp = code(x)
	tmp = sin(x) - x;
end

Wolfram

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

Alternative 1: 99.0% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 2.7557319223985893 \cdot 10^{-6}, -0.0001984126984126984\right), 0.008333333333333333\right), -0.16666666666666666\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  (* x (* x x))
  (fma
   (* x x)
   (fma
    (* x x)
    (fma (* x x) 2.7557319223985893e-6 -0.0001984126984126984)
    0.008333333333333333)
   -0.16666666666666666)))

C

double code(double x) {
	return (x * (x * x)) * fma((x * x), fma((x * x), fma((x * x), 2.7557319223985893e-6, -0.0001984126984126984), 0.008333333333333333), -0.16666666666666666);
}

Julia

function code(x)
	return Float64(Float64(x * Float64(x * x)) * fma(Float64(x * x), fma(Float64(x * x), fma(Float64(x * x), 2.7557319223985893e-6, -0.0001984126984126984), 0.008333333333333333), -0.16666666666666666))
end

Wolfram

code[x_] := N[(N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * N[(N[(x * x), $MachinePrecision] * 2.7557319223985893e-6 + -0.0001984126984126984), $MachinePrecision] + 0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 69.1%

   \[\sin x - x \]
2. Add Preprocessing
3. Step-by-step derivation

   1. sub-neg N/A

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

   2. +-commutative N/A

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

   3. flip-+ N/A

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

   4. /-lowering-/.f64 N/A

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

   5. sqr-neg N/A

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

   6. --lowering--.f64 N/A

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

   7. *-lowering-*.f64 N/A

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

   8. sqr-sin-a N/A

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

   9. cancel-sign-sub-inv N/A

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

   10. +-lowering-+.f64 N/A

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

   11. *-lowering-*.f64 N/A

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

   12. metadata-eval N/A

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

   13. count-2 N/A

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

   14. cos-lowering-cos.f64 N/A

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

   15. +-lowering-+.f64 N/A

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

   16. --lowering--.f64 N/A

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

   17. neg-lowering-neg.f64 N/A

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

   18. sin-lowering-sin.f64 54.2

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

4. Applied egg-rr 54.2%

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

5. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{3} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) - \frac{1}{6}\right)} \]
6. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{{x}^{3} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) - \frac{1}{6}\right)} \]

   2. cube-mult N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) - \frac{1}{6}\right) \]

   3. unpow2 N/A

      \[\leadsto \left(x \cdot \color{blue}{{x}^{2}}\right) \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) - \frac{1}{6}\right) \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\left(x \cdot {x}^{2}\right)} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) - \frac{1}{6}\right) \]

   5. unpow2 N/A

      \[\leadsto \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) - \frac{1}{6}\right) \]

   6. *-lowering-*.f64 N/A

      \[\leadsto \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) - \frac{1}{6}\right) \]

   7. sub-neg N/A

      \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \color{blue}{\left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)} \]

   8. metadata-eval N/A

      \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right)\right) + \color{blue}{\frac{-1}{6}}\right) \]

   9. accelerator-lowering-fma.f64 N/A

      \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right), \frac{-1}{6}\right)} \]

   10. unpow2 N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right), \frac{-1}{6}\right) \]

   11. *-lowering-*.f64 N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{120} + {x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right), \frac{-1}{6}\right) \]

   12. +-commutative N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \left(\frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}\right) + \frac{1}{120}}, \frac{-1}{6}\right) \]

   13. accelerator-lowering-fma.f64 N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}, \frac{1}{120}\right)}, \frac{-1}{6}\right) \]

   14. unpow2 N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}, \frac{1}{120}\right), \frac{-1}{6}\right) \]

   15. *-lowering-*.f64 N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{362880} \cdot {x}^{2} - \frac{1}{5040}, \frac{1}{120}\right), \frac{-1}{6}\right) \]

   16. sub-neg N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{1}{362880} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{5040}\right)\right)}, \frac{1}{120}\right), \frac{-1}{6}\right) \]

