Beckmann Distribution sample, tan2theta, alphax == alphay

Percentage Accurate: 56.4% → 98.9%

Time: 6.9s

Alternatives: 8

Speedup: 21.6×

Specification

\[\left(0.0001 \leq \alpha \land \alpha \leq 1\right) \land \left(2.328306437 \cdot 10^{-10} \leq u0 \land u0 \leq 1\right)\]

Math

\[\begin{array}{l} \\ \left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (* (* (- alpha) alpha) (log (- 1.0 u0))))

C

float code(float alpha, float u0) {
	return (-alpha * alpha) * logf((1.0f - u0));
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = (-alpha * alpha) * log((1.0e0 - u0))
end function

Julia

function code(alpha, u0)
	return Float32(Float32(Float32(-alpha) * alpha) * log(Float32(Float32(1.0) - u0)))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = (-alpha * alpha) * log((single(1.0) - u0));
end

Initial Program: 56.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (* (* (- alpha) alpha) (log (- 1.0 u0))))

C

float code(float alpha, float u0) {
	return (-alpha * alpha) * logf((1.0f - u0));
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = (-alpha * alpha) * log((1.0e0 - u0))
end function

Julia

function code(alpha, u0)
	return Float32(Float32(Float32(-alpha) * alpha) * log(Float32(Float32(1.0) - u0)))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = (-alpha * alpha) * log((single(1.0) - u0));
end

Alternative 1: 98.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \alpha \cdot \left(\alpha \cdot \left(-\mathsf{log1p}\left(-u0\right)\right)\right) \end{array} \]

FPCore

(FPCore (alpha u0) :precision binary32 (* alpha (* alpha (- (log1p (- u0))))))

C

float code(float alpha, float u0) {
	return alpha * (alpha * -log1pf(-u0));
}

Julia

function code(alpha, u0)
	return Float32(alpha * Float32(alpha * Float32(-log1p(Float32(-u0)))))
end

Derivation

1. Initial program 58.1%

   \[\left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \]
2. Step-by-step derivation

   1. associate-*l* 58.1%

      \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \log \left(1 - u0\right)\right)} \]

   2. sub-neg 58.1%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \log \color{blue}{\left(1 + \left(-u0\right)\right)}\right) \]

   3. log1p-def 99.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\mathsf{log1p}\left(-u0\right)}\right) \]

3. Simplified 99.0%

   \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \mathsf{log1p}\left(-u0\right)\right)} \]

4. Final simplification 99.0%

   \[\leadsto \alpha \cdot \left(\alpha \cdot \left(-\mathsf{log1p}\left(-u0\right)\right)\right) \]

Alternative 2: 91.2% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(u0 \cdot u0\right) \cdot \left(0.5 + u0 \cdot 0.3333333333333333\right)\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (* (* alpha alpha) (+ u0 (* (* u0 u0) (+ 0.5 (* u0 0.3333333333333333))))))

C

float code(float alpha, float u0) {
	return (alpha * alpha) * (u0 + ((u0 * u0) * (0.5f + (u0 * 0.3333333333333333f))));
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = (alpha * alpha) * (u0 + ((u0 * u0) * (0.5e0 + (u0 * 0.3333333333333333e0))))
end function

Julia

function code(alpha, u0)
	return Float32(Float32(alpha * alpha) * Float32(u0 + Float32(Float32(u0 * u0) * Float32(Float32(0.5) + Float32(u0 * Float32(0.3333333333333333))))))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = (alpha * alpha) * (u0 + ((u0 * u0) * (single(0.5) + (u0 * single(0.3333333333333333)))));
end

Derivation

1. Initial program 58.1%

   \[\left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \]
2. Step-by-step derivation

   1. associate-*l* 58.1%

      \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \log \left(1 - u0\right)\right)} \]

   2. sub-neg 58.1%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \log \color{blue}{\left(1 + \left(-u0\right)\right)}\right) \]

   3. log1p-def 99.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\mathsf{log1p}\left(-u0\right)}\right) \]

3. Simplified 99.0%

   \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \mathsf{log1p}\left(-u0\right)\right)} \]

