GTR1 distribution

Percentage Accurate: 98.5% → 98.6%

Time: 12.3s

Alternatives: 8

Speedup: 1.0×

Specification

\[\left(0 \leq cosTheta \land cosTheta \leq 1\right) \land \left(0.0001 \leq \alpha \land \alpha \leq 1\right)\]

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \alpha \cdot \alpha - 1\\ \frac{t_0}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(t_0 \cdot cosTheta\right) \cdot cosTheta\right)} \end{array} \end{array} \]

FPCore

(FPCore (cosTheta alpha)
 :precision binary32
 (let* ((t_0 (- (* alpha alpha) 1.0)))
   (/
    t_0
    (* (* PI (log (* alpha alpha))) (+ 1.0 (* (* t_0 cosTheta) cosTheta))))))

C

float code(float cosTheta, float alpha) {
	float t_0 = (alpha * alpha) - 1.0f;
	return t_0 / ((((float) M_PI) * logf((alpha * alpha))) * (1.0f + ((t_0 * cosTheta) * cosTheta)));
}

Julia

function code(cosTheta, alpha)
	t_0 = Float32(Float32(alpha * alpha) - Float32(1.0))
	return Float32(t_0 / Float32(Float32(Float32(pi) * log(Float32(alpha * alpha))) * Float32(Float32(1.0) + Float32(Float32(t_0 * cosTheta) * cosTheta))))
end

MATLAB

function tmp = code(cosTheta, alpha)
	t_0 = (alpha * alpha) - single(1.0);
	tmp = t_0 / ((single(pi) * log((alpha * alpha))) * (single(1.0) + ((t_0 * cosTheta) * cosTheta)));
end

Initial Program: 98.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \alpha \cdot \alpha - 1\\ \frac{t_0}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(t_0 \cdot cosTheta\right) \cdot cosTheta\right)} \end{array} \end{array} \]

FPCore

(FPCore (cosTheta alpha)
 :precision binary32
 (let* ((t_0 (- (* alpha alpha) 1.0)))
   (/
    t_0
    (* (* PI (log (* alpha alpha))) (+ 1.0 (* (* t_0 cosTheta) cosTheta))))))

C

float code(float cosTheta, float alpha) {
	float t_0 = (alpha * alpha) - 1.0f;
	return t_0 / ((((float) M_PI) * logf((alpha * alpha))) * (1.0f + ((t_0 * cosTheta) * cosTheta)));
}

Julia

function code(cosTheta, alpha)
	t_0 = Float32(Float32(alpha * alpha) - Float32(1.0))
	return Float32(t_0 / Float32(Float32(Float32(pi) * log(Float32(alpha * alpha))) * Float32(Float32(1.0) + Float32(Float32(t_0 * cosTheta) * cosTheta))))
end

MATLAB

function tmp = code(cosTheta, alpha)
	t_0 = (alpha * alpha) - single(1.0);
	tmp = t_0 / ((single(pi) * log((alpha * alpha))) * (single(1.0) + ((t_0 * cosTheta) * cosTheta)));
end

Alternative 1: 98.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \alpha \cdot \alpha + -1\\ \frac{t_0}{\log \left({\alpha}^{\left(\pi \cdot 2\right)}\right) \cdot \left(1 + cosTheta \cdot \left(t_0 \cdot cosTheta\right)\right)} \end{array} \end{array} \]

FPCore

(FPCore (cosTheta alpha)
 :precision binary32
 (let* ((t_0 (+ (* alpha alpha) -1.0)))
   (/
    t_0
    (* (log (pow alpha (* PI 2.0))) (+ 1.0 (* cosTheta (* t_0 cosTheta)))))))

C

float code(float cosTheta, float alpha) {
	float t_0 = (alpha * alpha) + -1.0f;
	return t_0 / (logf(powf(alpha, (((float) M_PI) * 2.0f))) * (1.0f + (cosTheta * (t_0 * cosTheta))));
}

Julia

function code(cosTheta, alpha)
	t_0 = Float32(Float32(alpha * alpha) + Float32(-1.0))
	return Float32(t_0 / Float32(log((alpha ^ Float32(Float32(pi) * Float32(2.0)))) * Float32(Float32(1.0) + Float32(cosTheta * Float32(t_0 * cosTheta)))))
end