   17. *-commutative N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{1}{362880}} + \left(\mathsf{neg}\left(\frac{1}{5040}\right)\right), \frac{1}{120}\right), \frac{-1}{6}\right) \]

   18. metadata-eval N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, {x}^{2} \cdot \frac{1}{362880} + \color{blue}{\frac{-1}{5040}}, \frac{1}{120}\right), \frac{-1}{6}\right) \]

   19. accelerator-lowering-fma.f64 N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{362880}, \frac{-1}{5040}\right)}, \frac{1}{120}\right), \frac{-1}{6}\right) \]

   20. unpow2 N/A

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{362880}, \frac{-1}{5040}\right), \frac{1}{120}\right), \frac{-1}{6}\right) \]

   21. *-lowering-*.f64 98.9

       \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(\color{blue}{x \cdot x}, 2.7557319223985893 \cdot 10^{-6}, -0.0001984126984126984\right), 0.008333333333333333\right), -0.16666666666666666\right) \]

7. Simplified 98.9%

   \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right) \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x \cdot x, 2.7557319223985893 \cdot 10^{-6}, -0.0001984126984126984\right), 0.008333333333333333\right), -0.16666666666666666\right)} \]
8. Add Preprocessing

Alternative 2: 99.0% accurate, 2.7× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot -0.0001984126984126984, 0.008333333333333333\right), -0.16666666666666666\right)\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  x
  (*
   x
   (*
    x
    (fma
     (* x x)
     (fma x (* x -0.0001984126984126984) 0.008333333333333333)
     -0.16666666666666666)))))

C

double code(double x) {
	return x * (x * (x * fma((x * x), fma(x, (x * -0.0001984126984126984), 0.008333333333333333), -0.16666666666666666)));
}

Julia

function code(x)
	return Float64(x * Float64(x * Float64(x * fma(Float64(x * x), fma(x, Float64(x * -0.0001984126984126984), 0.008333333333333333), -0.16666666666666666))))
end

Wolfram

code[x_] := N[(x * N[(x * N[(x * N[(N[(x * x), $MachinePrecision] * N[(x * N[(x * -0.0001984126984126984), $MachinePrecision] + 0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 69.1%

   \[\sin x - x \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{3} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right)} \]
4. Step-by-step derivation

   1. cube-mult N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right) \]

   2. unpow2 N/A

      \[\leadsto \left(x \cdot \color{blue}{{x}^{2}}\right) \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right) \]

   3. associate-*r* N/A

      \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right)} \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right)} \]

   5. unpow2 N/A

      \[\leadsto x \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right) \]

   6. associate-*l* N/A

      \[\leadsto x \cdot \color{blue}{\left(x \cdot \left(x \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right)\right)} \]

   7. *-lowering-*.f64 N/A

      \[\leadsto x \cdot \color{blue}{\left(x \cdot \left(x \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right)\right)} \]

   8. *-lowering-*.f64 N/A

      \[\leadsto x \cdot \left(x \cdot \color{blue}{\left(x \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) - \frac{1}{6}\right)\right)}\right) \]

   9. sub-neg N/A

      \[\leadsto x \cdot \left(x \cdot \left(x \cdot \color{blue}{\left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)}\right)\right) \]

   10. metadata-eval N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left({x}^{2} \cdot \left(\frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}\right) + \color{blue}{\frac{-1}{6}}\right)\right)\right) \]

   11. accelerator-lowering-fma.f64 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}, \frac{-1}{6}\right)}\right)\right) \]

   12. unpow2 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}, \frac{-1}{6}\right)\right)\right) \]

   13. *-lowering-*.f64 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{120} + \frac{-1}{5040} \cdot {x}^{2}, \frac{-1}{6}\right)\right)\right) \]

   14. +-commutative N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{\frac{-1}{5040} \cdot {x}^{2} + \frac{1}{120}}, \frac{-1}{6}\right)\right)\right) \]

   15. *-commutative N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{{x}^{2} \cdot \frac{-1}{5040}} + \frac{1}{120}, \frac{-1}{6}\right)\right)\right) \]

