UniformSampleCone, x

Percentage Accurate: 57.3% → 99.0%
Time: 5.6s
Alternatives: 14
Speedup: 3.4×

Specification

?
\[\left(\left(2.328306437 \cdot 10^{-10} \leq ux \land ux \leq 1\right) \land \left(2.328306437 \cdot 10^{-10} \leq uy \land uy \leq 1\right)\right) \land \left(0 \leq maxCos \land maxCos \leq 1\right)\]
\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(1 - ux\right) + ux \cdot maxCos\\ \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - t\_0 \cdot t\_0} \end{array} \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (let* ((t_0 (+ (- 1.0 ux) (* ux maxCos))))
   (* (cos (* (* uy 2.0) (PI))) (sqrt (- 1.0 (* t_0 t_0))))))
\begin{array}{l}

\\
\begin{array}{l}
t_0 := \left(1 - ux\right) + ux \cdot maxCos\\
\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - t\_0 \cdot t\_0}
\end{array}
\end{array}

Sampling outcomes in binary32 precision:

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Accuracy vs Speed?

Herbie found 14 alternatives:

AlternativeAccuracySpeedup
The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Initial Program: 57.3% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(1 - ux\right) + ux \cdot maxCos\\ \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - t\_0 \cdot t\_0} \end{array} \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (let* ((t_0 (+ (- 1.0 ux) (* ux maxCos))))
   (* (cos (* (* uy 2.0) (PI))) (sqrt (- 1.0 (* t_0 t_0))))))
\begin{array}{l}

\\
\begin{array}{l}
t_0 := \left(1 - ux\right) + ux \cdot maxCos\\
\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - t\_0 \cdot t\_0}
\end{array}
\end{array}

Alternative 1: 99.0% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \sqrt{\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), \mathsf{fma}\left(-2, maxCos, 2\right)\right) \cdot ux} \cdot \cos \left(\left(\mathsf{PI}\left(\right) \cdot uy\right) \cdot 2\right) \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (*
  (sqrt
   (* (fma (- ux) (fma (- maxCos 2.0) maxCos 1.0) (fma -2.0 maxCos 2.0)) ux))
  (cos (* (* (PI) uy) 2.0))))
\begin{array}{l}

\\
\sqrt{\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), \mathsf{fma}\left(-2, maxCos, 2\right)\right) \cdot ux} \cdot \cos \left(\left(\mathsf{PI}\left(\right) \cdot uy\right) \cdot 2\right)
\end{array}
Derivation
  1. Initial program 61.6%

    \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
  2. Add Preprocessing
  3. Taylor expanded in ux around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{ux \cdot \left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right)}} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    2. lower-*.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    3. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot ux} \]
    4. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right) + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    5. associate-*r*N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-1 \cdot ux\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    6. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    7. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(\mathsf{neg}\left(ux\right), {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    8. lower-neg.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    9. lower-pow.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    10. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    11. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  5. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux}} \]
  6. Taylor expanded in maxCos around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, 1 + maxCos \cdot \left(maxCos - 2\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  7. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, maxCos \cdot \left(maxCos - 2\right) + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \left(maxCos - 2\right) \cdot maxCos + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lower--.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  8. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  9. Step-by-step derivation
    1. lift--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. lift-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-ux\right) \cdot \mathsf{fma}\left(maxCos - 2, maxCos, 1\right) + 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. associate--l+N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-ux\right) \cdot \mathsf{fma}\left(maxCos - 2, maxCos, 1\right) + \left(2 - maxCos \cdot 2\right)\right) \cdot ux} \]
    4. lift-neg.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot \mathsf{fma}\left(maxCos - 2, maxCos, 1\right) + \left(2 - maxCos \cdot 2\right)\right) \cdot ux} \]
    5. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot ux\right) \cdot \mathsf{fma}\left(maxCos - 2, maxCos, 1\right) + \left(2 - maxCos \cdot 2\right)\right) \cdot ux} \]
    6. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\mathsf{fma}\left(-1 \cdot ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2 - maxCos \cdot 2\right) \cdot ux} \]
    7. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\mathsf{fma}\left(\mathsf{neg}\left(ux\right), \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2 - maxCos \cdot 2\right) \cdot ux} \]
    8. lift-neg.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2 - maxCos \cdot 2\right) \cdot ux} \]
    9. lower--.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2 - maxCos \cdot 2\right) \cdot ux} \]
  10. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2 - maxCos \cdot 2\right) \cdot ux} \]
  11. Applied rewrites99.2%

    \[\leadsto \color{blue}{\sqrt{\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), \mathsf{fma}\left(-2, maxCos, 2\right)\right) \cdot ux} \cdot \cos \left(\left(\mathsf{PI}\left(\right) \cdot uy\right) \cdot 2\right)} \]
  12. Add Preprocessing

Alternative 2: 98.3% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(ux \cdot 2 - 2, maxCos, -ux\right) + 2\right) \cdot ux} \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (*
  (cos (* (* uy 2.0) (PI)))
  (sqrt (* (+ (fma (- (* ux 2.0) 2.0) maxCos (- ux)) 2.0) ux))))
\begin{array}{l}

\\
\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(ux \cdot 2 - 2, maxCos, -ux\right) + 2\right) \cdot ux}
\end{array}
Derivation
  1. Initial program 61.6%

    \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
  2. Add Preprocessing
  3. Taylor expanded in ux around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{ux \cdot \left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right)}} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    2. lower-*.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    3. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot ux} \]
    4. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right) + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    5. associate-*r*N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-1 \cdot ux\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    6. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    7. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(\mathsf{neg}\left(ux\right), {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    8. lower-neg.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    9. lower-pow.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    10. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    11. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  5. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux}} \]
  6. Taylor expanded in maxCos around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 + \left(-1 \cdot ux + maxCos \cdot \left(2 \cdot ux - 2\right)\right)\right) \cdot ux} \]
  7. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot ux + maxCos \cdot \left(2 \cdot ux - 2\right)\right) + 2\right) \cdot ux} \]
    2. lower-+.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot ux + maxCos \cdot \left(2 \cdot ux - 2\right)\right) + 2\right) \cdot ux} \]
    3. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(maxCos \cdot \left(2 \cdot ux - 2\right) + -1 \cdot ux\right) + 2\right) \cdot ux} \]
    4. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(2 \cdot ux - 2\right) \cdot maxCos + -1 \cdot ux\right) + 2\right) \cdot ux} \]
    5. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(2 \cdot ux - 2, maxCos, -1 \cdot ux\right) + 2\right) \cdot ux} \]
    6. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(2 \cdot ux - 2, maxCos, -1 \cdot ux\right) + 2\right) \cdot ux} \]
    7. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(ux \cdot 2 - 2, maxCos, -1 \cdot ux\right) + 2\right) \cdot ux} \]
    8. lower-*.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(ux \cdot 2 - 2, maxCos, -1 \cdot ux\right) + 2\right) \cdot ux} \]
    9. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(ux \cdot 2 - 2, maxCos, \mathsf{neg}\left(ux\right)\right) + 2\right) \cdot ux} \]
    10. lift-neg.f3298.3