4. Taylor expanded in u0 around 0 90.4%

   \[\leadsto \color{blue}{u0 \cdot {\alpha}^{2} + \left(0.3333333333333333 \cdot \left({u0}^{3} \cdot {\alpha}^{2}\right) + 0.5 \cdot \left({u0}^{2} \cdot {\alpha}^{2}\right)\right)} \]
5. Step-by-step derivation

   1. *-commutative 90.4%

      \[\leadsto \color{blue}{{\alpha}^{2} \cdot u0} + \left(0.3333333333333333 \cdot \left({u0}^{3} \cdot {\alpha}^{2}\right) + 0.5 \cdot \left({u0}^{2} \cdot {\alpha}^{2}\right)\right) \]

   2. +-commutative 90.4%

      \[\leadsto {\alpha}^{2} \cdot u0 + \color{blue}{\left(0.5 \cdot \left({u0}^{2} \cdot {\alpha}^{2}\right) + 0.3333333333333333 \cdot \left({u0}^{3} \cdot {\alpha}^{2}\right)\right)} \]

   3. associate-*r* 90.4%

      \[\leadsto {\alpha}^{2} \cdot u0 + \left(\color{blue}{\left(0.5 \cdot {u0}^{2}\right) \cdot {\alpha}^{2}} + 0.3333333333333333 \cdot \left({u0}^{3} \cdot {\alpha}^{2}\right)\right) \]

   4. associate-*r* 90.4%

      \[\leadsto {\alpha}^{2} \cdot u0 + \left(\left(0.5 \cdot {u0}^{2}\right) \cdot {\alpha}^{2} + \color{blue}{\left(0.3333333333333333 \cdot {u0}^{3}\right) \cdot {\alpha}^{2}}\right) \]

   5. distribute-rgt-out 90.4%

      \[\leadsto {\alpha}^{2} \cdot u0 + \color{blue}{{\alpha}^{2} \cdot \left(0.5 \cdot {u0}^{2} + 0.3333333333333333 \cdot {u0}^{3}\right)} \]

   6. distribute-lft-out 90.3%

      \[\leadsto \color{blue}{{\alpha}^{2} \cdot \left(u0 + \left(0.5 \cdot {u0}^{2} + 0.3333333333333333 \cdot {u0}^{3}\right)\right)} \]

   7. unpow2 90.3%

      \[\leadsto \color{blue}{\left(\alpha \cdot \alpha\right)} \cdot \left(u0 + \left(0.5 \cdot {u0}^{2} + 0.3333333333333333 \cdot {u0}^{3}\right)\right) \]

   8. cube-mult 90.3%

      \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(0.5 \cdot {u0}^{2} + 0.3333333333333333 \cdot \color{blue}{\left(u0 \cdot \left(u0 \cdot u0\right)\right)}\right)\right) \]

   9. unpow2 90.3%

      \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(0.5 \cdot {u0}^{2} + 0.3333333333333333 \cdot \left(u0 \cdot \color{blue}{{u0}^{2}}\right)\right)\right) \]

   10. associate-*r* 90.3%

       \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(0.5 \cdot {u0}^{2} + \color{blue}{\left(0.3333333333333333 \cdot u0\right) \cdot {u0}^{2}}\right)\right) \]

   11. distribute-rgt-out 90.3%

       \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \color{blue}{{u0}^{2} \cdot \left(0.5 + 0.3333333333333333 \cdot u0\right)}\right) \]

   12. unpow2 90.3%

       \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \color{blue}{\left(u0 \cdot u0\right)} \cdot \left(0.5 + 0.3333333333333333 \cdot u0\right)\right) \]

6. Simplified 90.3%

   \[\leadsto \color{blue}{\left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(u0 \cdot u0\right) \cdot \left(0.5 + 0.3333333333333333 \cdot u0\right)\right)} \]

7. Final simplification 90.3%

   \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(u0 \cdot u0\right) \cdot \left(0.5 + u0 \cdot 0.3333333333333333\right)\right) \]

Alternative 3: 91.2% accurate, 7.2× speedup

Math

\[\begin{array}{l} \\ \alpha \cdot \left(\alpha \cdot \left(u0 - u0 \cdot \left(u0 \cdot \left(u0 \cdot -0.3333333333333333 + -0.5\right)\right)\right)\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (* alpha (* alpha (- u0 (* u0 (* u0 (+ (* u0 -0.3333333333333333) -0.5)))))))

