Beckmann Distribution sample, tan2theta, alphax == alphay

Percentage Accurate: 56.0% → 99.0%

Time: 10.4s

Alternatives: 10

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: 56.0% 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: 99.0% accurate, 1.0× speedup

Math

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

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(Float32(-alpha) * log1p(Float32(-u0))))
end

Derivation

1. Initial program 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Final simplification 99.1%

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

Alternative 2: 93.4% accurate, 5.7× speedup

Math

\[\begin{array}{l} \\ \alpha \cdot \left(u0 \cdot \left(\alpha + \left(0.5 + u0 \cdot \left(0.3333333333333333 + u0 \cdot 0.25\right)\right) \cdot \left(\alpha \cdot u0\right)\right)\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (*
  alpha
  (*
   u0
   (+
    alpha
    (* (+ 0.5 (* u0 (+ 0.3333333333333333 (* u0 0.25)))) (* alpha u0))))))

C

float code(float alpha, float u0) {
	return alpha * (u0 * (alpha + ((0.5f + (u0 * (0.3333333333333333f + (u0 * 0.25f)))) * (alpha * u0))));
}

Fortran

real(4) function code(alpha, u0)
    real(4), intent (in) :: alpha
    real(4), intent (in) :: u0
    code = alpha * (u0 * (alpha + ((0.5e0 + (u0 * (0.3333333333333333e0 + (u0 * 0.25e0)))) * (alpha * u0))))
end function

Julia

function code(alpha, u0)
	return Float32(alpha * Float32(u0 * Float32(alpha + Float32(Float32(Float32(0.5) + Float32(u0 * Float32(Float32(0.3333333333333333) + Float32(u0 * Float32(0.25))))) * Float32(alpha * u0)))))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = alpha * (u0 * (alpha + ((single(0.5) + (u0 * (single(0.3333333333333333) + (u0 * single(0.25))))) * (alpha * u0))));
end

Derivation

1. Initial program 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 91.9%

   \[\leadsto \alpha \cdot \color{blue}{\left(u0 \cdot \left(\alpha + u0 \cdot \left(0.5 \cdot \alpha + u0 \cdot \left(0.25 \cdot \left(\alpha \cdot u0\right) + 0.3333333333333333 \cdot \alpha\right)\right)\right)\right)} \]

6. Taylor expanded in alpha around 0 91.9%

   \[\leadsto \alpha \cdot \left(u0 \cdot \left(\alpha + \color{blue}{\alpha \cdot \left(u0 \cdot \left(0.5 + u0 \cdot \left(0.3333333333333333 + 0.25 \cdot u0\right)\right)\right)}\right)\right) \]
7. Step-by-step derivation

   1. associate-*r* 91.9%

      \[\leadsto \alpha \cdot \left(u0 \cdot \left(\alpha + \color{blue}{\left(\alpha \cdot u0\right) \cdot \left(0.5 + u0 \cdot \left(0.3333333333333333 + 0.25 \cdot u0\right)\right)}\right)\right) \]

   2. *-commutative 91.9%

      \[\leadsto \alpha \cdot \left(u0 \cdot \left(\alpha + \color{blue}{\left(u0 \cdot \alpha\right)} \cdot \left(0.5 + u0 \cdot \left(0.3333333333333333 + 0.25 \cdot u0\right)\right)\right)\right) \]

   3. *-commutative 91.9%

      \[\leadsto \alpha \cdot \left(u0 \cdot \left(\alpha + \left(u0 \cdot \alpha\right) \cdot \left(0.5 + u0 \cdot \left(0.3333333333333333 + \color{blue}{u0 \cdot 0.25}\right)\right)\right)\right) \]

8. Simplified 91.9%

   \[\leadsto \alpha \cdot \left(u0 \cdot \left(\alpha + \color{blue}{\left(u0 \cdot \alpha\right) \cdot \left(0.5 + u0 \cdot \left(0.3333333333333333 + u0 \cdot 0.25\right)\right)}\right)\right) \]

9. Final simplification 91.9%

   \[\leadsto \alpha \cdot \left(u0 \cdot \left(\alpha + \left(0.5 + u0 \cdot \left(0.3333333333333333 + u0 \cdot 0.25\right)\right) \cdot \left(\alpha \cdot u0\right)\right)\right) \]
10. Add Preprocessing

Alternative 3: 93.5% accurate, 5.7× speedup

Math

\[\begin{array}{l} \\ \alpha \cdot \left(\alpha \cdot \left(u0 + u0 \cdot \left(u0 \cdot \left(0.5 - u0 \cdot \left(u0 \cdot -0.25 - 0.3333333333333333\right)\right)\right)\right)\right) \end{array} \]

FPCore

(FPCore (alpha u0)
 :precision binary32
 (*
  alpha
  (*
   alpha
   (+ u0 (* u0 (* u0 (- 0.5 (* u0 (- (* u0 -0.25) 0.3333333333333333)))))))))