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(ux \cdot 2 - 2, maxCos, -ux\right) + 2\right) \cdot ux} \]
  8. Applied rewrites98.3%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(ux \cdot 2 - 2, maxCos, -ux\right) + 2\right) \cdot ux} \]
  9. Add Preprocessing

Alternative 3: 97.5% accurate, 1.1× speedup?

\[\begin{array}{l} \\ \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 - ux\right) - maxCos \cdot 2\right) \cdot ux} \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (* (cos (* (* uy 2.0) (PI))) (sqrt (* (- (- 2.0 ux) (* maxCos 2.0)) ux))))
\begin{array}{l}

\\
\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 - ux\right) - maxCos \cdot 2\right) \cdot ux}
\end{array}
Derivation
  1. Initial program 61.6%

    \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
  2. Add Preprocessing
  3. Taylor expanded in ux around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{ux \cdot \left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right)}} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    2. lower-*.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    3. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot ux} \]
    4. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right) + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    5. associate-*r*N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-1 \cdot ux\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    6. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    7. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(\mathsf{neg}\left(ux\right), {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    8. lower-neg.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    9. lower-pow.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    10. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    11. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  5. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux}} \]
  6. Taylor expanded in maxCos around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, 1 + maxCos \cdot \left(maxCos - 2\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  7. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, maxCos \cdot \left(maxCos - 2\right) + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \left(maxCos - 2\right) \cdot maxCos + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lower--.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  8. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  9. Taylor expanded in maxCos around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot ux\right) - maxCos \cdot 2\right) \cdot ux} \]
  10. Step-by-step derivation
    1. lower-+.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot ux\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + \left(\mathsf{neg}\left(ux\right)\right)\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. lift-neg.f3297.0

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + \left(-ux\right)\right) - maxCos \cdot 2\right) \cdot ux} \]
  11. Applied rewrites97.0%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + \left(-ux\right)\right) - maxCos \cdot 2\right) \cdot ux} \]
  12. Final simplification97.0%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 - ux\right) - maxCos \cdot 2\right) \cdot ux} \]
  13. Add Preprocessing

Alternative 4: 98.0% accurate, 1.1× speedup?

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\\ \mathbf{if}\;uy \leq 0.04600000008940697:\\ \;\;\;\;\left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, t\_0, \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(t\_0 \cdot t\_0\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux}\\ \mathbf{else}:\\ \;\;\;\;\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 - ux\right) \cdot ux}\\ \end{array} \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (let* ((t_0 (* (PI) (PI))))
   (if (<= uy 0.04600000008940697)
     (*
      (+
       1.0
       (*
        (* uy uy)
        (fma -2.0 t_0 (* (* 0.6666666666666666 (* uy uy)) (* t_0 t_0)))))
      (sqrt
       (*
        (- (fma (- ux) (fma (- maxCos 2.0) maxCos 1.0) 2.0) (* maxCos 2.0))
        ux)))
     (* (cos (* (* uy 2.0) (PI))) (sqrt (* (- 2.0 ux) ux))))))
\begin{array}{l}

\\
\begin{array}{l}
t_0 := \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\\
\mathbf{if}\;uy \leq 0.04600000008940697:\\
\;\;\;\;\left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, t\_0, \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(t\_0 \cdot t\_0\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux}\\

\mathbf{else}:\\
\;\;\;\;\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 - ux\right) \cdot ux}\\


\end{array}
\end{array}
Derivation
  1. Split input into 2 regimes
  2. if uy < 0.0460000001

    1. Initial program 61.5%

      \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
    2. Add Preprocessing
    3. Taylor expanded in ux around 0

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{ux \cdot \left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right)}} \]
    4. Step-by-step derivation
      1. *-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
      2. lower-*.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
      3. lower--.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot ux} \]
      4. +-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right) + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      5. associate-*r*N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-1 \cdot ux\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      6. mul-1-negN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      7. lower-fma.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(\mathsf{neg}\left(ux\right), {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      8. lower-neg.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      9. lower-pow.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      10. lower--.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      11. *-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      12. lower-*.f3299.4

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    5. Applied rewrites99.4%

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux}} \]
    6. Taylor expanded in maxCos around 0

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, 1 + maxCos \cdot \left(maxCos - 2\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    7. Step-by-step derivation
      1. +-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, maxCos \cdot \left(maxCos - 2\right) + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      2. *-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \left(maxCos - 2\right) \cdot maxCos + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      3. lower-fma.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      4. lower--.f3299.4

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    8. Applied rewrites99.4%

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    9. Taylor expanded in uy around 0

      \[\leadsto \color{blue}{\left(1 + {uy}^{2} \cdot \left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right)} \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    10. Step-by-step derivation
      1. lower-+.f32N/A

        \[\leadsto \left(1 + \color{blue}{{uy}^{2} \cdot \left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)}\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      2. lower-*.f32N/A

        \[\leadsto \left(1 + {uy}^{2} \cdot \color{blue}{\left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)}\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      3. unpow2N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\color{blue}{-2 \cdot {\mathsf{PI}\left(\right)}^{2}} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      4. lower-*.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\color{blue}{-2 \cdot {\mathsf{PI}\left(\right)}^{2}} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      5. lower-fma.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \color{blue}{{\mathsf{PI}\left(\right)}^{2}}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      6. unpow2N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \color{blue}{\mathsf{PI}\left(\right)}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      7. lower-*.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \color{blue}{\mathsf{PI}\left(\right)}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      8. lift-PI.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      9. lift-PI.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      10. associate-*r*N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      11. lower-*.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      12. lower-*.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      13. unpow2N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      14. lower-*.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      15. lower-pow.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      16. lift-PI.f3299.4

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    11. Applied rewrites99.4%

      \[\leadsto \color{blue}{\left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)} \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. Step-by-step derivation
      1. lift-PI.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      2. lift-pow.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      3. sqr-powN/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left({\mathsf{PI}\left(\right)}^{\left(\frac{4}{2}\right)} \cdot {\mathsf{PI}\left(\right)}^{\left(\frac{4}{2}\right)}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      4. metadata-evalN/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left({\mathsf{PI}\left(\right)}^{2} \cdot {\mathsf{PI}\left(\right)}^{\left(\frac{4}{2}\right)}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      5. metadata-evalN/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left({\mathsf{PI}\left(\right)}^{2} \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      6. lower-*.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left({\mathsf{PI}\left(\right)}^{2} \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      7. pow2N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      8. lift-*.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      9. lift-PI.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      10. lift-PI.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      11. pow2N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      12. lift-*.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      13. lift-PI.f32N/A

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      14. lift-PI.f3299.4

        \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    13. Applied rewrites99.4%

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]

    if 0.0460000001 < uy

    1. Initial program 61.9%

      \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
    2. Add Preprocessing
    3. Taylor expanded in ux around 0

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{ux \cdot \left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right)}} \]
    4. Step-by-step derivation
      1. *-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
      2. lower-*.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
      3. lower--.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot ux} \]
      4. +-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right) + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      5. associate-*r*N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-1 \cdot ux\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      6. mul-1-negN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      7. lower-fma.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(\mathsf{neg}\left(ux\right), {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      8. lower-neg.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      9. lower-pow.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      10. lower--.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
      11. *-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      12. lower-*.f3297.8