C

float code(float alpha, float u0) {
	return alpha * (alpha * (u0 - (u0 * (u0 * ((u0 * -0.3333333333333333f) + -0.5f)))));
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = alpha * (alpha * (u0 - (u0 * (u0 * ((u0 * (-0.3333333333333333e0)) + (-0.5e0))))))
end function

Julia

function code(alpha, u0)
	return Float32(alpha * Float32(alpha * Float32(u0 - Float32(u0 * Float32(u0 * Float32(Float32(u0 * Float32(-0.3333333333333333)) + Float32(-0.5)))))))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = alpha * (alpha * (u0 - (u0 * (u0 * ((u0 * single(-0.3333333333333333)) + single(-0.5))))));
end

Derivation

1. Initial program 58.1%

   \[\left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \]
2. Step-by-step derivation

   1. associate-*l* 58.1%

      \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \log \left(1 - u0\right)\right)} \]

   2. sub-neg 58.1%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \log \color{blue}{\left(1 + \left(-u0\right)\right)}\right) \]

   3. log1p-def 99.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\mathsf{log1p}\left(-u0\right)}\right) \]

3. Simplified 99.0%

   \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \mathsf{log1p}\left(-u0\right)\right)} \]

4. Taylor expanded in u0 around 0 90.3%

   \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(-0.3333333333333333 \cdot \left({u0}^{3} \cdot \alpha\right) + \left(-1 \cdot \left(u0 \cdot \alpha\right) + -0.5 \cdot \left({u0}^{2} \cdot \alpha\right)\right)\right)} \]
5. Step-by-step derivation

   1. associate-+r+ 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(\left(-0.3333333333333333 \cdot \left({u0}^{3} \cdot \alpha\right) + -1 \cdot \left(u0 \cdot \alpha\right)\right) + -0.5 \cdot \left({u0}^{2} \cdot \alpha\right)\right)} \]

   2. +-commutative 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(-0.5 \cdot \left({u0}^{2} \cdot \alpha\right) + \left(-0.3333333333333333 \cdot \left({u0}^{3} \cdot \alpha\right) + -1 \cdot \left(u0 \cdot \alpha\right)\right)\right)} \]

   3. associate-*r* 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \left(\color{blue}{\left(-0.5 \cdot {u0}^{2}\right) \cdot \alpha} + \left(-0.3333333333333333 \cdot \left({u0}^{3} \cdot \alpha\right) + -1 \cdot \left(u0 \cdot \alpha\right)\right)\right) \]

   4. *-commutative 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \left(\color{blue}{\alpha \cdot \left(-0.5 \cdot {u0}^{2}\right)} + \left(-0.3333333333333333 \cdot \left({u0}^{3} \cdot \alpha\right) + -1 \cdot \left(u0 \cdot \alpha\right)\right)\right) \]

   5. associate-*r* 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(-0.5 \cdot {u0}^{2}\right) + \left(\color{blue}{\left(-0.3333333333333333 \cdot {u0}^{3}\right) \cdot \alpha} + -1 \cdot \left(u0 \cdot \alpha\right)\right)\right) \]

   6. associate-*r* 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(-0.5 \cdot {u0}^{2}\right) + \left(\left(-0.3333333333333333 \cdot {u0}^{3}\right) \cdot \alpha + \color{blue}{\left(-1 \cdot u0\right) \cdot \alpha}\right)\right) \]

   7. distribute-rgt-out 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(-0.5 \cdot {u0}^{2}\right) + \color{blue}{\alpha \cdot \left(-0.3333333333333333 \cdot {u0}^{3} + -1 \cdot u0\right)}\right) \]

   8. distribute-lft-out 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(\alpha \cdot \left(-0.5 \cdot {u0}^{2} + \left(-0.3333333333333333 \cdot {u0}^{3} + -1 \cdot u0\right)\right)\right)} \]

   9. associate-+l+ 90.3%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\left(\left(-0.5 \cdot {u0}^{2} + -0.3333333333333333 \cdot {u0}^{3}\right) + -1 \cdot u0\right)}\right) \]