MATLAB

function tmp = code(cosTheta, alpha)
	t_0 = (alpha * alpha) + single(-1.0);
	tmp = t_0 / (log((alpha ^ (single(pi) * single(2.0)))) * (single(1.0) + (cosTheta * (t_0 * cosTheta))));
end

Derivation

1. Initial program 98.5%

   \[\frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]
2. Step-by-step derivation

   1. pow2 98.5%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \color{blue}{\left({\alpha}^{2}\right)}\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   2. log-pow 98.4%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \color{blue}{\left(2 \cdot \log \alpha\right)}\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   3. associate-*l* 98.4%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\color{blue}{\left(\left(\pi \cdot 2\right) \cdot \log \alpha\right)} \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   4. add-log-exp 98.4%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\color{blue}{\log \left(e^{\left(\pi \cdot 2\right) \cdot \log \alpha}\right)} \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   5. *-commutative 98.4%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\log \left(e^{\color{blue}{\log \alpha \cdot \left(\pi \cdot 2\right)}}\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   6. exp-to-pow 98.7%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\log \color{blue}{\left({\alpha}^{\left(\pi \cdot 2\right)}\right)} \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

3. Applied egg-rr 98.7%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\color{blue}{\log \left({\alpha}^{\left(\pi \cdot 2\right)}\right)} \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

4. Final simplification 98.7%

   \[\leadsto \frac{\alpha \cdot \alpha + -1}{\log \left({\alpha}^{\left(\pi \cdot 2\right)}\right) \cdot \left(1 + cosTheta \cdot \left(\left(\alpha \cdot \alpha + -1\right) \cdot cosTheta\right)\right)} \]

Alternative 2: 98.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \alpha \cdot \alpha + -1\\ \frac{t_0}{\left(1 + cosTheta \cdot \left(t_0 \cdot cosTheta\right)\right) \cdot \left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right)} \end{array} \end{array} \]

FPCore

(FPCore (cosTheta alpha)
 :precision binary32
 (let* ((t_0 (+ (* alpha alpha) -1.0)))
   (/
    t_0
    (* (+ 1.0 (* cosTheta (* t_0 cosTheta))) (* PI (log (* alpha alpha)))))))

C

float code(float cosTheta, float alpha) {
	float t_0 = (alpha * alpha) + -1.0f;
	return t_0 / ((1.0f + (cosTheta * (t_0 * cosTheta))) * (((float) M_PI) * logf((alpha * alpha))));
}

Julia

function code(cosTheta, alpha)
	t_0 = Float32(Float32(alpha * alpha) + Float32(-1.0))
	return Float32(t_0 / Float32(Float32(Float32(1.0) + Float32(cosTheta * Float32(t_0 * cosTheta))) * Float32(Float32(pi) * log(Float32(alpha * alpha)))))
end

MATLAB

function tmp = code(cosTheta, alpha)
	t_0 = (alpha * alpha) + single(-1.0);
	tmp = t_0 / ((single(1.0) + (cosTheta * (t_0 * cosTheta))) * (single(pi) * log((alpha * alpha))));
end

Derivation

1. Initial program 98.5%

   \[\frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

2. Final simplification 98.5%

   \[\leadsto \frac{\alpha \cdot \alpha + -1}{\left(1 + cosTheta \cdot \left(\left(\alpha \cdot \alpha + -1\right) \cdot cosTheta\right)\right) \cdot \left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right)} \]

Alternative 3: 97.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\alpha \cdot \alpha + -1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 - cosTheta \cdot cosTheta\right)} \end{array} \]

FPCore

(FPCore (cosTheta alpha)
 :precision binary32
 (/
  (+ (* alpha alpha) -1.0)
  (* (* PI (log (* alpha alpha))) (- 1.0 (* cosTheta cosTheta)))))

C

float code(float cosTheta, float alpha) {
	return ((alpha * alpha) + -1.0f) / ((((float) M_PI) * logf((alpha * alpha))) * (1.0f - (cosTheta * cosTheta)));
}

Julia

function code(cosTheta, alpha)
	return Float32(Float32(Float32(alpha * alpha) + Float32(-1.0)) / Float32(Float32(Float32(pi) * log(Float32(alpha * alpha))) * Float32(Float32(1.0) - Float32(cosTheta * cosTheta))))
end