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    5. Applied rewrites97.8%

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux}} \]
    6. Taylor expanded in maxCos around 0

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, 1 + maxCos \cdot \left(maxCos - 2\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    7. Step-by-step derivation
      1. +-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, maxCos \cdot \left(maxCos - 2\right) + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      2. *-commutativeN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \left(maxCos - 2\right) \cdot maxCos + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      3. lower-fma.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
      4. lower--.f3297.8

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    8. Applied rewrites97.8%

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    9. Taylor expanded in maxCos around 0

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 + -1 \cdot ux\right) \cdot ux} \]
    10. Step-by-step derivation
      1. lower-+.f32N/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 + -1 \cdot ux\right) \cdot ux} \]
      2. mul-1-negN/A

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 + \left(\mathsf{neg}\left(ux\right)\right)\right) \cdot ux} \]
      3. lift-neg.f3289.9

        \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 + \left(-ux\right)\right) \cdot ux} \]
    11. Applied rewrites89.9%

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 + \left(-ux\right)\right) \cdot ux} \]
  3. Recombined 2 regimes into one program.
  4. Final simplification98.0%

    \[\leadsto \begin{array}{l} \mathbf{if}\;uy \leq 0.04600000008940697:\\ \;\;\;\;\left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux}\\ \mathbf{else}:\\ \;\;\;\;\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(2 - ux\right) \cdot ux}\\ \end{array} \]
  5. Add Preprocessing

Alternative 5: 91.5% accurate, 1.6× speedup?

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\\ \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, t\_0, \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(t\_0 \cdot t\_0\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \end{array} \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (let* ((t_0 (* (PI) (PI))))
   (*
    (+
     1.0
     (*
      (* uy uy)
      (fma -2.0 t_0 (* (* 0.6666666666666666 (* uy uy)) (* t_0 t_0)))))
    (sqrt
     (*
      (- (fma (- ux) (fma (- maxCos 2.0) maxCos 1.0) 2.0) (* maxCos 2.0))
      ux)))))
\begin{array}{l}

\\
\begin{array}{l}
t_0 := \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\\
\left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, t\_0, \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(t\_0 \cdot t\_0\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux}
\end{array}
\end{array}
Derivation
  1. Initial program 61.6%

    \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
  2. Add Preprocessing
  3. Taylor expanded in ux around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{ux \cdot \left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right)}} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    2. lower-*.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    3. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot ux} \]
    4. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right) + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    5. associate-*r*N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-1 \cdot ux\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    6. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    7. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(\mathsf{neg}\left(ux\right), {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    8. lower-neg.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    9. lower-pow.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    10. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    11. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  5. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux}} \]
  6. Taylor expanded in maxCos around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, 1 + maxCos \cdot \left(maxCos - 2\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  7. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, maxCos \cdot \left(maxCos - 2\right) + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \left(maxCos - 2\right) \cdot maxCos + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lower--.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  8. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  9. Taylor expanded in uy around 0

    \[\leadsto \color{blue}{\left(1 + {uy}^{2} \cdot \left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right)} \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  10. Step-by-step derivation
    1. lower-+.f32N/A

      \[\leadsto \left(1 + \color{blue}{{uy}^{2} \cdot \left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)}\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. lower-*.f32N/A

      \[\leadsto \left(1 + {uy}^{2} \cdot \color{blue}{\left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)}\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\color{blue}{-2 \cdot {\mathsf{PI}\left(\right)}^{2}} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\color{blue}{-2 \cdot {\mathsf{PI}\left(\right)}^{2}} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    5. lower-fma.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \color{blue}{{\mathsf{PI}\left(\right)}^{2}}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    6. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \color{blue}{\mathsf{PI}\left(\right)}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    7. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \color{blue}{\mathsf{PI}\left(\right)}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    8. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    9. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    10. associate-*r*N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    11. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    13. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    14. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    15. lower-pow.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    16. lift-PI.f3291.6

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  11. Applied rewrites91.6%

    \[\leadsto \color{blue}{\left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)} \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  12. Step-by-step derivation
    1. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. lift-pow.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. sqr-powN/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left({\mathsf{PI}\left(\right)}^{\left(\frac{4}{2}\right)} \cdot {\mathsf{PI}\left(\right)}^{\left(\frac{4}{2}\right)}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. metadata-evalN/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left({\mathsf{PI}\left(\right)}^{2} \cdot {\mathsf{PI}\left(\right)}^{\left(\frac{4}{2}\right)}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    5. metadata-evalN/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left({\mathsf{PI}\left(\right)}^{2} \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    6. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left({\mathsf{PI}\left(\right)}^{2} \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    7. pow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    8. lift-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    9. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    10. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot {\mathsf{PI}\left(\right)}^{2}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    11. pow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lift-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    13. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    14. lift-PI.f3291.6

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  13. Applied rewrites91.6%

    \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right)\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  14. Add Preprocessing

Alternative 6: 88.2% accurate, 2.3× speedup?

\[\begin{array}{l} \\ \left(1 + \left(uy \cdot uy\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot -2\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (*
  (+ 1.0 (* (* uy uy) (* (* (PI) (PI)) -2.0)))
  (sqrt
   (*
    (- (fma (- ux) (fma (- maxCos 2.0) maxCos 1.0) 2.0) (* maxCos 2.0))
    ux))))
\begin{array}{l}

\\
\left(1 + \left(uy \cdot uy\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot -2\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux}
\end{array}
Derivation
  1. Initial program 61.6%

    \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
  2. Add Preprocessing
  3. Taylor expanded in ux around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{ux \cdot \left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right)}} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    2. lower-*.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    3. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot ux} \]
    4. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right) + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    5. associate-*r*N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-1 \cdot ux\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    6. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    7. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(\mathsf{neg}\left(ux\right), {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    8. lower-neg.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    9. lower-pow.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    10. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    11. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  5. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux}} \]
  6. Taylor expanded in maxCos around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, 1 + maxCos \cdot \left(maxCos - 2\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  7. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, maxCos \cdot \left(maxCos - 2\right) + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \left(maxCos - 2\right) \cdot maxCos + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lower--.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  8. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  9. Taylor expanded in uy around 0

    \[\leadsto \color{blue}{\left(1 + {uy}^{2} \cdot \left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right)} \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  10. Step-by-step derivation
    1. lower-+.f32N/A

      \[\leadsto \left(1 + \color{blue}{{uy}^{2} \cdot \left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)}\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. lower-*.f32N/A

      \[\leadsto \left(1 + {uy}^{2} \cdot \color{blue}{\left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)}\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\color{blue}{-2 \cdot {\mathsf{PI}\left(\right)}^{2}} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\color{blue}{-2 \cdot {\mathsf{PI}\left(\right)}^{2}} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    5. lower-fma.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \color{blue}{{\mathsf{PI}\left(\right)}^{2}}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    6. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \color{blue}{\mathsf{PI}\left(\right)}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    7. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \color{blue}{\mathsf{PI}\left(\right)}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    8. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    9. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    10. associate-*r*N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    11. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    13. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    14. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    15. lower-pow.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    16. lift-PI.f3291.6