   16. unpow2 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{\left(x \cdot x\right)} \cdot \frac{-1}{5040} + \frac{1}{120}, \frac{-1}{6}\right)\right)\right) \]

   17. associate-*l* N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{x \cdot \left(x \cdot \frac{-1}{5040}\right)} + \frac{1}{120}, \frac{-1}{6}\right)\right)\right) \]

   18. accelerator-lowering-fma.f64 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \color{blue}{\mathsf{fma}\left(x, x \cdot \frac{-1}{5040}, \frac{1}{120}\right)}, \frac{-1}{6}\right)\right)\right) \]

   19. *-lowering-*.f64 98.9

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, \color{blue}{x \cdot -0.0001984126984126984}, 0.008333333333333333\right), -0.16666666666666666\right)\right)\right) \]

5. Simplified 98.9%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot -0.0001984126984126984, 0.008333333333333333\right), -0.16666666666666666\right)\right)\right)} \]
6. Add Preprocessing

Alternative 3: 98.8% accurate, 3.9× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.008333333333333333, -0.16666666666666666\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* (* x x) (* x (fma x (* x 0.008333333333333333) -0.16666666666666666))))

C

double code(double x) {
	return (x * x) * (x * fma(x, (x * 0.008333333333333333), -0.16666666666666666));
}

Julia

function code(x)
	return Float64(Float64(x * x) * Float64(x * fma(x, Float64(x * 0.008333333333333333), -0.16666666666666666)))
end

Wolfram

code[x_] := N[(N[(x * x), $MachinePrecision] * N[(x * N[(x * N[(x * 0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 69.1%

   \[\sin x - x \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{3} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)} \]
4. Step-by-step derivation

   1. cube-mult N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right) \]

   2. unpow2 N/A

      \[\leadsto \left(x \cdot \color{blue}{{x}^{2}}\right) \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right) \]

   3. associate-*r* N/A

      \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)} \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)} \]

   5. unpow2 N/A

      \[\leadsto x \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right) \]

   6. associate-*l* N/A

      \[\leadsto x \cdot \color{blue}{\left(x \cdot \left(x \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)\right)} \]

   7. *-lowering-*.f64 N/A

      \[\leadsto x \cdot \color{blue}{\left(x \cdot \left(x \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)\right)} \]

   8. *-lowering-*.f64 N/A

      \[\leadsto x \cdot \left(x \cdot \color{blue}{\left(x \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)}\right) \]

   9. sub-neg N/A

      \[\leadsto x \cdot \left(x \cdot \left(x \cdot \color{blue}{\left(\frac{1}{120} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)}\right)\right) \]

   10. *-commutative N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left(\color{blue}{{x}^{2} \cdot \frac{1}{120}} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)\right)\right) \]

   11. unpow2 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{120} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)\right)\right) \]

   12. associate-*l* N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left(\color{blue}{x \cdot \left(x \cdot \frac{1}{120}\right)} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)\right)\right) \]

   13. metadata-eval N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left(x \cdot \left(x \cdot \frac{1}{120}\right) + \color{blue}{\frac{-1}{6}}\right)\right)\right) \]

   14. accelerator-lowering-fma.f64 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \color{blue}{\mathsf{fma}\left(x, x \cdot \frac{1}{120}, \frac{-1}{6}\right)}\right)\right) \]

   15. *-lowering-*.f64 98.7

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x, \color{blue}{x \cdot 0.008333333333333333}, -0.16666666666666666\right)\right)\right) \]

5. Simplified 98.7%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.008333333333333333, -0.16666666666666666\right)\right)\right)} \]
6. Step-by-step derivation

   1. associate-*r* N/A

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot \left(x \cdot \left(x \cdot \left(x \cdot \frac{1}{120}\right) + \frac{-1}{6}\right)\right)} \]

