Linear.Quaternion:$cexp from linear-1.19.1.3

Percentage Accurate: 99.8% → 99.8%

Time: 6.3s

Alternatives: 6

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x \cdot \frac{\sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (/ (sin y) y)))

C

double code(double x, double y) {
	return x * (sin(y) / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (sin(y) / y)
end function

Java

public static double code(double x, double y) {
	return x * (Math.sin(y) / y);
}

Python

def code(x, y):
	return x * (math.sin(y) / y)

Julia

function code(x, y)
	return Float64(x * Float64(sin(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (sin(y) / y);
end

Wolfram

code[x_, y_] := N[(x * N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \frac{\sin y}{y} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* x (/ (sin y) y)))

C

double code(double x, double y) {
	return x * (sin(y) / y);
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x * (sin(y) / y)
end function

Java

public static double code(double x, double y) {
	return x * (Math.sin(y) / y);
}

Python

def code(x, y):
	return x * (math.sin(y) / y)

Julia

function code(x, y)
	return Float64(x * Float64(sin(y) / y))
end

MATLAB

function tmp = code(x, y)
	tmp = x * (sin(y) / y);
end

Wolfram

code[x_, y_] := N[(x * N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\sin y}{y} \cdot x \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (* (/ (sin y) y) x))

C

double code(double x, double y) {
	return (sin(y) / y) * x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (sin(y) / y) * x
end function

Java

public static double code(double x, double y) {
	return (Math.sin(y) / y) * x;
}

Python

def code(x, y):
	return (math.sin(y) / y) * x

Julia

function code(x, y)
	return Float64(Float64(sin(y) / y) * x)
end

MATLAB

function tmp = code(x, y)
	tmp = (sin(y) / y) * x;
end

Wolfram

code[x_, y_] := N[(N[(N[Sin[y], $MachinePrecision] / y), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing

3. Final simplification 99.8%

   \[\leadsto \frac{\sin y}{y} \cdot x \]
4. Add Preprocessing

Alternative 2: 63.7% accurate, 2.6× speedup

Math

\[\begin{array}{l} \\ \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.00205026455026455, y \cdot y, 0.019444444444444445\right), y \cdot y, 0.16666666666666666\right), y \cdot y, 1\right)} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (/
  x
  (fma
   (fma
    (fma 0.00205026455026455 (* y y) 0.019444444444444445)
    (* y y)
    0.16666666666666666)
   (* y y)
   1.0)))

C

double code(double x, double y) {
	return x / fma(fma(fma(0.00205026455026455, (y * y), 0.019444444444444445), (y * y), 0.16666666666666666), (y * y), 1.0);
}

Julia

function code(x, y)
	return Float64(x / fma(fma(fma(0.00205026455026455, Float64(y * y), 0.019444444444444445), Float64(y * y), 0.16666666666666666), Float64(y * y), 1.0))
end

Wolfram

code[x_, y_] := N[(x / N[(N[(N[(0.00205026455026455 * N[(y * y), $MachinePrecision] + 0.019444444444444445), $MachinePrecision] * N[(y * y), $MachinePrecision] + 0.16666666666666666), $MachinePrecision] * N[(y * y), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot \frac{\sin y}{y}} \]

   2. lift-/.f64 N/A

      \[\leadsto x \cdot \color{blue}{\frac{\sin y}{y}} \]

   3. clear-num N/A

      \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

   4. un-div-inv N/A

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   5. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   6. lower-/.f64 99.8

      \[\leadsto \frac{x}{\color{blue}{\frac{y}{\sin y}}} \]

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

5. Taylor expanded in y around 0

   \[\leadsto \frac{x}{\color{blue}{1 + {y}^{2} \cdot \left(\frac{1}{6} + {y}^{2} \cdot \left(\frac{7}{360} + \frac{31}{15120} \cdot {y}^{2}\right)\right)}} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{x}{\color{blue}{{y}^{2} \cdot \left(\frac{1}{6} + {y}^{2} \cdot \left(\frac{7}{360} + \frac{31}{15120} \cdot {y}^{2}\right)\right) + 1}} \]