MATLAB

function tmp = code(cosTheta, alpha)
	tmp = ((alpha * alpha) + single(-1.0)) / ((single(pi) * log((alpha * alpha))) * (single(1.0) - (cosTheta * cosTheta)));
end

Derivation

1. Initial program 98.5%

   \[\frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

2. Taylor expanded in alpha around 0 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-1 \cdot cosTheta\right)} \cdot cosTheta\right)} \]
3. Step-by-step derivation

   1. mul-1-neg 97.8%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

4. Simplified 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

5. Final simplification 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha + -1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 - cosTheta \cdot cosTheta\right)} \]

Alternative 4: 95.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ 0.5 \cdot \frac{\alpha + 1}{\frac{\pi \cdot \log \alpha}{\alpha + -1}} \end{array} \]

FPCore

(FPCore (cosTheta alpha)
 :precision binary32
 (* 0.5 (/ (+ alpha 1.0) (/ (* PI (log alpha)) (+ alpha -1.0)))))

C

float code(float cosTheta, float alpha) {
	return 0.5f * ((alpha + 1.0f) / ((((float) M_PI) * logf(alpha)) / (alpha + -1.0f)));
}

Julia

function code(cosTheta, alpha)
	return Float32(Float32(0.5) * Float32(Float32(alpha + Float32(1.0)) / Float32(Float32(Float32(pi) * log(alpha)) / Float32(alpha + Float32(-1.0)))))
end

MATLAB

function tmp = code(cosTheta, alpha)
	tmp = single(0.5) * ((alpha + single(1.0)) / ((single(pi) * log(alpha)) / (alpha + single(-1.0))));
end

Derivation

1. Initial program 98.5%

   \[\frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]
2. Step-by-step derivation

   1. pow2 98.5%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \color{blue}{\left({\alpha}^{2}\right)}\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   2. log-pow 98.4%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \color{blue}{\left(2 \cdot \log \alpha\right)}\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   3. associate-*l* 98.4%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\color{blue}{\left(\left(\pi \cdot 2\right) \cdot \log \alpha\right)} \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   4. add-log-exp 98.4%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\color{blue}{\log \left(e^{\left(\pi \cdot 2\right) \cdot \log \alpha}\right)} \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   5. *-commutative 98.4%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\log \left(e^{\color{blue}{\log \alpha \cdot \left(\pi \cdot 2\right)}}\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

   6. exp-to-pow 98.7%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\log \color{blue}{\left({\alpha}^{\left(\pi \cdot 2\right)}\right)} \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

3. Applied egg-rr 98.7%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\color{blue}{\log \left({\alpha}^{\left(\pi \cdot 2\right)}\right)} \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]
4. Step-by-step derivation

   1. difference-of-sqr-1 98.4%

      \[\leadsto \frac{\color{blue}{\left(\alpha + 1\right) \cdot \left(\alpha - 1\right)}}{\log \left({\alpha}^{\left(\pi \cdot 2\right)}\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

5. Applied egg-rr 98.4%

   \[\leadsto \frac{\color{blue}{\left(\alpha + 1\right) \cdot \left(\alpha - 1\right)}}{\log \left({\alpha}^{\left(\pi \cdot 2\right)}\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

6. Taylor expanded in cosTheta around 0 95.2%

   \[\leadsto \color{blue}{0.5 \cdot \frac{\left(1 + \alpha\right) \cdot \left(\alpha - 1\right)}{\pi \cdot \log \alpha}} \]
7. Step-by-step derivation

   1. sub-neg 95.2%

      \[\leadsto 0.5 \cdot \frac{\left(1 + \alpha\right) \cdot \color{blue}{\left(\alpha + \left(-1\right)\right)}}{\pi \cdot \log \alpha} \]

   2. metadata-eval 95.2%

      \[\leadsto 0.5 \cdot \frac{\left(1 + \alpha\right) \cdot \left(\alpha + \color{blue}{-1}\right)}{\pi \cdot \log \alpha} \]

   3. associate-/l* 95.1%

      \[\leadsto 0.5 \cdot \color{blue}{\frac{1 + \alpha}{\frac{\pi \cdot \log \alpha}{\alpha + -1}}} \]

   4. +-commutative 95.1%

      \[\leadsto 0.5 \cdot \frac{\color{blue}{\alpha + 1}}{\frac{\pi \cdot \log \alpha}{\alpha + -1}} \]

