HairBSDF, gamma for a refracted ray

Percentage Accurate: 91.4% → 98.6%
Time: 12.6s
Alternatives: 4
Speedup: 1.5×

Specification

?
\[\left(\left(-1 \leq sinTheta\_O \land sinTheta\_O \leq 1\right) \land \left(-1 \leq h \land h \leq 1\right)\right) \land \left(0 \leq eta \land eta \leq 10\right)\]
\[\begin{array}{l} \\ \sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right) \end{array} \]
(FPCore (sinTheta_O h eta)
 :precision binary32
 (asin
  (/
   h
   (sqrt
    (-
     (* eta eta)
     (/
      (* sinTheta_O sinTheta_O)
      (sqrt (- 1.0 (* sinTheta_O sinTheta_O)))))))))
float code(float sinTheta_O, float h, float eta) {
	return asinf((h / sqrtf(((eta * eta) - ((sinTheta_O * sinTheta_O) / sqrtf((1.0f - (sinTheta_O * sinTheta_O))))))));
}
real(4) function code(sintheta_o, h, eta)
    real(4), intent (in) :: sintheta_o
    real(4), intent (in) :: h
    real(4), intent (in) :: eta
    code = asin((h / sqrt(((eta * eta) - ((sintheta_o * sintheta_o) / sqrt((1.0e0 - (sintheta_o * sintheta_o))))))))
end function
function code(sinTheta_O, h, eta)
	return asin(Float32(h / sqrt(Float32(Float32(eta * eta) - Float32(Float32(sinTheta_O * sinTheta_O) / sqrt(Float32(Float32(1.0) - Float32(sinTheta_O * sinTheta_O))))))))
end
function tmp = code(sinTheta_O, h, eta)
	tmp = asin((h / sqrt(((eta * eta) - ((sinTheta_O * sinTheta_O) / sqrt((single(1.0) - (sinTheta_O * sinTheta_O))))))));
end
\begin{array}{l}

\\
\sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right)
\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 4 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: 91.4% accurate, 1.0× speedup?

\[\begin{array}{l} \\ \sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right) \end{array} \]
(FPCore (sinTheta_O h eta)
 :precision binary32
 (asin
  (/
   h
   (sqrt
    (-
     (* eta eta)
     (/
      (* sinTheta_O sinTheta_O)
      (sqrt (- 1.0 (* sinTheta_O sinTheta_O)))))))))
float code(float sinTheta_O, float h, float eta) {
	return asinf((h / sqrtf(((eta * eta) - ((sinTheta_O * sinTheta_O) / sqrtf((1.0f - (sinTheta_O * sinTheta_O))))))));
}
real(4) function code(sintheta_o, h, eta)
    real(4), intent (in) :: sintheta_o
    real(4), intent (in) :: h
    real(4), intent (in) :: eta
    code = asin((h / sqrt(((eta * eta) - ((sintheta_o * sintheta_o) / sqrt((1.0e0 - (sintheta_o * sintheta_o))))))))
end function
function code(sinTheta_O, h, eta)
	return asin(Float32(h / sqrt(Float32(Float32(eta * eta) - Float32(Float32(sinTheta_O * sinTheta_O) / sqrt(Float32(Float32(1.0) - Float32(sinTheta_O * sinTheta_O))))))))
end
function tmp = code(sinTheta_O, h, eta)
	tmp = asin((h / sqrt(((eta * eta) - ((sinTheta_O * sinTheta_O) / sqrt((single(1.0) - (sinTheta_O * sinTheta_O))))))));
end
\begin{array}{l}

\\
\sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right)
\end{array}

Alternative 1: 98.6% accurate, 1.1× speedup?