   2. *-commutative N/A

      \[\leadsto \frac{x}{\color{blue}{\left(\frac{1}{6} + {y}^{2} \cdot \left(\frac{7}{360} + \frac{31}{15120} \cdot {y}^{2}\right)\right) \cdot {y}^{2}} + 1} \]

   3. lower-fma.f64 N/A

      \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(\frac{1}{6} + {y}^{2} \cdot \left(\frac{7}{360} + \frac{31}{15120} \cdot {y}^{2}\right), {y}^{2}, 1\right)}} \]

   4. +-commutative N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\color{blue}{{y}^{2} \cdot \left(\frac{7}{360} + \frac{31}{15120} \cdot {y}^{2}\right) + \frac{1}{6}}, {y}^{2}, 1\right)} \]

   5. *-commutative N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\color{blue}{\left(\frac{7}{360} + \frac{31}{15120} \cdot {y}^{2}\right) \cdot {y}^{2}} + \frac{1}{6}, {y}^{2}, 1\right)} \]

   6. lower-fma.f64 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{7}{360} + \frac{31}{15120} \cdot {y}^{2}, {y}^{2}, \frac{1}{6}\right)}, {y}^{2}, 1\right)} \]

   7. +-commutative N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\frac{31}{15120} \cdot {y}^{2} + \frac{7}{360}}, {y}^{2}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   8. lower-fma.f64 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{31}{15120}, {y}^{2}, \frac{7}{360}\right)}, {y}^{2}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   9. unpow2 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{31}{15120}, \color{blue}{y \cdot y}, \frac{7}{360}\right), {y}^{2}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   10. lower-*.f64 N/A

       \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{31}{15120}, \color{blue}{y \cdot y}, \frac{7}{360}\right), {y}^{2}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   11. unpow2 N/A

       \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{31}{15120}, y \cdot y, \frac{7}{360}\right), \color{blue}{y \cdot y}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   12. lower-*.f64 N/A

       \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{31}{15120}, y \cdot y, \frac{7}{360}\right), \color{blue}{y \cdot y}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   13. unpow2 N/A

       \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(\frac{31}{15120}, y \cdot y, \frac{7}{360}\right), y \cdot y, \frac{1}{6}\right), \color{blue}{y \cdot y}, 1\right)} \]

   14. lower-*.f64 64.4

       \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.00205026455026455, y \cdot y, 0.019444444444444445\right), y \cdot y, 0.16666666666666666\right), \color{blue}{y \cdot y}, 1\right)} \]

7. Applied rewrites 64.4%

   \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(\mathsf{fma}\left(0.00205026455026455, y \cdot y, 0.019444444444444445\right), y \cdot y, 0.16666666666666666\right), y \cdot y, 1\right)}} \]
8. Add Preprocessing

Alternative 3: 63.7% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(0.019444444444444445, y \cdot y, 0.16666666666666666\right), y \cdot y, 1\right)} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (/
  x
  (fma (fma 0.019444444444444445 (* y y) 0.16666666666666666) (* y y) 1.0)))

C

double code(double x, double y) {
	return x / fma(fma(0.019444444444444445, (y * y), 0.16666666666666666), (y * y), 1.0);
}

Julia

function code(x, y)
	return Float64(x / fma(fma(0.019444444444444445, Float64(y * y), 0.16666666666666666), Float64(y * y), 1.0))
end

Wolfram

code[x_, y_] := N[(x / N[(N[(0.019444444444444445 * N[(y * y), $MachinePrecision] + 0.16666666666666666), $MachinePrecision] * N[(y * y), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot \frac{\sin y}{y}} \]

   2. lift-/.f64 N/A

      \[\leadsto x \cdot \color{blue}{\frac{\sin y}{y}} \]

   3. clear-num N/A

      \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

   4. un-div-inv N/A

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   5. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   6. lower-/.f64 99.8

      \[\leadsto \frac{x}{\color{blue}{\frac{y}{\sin y}}} \]

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

5. Taylor expanded in y around 0

   \[\leadsto \frac{x}{\color{blue}{1 + {y}^{2} \cdot \left(\frac{1}{6} + \frac{7}{360} \cdot {y}^{2}\right)}} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{x}{\color{blue}{{y}^{2} \cdot \left(\frac{1}{6} + \frac{7}{360} \cdot {y}^{2}\right) + 1}} \]