   5. +-commutative 95.1%

      \[\leadsto 0.5 \cdot \frac{\alpha + 1}{\frac{\pi \cdot \log \alpha}{\color{blue}{-1 + \alpha}}} \]

8. Simplified 95.1%

   \[\leadsto \color{blue}{0.5 \cdot \frac{\alpha + 1}{\frac{\pi \cdot \log \alpha}{-1 + \alpha}}} \]

9. Final simplification 95.1%

   \[\leadsto 0.5 \cdot \frac{\alpha + 1}{\frac{\pi \cdot \log \alpha}{\alpha + -1}} \]

Alternative 5: 95.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\alpha + 1\right) \cdot \frac{\alpha + -1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \end{array} \]

FPCore

(FPCore (cosTheta alpha)
 :precision binary32
 (* (+ alpha 1.0) (/ (+ alpha -1.0) (* PI (* 2.0 (log alpha))))))

C

float code(float cosTheta, float alpha) {
	return (alpha + 1.0f) * ((alpha + -1.0f) / (((float) M_PI) * (2.0f * logf(alpha))));
}

Julia

function code(cosTheta, alpha)
	return Float32(Float32(alpha + Float32(1.0)) * Float32(Float32(alpha + Float32(-1.0)) / Float32(Float32(pi) * Float32(Float32(2.0) * log(alpha)))))
end

MATLAB

function tmp = code(cosTheta, alpha)
	tmp = (alpha + single(1.0)) * ((alpha + single(-1.0)) / (single(pi) * (single(2.0) * log(alpha))));
end

Derivation

1. Initial program 98.5%

   \[\frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

2. Taylor expanded in alpha around 0 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-1 \cdot cosTheta\right)} \cdot cosTheta\right)} \]
3. Step-by-step derivation

   1. mul-1-neg 97.8%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

4. Simplified 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

5. Taylor expanded in cosTheta around 0 95.5%

   \[\leadsto \color{blue}{\frac{{\alpha}^{2} - 1}{\pi \cdot \log \left({\alpha}^{2}\right)}} \]
6. Step-by-step derivation

   1. log-pow 95.5%

      \[\leadsto \frac{{\alpha}^{2} - 1}{\pi \cdot \color{blue}{\left(2 \cdot \log \alpha\right)}} \]

   2. unpow2 95.5%

      \[\leadsto \frac{\color{blue}{\alpha \cdot \alpha} - 1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   3. fma-neg 95.5%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\alpha, \alpha, -1\right)}}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   4. metadata-eval 95.5%

      \[\leadsto \frac{\mathsf{fma}\left(\alpha, \alpha, \color{blue}{-1}\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

7. Simplified 95.5%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\alpha, \alpha, -1\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)}} \]
8. Step-by-step derivation

   1. metadata-eval 95.5%

      \[\leadsto \frac{\mathsf{fma}\left(\alpha, \alpha, \color{blue}{-1}\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   2. fma-neg 95.5%

      \[\leadsto \frac{\color{blue}{\alpha \cdot \alpha - 1}}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   3. div-inv 95.2%

      \[\leadsto \color{blue}{\left(\alpha \cdot \alpha - 1\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)}} \]

   4. difference-of-sqr-1 95.1%

      \[\leadsto \color{blue}{\left(\left(\alpha + 1\right) \cdot \left(\alpha - 1\right)\right)} \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   5. *-un-lft-identity 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\color{blue}{1 \cdot \alpha} - 1\right)\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   6. fma-neg 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \color{blue}{\mathsf{fma}\left(1, \alpha, -1\right)}\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   7. metadata-eval 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \mathsf{fma}\left(1, \alpha, \color{blue}{-1}\right)\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   8. fma-def 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \color{blue}{\left(1 \cdot \alpha + -1\right)}\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   9. *-un-lft-identity 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\color{blue}{\alpha} + -1\right)\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   10. associate-*r* 95.1%

       \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)\right) \cdot \frac{1}{\color{blue}{\left(\pi \cdot 2\right) \cdot \log \alpha}} \]

   11. *-commutative 95.1%

       \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)\right) \cdot \frac{1}{\color{blue}{\left(2 \cdot \pi\right)} \cdot \log \alpha} \]

   12. *-commutative 95.1%

       \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)\right) \cdot \frac{1}{\color{blue}{\log \alpha \cdot \left(2 \cdot \pi\right)}} \]