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  11. Applied rewrites91.6%

    \[\leadsto \color{blue}{\left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)} \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  12. Taylor expanded in uy around 0

    \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(-2 \cdot \color{blue}{{\mathsf{PI}\left(\right)}^{2}}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  13. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left({\mathsf{PI}\left(\right)}^{2} \cdot -2\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left({\mathsf{PI}\left(\right)}^{2} \cdot -2\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. pow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot -2\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lift-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot -2\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    5. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot -2\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    6. lift-PI.f3289.0

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot -2\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  14. Applied rewrites89.0%

    \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\left(\mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \color{blue}{-2}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  15. Add Preprocessing

Alternative 7: 79.9% accurate, 3.4× speedup?

\[\begin{array}{l} \\ 1 \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \end{array} \]
(FPCore (ux uy maxCos)
 :precision binary32
 (*
  1.0
  (sqrt
   (*
    (- (fma (- ux) (fma (- maxCos 2.0) maxCos 1.0) 2.0) (* maxCos 2.0))
    ux))))
float code(float ux, float uy, float maxCos) {
	return 1.0f * sqrtf(((fmaf(-ux, fmaf((maxCos - 2.0f), maxCos, 1.0f), 2.0f) - (maxCos * 2.0f)) * ux));
}
function code(ux, uy, maxCos)
	return Float32(Float32(1.0) * sqrt(Float32(Float32(fma(Float32(-ux), fma(Float32(maxCos - Float32(2.0)), maxCos, Float32(1.0)), Float32(2.0)) - Float32(maxCos * Float32(2.0))) * ux)))
end
\begin{array}{l}

\\
1 \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux}
\end{array}
Derivation
  1. Initial program 61.6%

    \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
  2. Add Preprocessing
  3. Taylor expanded in ux around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{ux \cdot \left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right)}} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    2. lower-*.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot \color{blue}{ux}} \]
    3. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(2 + -1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right)\right) - 2 \cdot maxCos\right) \cdot ux} \]
    4. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(-1 \cdot \left(ux \cdot {\left(maxCos - 1\right)}^{2}\right) + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    5. associate-*r*N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(-1 \cdot ux\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    6. mul-1-negN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\left(\left(\mathsf{neg}\left(ux\right)\right) \cdot {\left(maxCos - 1\right)}^{2} + 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    7. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(\mathsf{neg}\left(ux\right), {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    8. lower-neg.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    9. lower-pow.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    10. lower--.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - 2 \cdot maxCos\right) \cdot ux} \]
    11. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  5. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{\left(\mathsf{fma}\left(-ux, {\left(maxCos - 1\right)}^{2}, 2\right) - maxCos \cdot 2\right) \cdot ux}} \]
  6. Taylor expanded in maxCos around 0

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, 1 + maxCos \cdot \left(maxCos - 2\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  7. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, maxCos \cdot \left(maxCos - 2\right) + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. *-commutativeN/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \left(maxCos - 2\right) \cdot maxCos + 1, 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. lower-fma.f32N/A

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lower--.f3299.2

      \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  8. Applied rewrites99.2%

    \[\leadsto \cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  9. Taylor expanded in uy around 0

    \[\leadsto \color{blue}{\left(1 + {uy}^{2} \cdot \left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right)} \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  10. Step-by-step derivation
    1. lower-+.f32N/A

      \[\leadsto \left(1 + \color{blue}{{uy}^{2} \cdot \left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)}\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. lower-*.f32N/A

      \[\leadsto \left(1 + {uy}^{2} \cdot \color{blue}{\left(-2 \cdot {\mathsf{PI}\left(\right)}^{2} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)}\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    3. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\color{blue}{-2 \cdot {\mathsf{PI}\left(\right)}^{2}} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    4. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \left(\color{blue}{-2 \cdot {\mathsf{PI}\left(\right)}^{2}} + \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    5. lower-fma.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \color{blue}{{\mathsf{PI}\left(\right)}^{2}}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    6. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \color{blue}{\mathsf{PI}\left(\right)}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    7. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \color{blue}{\mathsf{PI}\left(\right)}, \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    8. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    9. lift-PI.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \frac{2}{3} \cdot \left({uy}^{2} \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    10. associate-*r*N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    11. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    12. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot {uy}^{2}\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    13. unpow2N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    14. lower-*.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    15. lower-pow.f32N/A

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(\frac{2}{3} \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    16. lift-PI.f3291.6

      \[\leadsto \left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right) \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  11. Applied rewrites91.6%

    \[\leadsto \color{blue}{\left(1 + \left(uy \cdot uy\right) \cdot \mathsf{fma}\left(-2, \mathsf{PI}\left(\right) \cdot \mathsf{PI}\left(\right), \left(0.6666666666666666 \cdot \left(uy \cdot uy\right)\right) \cdot {\mathsf{PI}\left(\right)}^{4}\right)\right)} \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  12. Taylor expanded in uy around 0

    \[\leadsto 1 \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
  13. Step-by-step derivation
    1. Applied rewrites80.7%

      \[\leadsto 1 \cdot \sqrt{\left(\mathsf{fma}\left(-ux, \mathsf{fma}\left(maxCos - 2, maxCos, 1\right), 2\right) - maxCos \cdot 2\right) \cdot ux} \]
    2. Add Preprocessing

    Alternative 8: 49.5% accurate, 3.4× speedup?

    \[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - \left(ux - ux \cdot maxCos\right)\\ 1 \cdot \sqrt{1 - t\_0 \cdot t\_0} \end{array} \end{array} \]
    (FPCore (ux uy maxCos)
     :precision binary32
     (let* ((t_0 (- 1.0 (- ux (* ux maxCos)))))
       (* 1.0 (sqrt (- 1.0 (* t_0 t_0))))))
    float code(float ux, float uy, float maxCos) {
    	float t_0 = 1.0f - (ux - (ux * maxCos));
    	return 1.0f * sqrtf((1.0f - (t_0 * t_0)));
    }
    
    module fmin_fmax_functions
        implicit none
        private
        public fmax
        public fmin
    
        interface fmax
            module procedure fmax88
            module procedure fmax44
            module procedure fmax84
            module procedure fmax48
        end interface
        interface fmin
            module procedure fmin88
            module procedure fmin44
            module procedure fmin84
            module procedure fmin48
        end interface
    contains
        real(8) function fmax88(x, y) result (res)
            real(8), intent (in) :: x
            real(8), intent (in) :: y
            res = merge(y, merge(x, max(x, y), y /= y), x /= x)
        end function
        real(4) function fmax44(x, y) result (res)
            real(4), intent (in) :: x
            real(4), intent (in) :: y
            res = merge(y, merge(x, max(x, y), y /= y), x /= x)
        end function
        real(8) function fmax84(x, y) result(res)
            real(8), intent (in) :: x
            real(4), intent (in) :: y
            res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
        end function
        real(8) function fmax48(x, y) result(res)
            real(4), intent (in) :: x
            real(8), intent (in) :: y
            res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
        end function
        real(8) function fmin88(x, y) result (res)
            real(8), intent (in) :: x
            real(8), intent (in) :: y
            res = merge(y, merge(x, min(x, y), y /= y), x /= x)
        end function
        real(4) function fmin44(x, y) result (res)
            real(4), intent (in) :: x
            real(4), intent (in) :: y
            res = merge(y, merge(x, min(x, y), y /= y), x /= x)
        end function
        real(8) function fmin84(x, y) result(res)
            real(8), intent (in) :: x
            real(4), intent (in) :: y
            res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
        end function
        real(8) function fmin48(x, y) result(res)
            real(4), intent (in) :: x
            real(8), intent (in) :: y
            res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
        end function
    end module
    