   2. *-commutative N/A

      \[\leadsto \frac{x}{\color{blue}{\left(\frac{1}{6} + \frac{7}{360} \cdot {y}^{2}\right) \cdot {y}^{2}} + 1} \]

   3. lower-fma.f64 N/A

      \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(\frac{1}{6} + \frac{7}{360} \cdot {y}^{2}, {y}^{2}, 1\right)}} \]

   4. +-commutative N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\color{blue}{\frac{7}{360} \cdot {y}^{2} + \frac{1}{6}}, {y}^{2}, 1\right)} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{7}{360}, {y}^{2}, \frac{1}{6}\right)}, {y}^{2}, 1\right)} \]

   6. unpow2 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{7}{360}, \color{blue}{y \cdot y}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   7. lower-*.f64 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{7}{360}, \color{blue}{y \cdot y}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   8. unpow2 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{7}{360}, y \cdot y, \frac{1}{6}\right), \color{blue}{y \cdot y}, 1\right)} \]

   9. lower-*.f64 64.3

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(0.019444444444444445, y \cdot y, 0.16666666666666666\right), \color{blue}{y \cdot y}, 1\right)} \]

7. Applied rewrites 64.3%

   \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.019444444444444445, y \cdot y, 0.16666666666666666\right), y \cdot y, 1\right)}} \]
8. Add Preprocessing

Alternative 4: 63.8% accurate, 3.9× speedup

Math

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

FPCore

(FPCore (x y)
 :precision binary64
 (* (/ (- -1.0) (fma 0.16666666666666666 (* y y) 1.0)) x))

C

double code(double x, double y) {
	return (-(-1.0) / fma(0.16666666666666666, (y * y), 1.0)) * x;
}

Julia

function code(x, y)
	return Float64(Float64(Float64(-(-1.0)) / fma(0.16666666666666666, Float64(y * y), 1.0)) * x)
end

Wolfram

code[x_, y_] := N[(N[((--1.0) / N[(0.16666666666666666 * N[(y * y), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot \frac{\sin y}{y}} \]

   2. lift-/.f64 N/A

      \[\leadsto x \cdot \color{blue}{\frac{\sin y}{y}} \]

   3. clear-num N/A

      \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

   4. un-div-inv N/A

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   5. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   6. lower-/.f64 99.8

      \[\leadsto \frac{x}{\color{blue}{\frac{y}{\sin y}}} \]

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

5. Taylor expanded in y around 0

   \[\leadsto \frac{x}{\color{blue}{1 + {y}^{2} \cdot \left(\frac{1}{6} + \frac{7}{360} \cdot {y}^{2}\right)}} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{x}{\color{blue}{{y}^{2} \cdot \left(\frac{1}{6} + \frac{7}{360} \cdot {y}^{2}\right) + 1}} \]

   2. *-commutative N/A

      \[\leadsto \frac{x}{\color{blue}{\left(\frac{1}{6} + \frac{7}{360} \cdot {y}^{2}\right) \cdot {y}^{2}} + 1} \]

   3. lower-fma.f64 N/A

      \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(\frac{1}{6} + \frac{7}{360} \cdot {y}^{2}, {y}^{2}, 1\right)}} \]

   4. +-commutative N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\color{blue}{\frac{7}{360} \cdot {y}^{2} + \frac{1}{6}}, {y}^{2}, 1\right)} \]

   5. lower-fma.f64 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(\frac{7}{360}, {y}^{2}, \frac{1}{6}\right)}, {y}^{2}, 1\right)} \]

   6. unpow2 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{7}{360}, \color{blue}{y \cdot y}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   7. lower-*.f64 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{7}{360}, \color{blue}{y \cdot y}, \frac{1}{6}\right), {y}^{2}, 1\right)} \]

   8. unpow2 N/A

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(\frac{7}{360}, y \cdot y, \frac{1}{6}\right), \color{blue}{y \cdot y}, 1\right)} \]

   9. lower-*.f64 64.3

      \[\leadsto \frac{x}{\mathsf{fma}\left(\mathsf{fma}\left(0.019444444444444445, y \cdot y, 0.16666666666666666\right), \color{blue}{y \cdot y}, 1\right)} \]