   13. *-commutative 95.1%

       \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)\right) \cdot \frac{1}{\log \alpha \cdot \color{blue}{\left(\pi \cdot 2\right)}} \]

   14. associate-*r* 95.0%

       \[\leadsto \color{blue}{\left(\alpha + 1\right) \cdot \left(\left(\alpha + -1\right) \cdot \frac{1}{\log \alpha \cdot \left(\pi \cdot 2\right)}\right)} \]

   15. *-commutative 95.0%

       \[\leadsto \color{blue}{\left(\left(\alpha + -1\right) \cdot \frac{1}{\log \alpha \cdot \left(\pi \cdot 2\right)}\right) \cdot \left(\alpha + 1\right)} \]

9. Applied egg-rr 95.2%

   \[\leadsto \color{blue}{\frac{\alpha + -1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \cdot \left(\alpha + 1\right)} \]

10. Final simplification 95.2%

    \[\leadsto \left(\alpha + 1\right) \cdot \frac{\alpha + -1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

Alternative 6: 95.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)}{\left(\pi \cdot 2\right) \cdot \log \alpha} \end{array} \]

FPCore

(FPCore (cosTheta alpha)
 :precision binary32
 (/ (* (+ alpha 1.0) (+ alpha -1.0)) (* (* PI 2.0) (log alpha))))

C

float code(float cosTheta, float alpha) {
	return ((alpha + 1.0f) * (alpha + -1.0f)) / ((((float) M_PI) * 2.0f) * logf(alpha));
}

Julia

function code(cosTheta, alpha)
	return Float32(Float32(Float32(alpha + Float32(1.0)) * Float32(alpha + Float32(-1.0))) / Float32(Float32(Float32(pi) * Float32(2.0)) * log(alpha)))
end

MATLAB

function tmp = code(cosTheta, alpha)
	tmp = ((alpha + single(1.0)) * (alpha + single(-1.0))) / ((single(pi) * single(2.0)) * log(alpha));
end

Derivation

1. Initial program 98.5%

   \[\frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

2. Taylor expanded in alpha around 0 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-1 \cdot cosTheta\right)} \cdot cosTheta\right)} \]
3. Step-by-step derivation

   1. mul-1-neg 97.8%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

4. Simplified 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

5. Taylor expanded in cosTheta around 0 95.5%

   \[\leadsto \color{blue}{\frac{{\alpha}^{2} - 1}{\pi \cdot \log \left({\alpha}^{2}\right)}} \]
6. Step-by-step derivation

   1. log-pow 95.5%

      \[\leadsto \frac{{\alpha}^{2} - 1}{\pi \cdot \color{blue}{\left(2 \cdot \log \alpha\right)}} \]

   2. unpow2 95.5%

      \[\leadsto \frac{\color{blue}{\alpha \cdot \alpha} - 1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   3. fma-neg 95.5%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\alpha, \alpha, -1\right)}}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   4. metadata-eval 95.5%

      \[\leadsto \frac{\mathsf{fma}\left(\alpha, \alpha, \color{blue}{-1}\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

7. Simplified 95.5%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\alpha, \alpha, -1\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)}} \]
8. Step-by-step derivation

   1. metadata-eval 95.5%

      \[\leadsto \frac{\mathsf{fma}\left(\alpha, \alpha, \color{blue}{-1}\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   2. fma-neg 95.5%

      \[\leadsto \frac{\color{blue}{\alpha \cdot \alpha - 1}}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   3. div-inv 95.2%

      \[\leadsto \color{blue}{\left(\alpha \cdot \alpha - 1\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)}} \]

   4. difference-of-sqr-1 95.1%

      \[\leadsto \color{blue}{\left(\left(\alpha + 1\right) \cdot \left(\alpha - 1\right)\right)} \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   5. *-un-lft-identity 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\color{blue}{1 \cdot \alpha} - 1\right)\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   6. fma-neg 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \color{blue}{\mathsf{fma}\left(1, \alpha, -1\right)}\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   7. metadata-eval 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \mathsf{fma}\left(1, \alpha, \color{blue}{-1}\right)\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   8. fma-def 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \color{blue}{\left(1 \cdot \alpha + -1\right)}\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   9. *-un-lft-identity 95.1%