   10. mul-1-neg 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\left(-0.5 \cdot {u0}^{2} + -0.3333333333333333 \cdot {u0}^{3}\right) + \color{blue}{\left(-u0\right)}\right)\right) \]

   11. unsub-neg 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\left(\left(-0.5 \cdot {u0}^{2} + -0.3333333333333333 \cdot {u0}^{3}\right) - u0\right)}\right) \]

   12. *-commutative 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\left(\color{blue}{{u0}^{2} \cdot -0.5} + -0.3333333333333333 \cdot {u0}^{3}\right) - u0\right)\right) \]

   13. *-commutative 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\left({u0}^{2} \cdot -0.5 + \color{blue}{{u0}^{3} \cdot -0.3333333333333333}\right) - u0\right)\right) \]

   14. unpow3 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\left({u0}^{2} \cdot -0.5 + \color{blue}{\left(\left(u0 \cdot u0\right) \cdot u0\right)} \cdot -0.3333333333333333\right) - u0\right)\right) \]

   15. unpow2 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\left({u0}^{2} \cdot -0.5 + \left(\color{blue}{{u0}^{2}} \cdot u0\right) \cdot -0.3333333333333333\right) - u0\right)\right) \]

   16. associate-*l* 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\left({u0}^{2} \cdot -0.5 + \color{blue}{{u0}^{2} \cdot \left(u0 \cdot -0.3333333333333333\right)}\right) - u0\right)\right) \]

   17. distribute-lft-out 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\color{blue}{{u0}^{2} \cdot \left(-0.5 + u0 \cdot -0.3333333333333333\right)} - u0\right)\right) \]

   18. unpow2 90.3%

       \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\color{blue}{\left(u0 \cdot u0\right)} \cdot \left(-0.5 + u0 \cdot -0.3333333333333333\right) - u0\right)\right) \]

6. Simplified 90.3%

   \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(\alpha \cdot \left(\left(u0 \cdot u0\right) \cdot \left(-0.5 + u0 \cdot -0.3333333333333333\right) - u0\right)\right)} \]
7. Step-by-step derivation

   1. distribute-lft-neg-out 90.3%

      \[\leadsto \color{blue}{-\alpha \cdot \left(\alpha \cdot \left(\left(u0 \cdot u0\right) \cdot \left(-0.5 + u0 \cdot -0.3333333333333333\right) - u0\right)\right)} \]

   2. associate-*l* 90.4%

      \[\leadsto -\alpha \cdot \left(\alpha \cdot \left(\color{blue}{u0 \cdot \left(u0 \cdot \left(-0.5 + u0 \cdot -0.3333333333333333\right)\right)} - u0\right)\right) \]

   3. +-commutative 90.4%

      \[\leadsto -\alpha \cdot \left(\alpha \cdot \left(u0 \cdot \left(u0 \cdot \color{blue}{\left(u0 \cdot -0.3333333333333333 + -0.5\right)}\right) - u0\right)\right) \]

8. Applied egg-rr 90.4%

   \[\leadsto \color{blue}{-\alpha \cdot \left(\alpha \cdot \left(u0 \cdot \left(u0 \cdot \left(u0 \cdot -0.3333333333333333 + -0.5\right)\right) - u0\right)\right)} \]

9. Final simplification 90.4%

   \[\leadsto \alpha \cdot \left(\alpha \cdot \left(u0 - u0 \cdot \left(u0 \cdot \left(u0 \cdot -0.3333333333333333 + -0.5\right)\right)\right)\right) \]

Alternative 4: 86.9% accurate, 9.8× speedup

Math

\[\begin{array}{l} \\ \left(\alpha \cdot \alpha\right) \cdot \left(u0 \cdot \left(u0 \cdot 0.5 + 1\right)\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (* (* alpha alpha) (* u0 (+ (* u0 0.5) 1.0))))

C

float code(float alpha, float u0) {
	return (alpha * alpha) * (u0 * ((u0 * 0.5f) + 1.0f));
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = (alpha * alpha) * (u0 * ((u0 * 0.5e0) + 1.0e0))
end function