   2. *-commutative N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot \left(x \cdot \frac{1}{120}\right) + \frac{-1}{6}\right)\right) \cdot \left(x \cdot x\right)} \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot \left(x \cdot \frac{1}{120}\right) + \frac{-1}{6}\right)\right) \cdot \left(x \cdot x\right)} \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot \left(x \cdot \frac{1}{120}\right) + \frac{-1}{6}\right)\right)} \cdot \left(x \cdot x\right) \]

   5. accelerator-lowering-fma.f64 N/A

      \[\leadsto \left(x \cdot \color{blue}{\mathsf{fma}\left(x, x \cdot \frac{1}{120}, \frac{-1}{6}\right)}\right) \cdot \left(x \cdot x\right) \]

   6. *-lowering-*.f64 N/A

      \[\leadsto \left(x \cdot \mathsf{fma}\left(x, \color{blue}{x \cdot \frac{1}{120}}, \frac{-1}{6}\right)\right) \cdot \left(x \cdot x\right) \]

   7. *-lowering-*.f64 98.7

      \[\leadsto \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.008333333333333333, -0.16666666666666666\right)\right) \cdot \color{blue}{\left(x \cdot x\right)} \]

7. Applied egg-rr 98.7%

   \[\leadsto \color{blue}{\left(x \cdot \mathsf{fma}\left(x, x \cdot 0.008333333333333333, -0.16666666666666666\right)\right) \cdot \left(x \cdot x\right)} \]

8. Final simplification 98.7%

   \[\leadsto \left(x \cdot x\right) \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.008333333333333333, -0.16666666666666666\right)\right) \]
9. Add Preprocessing

Alternative 4: 98.8% accurate, 3.9× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.008333333333333333, -0.16666666666666666\right)\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* x (* x (* x (fma x (* x 0.008333333333333333) -0.16666666666666666)))))

C

double code(double x) {
	return x * (x * (x * fma(x, (x * 0.008333333333333333), -0.16666666666666666)));
}

Julia

function code(x)
	return Float64(x * Float64(x * Float64(x * fma(x, Float64(x * 0.008333333333333333), -0.16666666666666666))))
end

Wolfram

code[x_] := N[(x * N[(x * N[(x * N[(x * N[(x * 0.008333333333333333), $MachinePrecision] + -0.16666666666666666), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 69.1%

   \[\sin x - x \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{{x}^{3} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)} \]
4. Step-by-step derivation

   1. cube-mult N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right) \]

   2. unpow2 N/A

      \[\leadsto \left(x \cdot \color{blue}{{x}^{2}}\right) \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right) \]

   3. associate-*r* N/A

      \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)} \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot \left({x}^{2} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)} \]

   5. unpow2 N/A

      \[\leadsto x \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right) \]

   6. associate-*l* N/A

      \[\leadsto x \cdot \color{blue}{\left(x \cdot \left(x \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)\right)} \]

   7. *-lowering-*.f64 N/A

      \[\leadsto x \cdot \color{blue}{\left(x \cdot \left(x \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)\right)} \]

   8. *-lowering-*.f64 N/A

      \[\leadsto x \cdot \left(x \cdot \color{blue}{\left(x \cdot \left(\frac{1}{120} \cdot {x}^{2} - \frac{1}{6}\right)\right)}\right) \]

   9. sub-neg N/A

      \[\leadsto x \cdot \left(x \cdot \left(x \cdot \color{blue}{\left(\frac{1}{120} \cdot {x}^{2} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)}\right)\right) \]

   10. *-commutative N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left(\color{blue}{{x}^{2} \cdot \frac{1}{120}} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)\right)\right) \]

   11. unpow2 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{120} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)\right)\right) \]

   12. associate-*l* N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left(\color{blue}{x \cdot \left(x \cdot \frac{1}{120}\right)} + \left(\mathsf{neg}\left(\frac{1}{6}\right)\right)\right)\right)\right) \]

   13. metadata-eval N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \left(x \cdot \left(x \cdot \frac{1}{120}\right) + \color{blue}{\frac{-1}{6}}\right)\right)\right) \]