    real(4) function code(ux, uy, maxcos)
    use fmin_fmax_functions
        real(4), intent (in) :: ux
        real(4), intent (in) :: uy
        real(4), intent (in) :: maxcos
        real(4) :: t_0
        t_0 = 1.0e0 - (ux - (ux * maxcos))
        code = 1.0e0 * sqrt((1.0e0 - (t_0 * t_0)))
    end function
    
    function code(ux, uy, maxCos)
    	t_0 = Float32(Float32(1.0) - Float32(ux - Float32(ux * maxCos)))
    	return Float32(Float32(1.0) * sqrt(Float32(Float32(1.0) - Float32(t_0 * t_0))))
    end
    
    function tmp = code(ux, uy, maxCos)
    	t_0 = single(1.0) - (ux - (ux * maxCos));
    	tmp = single(1.0) * sqrt((single(1.0) - (t_0 * t_0)));
    end
    
    \begin{array}{l}
    
    \\
    \begin{array}{l}
    t_0 := 1 - \left(ux - ux \cdot maxCos\right)\\
    1 \cdot \sqrt{1 - t\_0 \cdot t\_0}
    \end{array}
    \end{array}
    
    Derivation
    1. Initial program 61.6%

      \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
    2. Add Preprocessing
    3. Taylor expanded in uy around 0

      \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
    4. Step-by-step derivation
      1. Applied rewrites53.2%

        \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
      2. Step-by-step derivation
        1. lift--.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\left(1 - ux\right)} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        2. flip--N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 \cdot 1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        3. lower-/.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 \cdot 1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        4. metadata-evalN/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{\color{blue}{1} - ux \cdot ux}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        5. unpow2N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{{ux}^{2}}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        6. lower--.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{\color{blue}{1 - {ux}^{2}}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        7. unpow2N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        8. lower-*.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        9. lower-+.f3253.4

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - ux \cdot ux}{\color{blue}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
      3. Applied rewrites53.4%

        \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
      4. Step-by-step derivation
        1. lift-+.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(\frac{1 - ux \cdot ux}{1 + ux} + ux \cdot maxCos\right)} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        2. lift-+.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - ux \cdot ux}{\color{blue}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        3. lift-/.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        4. lift--.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{\color{blue}{1 - ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        5. lift-*.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        6. pow2N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{{ux}^{2}}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        7. metadata-evalN/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{\color{blue}{1 \cdot 1} - {ux}^{2}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        8. pow2N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 \cdot 1 - \color{blue}{ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        9. flip--N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\left(1 - ux\right)} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        10. associate-+l-N/A

          \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(1 - \left(ux - ux \cdot maxCos\right)\right)} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        11. lower--.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(1 - \left(ux - ux \cdot maxCos\right)\right)} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        12. lower--.f3253.2

          \[\leadsto 1 \cdot \sqrt{1 - \left(1 - \color{blue}{\left(ux - ux \cdot maxCos\right)}\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        13. lift-+.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(1 - \left(ux - ux \cdot maxCos\right)\right) \cdot \color{blue}{\left(\left(1 - ux\right) + ux \cdot maxCos\right)}} \]
        14. lift--.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(1 - \left(ux - ux \cdot maxCos\right)\right) \cdot \left(\color{blue}{\left(1 - ux\right)} + ux \cdot maxCos\right)} \]
        15. associate-+l-N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(1 - \left(ux - ux \cdot maxCos\right)\right) \cdot \color{blue}{\left(1 - \left(ux - ux \cdot maxCos\right)\right)}} \]
        16. lower--.f32N/A

          \[\leadsto 1 \cdot \sqrt{1 - \left(1 - \left(ux - ux \cdot maxCos\right)\right) \cdot \color{blue}{\left(1 - \left(ux - ux \cdot maxCos\right)\right)}} \]
        17. lower--.f3253.3

          \[\leadsto 1 \cdot \sqrt{1 - \left(1 - \left(ux - ux \cdot maxCos\right)\right) \cdot \left(1 - \color{blue}{\left(ux - ux \cdot maxCos\right)}\right)} \]
      5. Applied rewrites53.3%

        \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(1 - \left(ux - ux \cdot maxCos\right)\right) \cdot \left(1 - \left(ux - ux \cdot maxCos\right)\right)}} \]
      6. Add Preprocessing

      Alternative 9: 49.4% accurate, 3.7× speedup?

      \[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(maxCos, ux, 1 - ux\right)\\ 1 \cdot \sqrt{1 - t\_0 \cdot t\_0} \end{array} \end{array} \]
      (FPCore (ux uy maxCos)
       :precision binary32
       (let* ((t_0 (fma maxCos ux (- 1.0 ux)))) (* 1.0 (sqrt (- 1.0 (* t_0 t_0))))))
      float code(float ux, float uy, float maxCos) {
      	float t_0 = fmaf(maxCos, ux, (1.0f - ux));
      	return 1.0f * sqrtf((1.0f - (t_0 * t_0)));
      }
      
      function code(ux, uy, maxCos)
      	t_0 = fma(maxCos, ux, Float32(Float32(1.0) - ux))
      	return Float32(Float32(1.0) * sqrt(Float32(Float32(1.0) - Float32(t_0 * t_0))))
      end
      
      \begin{array}{l}
      
      \\
      \begin{array}{l}
      t_0 := \mathsf{fma}\left(maxCos, ux, 1 - ux\right)\\
      1 \cdot \sqrt{1 - t\_0 \cdot t\_0}
      \end{array}
      \end{array}
      
      Derivation
      1. Initial program 61.6%

        \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
      2. Add Preprocessing
      3. Taylor expanded in uy around 0

        \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
      4. Step-by-step derivation
        1. Applied rewrites53.2%

          \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        2. Step-by-step derivation
          1. lift-+.f32N/A

            \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(\left(1 - ux\right) + ux \cdot maxCos\right)} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          2. lift--.f32N/A

            \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\left(1 - ux\right)} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          3. +-commutativeN/A

            \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(ux \cdot maxCos + \left(1 - ux\right)\right)} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          4. lift-*.f32N/A

            \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{ux \cdot maxCos} + \left(1 - ux\right)\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          5. *-commutativeN/A

            \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{maxCos \cdot ux} + \left(1 - ux\right)\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          6. lift-fma.f32N/A

            \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\mathsf{fma}\left(maxCos, ux, 1 - ux\right)} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          7. lift--.f3253.2

            \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos, ux, \color{blue}{1 - ux}\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          8. lift-+.f32N/A