      \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\color{blue}{\alpha} + -1\right)\right) \cdot \frac{1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   10. associate-*r* 95.1%

       \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)\right) \cdot \frac{1}{\color{blue}{\left(\pi \cdot 2\right) \cdot \log \alpha}} \]

   11. *-commutative 95.1%

       \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)\right) \cdot \frac{1}{\color{blue}{\left(2 \cdot \pi\right)} \cdot \log \alpha} \]

   12. *-commutative 95.1%

       \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)\right) \cdot \frac{1}{\color{blue}{\log \alpha \cdot \left(2 \cdot \pi\right)}} \]

   13. *-commutative 95.1%

       \[\leadsto \left(\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)\right) \cdot \frac{1}{\log \alpha \cdot \color{blue}{\left(\pi \cdot 2\right)}} \]

   14. associate-*r* 95.0%

       \[\leadsto \color{blue}{\left(\alpha + 1\right) \cdot \left(\left(\alpha + -1\right) \cdot \frac{1}{\log \alpha \cdot \left(\pi \cdot 2\right)}\right)} \]

   15. un-div-inv 95.2%

       \[\leadsto \left(\alpha + 1\right) \cdot \color{blue}{\frac{\alpha + -1}{\log \alpha \cdot \left(\pi \cdot 2\right)}} \]

   16. *-commutative 95.2%

       \[\leadsto \left(\alpha + 1\right) \cdot \frac{\alpha + -1}{\log \alpha \cdot \color{blue}{\left(2 \cdot \pi\right)}} \]

   17. *-commutative 95.2%

       \[\leadsto \left(\alpha + 1\right) \cdot \frac{\alpha + -1}{\color{blue}{\left(2 \cdot \pi\right) \cdot \log \alpha}} \]

   18. *-commutative 95.2%

       \[\leadsto \left(\alpha + 1\right) \cdot \frac{\alpha + -1}{\color{blue}{\left(\pi \cdot 2\right)} \cdot \log \alpha} \]

9. Applied egg-rr 95.2%

   \[\leadsto \color{blue}{\left(\alpha + 1\right) \cdot \frac{\alpha + -1}{\pi \cdot \left(2 \cdot \log \alpha\right)}} \]
10. Step-by-step derivation

    1. associate-*r/ 95.2%

       \[\leadsto \color{blue}{\frac{\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)}} \]

    2. associate-*r* 95.2%

       \[\leadsto \frac{\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)}{\color{blue}{\left(\pi \cdot 2\right) \cdot \log \alpha}} \]

    3. *-commutative 95.2%

       \[\leadsto \frac{\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)}{\color{blue}{\left(2 \cdot \pi\right)} \cdot \log \alpha} \]

11. Simplified 95.2%

    \[\leadsto \color{blue}{\frac{\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)}{\left(2 \cdot \pi\right) \cdot \log \alpha}} \]

12. Final simplification 95.2%

    \[\leadsto \frac{\left(\alpha + 1\right) \cdot \left(\alpha + -1\right)}{\left(\pi \cdot 2\right) \cdot \log \alpha} \]

Alternative 7: 66.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{-0.5}{\pi \cdot \log \alpha} \end{array} \]

FPCore

(FPCore (cosTheta alpha) :precision binary32 (/ -0.5 (* PI (log alpha))))

C

float code(float cosTheta, float alpha) {
	return -0.5f / (((float) M_PI) * logf(alpha));
}

Julia

function code(cosTheta, alpha)
	return Float32(Float32(-0.5) / Float32(Float32(pi) * log(alpha)))
end

MATLAB

function tmp = code(cosTheta, alpha)
	tmp = single(-0.5) / (single(pi) * log(alpha));
end

Derivation

1. Initial program 98.5%

   \[\frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

2. Taylor expanded in alpha around 0 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-1 \cdot cosTheta\right)} \cdot cosTheta\right)} \]
3. Step-by-step derivation

   1. mul-1-neg 97.8%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

4. Simplified 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

5. Taylor expanded in cosTheta around 0 95.5%

   \[\leadsto \color{blue}{\frac{{\alpha}^{2} - 1}{\pi \cdot \log \left({\alpha}^{2}\right)}} \]
6. Step-by-step derivation

   1. log-pow 95.5%

      \[\leadsto \frac{{\alpha}^{2} - 1}{\pi \cdot \color{blue}{\left(2 \cdot \log \alpha\right)}} \]