   14. accelerator-lowering-fma.f64 N/A

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \color{blue}{\mathsf{fma}\left(x, x \cdot \frac{1}{120}, \frac{-1}{6}\right)}\right)\right) \]

   15. *-lowering-*.f64 98.7

       \[\leadsto x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x, \color{blue}{x \cdot 0.008333333333333333}, -0.16666666666666666\right)\right)\right) \]

5. Simplified 98.7%

   \[\leadsto \color{blue}{x \cdot \left(x \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.008333333333333333, -0.16666666666666666\right)\right)\right)} \]
6. Add Preprocessing

Alternative 5: 98.4% accurate, 4.7× speedup

Math

\[\begin{array}{l} \\ x \cdot \frac{x \cdot x}{-6} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (/ (* x x) -6.0)))

C

double code(double x) {
	return x * ((x * x) / -6.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x * ((x * x) / (-6.0d0))
end function

Java

public static double code(double x) {
	return x * ((x * x) / -6.0);
}

Python

def code(x):
	return x * ((x * x) / -6.0)

Julia

function code(x)
	return Float64(x * Float64(Float64(x * x) / -6.0))
end

MATLAB

function tmp = code(x)
	tmp = x * ((x * x) / -6.0);
end

Wolfram

code[x_] := N[(x * N[(N[(x * x), $MachinePrecision] / -6.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 69.1%

   \[\sin x - x \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{-1}{6} \cdot {x}^{3}} \]
4. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\frac{-1}{6} \cdot {x}^{3}} \]

   2. cube-mult N/A

      \[\leadsto \frac{-1}{6} \cdot \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} \]

   3. unpow2 N/A

      \[\leadsto \frac{-1}{6} \cdot \left(x \cdot \color{blue}{{x}^{2}}\right) \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \frac{-1}{6} \cdot \color{blue}{\left(x \cdot {x}^{2}\right)} \]

   5. unpow2 N/A

      \[\leadsto \frac{-1}{6} \cdot \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \]

   6. *-lowering-*.f64 98.4

      \[\leadsto -0.16666666666666666 \cdot \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \]

5. Simplified 98.4%

   \[\leadsto \color{blue}{-0.16666666666666666 \cdot \left(x \cdot \left(x \cdot x\right)\right)} \]
6. Step-by-step derivation

   1. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\frac{-1}{6} \cdot x\right) \cdot \left(x \cdot x\right)} \]

   2. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left(\frac{-1}{6} \cdot x\right) \cdot x\right) \cdot x} \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\left(\left(\frac{-1}{6} \cdot x\right) \cdot x\right) \cdot x} \]

   4. *-commutative N/A

      \[\leadsto \left(\color{blue}{\left(x \cdot \frac{-1}{6}\right)} \cdot x\right) \cdot x \]

   5. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\left(\left(x \cdot \frac{-1}{6}\right) \cdot x\right)} \cdot x \]

   6. *-lowering-*.f64 98.4

      \[\leadsto \left(\color{blue}{\left(x \cdot -0.16666666666666666\right)} \cdot x\right) \cdot x \]

7. Applied egg-rr 98.4%

   \[\leadsto \color{blue}{\left(\left(x \cdot -0.16666666666666666\right) \cdot x\right) \cdot x} \]
8. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot \frac{-1}{6}\right)\right)} \cdot x \]

   2. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left(x \cdot x\right) \cdot \frac{-1}{6}\right)} \cdot x \]

   3. unpow1 N/A

      \[\leadsto \left(\color{blue}{{\left(x \cdot x\right)}^{1}} \cdot \frac{-1}{6}\right) \cdot x \]

   4. metadata-eval N/A

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

   5. pow-prod-up N/A

      \[\leadsto \left(\color{blue}{\left({\left(x \cdot x\right)}^{-1} \cdot {\left(x \cdot x\right)}^{2}\right)} \cdot \frac{-1}{6}\right) \cdot x \]

   6. inv-pow N/A

      \[\leadsto \left(\left(\color{blue}{\frac{1}{x \cdot x}} \cdot {\left(x \cdot x\right)}^{2}\right) \cdot \frac{-1}{6}\right) \cdot x \]