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;sinTheta\_O \cdot sinTheta\_O \leq 0:\\ \;\;\;\;\sin^{-1} \left(\frac{h}{eta}\right)\\ \mathbf{else}:\\ \;\;\;\;\sin^{-1} \left(\frac{h}{\sqrt{\left(eta - sinTheta\_O\right) \cdot \left(eta + sinTheta\_O\right)}}\right)\\ \end{array} \end{array} \]
(FPCore (sinTheta_O h eta)
 :precision binary32
 (if (<= (* sinTheta_O sinTheta_O) 0.0)
   (asin (/ h eta))
   (asin (/ h (sqrt (* (- eta sinTheta_O) (+ eta sinTheta_O)))))))
float code(float sinTheta_O, float h, float eta) {
	float tmp;
	if ((sinTheta_O * sinTheta_O) <= 0.0f) {
		tmp = asinf((h / eta));
	} else {
		tmp = asinf((h / sqrtf(((eta - sinTheta_O) * (eta + sinTheta_O)))));
	}
	return tmp;
}
real(4) function code(sintheta_o, h, eta)
    real(4), intent (in) :: sintheta_o
    real(4), intent (in) :: h
    real(4), intent (in) :: eta
    real(4) :: tmp
    if ((sintheta_o * sintheta_o) <= 0.0e0) then
        tmp = asin((h / eta))
    else
        tmp = asin((h / sqrt(((eta - sintheta_o) * (eta + sintheta_o)))))
    end if
    code = tmp
end function
function code(sinTheta_O, h, eta)
	tmp = Float32(0.0)
	if (Float32(sinTheta_O * sinTheta_O) <= Float32(0.0))
		tmp = asin(Float32(h / eta));
	else
		tmp = asin(Float32(h / sqrt(Float32(Float32(eta - sinTheta_O) * Float32(eta + sinTheta_O)))));
	end
	return tmp
end
function tmp_2 = code(sinTheta_O, h, eta)
	tmp = single(0.0);
	if ((sinTheta_O * sinTheta_O) <= single(0.0))
		tmp = asin((h / eta));
	else
		tmp = asin((h / sqrt(((eta - sinTheta_O) * (eta + sinTheta_O)))));
	end
	tmp_2 = tmp;
end
\begin{array}{l}

\\
\begin{array}{l}
\mathbf{if}\;sinTheta\_O \cdot sinTheta\_O \leq 0:\\
\;\;\;\;\sin^{-1} \left(\frac{h}{eta}\right)\\

\mathbf{else}:\\
\;\;\;\;\sin^{-1} \left(\frac{h}{\sqrt{\left(eta - sinTheta\_O\right) \cdot \left(eta + sinTheta\_O\right)}}\right)\\


\end{array}
\end{array}
Derivation
  1. Split input into 2 regimes
  2. if (*.f32 sinTheta_O sinTheta_O) < 0.0

    1. Initial program 82.3%

      \[\sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right) \]
    2. Add Preprocessing
    3. Taylor expanded in eta around inf

      \[\leadsto \sin^{-1} \color{blue}{\left(\frac{h}{eta}\right)} \]
    4. Step-by-step derivation
      1. lower-/.f3299.3

        \[\leadsto \sin^{-1} \color{blue}{\left(\frac{h}{eta}\right)} \]
    5. Applied rewrites99.3%

      \[\leadsto \sin^{-1} \color{blue}{\left(\frac{h}{eta}\right)} \]

    if 0.0 < (*.f32 sinTheta_O sinTheta_O)

    1. Initial program 99.6%

      \[\sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right) \]
    2. Add Preprocessing
    3. Taylor expanded in sinTheta_O around 0

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{-1 \cdot {sinTheta\_O}^{2} + {eta}^{2}}}}\right) \]
    4. Step-by-step derivation
      1. +-commutativeN/A

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{{eta}^{2} + -1 \cdot {sinTheta\_O}^{2}}}}\right) \]
      2. mul-1-negN/A

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{{eta}^{2} + \color{blue}{\left(\mathsf{neg}\left({sinTheta\_O}^{2}\right)\right)}}}\right) \]
      3. unsub-negN/A

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{{eta}^{2} - {sinTheta\_O}^{2}}}}\right) \]
      4. unpow2N/A

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{eta \cdot eta} - {sinTheta\_O}^{2}}}\right) \]
      5. unpow2N/A

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \color{blue}{sinTheta\_O \cdot sinTheta\_O}}}\right) \]
      6. difference-of-squaresN/A

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
      7. lower-*.f32N/A

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
      8. lower-+.f32N/A

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right)} \cdot \left(eta - sinTheta\_O\right)}}\right) \]
      9. lower--.f3299.4

        \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\left(eta + sinTheta\_O\right) \cdot \color{blue}{\left(eta - sinTheta\_O\right)}}}\right) \]
    5. Applied rewrites99.4%