            \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos, ux, 1 - ux\right) \cdot \color{blue}{\left(\left(1 - ux\right) + ux \cdot maxCos\right)}} \]
          9. lift--.f32N/A

            \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos, ux, 1 - ux\right) \cdot \left(\color{blue}{\left(1 - ux\right)} + ux \cdot maxCos\right)} \]
          10. +-commutativeN/A

            \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos, ux, 1 - ux\right) \cdot \color{blue}{\left(ux \cdot maxCos + \left(1 - ux\right)\right)}} \]
          11. lift-*.f32N/A

            \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos, ux, 1 - ux\right) \cdot \left(\color{blue}{ux \cdot maxCos} + \left(1 - ux\right)\right)} \]
          12. *-commutativeN/A

            \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos, ux, 1 - ux\right) \cdot \left(\color{blue}{maxCos \cdot ux} + \left(1 - ux\right)\right)} \]
          13. lift-fma.f32N/A

            \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos, ux, 1 - ux\right) \cdot \color{blue}{\mathsf{fma}\left(maxCos, ux, 1 - ux\right)}} \]
          14. lift--.f3253.2

            \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos, ux, 1 - ux\right) \cdot \mathsf{fma}\left(maxCos, ux, \color{blue}{1 - ux}\right)} \]
        3. Applied rewrites53.2%

          \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\mathsf{fma}\left(maxCos, ux, 1 - ux\right) \cdot \mathsf{fma}\left(maxCos, ux, 1 - ux\right)}} \]
        4. Add Preprocessing

        Alternative 10: 49.4% accurate, 4.0× speedup?

        \[\begin{array}{l} \\ \sqrt{1 - \left(\left(1 + maxCos \cdot ux\right) - ux\right) \cdot \mathsf{fma}\left(maxCos, ux, 1 - ux\right)} \end{array} \]
        (FPCore (ux uy maxCos)
         :precision binary32
         (sqrt (- 1.0 (* (- (+ 1.0 (* maxCos ux)) ux) (fma maxCos ux (- 1.0 ux))))))
        float code(float ux, float uy, float maxCos) {
        	return sqrtf((1.0f - (((1.0f + (maxCos * ux)) - ux) * fmaf(maxCos, ux, (1.0f - ux)))));
        }
        
        function code(ux, uy, maxCos)
        	return sqrt(Float32(Float32(1.0) - Float32(Float32(Float32(Float32(1.0) + Float32(maxCos * ux)) - ux) * fma(maxCos, ux, Float32(Float32(1.0) - ux)))))
        end
        
        \begin{array}{l}
        
        \\
        \sqrt{1 - \left(\left(1 + maxCos \cdot ux\right) - ux\right) \cdot \mathsf{fma}\left(maxCos, ux, 1 - ux\right)}
        \end{array}
        
        Derivation
        1. Initial program 61.6%

          \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        2. Add Preprocessing
        3. Taylor expanded in uy around 0

          \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
        4. Step-by-step derivation
          1. Applied rewrites53.2%

            \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          2. Step-by-step derivation
            1. lift--.f32N/A

              \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\left(1 - ux\right)} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            2. flip--N/A

              \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 \cdot 1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            3. lower-/.f32N/A

              \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 \cdot 1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            4. metadata-evalN/A

              \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{\color{blue}{1} - ux \cdot ux}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            5. unpow2N/A

              \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{{ux}^{2}}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            6. lower--.f32N/A

              \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{\color{blue}{1 - {ux}^{2}}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            7. unpow2N/A

              \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            8. lower-*.f32N/A

              \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            9. lower-+.f3253.4

              \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - ux \cdot ux}{\color{blue}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          3. Applied rewrites53.4%

            \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          4. Taylor expanded in uy around 0

            \[\leadsto \color{blue}{\sqrt{1 - \left(\left(1 + maxCos \cdot ux\right) - ux\right) \cdot \left(\left(maxCos \cdot ux + \frac{1}{1 + ux}\right) - \frac{{ux}^{2}}{1 + ux}\right)}} \]
          5. Step-by-step derivation
            1. fp-cancel-sub-sign-invN/A

              \[\leadsto \sqrt{1 - \color{blue}{\left(\left(1 + maxCos \cdot ux\right) - ux\right)} \cdot \left(\left(maxCos \cdot ux + \frac{1}{1 + ux}\right) - \frac{{ux}^{2}}{1 + ux}\right)} \]
            2. metadata-evalN/A

              \[\leadsto \sqrt{1 - \left(\left(1 + maxCos \cdot ux\right) - ux\right) \cdot \left(\left(maxCos \cdot ux + \frac{1}{1 + ux}\right) - \frac{{ux}^{2}}{1 + ux}\right)} \]
            3. flip--N/A

              \[\leadsto \sqrt{1 - \left(\left(1 + maxCos \cdot ux\right) - ux\right) \cdot \left(\left(maxCos \cdot ux + \frac{1}{1 + ux}\right) - \frac{{ux}^{2}}{1 + ux}\right)} \]
            4. fp-cancel-sub-sign-invN/A

              \[\leadsto \sqrt{1 - \color{blue}{\left(\left(1 + maxCos \cdot ux\right) - ux\right)} \cdot \left(\left(maxCos \cdot ux + \frac{1}{1 + ux}\right) - \frac{{ux}^{2}}{1 + ux}\right)} \]
          6. Applied rewrites53.1%

            \[\leadsto \color{blue}{\sqrt{1 - \left(\left(1 + maxCos \cdot ux\right) - ux\right) \cdot \mathsf{fma}\left(maxCos, ux, 1 - ux\right)}} \]
          7. Add Preprocessing

          Alternative 11: 48.1% accurate, 4.1× speedup?

          \[\begin{array}{l} \\ 1 \cdot \sqrt{1 - \left(1 - ux\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \end{array} \]
          (FPCore (ux uy maxCos)
           :precision binary32
           (* 1.0 (sqrt (- 1.0 (* (- 1.0 ux) (+ (- 1.0 ux) (* ux maxCos)))))))
          float code(float ux, float uy, float maxCos) {
          	return 1.0f * sqrtf((1.0f - ((1.0f - ux) * ((1.0f - ux) + (ux * maxCos)))));
          }
          
          module fmin_fmax_functions
              implicit none
              private
              public fmax
              public fmin
          