   7. pow2 N/A

      \[\leadsto \left(\left(\frac{1}{x \cdot x} \cdot \color{blue}{\left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)}\right) \cdot \frac{-1}{6}\right) \cdot x \]

   8. associate-*l/ N/A

      \[\leadsto \left(\color{blue}{\frac{1 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)}{x \cdot x}} \cdot \frac{-1}{6}\right) \cdot x \]

   9. *-lft-identity N/A

      \[\leadsto \left(\frac{\color{blue}{\left(x \cdot x\right) \cdot \left(x \cdot x\right)}}{x \cdot x} \cdot \frac{-1}{6}\right) \cdot x \]

   10. associate-/r/ N/A

       \[\leadsto \color{blue}{\frac{\left(x \cdot x\right) \cdot \left(x \cdot x\right)}{\frac{x \cdot x}{\frac{-1}{6}}}} \cdot x \]

   11. div-inv N/A

       \[\leadsto \frac{\left(x \cdot x\right) \cdot \left(x \cdot x\right)}{\color{blue}{\left(x \cdot x\right) \cdot \frac{1}{\frac{-1}{6}}}} \cdot x \]

   12. associate-/r* N/A

       \[\leadsto \color{blue}{\frac{\frac{\left(x \cdot x\right) \cdot \left(x \cdot x\right)}{x \cdot x}}{\frac{1}{\frac{-1}{6}}}} \cdot x \]

   13. *-lft-identity N/A

       \[\leadsto \frac{\frac{\color{blue}{1 \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)}}{x \cdot x}}{\frac{1}{\frac{-1}{6}}} \cdot x \]

   14. associate-*l/ N/A

       \[\leadsto \frac{\color{blue}{\frac{1}{x \cdot x} \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)}}{\frac{1}{\frac{-1}{6}}} \cdot x \]

   15. inv-pow N/A

       \[\leadsto \frac{\color{blue}{{\left(x \cdot x\right)}^{-1}} \cdot \left(\left(x \cdot x\right) \cdot \left(x \cdot x\right)\right)}{\frac{1}{\frac{-1}{6}}} \cdot x \]

   16. pow2 N/A

       \[\leadsto \frac{{\left(x \cdot x\right)}^{-1} \cdot \color{blue}{{\left(x \cdot x\right)}^{2}}}{\frac{1}{\frac{-1}{6}}} \cdot x \]

   17. pow-prod-up N/A

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

   18. metadata-eval N/A

       \[\leadsto \frac{{\left(x \cdot x\right)}^{\color{blue}{1}}}{\frac{1}{\frac{-1}{6}}} \cdot x \]

   19. unpow1 N/A

       \[\leadsto \frac{\color{blue}{x \cdot x}}{\frac{1}{\frac{-1}{6}}} \cdot x \]

   20. /-lowering-/.f64 N/A

       \[\leadsto \color{blue}{\frac{x \cdot x}{\frac{1}{\frac{-1}{6}}}} \cdot x \]

   21. *-lowering-*.f64 N/A

       \[\leadsto \frac{\color{blue}{x \cdot x}}{\frac{1}{\frac{-1}{6}}} \cdot x \]

   22. metadata-eval 98.5

       \[\leadsto \frac{x \cdot x}{\color{blue}{-6}} \cdot x \]

9. Applied egg-rr 98.5%

   \[\leadsto \color{blue}{\frac{x \cdot x}{-6}} \cdot x \]

10. Final simplification 98.5%

    \[\leadsto x \cdot \frac{x \cdot x}{-6} \]
11. Add Preprocessing

Alternative 6: 98.4% accurate, 6.5× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot x\right) \cdot \left(x \cdot -0.16666666666666666\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (* x x) (* x -0.16666666666666666)))

C

double code(double x) {
	return (x * x) * (x * -0.16666666666666666);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * x) * (x * (-0.16666666666666666d0))
end function