Julia

function code(alpha, u0)
	return Float32(Float32(alpha * alpha) * Float32(u0 * Float32(Float32(u0 * Float32(0.5)) + Float32(1.0))))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = (alpha * alpha) * (u0 * ((u0 * single(0.5)) + single(1.0)));
end

Derivation

1. Initial program 58.1%

   \[\left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \]
2. Step-by-step derivation

   1. associate-*l* 58.1%

      \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \log \left(1 - u0\right)\right)} \]

   2. sub-neg 58.1%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \log \color{blue}{\left(1 + \left(-u0\right)\right)}\right) \]

   3. log1p-def 99.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\mathsf{log1p}\left(-u0\right)}\right) \]

3. Simplified 99.0%

   \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \mathsf{log1p}\left(-u0\right)\right)} \]

4. Taylor expanded in u0 around 0 86.0%

   \[\leadsto \color{blue}{u0 \cdot {\alpha}^{2} + 0.5 \cdot \left({u0}^{2} \cdot {\alpha}^{2}\right)} \]
5. Step-by-step derivation

   1. associate-*r* 86.0%

      \[\leadsto u0 \cdot {\alpha}^{2} + \color{blue}{\left(0.5 \cdot {u0}^{2}\right) \cdot {\alpha}^{2}} \]

   2. distribute-rgt-out 85.9%

      \[\leadsto \color{blue}{{\alpha}^{2} \cdot \left(u0 + 0.5 \cdot {u0}^{2}\right)} \]

   3. unpow2 85.9%

      \[\leadsto \color{blue}{\left(\alpha \cdot \alpha\right)} \cdot \left(u0 + 0.5 \cdot {u0}^{2}\right) \]

   4. unpow2 85.9%

      \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + 0.5 \cdot \color{blue}{\left(u0 \cdot u0\right)}\right) \]

   5. associate-*r* 85.9%

      \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \color{blue}{\left(0.5 \cdot u0\right) \cdot u0}\right) \]

   6. distribute-rgt1-in 85.7%

      \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \color{blue}{\left(\left(0.5 \cdot u0 + 1\right) \cdot u0\right)} \]

6. Simplified 85.7%

   \[\leadsto \color{blue}{\left(\alpha \cdot \alpha\right) \cdot \left(\left(0.5 \cdot u0 + 1\right) \cdot u0\right)} \]

7. Final simplification 85.7%

   \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 \cdot \left(u0 \cdot 0.5 + 1\right)\right) \]

Alternative 5: 87.1% accurate, 9.8× speedup

Math

\[\begin{array}{l} \\ \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(u0 \cdot u0\right) \cdot 0.5\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (* (* alpha alpha) (+ u0 (* (* u0 u0) 0.5))))

C

float code(float alpha, float u0) {
	return (alpha * alpha) * (u0 + ((u0 * u0) * 0.5f));
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = (alpha * alpha) * (u0 + ((u0 * u0) * 0.5e0))
end function

Julia

function code(alpha, u0)
	return Float32(Float32(alpha * alpha) * Float32(u0 + Float32(Float32(u0 * u0) * Float32(0.5))))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = (alpha * alpha) * (u0 + ((u0 * u0) * single(0.5)));
end

Derivation

1. Initial program 58.1%

   \[\left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \]
2. Step-by-step derivation

   1. associate-*l* 58.1%

      \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \log \left(1 - u0\right)\right)} \]

   2. sub-neg 58.1%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \log \color{blue}{\left(1 + \left(-u0\right)\right)}\right) \]

   3. log1p-def 99.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\mathsf{log1p}\left(-u0\right)}\right) \]

3. Simplified 99.0%

   \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \mathsf{log1p}\left(-u0\right)\right)} \]

4. Taylor expanded in u0 around 0 86.0%

   \[\leadsto \color{blue}{u0 \cdot {\alpha}^{2} + 0.5 \cdot \left({u0}^{2} \cdot {\alpha}^{2}\right)} \]
5. Step-by-step derivation

   1. associate-*r* 86.0%

      \[\leadsto u0 \cdot {\alpha}^{2} + \color{blue}{\left(0.5 \cdot {u0}^{2}\right) \cdot {\alpha}^{2}} \]