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
  3. Recombined 2 regimes into one program.
  4. Final simplification99.4%

    \[\leadsto \begin{array}{l} \mathbf{if}\;sinTheta\_O \cdot sinTheta\_O \leq 0:\\ \;\;\;\;\sin^{-1} \left(\frac{h}{eta}\right)\\ \mathbf{else}:\\ \;\;\;\;\sin^{-1} \left(\frac{h}{\sqrt{\left(eta - sinTheta\_O\right) \cdot \left(eta + sinTheta\_O\right)}}\right)\\ \end{array} \]
  5. Add Preprocessing

Alternative 2: 98.4% accurate, 1.1× speedup?

\[\begin{array}{l} \\ \sin^{-1} \left(\frac{h}{\sqrt{eta - sinTheta\_O} \cdot \sqrt{eta + sinTheta\_O}}\right) \end{array} \]
(FPCore (sinTheta_O h eta)
 :precision binary32
 (asin (/ h (* (sqrt (- eta sinTheta_O)) (sqrt (+ eta sinTheta_O))))))
float code(float sinTheta_O, float h, float eta) {
	return asinf((h / (sqrtf((eta - sinTheta_O)) * sqrtf((eta + sinTheta_O)))));
}
real(4) function code(sintheta_o, h, eta)
    real(4), intent (in) :: sintheta_o
    real(4), intent (in) :: h
    real(4), intent (in) :: eta
    code = asin((h / (sqrt((eta - sintheta_o)) * sqrt((eta + sintheta_o)))))
end function
function code(sinTheta_O, h, eta)
	return asin(Float32(h / Float32(sqrt(Float32(eta - sinTheta_O)) * sqrt(Float32(eta + sinTheta_O)))))
end
function tmp = code(sinTheta_O, h, eta)
	tmp = asin((h / (sqrt((eta - sinTheta_O)) * sqrt((eta + sinTheta_O)))));
end
\begin{array}{l}

\\
\sin^{-1} \left(\frac{h}{\sqrt{eta - sinTheta\_O} \cdot \sqrt{eta + sinTheta\_O}}\right)
\end{array}
Derivation
  1. Initial program 89.9%

    \[\sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in sinTheta_O around 0

    \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{-1 \cdot {sinTheta\_O}^{2} + {eta}^{2}}}}\right) \]
  4. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{{eta}^{2} + -1 \cdot {sinTheta\_O}^{2}}}}\right) \]
    2. mul-1-negN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{{eta}^{2} + \color{blue}{\left(\mathsf{neg}\left({sinTheta\_O}^{2}\right)\right)}}}\right) \]
    3. unsub-negN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{{eta}^{2} - {sinTheta\_O}^{2}}}}\right) \]
    4. unpow2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{eta \cdot eta} - {sinTheta\_O}^{2}}}\right) \]
    5. unpow2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \color{blue}{sinTheta\_O \cdot sinTheta\_O}}}\right) \]
    6. difference-of-squaresN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
    7. lower-*.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
    8. lower-+.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right)} \cdot \left(eta - sinTheta\_O\right)}}\right) \]
    9. lower--.f3289.8

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\left(eta + sinTheta\_O\right) \cdot \color{blue}{\left(eta - sinTheta\_O\right)}}}\right) \]
  5. Applied rewrites89.8%

    \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
  6. Step-by-step derivation
    1. lift-+.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right)} \cdot \left(eta - sinTheta\_O\right)}}\right) \]
    2. lift--.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\left(eta + sinTheta\_O\right) \cdot \color{blue}{\left(eta - sinTheta\_O\right)}}}\right) \]
    3. *-commutativeN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta - sinTheta\_O\right) \cdot \left(eta + sinTheta\_O\right)}}}\right) \]
    4. sqrt-prodN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\sqrt{eta - sinTheta\_O} \cdot \sqrt{eta + sinTheta\_O}}}\right) \]
    5. pow1/2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{{\left(eta - sinTheta\_O\right)}^{\frac{1}{2}}} \cdot \sqrt{eta + sinTheta\_O}}\right) \]
    6. pow1/2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{{\left(eta - sinTheta\_O\right)}^{\frac{1}{2}} \cdot \color{blue}{{\left(eta + sinTheta\_O\right)}^{\frac{1}{2}}}}\right) \]
    7. lower-*.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{{\left(eta - sinTheta\_O\right)}^{\frac{1}{2}} \cdot {\left(eta + sinTheta\_O\right)}^{\frac{1}{2}}}}\right) \]
    8. pow1/2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\sqrt{eta - sinTheta\_O}} \cdot {\left(eta + sinTheta\_O\right)}^{\frac{1}{2}}}\right) \]
    9. lower-sqrt.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\sqrt{eta - sinTheta\_O}} \cdot {\left(eta + sinTheta\_O\right)}^{\frac{1}{2}}}\right) \]
    10. pow1/2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{eta - sinTheta\_O} \cdot \color{blue}{\sqrt{eta + sinTheta\_O}}}\right) \]
    11. lower-sqrt.f3298.6