Java

public static double code(double x) {
	return (x * x) * (x * -0.16666666666666666);
}

Python

def code(x):
	return (x * x) * (x * -0.16666666666666666)

Julia

function code(x)
	return Float64(Float64(x * x) * Float64(x * -0.16666666666666666))
end

MATLAB

function tmp = code(x)
	tmp = (x * x) * (x * -0.16666666666666666);
end

Wolfram

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

Derivation

1. Initial program 69.1%

   \[\sin x - x \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{-1}{6} \cdot {x}^{3}} \]
4. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\frac{-1}{6} \cdot {x}^{3}} \]

   2. cube-mult N/A

      \[\leadsto \frac{-1}{6} \cdot \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} \]

   3. unpow2 N/A

      \[\leadsto \frac{-1}{6} \cdot \left(x \cdot \color{blue}{{x}^{2}}\right) \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \frac{-1}{6} \cdot \color{blue}{\left(x \cdot {x}^{2}\right)} \]

   5. unpow2 N/A

      \[\leadsto \frac{-1}{6} \cdot \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \]

   6. *-lowering-*.f64 98.4

      \[\leadsto -0.16666666666666666 \cdot \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \]

5. Simplified 98.4%

   \[\leadsto \color{blue}{-0.16666666666666666 \cdot \left(x \cdot \left(x \cdot x\right)\right)} \]
6. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \color{blue}{\left(x \cdot \left(x \cdot x\right)\right) \cdot \frac{-1}{6}} \]

   2. associate-*r* N/A

      \[\leadsto \color{blue}{\left(\left(x \cdot x\right) \cdot x\right)} \cdot \frac{-1}{6} \]

   3. associate-*l* N/A

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot \left(x \cdot \frac{-1}{6}\right)} \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot \left(x \cdot \frac{-1}{6}\right)} \]

   5. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\left(x \cdot x\right)} \cdot \left(x \cdot \frac{-1}{6}\right) \]

   6. *-lowering-*.f64 98.5

      \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{\left(x \cdot -0.16666666666666666\right)} \]

7. Applied egg-rr 98.5%

   \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot \left(x \cdot -0.16666666666666666\right)} \]
8. Add Preprocessing

Alternative 7: 98.4% accurate, 6.5× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* (* x (* x x)) -0.16666666666666666))

C

double code(double x) {
	return (x * (x * x)) * -0.16666666666666666;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (x * (x * x)) * (-0.16666666666666666d0)
end function

Java

public static double code(double x) {
	return (x * (x * x)) * -0.16666666666666666;
}

Python

def code(x):
	return (x * (x * x)) * -0.16666666666666666

Julia

function code(x)
	return Float64(Float64(x * Float64(x * x)) * -0.16666666666666666)
end

MATLAB

function tmp = code(x)
	tmp = (x * (x * x)) * -0.16666666666666666;
end

Wolfram

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

Derivation

1. Initial program 69.1%

   \[\sin x - x \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{-1}{6} \cdot {x}^{3}} \]
4. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \color{blue}{\frac{-1}{6} \cdot {x}^{3}} \]

   2. cube-mult N/A

      \[\leadsto \frac{-1}{6} \cdot \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} \]

   3. unpow2 N/A

      \[\leadsto \frac{-1}{6} \cdot \left(x \cdot \color{blue}{{x}^{2}}\right) \]

   4. *-lowering-*.f64 N/A

      \[\leadsto \frac{-1}{6} \cdot \color{blue}{\left(x \cdot {x}^{2}\right)} \]

   5. unpow2 N/A

      \[\leadsto \frac{-1}{6} \cdot \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \]

   6. *-lowering-*.f64 98.4

      \[\leadsto -0.16666666666666666 \cdot \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) \]

5. Simplified 98.4%

   \[\leadsto \color{blue}{-0.16666666666666666 \cdot \left(x \cdot \left(x \cdot x\right)\right)} \]

6. Final simplification 98.4%

   \[\leadsto \left(x \cdot \left(x \cdot x\right)\right) \cdot -0.16666666666666666 \]
7. Add Preprocessing