              interface fmax
                  module procedure fmax88
                  module procedure fmax44
                  module procedure fmax84
                  module procedure fmax48
              end interface
              interface fmin
                  module procedure fmin88
                  module procedure fmin44
                  module procedure fmin84
                  module procedure fmin48
              end interface
          contains
              real(8) function fmax88(x, y) result (res)
                  real(8), intent (in) :: x
                  real(8), intent (in) :: y
                  res = merge(y, merge(x, max(x, y), y /= y), x /= x)
              end function
              real(4) function fmax44(x, y) result (res)
                  real(4), intent (in) :: x
                  real(4), intent (in) :: y
                  res = merge(y, merge(x, max(x, y), y /= y), x /= x)
              end function
              real(8) function fmax84(x, y) result(res)
                  real(8), intent (in) :: x
                  real(4), intent (in) :: y
                  res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
              end function
              real(8) function fmax48(x, y) result(res)
                  real(4), intent (in) :: x
                  real(8), intent (in) :: y
                  res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
              end function
              real(8) function fmin88(x, y) result (res)
                  real(8), intent (in) :: x
                  real(8), intent (in) :: y
                  res = merge(y, merge(x, min(x, y), y /= y), x /= x)
              end function
              real(4) function fmin44(x, y) result (res)
                  real(4), intent (in) :: x
                  real(4), intent (in) :: y
                  res = merge(y, merge(x, min(x, y), y /= y), x /= x)
              end function
              real(8) function fmin84(x, y) result(res)
                  real(8), intent (in) :: x
                  real(4), intent (in) :: y
                  res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
              end function
              real(8) function fmin48(x, y) result(res)
                  real(4), intent (in) :: x
                  real(8), intent (in) :: y
                  res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
              end function
          end module
          
          real(4) function code(ux, uy, maxcos)
          use fmin_fmax_functions
              real(4), intent (in) :: ux
              real(4), intent (in) :: uy
              real(4), intent (in) :: maxcos
              code = 1.0e0 * sqrt((1.0e0 - ((1.0e0 - ux) * ((1.0e0 - ux) + (ux * maxcos)))))
          end function
          
          function code(ux, uy, maxCos)
          	return Float32(Float32(1.0) * sqrt(Float32(Float32(1.0) - Float32(Float32(Float32(1.0) - ux) * Float32(Float32(Float32(1.0) - ux) + Float32(ux * maxCos))))))
          end
          
          function tmp = code(ux, uy, maxCos)
          	tmp = single(1.0) * sqrt((single(1.0) - ((single(1.0) - ux) * ((single(1.0) - ux) + (ux * maxCos)))));
          end
          
          \begin{array}{l}
          
          \\
          1 \cdot \sqrt{1 - \left(1 - ux\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)}
          \end{array}
          
          Derivation
          1. Initial program 61.6%

            \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          2. Add Preprocessing
          3. Taylor expanded in uy around 0

            \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
          4. Step-by-step derivation
            1. Applied rewrites53.2%

              \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            2. Step-by-step derivation
              1. lift--.f32N/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\left(1 - ux\right)} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              2. flip--N/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 \cdot 1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              3. lower-/.f32N/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 \cdot 1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              4. metadata-evalN/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{\color{blue}{1} - ux \cdot ux}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              5. unpow2N/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{{ux}^{2}}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              6. lower--.f32N/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{\color{blue}{1 - {ux}^{2}}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              7. unpow2N/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              8. lower-*.f32N/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - \color{blue}{ux \cdot ux}}{1 + ux} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              9. lower-+.f3253.4

                \[\leadsto 1 \cdot \sqrt{1 - \left(\frac{1 - ux \cdot ux}{\color{blue}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            3. Applied rewrites53.4%

              \[\leadsto 1 \cdot \sqrt{1 - \left(\color{blue}{\frac{1 - ux \cdot ux}{1 + ux}} + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            4. Taylor expanded in maxCos around 0

              \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(\frac{1}{1 + ux} - \frac{{ux}^{2}}{1 + ux}\right)} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            5. Step-by-step derivation
              1. sub-divN/A

                \[\leadsto 1 \cdot \sqrt{1 - \frac{1 - {ux}^{2}}{\color{blue}{1 + ux}} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              2. metadata-evalN/A

                \[\leadsto 1 \cdot \sqrt{1 - \frac{1 \cdot 1 - {ux}^{2}}{1 + ux} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              3. pow2N/A

                \[\leadsto 1 \cdot \sqrt{1 - \frac{1 \cdot 1 - ux \cdot ux}{1 + ux} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              4. flip--N/A

                \[\leadsto 1 \cdot \sqrt{1 - \left(1 - \color{blue}{ux}\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              5. lift--.f3251.3

                \[\leadsto 1 \cdot \sqrt{1 - \left(1 - \color{blue}{ux}\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            6. Applied rewrites51.3%

              \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(1 - ux\right)} \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            7. Add Preprocessing

            Alternative 12: 41.0% accurate, 5.0× speedup?

            \[\begin{array}{l} \\ 1 \cdot \sqrt{1 - \mathsf{fma}\left(\left(maxCos + maxCos\right) - 2, ux, 1\right)} \end{array} \]
            (FPCore (ux uy maxCos)
             :precision binary32
             (* 1.0 (sqrt (- 1.0 (fma (- (+ maxCos maxCos) 2.0) ux 1.0)))))
            float code(float ux, float uy, float maxCos) {
            	return 1.0f * sqrtf((1.0f - fmaf(((maxCos + maxCos) - 2.0f), ux, 1.0f)));
            }
            
            function code(ux, uy, maxCos)
            	return Float32(Float32(1.0) * sqrt(Float32(Float32(1.0) - fma(Float32(Float32(maxCos + maxCos) - Float32(2.0)), ux, Float32(1.0)))))
            end
            
            \begin{array}{l}
            
            \\
            1 \cdot \sqrt{1 - \mathsf{fma}\left(\left(maxCos + maxCos\right) - 2, ux, 1\right)}
            \end{array}
            
            Derivation
            1. Initial program 61.6%

              \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            2. Add Preprocessing
            3. Taylor expanded in uy around 0

              \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
            4. Step-by-step derivation
              1. Applied rewrites53.2%

                \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              2. Taylor expanded in ux around 0

                \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(1 + ux \cdot \left(2 \cdot maxCos - 2\right)\right)}} \]
              3. Step-by-step derivation
                1. +-commutativeN/A

                  \[\leadsto 1 \cdot \sqrt{1 - \left(ux \cdot \left(2 \cdot maxCos - 2\right) + \color{blue}{1}\right)} \]
                2. *-commutativeN/A

                  \[\leadsto 1 \cdot \sqrt{1 - \left(\left(2 \cdot maxCos - 2\right) \cdot ux + 1\right)} \]
                3. lower-fma.f32N/A

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(2 \cdot maxCos - 2, \color{blue}{ux}, 1\right)} \]
                4. lower--.f32N/A

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(2 \cdot maxCos - 2, ux, 1\right)} \]
                5. *-commutativeN/A

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos \cdot 2 - 2, ux, 1\right)} \]
                6. lift-*.f3243.2

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos \cdot 2 - 2, ux, 1\right)} \]
              4. Applied rewrites43.2%

                \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\mathsf{fma}\left(maxCos \cdot 2 - 2, ux, 1\right)}} \]
              5. Step-by-step derivation
                1. lift-*.f32N/A

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos \cdot 2 - 2, ux, 1\right)} \]
                2. *-commutativeN/A

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(2 \cdot maxCos - 2, ux, 1\right)} \]
                3. count-2-revN/A

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(\left(maxCos + maxCos\right) - 2, ux, 1\right)} \]
                4. lower-+.f3243.2

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(\left(maxCos + maxCos\right) - 2, ux, 1\right)} \]
              6. Applied rewrites43.2%

                \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(\left(maxCos + maxCos\right) - 2, ux, 1\right)} \]
              7. Add Preprocessing

              Alternative 13: 40.3% accurate, 6.2× speedup?