   2. unpow2 95.5%

      \[\leadsto \frac{\color{blue}{\alpha \cdot \alpha} - 1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   3. fma-neg 95.5%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\alpha, \alpha, -1\right)}}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   4. metadata-eval 95.5%

      \[\leadsto \frac{\mathsf{fma}\left(\alpha, \alpha, \color{blue}{-1}\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

7. Simplified 95.5%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\alpha, \alpha, -1\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)}} \]

8. Taylor expanded in alpha around 0 68.2%

   \[\leadsto \color{blue}{\frac{-0.5}{\pi \cdot \log \alpha}} \]

9. Final simplification 68.2%

   \[\leadsto \frac{-0.5}{\pi \cdot \log \alpha} \]

Alternative 8: 66.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{-0.5}{\pi}}{\log \alpha} \end{array} \]

FPCore

(FPCore (cosTheta alpha) :precision binary32 (/ (/ -0.5 PI) (log alpha)))

C

float code(float cosTheta, float alpha) {
	return (-0.5f / ((float) M_PI)) / logf(alpha);
}

Julia

function code(cosTheta, alpha)
	return Float32(Float32(Float32(-0.5) / Float32(pi)) / log(alpha))
end

MATLAB

function tmp = code(cosTheta, alpha)
	tmp = (single(-0.5) / single(pi)) / log(alpha);
end

Derivation

1. Initial program 98.5%

   \[\frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \left(\left(\alpha \cdot \alpha - 1\right) \cdot cosTheta\right) \cdot cosTheta\right)} \]

2. Taylor expanded in alpha around 0 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-1 \cdot cosTheta\right)} \cdot cosTheta\right)} \]
3. Step-by-step derivation

   1. mul-1-neg 97.8%

      \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

4. Simplified 97.8%

   \[\leadsto \frac{\alpha \cdot \alpha - 1}{\left(\pi \cdot \log \left(\alpha \cdot \alpha\right)\right) \cdot \left(1 + \color{blue}{\left(-cosTheta\right)} \cdot cosTheta\right)} \]

5. Taylor expanded in cosTheta around 0 95.5%

   \[\leadsto \color{blue}{\frac{{\alpha}^{2} - 1}{\pi \cdot \log \left({\alpha}^{2}\right)}} \]
6. Step-by-step derivation

   1. log-pow 95.5%

      \[\leadsto \frac{{\alpha}^{2} - 1}{\pi \cdot \color{blue}{\left(2 \cdot \log \alpha\right)}} \]

   2. unpow2 95.5%

      \[\leadsto \frac{\color{blue}{\alpha \cdot \alpha} - 1}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   3. fma-neg 95.5%

      \[\leadsto \frac{\color{blue}{\mathsf{fma}\left(\alpha, \alpha, -1\right)}}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

   4. metadata-eval 95.5%

      \[\leadsto \frac{\mathsf{fma}\left(\alpha, \alpha, \color{blue}{-1}\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)} \]

7. Simplified 95.5%

   \[\leadsto \color{blue}{\frac{\mathsf{fma}\left(\alpha, \alpha, -1\right)}{\pi \cdot \left(2 \cdot \log \alpha\right)}} \]

8. Taylor expanded in alpha around 0 68.2%

   \[\leadsto \color{blue}{\frac{-0.5}{\pi \cdot \log \alpha}} \]
9. Step-by-step derivation

   1. associate-/r* 68.2%

      \[\leadsto \color{blue}{\frac{\frac{-0.5}{\pi}}{\log \alpha}} \]

10. Simplified 68.2%

    \[\leadsto \color{blue}{\frac{\frac{-0.5}{\pi}}{\log \alpha}} \]

11. Final simplification 68.2%

    \[\leadsto \frac{\frac{-0.5}{\pi}}{\log \alpha} \]

Reproduce

herbie shell --seed 2023307 
(FPCore (cosTheta alpha)
  :name "GTR1 distribution"
  :precision binary32
  :pre (and (and (<= 0.0 cosTheta) (<= cosTheta 1.0)) (and (<= 0.0001 alpha) (<= alpha 1.0)))
  (/ (- (* alpha alpha) 1.0) (* (* PI (log (* alpha alpha))) (+ 1.0 (* (* (- (* alpha alpha) 1.0) cosTheta) cosTheta)))))