7. Applied rewrites 64.3%

   \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(0.019444444444444445, y \cdot y, 0.16666666666666666\right), y \cdot y, 1\right)}} \]

8. Taylor expanded in y around 0

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

10. Applied rewrites 64.3%

    \[\leadsto \frac{x}{\mathsf{fma}\left(0.16666666666666666, \color{blue}{y} \cdot y, 1\right)} \]
11. Step-by-step derivation

    1. lift-/.f64 N/A

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

    2. frac-2neg N/A

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

    3. div-inv N/A

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

    4. lower-*.f64 N/A

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

    5. lower-neg.f64 N/A

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

    6. frac-2neg N/A

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

    7. metadata-eval N/A

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

    8. remove-double-neg N/A

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

    9. lower-/.f64 64.3

       \[\leadsto \left(-x\right) \cdot \color{blue}{\frac{-1}{\mathsf{fma}\left(0.16666666666666666, y \cdot y, 1\right)}} \]

12. Applied rewrites 64.3%

    \[\leadsto \color{blue}{\left(-x\right) \cdot \frac{-1}{\mathsf{fma}\left(0.16666666666666666, y \cdot y, 1\right)}} \]

13. Final simplification 64.3%

    \[\leadsto \frac{--1}{\mathsf{fma}\left(0.16666666666666666, y \cdot y, 1\right)} \cdot x \]
14. Add Preprocessing

Alternative 5: 63.8% accurate, 5.1× speedup

Math

\[\begin{array}{l} \\ \frac{x}{\mathsf{fma}\left(0.16666666666666666, y \cdot y, 1\right)} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (/ x (fma 0.16666666666666666 (* y y) 1.0)))

C

double code(double x, double y) {
	return x / fma(0.16666666666666666, (y * y), 1.0);
}

Julia

function code(x, y)
	return Float64(x / fma(0.16666666666666666, Float64(y * y), 1.0))
end

Wolfram

code[x_, y_] := N[(x / N[(0.16666666666666666 * N[(y * y), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{x \cdot \frac{\sin y}{y}} \]

   2. lift-/.f64 N/A

      \[\leadsto x \cdot \color{blue}{\frac{\sin y}{y}} \]

   3. clear-num N/A

      \[\leadsto x \cdot \color{blue}{\frac{1}{\frac{y}{\sin y}}} \]

   4. un-div-inv N/A

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   5. lower-/.f64 N/A

      \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

   6. lower-/.f64 99.8

      \[\leadsto \frac{x}{\color{blue}{\frac{y}{\sin y}}} \]

4. Applied rewrites 99.8%

   \[\leadsto \color{blue}{\frac{x}{\frac{y}{\sin y}}} \]

5. Taylor expanded in y around 0

   \[\leadsto \frac{x}{\color{blue}{1 + \frac{1}{6} \cdot {y}^{2}}} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{x}{\color{blue}{\frac{1}{6} \cdot {y}^{2} + 1}} \]

   2. lower-fma.f64 N/A

      \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(\frac{1}{6}, {y}^{2}, 1\right)}} \]

   3. unpow2 N/A

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

   4. lower-*.f64 64.3

      \[\leadsto \frac{x}{\mathsf{fma}\left(0.16666666666666666, \color{blue}{y \cdot y}, 1\right)} \]

7. Applied rewrites 64.3%

   \[\leadsto \frac{x}{\color{blue}{\mathsf{fma}\left(0.16666666666666666, y \cdot y, 1\right)}} \]
8. Add Preprocessing

Alternative 6: 51.0% accurate, 19.5× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_] := N[(1.0 * x), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \frac{\sin y}{y} \]
2. Add Preprocessing

3. Taylor expanded in y around 0

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

5. Applied rewrites 51.0%

   \[\leadsto x \cdot \color{blue}{1} \]

6. Final simplification 51.0%

   \[\leadsto 1 \cdot x \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024298 
(FPCore (x y)
  :name "Linear.Quaternion:$cexp from linear-1.19.1.3"
  :precision binary64
  (* x (/ (sin y) y)))