C

float code(float alpha, float u0) {
	return alpha * (alpha * (u0 + (u0 * (u0 * (0.5f - (u0 * ((u0 * -0.25f) - 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 * ((u0 * (-0.25e0)) - 0.3333333333333333e0)))))))
end function

Julia

function code(alpha, u0)
	return Float32(alpha * Float32(alpha * Float32(u0 + Float32(u0 * Float32(u0 * Float32(Float32(0.5) - Float32(u0 * Float32(Float32(u0 * Float32(-0.25)) - Float32(0.3333333333333333)))))))))
end

MATLAB

function tmp = code(alpha, u0)
	tmp = alpha * (alpha * (u0 + (u0 * (u0 * (single(0.5) - (u0 * ((u0 * single(-0.25)) - single(0.3333333333333333))))))));
end

Derivation

1. Initial program 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 91.7%

   \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(u0 \cdot \left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right) - 1\right)\right)}\right) \]
6. Step-by-step derivation

   1. sub-neg 91.7%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(u0 \cdot \color{blue}{\left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right) + \left(-1\right)\right)}\right)\right) \]

   2. distribute-rgt-in 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(\left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)}\right) \]

   3. fma-neg 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \color{blue}{\mathsf{fma}\left(u0, -0.25 \cdot u0 - 0.3333333333333333, -0.5\right)}\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   4. *-commutative 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \color{blue}{u0 \cdot -0.25} - 0.3333333333333333, -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   5. fma-neg 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \color{blue}{\mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right)}, -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   6. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, \color{blue}{-0.3333333333333333}\right), -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   7. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), \color{blue}{-0.5}\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   8. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), -0.5\right)\right) \cdot u0 + \color{blue}{-1} \cdot u0\right)\right) \]

7. Applied egg-rr 92.0%

   \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), -0.5\right)\right) \cdot u0 + -1 \cdot u0\right)}\right) \]

8. Taylor expanded in u0 around 0 92.0%

   \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\color{blue}{\left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right)\right)} \cdot u0 + -1 \cdot u0\right)\right) \]

9. Final simplification 92.0%

   \[\leadsto \alpha \cdot \left(\alpha \cdot \left(u0 + u0 \cdot \left(u0 \cdot \left(0.5 - u0 \cdot \left(u0 \cdot -0.25 - 0.3333333333333333\right)\right)\right)\right)\right) \]
10. Add Preprocessing

Alternative 4: 91.4% accurate, 7.2× speedup

Math

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

FPCore

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

C

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

Fortran

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

Julia

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

MATLAB

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

Derivation

1. Initial program 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 91.9%

   \[\leadsto \alpha \cdot \color{blue}{\left(u0 \cdot \left(\alpha + u0 \cdot \left(0.5 \cdot \alpha + u0 \cdot \left(0.25 \cdot \left(\alpha \cdot u0\right) + 0.3333333333333333 \cdot \alpha\right)\right)\right)\right)} \]

6. Taylor expanded in u0 around 0 90.1%

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

   1. +-commutative 90.1%

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

   2. *-commutative 90.1%

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

   3. *-commutative 90.1%

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

   4. associate-*r* 90.1%

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

   5. *-commutative 90.1%

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

   6. distribute-lft-out 90.1%

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

   7. *-commutative 90.1%

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

8. Simplified 90.1%

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

9. Final simplification 90.1%

   \[\leadsto \alpha \cdot \left(u0 \cdot \left(\alpha + u0 \cdot \left(\alpha \cdot \left(0.5 + u0 \cdot 0.3333333333333333\right)\right)\right)\right) \]
10. Add Preprocessing

Alternative 5: 91.4% accurate, 7.2× speedup

Math

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

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(alpha * Float32(alpha * Float32(u0 + Float32(u0 * Float32(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 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 91.7%

   \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(u0 \cdot \left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right) - 1\right)\right)}\right) \]
6. Step-by-step derivation

   1. sub-neg 91.7%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(u0 \cdot \color{blue}{\left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right) + \left(-1\right)\right)}\right)\right) \]

   2. distribute-rgt-in 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(\left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)}\right) \]

   3. fma-neg 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \color{blue}{\mathsf{fma}\left(u0, -0.25 \cdot u0 - 0.3333333333333333, -0.5\right)}\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   4. *-commutative 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \color{blue}{u0 \cdot -0.25} - 0.3333333333333333, -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   5. fma-neg 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \color{blue}{\mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right)}, -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   6. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, \color{blue}{-0.3333333333333333}\right), -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   7. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), \color{blue}{-0.5}\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   8. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), -0.5\right)\right) \cdot u0 + \color{blue}{-1} \cdot u0\right)\right) \]

7. Applied egg-rr 92.0%

   \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), -0.5\right)\right) \cdot u0 + -1 \cdot u0\right)}\right) \]

8. Taylor expanded in u0 around 0 90.2%

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

9. Final simplification 90.2%

   \[\leadsto \alpha \cdot \left(\alpha \cdot \left(u0 + u0 \cdot \left(u0 \cdot \left(0.5 - u0 \cdot -0.3333333333333333\right)\right)\right)\right) \]
10. Add Preprocessing