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{eta - sinTheta\_O} \cdot \color{blue}{\sqrt{eta + sinTheta\_O}}}\right) \]
  7. Applied rewrites98.6%

    \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\sqrt{eta - sinTheta\_O} \cdot \sqrt{eta + sinTheta\_O}}}\right) \]
  8. Add Preprocessing

Alternative 3: 97.9% accurate, 1.2× speedup?

\[\begin{array}{l} \\ \sin^{-1} \left(\frac{h}{\mathsf{fma}\left(sinTheta\_O, \mathsf{fma}\left(-0.5, \frac{sinTheta\_O}{eta}, 0\right), eta\right)}\right) \end{array} \]
(FPCore (sinTheta_O h eta)
 :precision binary32
 (asin (/ h (fma sinTheta_O (fma -0.5 (/ sinTheta_O eta) 0.0) eta))))
float code(float sinTheta_O, float h, float eta) {
	return asinf((h / fmaf(sinTheta_O, fmaf(-0.5f, (sinTheta_O / eta), 0.0f), eta)));
}
function code(sinTheta_O, h, eta)
	return asin(Float32(h / fma(sinTheta_O, fma(Float32(-0.5), Float32(sinTheta_O / eta), Float32(0.0)), eta)))
end
\begin{array}{l}

\\
\sin^{-1} \left(\frac{h}{\mathsf{fma}\left(sinTheta\_O, \mathsf{fma}\left(-0.5, \frac{sinTheta\_O}{eta}, 0\right), eta\right)}\right)
\end{array}
Derivation
  1. Initial program 89.9%

    \[\sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in sinTheta_O around 0

    \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{-1 \cdot {sinTheta\_O}^{2} + {eta}^{2}}}}\right) \]
  4. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{{eta}^{2} + -1 \cdot {sinTheta\_O}^{2}}}}\right) \]
    2. mul-1-negN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{{eta}^{2} + \color{blue}{\left(\mathsf{neg}\left({sinTheta\_O}^{2}\right)\right)}}}\right) \]
    3. unsub-negN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{{eta}^{2} - {sinTheta\_O}^{2}}}}\right) \]
    4. unpow2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{eta \cdot eta} - {sinTheta\_O}^{2}}}\right) \]
    5. unpow2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \color{blue}{sinTheta\_O \cdot sinTheta\_O}}}\right) \]
    6. difference-of-squaresN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
    7. lower-*.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
    8. lower-+.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right)} \cdot \left(eta - sinTheta\_O\right)}}\right) \]
    9. lower--.f3289.8

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\left(eta + sinTheta\_O\right) \cdot \color{blue}{\left(eta - sinTheta\_O\right)}}}\right) \]
  5. Applied rewrites89.8%

    \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right) \cdot \left(eta - sinTheta\_O\right)}}}\right) \]
  6. Step-by-step derivation
    1. lift-+.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta + sinTheta\_O\right)} \cdot \left(eta - sinTheta\_O\right)}}\right) \]
    2. lift--.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\left(eta + sinTheta\_O\right) \cdot \color{blue}{\left(eta - sinTheta\_O\right)}}}\right) \]
    3. *-commutativeN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{\color{blue}{\left(eta - sinTheta\_O\right) \cdot \left(eta + sinTheta\_O\right)}}}\right) \]
    4. sqrt-prodN/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\sqrt{eta - sinTheta\_O} \cdot \sqrt{eta + sinTheta\_O}}}\right) \]
    5. pow1/2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{{\left(eta - sinTheta\_O\right)}^{\frac{1}{2}}} \cdot \sqrt{eta + sinTheta\_O}}\right) \]
    6. pow1/2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{{\left(eta - sinTheta\_O\right)}^{\frac{1}{2}} \cdot \color{blue}{{\left(eta + sinTheta\_O\right)}^{\frac{1}{2}}}}\right) \]
    7. lower-*.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{{\left(eta - sinTheta\_O\right)}^{\frac{1}{2}} \cdot {\left(eta + sinTheta\_O\right)}^{\frac{1}{2}}}}\right) \]
    8. pow1/2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\sqrt{eta - sinTheta\_O}} \cdot {\left(eta + sinTheta\_O\right)}^{\frac{1}{2}}}\right) \]
    9. lower-sqrt.f32N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\sqrt{eta - sinTheta\_O}} \cdot {\left(eta + sinTheta\_O\right)}^{\frac{1}{2}}}\right) \]
    10. pow1/2N/A