   2. distribute-rgt-out 85.9%

      \[\leadsto \color{blue}{{\alpha}^{2} \cdot \left(u0 + 0.5 \cdot {u0}^{2}\right)} \]

   3. unpow2 85.9%

      \[\leadsto \color{blue}{\left(\alpha \cdot \alpha\right)} \cdot \left(u0 + 0.5 \cdot {u0}^{2}\right) \]

   4. *-commutative 85.9%

      \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \color{blue}{{u0}^{2} \cdot 0.5}\right) \]

   5. unpow2 85.9%

      \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \color{blue}{\left(u0 \cdot u0\right)} \cdot 0.5\right) \]

6. Simplified 85.9%

   \[\leadsto \color{blue}{\left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(u0 \cdot u0\right) \cdot 0.5\right)} \]

7. Final simplification 85.9%

   \[\leadsto \left(\alpha \cdot \alpha\right) \cdot \left(u0 + \left(u0 \cdot u0\right) \cdot 0.5\right) \]

Alternative 6: 87.1% accurate, 9.8× speedup

Math

\[\begin{array}{l} \\ \alpha \cdot \left(\alpha \cdot \left(u0 - u0 \cdot \left(u0 \cdot -0.5\right)\right)\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (* alpha (* alpha (- u0 (* u0 (* u0 -0.5))))))

C

float code(float alpha, float u0) {
	return alpha * (alpha * (u0 - (u0 * (u0 * -0.5f))));
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = alpha * (alpha * (u0 - (u0 * (u0 * (-0.5e0)))))
end function

Julia

function code(alpha, u0)
	return Float32(alpha * Float32(alpha * Float32(u0 - Float32(u0 * Float32(u0 * Float32(-0.5))))))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = alpha * (alpha * (u0 - (u0 * (u0 * single(-0.5)))));
end

Derivation

1. Initial program 58.1%

   \[\left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \]
2. Step-by-step derivation

   1. associate-*l* 58.1%

      \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \log \left(1 - u0\right)\right)} \]

   2. sub-neg 58.1%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \log \color{blue}{\left(1 + \left(-u0\right)\right)}\right) \]

   3. log1p-def 99.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\mathsf{log1p}\left(-u0\right)}\right) \]

3. Simplified 99.0%

   \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \mathsf{log1p}\left(-u0\right)\right)} \]

4. Taylor expanded in u0 around 0 86.0%

   \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(-1 \cdot \left(u0 \cdot \alpha\right) + -0.5 \cdot \left({u0}^{2} \cdot \alpha\right)\right)} \]
5. Step-by-step derivation

   1. +-commutative 86.0%

      \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(-0.5 \cdot \left({u0}^{2} \cdot \alpha\right) + -1 \cdot \left(u0 \cdot \alpha\right)\right)} \]

   2. mul-1-neg 86.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(-0.5 \cdot \left({u0}^{2} \cdot \alpha\right) + \color{blue}{\left(-u0 \cdot \alpha\right)}\right) \]

   3. unsub-neg 86.0%

      \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(-0.5 \cdot \left({u0}^{2} \cdot \alpha\right) - u0 \cdot \alpha\right)} \]

   4. associate-*r* 86.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\color{blue}{\left(-0.5 \cdot {u0}^{2}\right) \cdot \alpha} - u0 \cdot \alpha\right) \]

   5. distribute-rgt-out-- 86.0%

      \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(\alpha \cdot \left(-0.5 \cdot {u0}^{2} - u0\right)\right)} \]

   6. *-commutative 86.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\color{blue}{{u0}^{2} \cdot -0.5} - u0\right)\right) \]

   7. unpow2 86.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\color{blue}{\left(u0 \cdot u0\right)} \cdot -0.5 - u0\right)\right) \]

   8. associate-*l* 86.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \left(\color{blue}{u0 \cdot \left(u0 \cdot -0.5\right)} - u0\right)\right) \]

6. Simplified 86.0%

   \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(\alpha \cdot \left(u0 \cdot \left(u0 \cdot -0.5\right) - u0\right)\right)} \]

7. Final simplification 86.0%

   \[\leadsto \alpha \cdot \left(\alpha \cdot \left(u0 - u0 \cdot \left(u0 \cdot -0.5\right)\right)\right) \]