Alternative 6: 87.3% accurate, 8.3× speedup

Math

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

FPCore

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

C

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

Fortran

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

Julia

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

MATLAB

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

Derivation

1. Initial program 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 91.9%

   \[\leadsto \alpha \cdot \color{blue}{\left(u0 \cdot \left(\alpha + u0 \cdot \left(0.5 \cdot \alpha + u0 \cdot \left(0.25 \cdot \left(\alpha \cdot u0\right) + 0.3333333333333333 \cdot \alpha\right)\right)\right)\right)} \]

6. Taylor expanded in u0 around 0 86.2%

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

   1. associate-*r* 86.2%

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

   2. *-commutative 86.2%

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

8. Simplified 86.2%

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

   1. distribute-lft-in 86.3%

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

   2. *-commutative 86.3%

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

10. Applied egg-rr 86.3%

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

11. Final simplification 86.3%

    \[\leadsto \alpha \cdot \left(u0 \cdot \left(u0 \cdot \left(\alpha \cdot 0.5\right)\right) + \alpha \cdot u0\right) \]
12. Add Preprocessing

Alternative 7: 87.1% accurate, 9.8× speedup

Math

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

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(alpha * Float32(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 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 91.9%

   \[\leadsto \alpha \cdot \color{blue}{\left(u0 \cdot \left(\alpha + u0 \cdot \left(0.5 \cdot \alpha + u0 \cdot \left(0.25 \cdot \left(\alpha \cdot u0\right) + 0.3333333333333333 \cdot \alpha\right)\right)\right)\right)} \]

6. Taylor expanded in u0 around 0 86.2%

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

   1. associate-*r* 86.2%

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

   2. *-commutative 86.2%

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

8. Simplified 86.2%

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

9. Taylor expanded in alpha around 0 86.1%

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

10. Final simplification 86.1%

    \[\leadsto \alpha \cdot \left(\alpha \cdot \left(u0 \cdot \left(u0 \cdot 0.5 + 1\right)\right)\right) \]
11. Add Preprocessing

Alternative 8: 87.3% accurate, 9.8× speedup

Math

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

FPCore

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

C

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

Fortran

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

Julia

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

MATLAB

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

Derivation

1. Initial program 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 86.2%

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

6. Final simplification 86.2%

   \[\leadsto \alpha \cdot \left(u0 \cdot \left(\alpha + 0.5 \cdot \left(\alpha \cdot u0\right)\right)\right) \]
7. Add Preprocessing

Alternative 9: 87.3% 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 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 91.7%

   \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(u0 \cdot \left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right) - 1\right)\right)}\right) \]
6. Step-by-step derivation

   1. sub-neg 91.7%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(u0 \cdot \color{blue}{\left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right) + \left(-1\right)\right)}\right)\right) \]

   2. distribute-rgt-in 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(\left(u0 \cdot \left(u0 \cdot \left(-0.25 \cdot u0 - 0.3333333333333333\right) - 0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)}\right) \]

   3. fma-neg 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \color{blue}{\mathsf{fma}\left(u0, -0.25 \cdot u0 - 0.3333333333333333, -0.5\right)}\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   4. *-commutative 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \color{blue}{u0 \cdot -0.25} - 0.3333333333333333, -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   5. fma-neg 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \color{blue}{\mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right)}, -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   6. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, \color{blue}{-0.3333333333333333}\right), -0.5\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   7. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), \color{blue}{-0.5}\right)\right) \cdot u0 + \left(-1\right) \cdot u0\right)\right) \]

   8. metadata-eval 92.0%

      \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), -0.5\right)\right) \cdot u0 + \color{blue}{-1} \cdot u0\right)\right) \]

7. Applied egg-rr 92.0%

   \[\leadsto \alpha \cdot \left(\left(-\alpha\right) \cdot \color{blue}{\left(\left(u0 \cdot \mathsf{fma}\left(u0, \mathsf{fma}\left(u0, -0.25, -0.3333333333333333\right), -0.5\right)\right) \cdot u0 + -1 \cdot u0\right)}\right) \]

8. Taylor expanded in u0 around 0 86.3%

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

9. Final simplification 86.3%

   \[\leadsto \alpha \cdot \left(\alpha \cdot \left(u0 - u0 \cdot \left(u0 \cdot -0.5\right)\right)\right) \]
10. Add Preprocessing

Alternative 10: 74.5% 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 57.7%

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

   1. associate-*l* 57.8%

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

   2. distribute-lft-neg-out 57.8%

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

   3. distribute-rgt-neg-in 57.8%

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

   4. distribute-lft-neg-out 57.8%

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

   5. sub-neg 57.8%

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

   6. log1p-define 99.1%

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

3. Simplified 99.1%

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

5. Taylor expanded in u0 around 0 73.2%

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

6. Final simplification 73.2%

   \[\leadsto \alpha \cdot \left(\alpha \cdot u0\right) \]
7. Add Preprocessing

Reproduce

herbie shell --seed 2024075 
(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))))