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{eta - sinTheta\_O} \cdot \color{blue}{\sqrt{eta + sinTheta\_O}}}\right) \]
    11. lower-sqrt.f3298.6

      \[\leadsto \sin^{-1} \left(\frac{h}{\sqrt{eta - sinTheta\_O} \cdot \color{blue}{\sqrt{eta + sinTheta\_O}}}\right) \]
  7. Applied rewrites98.6%

    \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\sqrt{eta - sinTheta\_O} \cdot \sqrt{eta + sinTheta\_O}}}\right) \]
  8. Taylor expanded in sinTheta_O around 0

    \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{eta + sinTheta\_O \cdot \left(\frac{-1}{2} \cdot \frac{sinTheta\_O \cdot \left(1 + \frac{1}{4} \cdot \frac{{\left(eta + -1 \cdot eta\right)}^{2}}{{eta}^{2}}\right)}{eta} + \frac{1}{2} \cdot \frac{eta + -1 \cdot eta}{eta}\right)}}\right) \]
  9. Applied rewrites98.2%

    \[\leadsto \sin^{-1} \left(\frac{h}{\color{blue}{\mathsf{fma}\left(sinTheta\_O, \mathsf{fma}\left(-0.5, \frac{sinTheta\_O}{eta}, 0\right), eta\right)}}\right) \]
  10. Add Preprocessing

Alternative 4: 95.3% accurate, 1.5× speedup?

\[\begin{array}{l} \\ \sin^{-1} \left(\frac{h}{eta}\right) \end{array} \]
(FPCore (sinTheta_O h eta) :precision binary32 (asin (/ h eta)))
float code(float sinTheta_O, float h, float eta) {
	return asinf((h / eta));
}
real(4) function code(sintheta_o, h, eta)
    real(4), intent (in) :: sintheta_o
    real(4), intent (in) :: h
    real(4), intent (in) :: eta
    code = asin((h / eta))
end function
function code(sinTheta_O, h, eta)
	return asin(Float32(h / eta))
end
function tmp = code(sinTheta_O, h, eta)
	tmp = asin((h / eta));
end
\begin{array}{l}

\\
\sin^{-1} \left(\frac{h}{eta}\right)
\end{array}
Derivation
  1. Initial program 89.9%

    \[\sin^{-1} \left(\frac{h}{\sqrt{eta \cdot eta - \frac{sinTheta\_O \cdot sinTheta\_O}{\sqrt{1 - sinTheta\_O \cdot sinTheta\_O}}}}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in eta around inf

    \[\leadsto \sin^{-1} \color{blue}{\left(\frac{h}{eta}\right)} \]
  4. Step-by-step derivation
    1. lower-/.f3296.1

      \[\leadsto \sin^{-1} \color{blue}{\left(\frac{h}{eta}\right)} \]
  5. Applied rewrites96.1%

    \[\leadsto \sin^{-1} \color{blue}{\left(\frac{h}{eta}\right)} \]
  6. Add Preprocessing

Reproduce

?
herbie shell --seed 2024216 
(FPCore (sinTheta_O h eta)
  :name "HairBSDF, gamma for a refracted ray"
  :precision binary32
  :pre (and (and (and (<= -1.0 sinTheta_O) (<= sinTheta_O 1.0)) (and (<= -1.0 h) (<= h 1.0))) (and (<= 0.0 eta) (<= eta 10.0)))
  (asin (/ h (sqrt (- (* eta eta) (/ (* sinTheta_O sinTheta_O) (sqrt (- 1.0 (* sinTheta_O sinTheta_O)))))))))