Alternative 7: 74.2% accurate, 21.6× speedup

Math

\[\begin{array}{l} \\ u0 \cdot \left(\alpha \cdot \alpha\right) \end{array} \]

FPCore

(FPCore (alpha u0) :precision binary32 (* u0 (* alpha alpha)))

C

float code(float alpha, float u0) {
	return u0 * (alpha * alpha);
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = u0 * (alpha * alpha)
end function

Julia

function code(alpha, u0)
	return Float32(u0 * Float32(alpha * alpha))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = u0 * (alpha * alpha);
end

Derivation

1. Initial program 58.1%

   \[\left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \]
2. Step-by-step derivation

   1. associate-*l* 58.1%

      \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \log \left(1 - u0\right)\right)} \]

   2. sub-neg 58.1%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \log \color{blue}{\left(1 + \left(-u0\right)\right)}\right) \]

   3. log1p-def 99.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\mathsf{log1p}\left(-u0\right)}\right) \]

3. Simplified 99.0%

   \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \mathsf{log1p}\left(-u0\right)\right)} \]

4. Taylor expanded in u0 around 0 72.9%

   \[\leadsto \color{blue}{u0 \cdot {\alpha}^{2}} \]
5. Step-by-step derivation

   1. unpow2 72.9%

      \[\leadsto u0 \cdot \color{blue}{\left(\alpha \cdot \alpha\right)} \]

6. Simplified 72.9%

   \[\leadsto \color{blue}{u0 \cdot \left(\alpha \cdot \alpha\right)} \]

7. Final simplification 72.9%

   \[\leadsto u0 \cdot \left(\alpha \cdot \alpha\right) \]

Alternative 8: 74.2% accurate, 21.6× speedup

Math

\[\begin{array}{l} \\ \alpha \cdot \left(\alpha \cdot u0\right) \end{array} \]

FPCore

(FPCore (alpha u0) :precision binary32 (* alpha (* alpha u0)))

C

float code(float alpha, float u0) {
	return alpha * (alpha * u0);
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = alpha * (alpha * u0)
end function

Julia

function code(alpha, u0)
	return Float32(alpha * Float32(alpha * u0))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = alpha * (alpha * u0);
end

Derivation

1. Initial program 58.1%

   \[\left(\left(-\alpha\right) \cdot \alpha\right) \cdot \log \left(1 - u0\right) \]
2. Step-by-step derivation

   1. associate-*l* 58.1%

      \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \log \left(1 - u0\right)\right)} \]

   2. sub-neg 58.1%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \log \color{blue}{\left(1 + \left(-u0\right)\right)}\right) \]

   3. log1p-def 99.0%

      \[\leadsto \left(-\alpha\right) \cdot \left(\alpha \cdot \color{blue}{\mathsf{log1p}\left(-u0\right)}\right) \]

3. Simplified 99.0%

   \[\leadsto \color{blue}{\left(-\alpha\right) \cdot \left(\alpha \cdot \mathsf{log1p}\left(-u0\right)\right)} \]

4. Taylor expanded in u0 around 0 72.9%

   \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(-1 \cdot \left(u0 \cdot \alpha\right)\right)} \]
5. Step-by-step derivation

   1. mul-1-neg 72.9%

      \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(-u0 \cdot \alpha\right)} \]

   2. distribute-rgt-neg-in 72.9%

      \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(u0 \cdot \left(-\alpha\right)\right)} \]

6. Simplified 72.9%

   \[\leadsto \left(-\alpha\right) \cdot \color{blue}{\left(u0 \cdot \left(-\alpha\right)\right)} \]

7. Final simplification 72.9%

   \[\leadsto \alpha \cdot \left(\alpha \cdot u0\right) \]

Reproduce

herbie shell --seed 2023194 
(FPCore (alpha u0)
  :name "Beckmann Distribution sample, tan2theta, alphax == alphay"
  :precision binary32
  :pre (and (and (<= 0.0001 alpha) (<= alpha 1.0)) (and (<= 2.328306437e-10 u0) (<= u0 1.0)))
  (* (* (- alpha) alpha) (log (- 1.0 u0))))