Alternative 8: 67.2% accurate, 104.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x) :precision binary64 0.0)

C

double code(double x) {
	return 0.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 0.0d0
end function

Java

public static double code(double x) {
	return 0.0;
}

Python

def code(x):
	return 0.0

Julia

function code(x)
	return 0.0
end

MATLAB

function tmp = code(x)
	tmp = 0.0;
end

Wolfram

code[x_] := 0.0

Derivation

1. Initial program 69.1%

   \[\sin x - x \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

5. Simplified 67.3%

   \[\leadsto \color{blue}{x} - x \]
6. Step-by-step derivation

   1. +-inverses 67.3

      \[\leadsto \color{blue}{0} \]

7. Applied egg-rr 67.3%

   \[\leadsto \color{blue}{0} \]
8. Add Preprocessing

Developer Target 1: 99.8% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\left|x\right| < 0.07:\\ \;\;\;\;-\left(\left(\frac{{x}^{3}}{6} - \frac{{x}^{5}}{120}\right) + \frac{{x}^{7}}{5040}\right)\\ \mathbf{else}:\\ \;\;\;\;\sin x - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (< (fabs x) 0.07)
   (- (+ (- (/ (pow x 3.0) 6.0) (/ (pow x 5.0) 120.0)) (/ (pow x 7.0) 5040.0)))
   (- (sin x) x)))

C

double code(double x) {
	double tmp;
	if (fabs(x) < 0.07) {
		tmp = -(((pow(x, 3.0) / 6.0) - (pow(x, 5.0) / 120.0)) + (pow(x, 7.0) / 5040.0));
	} else {
		tmp = sin(x) - x;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (abs(x) < 0.07d0) then
        tmp = -((((x ** 3.0d0) / 6.0d0) - ((x ** 5.0d0) / 120.0d0)) + ((x ** 7.0d0) / 5040.0d0))
    else
        tmp = sin(x) - x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (Math.abs(x) < 0.07) {
		tmp = -(((Math.pow(x, 3.0) / 6.0) - (Math.pow(x, 5.0) / 120.0)) + (Math.pow(x, 7.0) / 5040.0));
	} else {
		tmp = Math.sin(x) - x;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if math.fabs(x) < 0.07:
		tmp = -(((math.pow(x, 3.0) / 6.0) - (math.pow(x, 5.0) / 120.0)) + (math.pow(x, 7.0) / 5040.0))
	else:
		tmp = math.sin(x) - x
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (abs(x) < 0.07)
		tmp = Float64(-Float64(Float64(Float64((x ^ 3.0) / 6.0) - Float64((x ^ 5.0) / 120.0)) + Float64((x ^ 7.0) / 5040.0)));
	else
		tmp = Float64(sin(x) - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (abs(x) < 0.07)
		tmp = -((((x ^ 3.0) / 6.0) - ((x ^ 5.0) / 120.0)) + ((x ^ 7.0) / 5040.0));
	else
		tmp = sin(x) - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[Less[N[Abs[x], $MachinePrecision], 0.07], (-N[(N[(N[(N[Power[x, 3.0], $MachinePrecision] / 6.0), $MachinePrecision] - N[(N[Power[x, 5.0], $MachinePrecision] / 120.0), $MachinePrecision]), $MachinePrecision] + N[(N[Power[x, 7.0], $MachinePrecision] / 5040.0), $MachinePrecision]), $MachinePrecision]), N[(N[Sin[x], $MachinePrecision] - x), $MachinePrecision]]

Reproduce

herbie shell --seed 2024205 
(FPCore (x)
  :name "bug500 (missed optimization)"
  :precision binary64
  :pre (and (< -1000.0 x) (< x 1000.0))

  :alt
  (! :herbie-platform default (if (< (fabs x) 7/100) (- (+ (- (/ (pow x 3) 6) (/ (pow x 5) 120)) (/ (pow x 7) 5040))) (- (sin x) x)))

  (- (sin x) x))