              \[\begin{array}{l} \\ 1 \cdot \sqrt{1 - \mathsf{fma}\left(-2, ux, 1\right)} \end{array} \]
              (FPCore (ux uy maxCos)
               :precision binary32
               (* 1.0 (sqrt (- 1.0 (fma -2.0 ux 1.0)))))
              float code(float ux, float uy, float maxCos) {
              	return 1.0f * sqrtf((1.0f - fmaf(-2.0f, ux, 1.0f)));
              }
              
              function code(ux, uy, maxCos)
              	return Float32(Float32(1.0) * sqrt(Float32(Float32(1.0) - fma(Float32(-2.0), ux, Float32(1.0)))))
              end
              
              \begin{array}{l}
              
              \\
              1 \cdot \sqrt{1 - \mathsf{fma}\left(-2, ux, 1\right)}
              \end{array}
              
              Derivation
              1. Initial program 61.6%

                \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              2. Add Preprocessing
              3. Taylor expanded in uy around 0

                \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
              4. Step-by-step derivation
                1. Applied rewrites53.2%

                  \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
                2. Taylor expanded in ux around 0

                  \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\left(1 + ux \cdot \left(2 \cdot maxCos - 2\right)\right)}} \]
                3. Step-by-step derivation
                  1. +-commutativeN/A

                    \[\leadsto 1 \cdot \sqrt{1 - \left(ux \cdot \left(2 \cdot maxCos - 2\right) + \color{blue}{1}\right)} \]
                  2. *-commutativeN/A

                    \[\leadsto 1 \cdot \sqrt{1 - \left(\left(2 \cdot maxCos - 2\right) \cdot ux + 1\right)} \]
                  3. lower-fma.f32N/A

                    \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(2 \cdot maxCos - 2, \color{blue}{ux}, 1\right)} \]
                  4. lower--.f32N/A

                    \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(2 \cdot maxCos - 2, ux, 1\right)} \]
                  5. *-commutativeN/A

                    \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos \cdot 2 - 2, ux, 1\right)} \]
                  6. lift-*.f3243.2

                    \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(maxCos \cdot 2 - 2, ux, 1\right)} \]
                4. Applied rewrites43.2%

                  \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{\mathsf{fma}\left(maxCos \cdot 2 - 2, ux, 1\right)}} \]
                5. Taylor expanded in maxCos around 0

                  \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(-2, ux, 1\right)} \]
                6. Step-by-step derivation
                  1. Applied rewrites42.3%

                    \[\leadsto 1 \cdot \sqrt{1 - \mathsf{fma}\left(-2, ux, 1\right)} \]
                  2. Add Preprocessing

                  Alternative 14: 6.6% accurate, 8.2× speedup?

                  \[\begin{array}{l} \\ 1 \cdot \sqrt{1 - 1} \end{array} \]
                  (FPCore (ux uy maxCos) :precision binary32 (* 1.0 (sqrt (- 1.0 1.0))))
                  float code(float ux, float uy, float maxCos) {
                  	return 1.0f * sqrtf((1.0f - 1.0f));
                  }
                  
                  module fmin_fmax_functions
                      implicit none
                      private
                      public fmax
                      public fmin
                  
                      interface fmax
                          module procedure fmax88
                          module procedure fmax44
                          module procedure fmax84
                          module procedure fmax48
                      end interface
                      interface fmin
                          module procedure fmin88
                          module procedure fmin44
                          module procedure fmin84
                          module procedure fmin48
                      end interface
                  contains
                      real(8) function fmax88(x, y) result (res)
                          real(8), intent (in) :: x
                          real(8), intent (in) :: y
                          res = merge(y, merge(x, max(x, y), y /= y), x /= x)
                      end function
                      real(4) function fmax44(x, y) result (res)
                          real(4), intent (in) :: x
                          real(4), intent (in) :: y
                          res = merge(y, merge(x, max(x, y), y /= y), x /= x)
                      end function
                      real(8) function fmax84(x, y) result(res)
                          real(8), intent (in) :: x
                          real(4), intent (in) :: y
                          res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
                      end function
                      real(8) function fmax48(x, y) result(res)
                          real(4), intent (in) :: x
                          real(8), intent (in) :: y
                          res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
                      end function
                      real(8) function fmin88(x, y) result (res)
                          real(8), intent (in) :: x
                          real(8), intent (in) :: y
                          res = merge(y, merge(x, min(x, y), y /= y), x /= x)
                      end function
                      real(4) function fmin44(x, y) result (res)
                          real(4), intent (in) :: x
                          real(4), intent (in) :: y
                          res = merge(y, merge(x, min(x, y), y /= y), x /= x)
                      end function
                      real(8) function fmin84(x, y) result(res)
                          real(8), intent (in) :: x
                          real(4), intent (in) :: y
                          res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
                      end function
                      real(8) function fmin48(x, y) result(res)
                          real(4), intent (in) :: x
                          real(8), intent (in) :: y
                          res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
                      end function
                  end module
                  
                  real(4) function code(ux, uy, maxcos)
                  use fmin_fmax_functions
                      real(4), intent (in) :: ux
                      real(4), intent (in) :: uy
                      real(4), intent (in) :: maxcos
                      code = 1.0e0 * sqrt((1.0e0 - 1.0e0))
                  end function
                  
                  function code(ux, uy, maxCos)
                  	return Float32(Float32(1.0) * sqrt(Float32(Float32(1.0) - Float32(1.0))))
                  end
                  
                  function tmp = code(ux, uy, maxCos)
                  	tmp = single(1.0) * sqrt((single(1.0) - single(1.0)));
                  end
                  
                  \begin{array}{l}
                  
                  \\
                  1 \cdot \sqrt{1 - 1}
                  \end{array}
                  
                  Derivation
                  1. Initial program 61.6%

                    \[\cos \left(\left(uy \cdot 2\right) \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
                  2. Add Preprocessing
                  3. Taylor expanded in uy around 0

                    \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
                  4. Step-by-step derivation
                    1. Applied rewrites53.2%

                      \[\leadsto \color{blue}{1} \cdot \sqrt{1 - \left(\left(1 - ux\right) + ux \cdot maxCos\right) \cdot \left(\left(1 - ux\right) + ux \cdot maxCos\right)} \]
                    2. Taylor expanded in ux around 0

                      \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{1}} \]
                    3. Step-by-step derivation
                      1. Applied rewrites6.6%

                        \[\leadsto 1 \cdot \sqrt{1 - \color{blue}{1}} \]
                      2. Add Preprocessing

                      Reproduce

                      ?
                      herbie shell --seed 2025051 
                      (FPCore (ux uy maxCos)
                        :name "UniformSampleCone, x"
                        :precision binary32
                        :pre (and (and (and (<= 2.328306437e-10 ux) (<= ux 1.0)) (and (<= 2.328306437e-10 uy) (<= uy 1.0))) (and (<= 0.0 maxCos) (<= maxCos 1.0)))
                        (* (cos (* (* uy 2.0) (PI))) (sqrt (- 1.0 (* (+ (- 1.0 ux) (* ux maxCos)) (+ (- 1.0 ux) (* ux maxCos)))))))