Trowbridge-Reitz Sample, near normal, slope_x

Percentage Accurate: 99.0% → 99.0%

Time: 10.1s

Alternatives: 9

Speedup: 1.0×

Specification

\[\left(\left(cosTheta\_i > 0.9999 \land cosTheta\_i \leq 1\right) \land \left(2.328306437 \cdot 10^{-10} \leq u1 \land u1 \leq 1\right)\right) \land \left(2.328306437 \cdot 10^{-10} \leq u2 \land u2 \leq 1\right)\]

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (* (sqrt (/ u1 (- 1.0 u1))) (cos (* 6.28318530718 u2))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1))) * cosf((6.28318530718f * u2));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1))) * cos((6.28318530718e0 * u2))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * cos(Float32(Float32(6.28318530718) * u2)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1))) * cos((single(6.28318530718) * u2));
end

Initial Program: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (* (sqrt (/ u1 (- 1.0 u1))) (cos (* 6.28318530718 u2))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1))) * cosf((6.28318530718f * u2));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1))) * cos((6.28318530718e0 * u2))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * cos(Float32(Float32(6.28318530718) * u2)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1))) * cos((single(6.28318530718) * u2));
end

Alternative 1: 99.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (* (sqrt (/ u1 (- 1.0 u1))) (cos (* 6.28318530718 u2))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1))) * cosf((6.28318530718f * u2));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1))) * cos((6.28318530718e0 * u2))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * cos(Float32(Float32(6.28318530718) * u2)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1))) * cos((single(6.28318530718) * u2));
end

Derivation

1. Initial program 98.9%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 2: 94.3% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \cos \left(6.28318530718 \cdot u2\right)\\ \mathbf{if}\;t\_0 \leq 0.9929999709129333:\\ \;\;\;\;t\_0 \cdot \sqrt{u1}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{\frac{u1}{1 - u1}} \cdot \left(1 + -19.739208802181317 \cdot \left(u2 \cdot u2\right)\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (let* ((t_0 (cos (* 6.28318530718 u2))))
   (if (<= t_0 0.9929999709129333)
     (* t_0 (sqrt u1))
     (* (sqrt (/ u1 (- 1.0 u1))) (+ 1.0 (* -19.739208802181317 (* u2 u2)))))))

C

float code(float cosTheta_i, float u1, float u2) {
	float t_0 = cosf((6.28318530718f * u2));
	float tmp;
	if (t_0 <= 0.9929999709129333f) {
		tmp = t_0 * sqrtf(u1);
	} else {
		tmp = sqrtf((u1 / (1.0f - u1))) * (1.0f + (-19.739208802181317f * (u2 * u2)));
	}
	return tmp;
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    real(4) :: t_0
    real(4) :: tmp
    t_0 = cos((6.28318530718e0 * u2))
    if (t_0 <= 0.9929999709129333e0) then
        tmp = t_0 * sqrt(u1)
    else
        tmp = sqrt((u1 / (1.0e0 - u1))) * (1.0e0 + ((-19.739208802181317e0) * (u2 * u2)))
    end if
    code = tmp
end function

Julia

function code(cosTheta_i, u1, u2)
	t_0 = cos(Float32(Float32(6.28318530718) * u2))
	tmp = Float32(0.0)
	if (t_0 <= Float32(0.9929999709129333))
		tmp = Float32(t_0 * sqrt(u1));
	else
		tmp = Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * Float32(Float32(1.0) + Float32(Float32(-19.739208802181317) * Float32(u2 * u2))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(cosTheta_i, u1, u2)
	t_0 = cos((single(6.28318530718) * u2));
	tmp = single(0.0);
	if (t_0 <= single(0.9929999709129333))
		tmp = t_0 * sqrt(u1);
	else
		tmp = sqrt((u1 / (single(1.0) - u1))) * (single(1.0) + (single(-19.739208802181317) * (u2 * u2)));
	end
	tmp_2 = tmp;
end

Derivation

1. Split input into 2 regimes

2. if (cos.f32 (*.f32 #s(literal 314159265359/50000000000 binary32) u2)) < 0.992999971

   1. Initial program 96.6%

      \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   2. Add Preprocessing

   3. Taylor expanded in u1 around 0 77.5%

      \[\leadsto \color{blue}{\sqrt{u1} \cdot \cos \left(6.28318530718 \cdot u2\right)} \]

   if 0.992999971 < (cos.f32 (*.f32 #s(literal 314159265359/50000000000 binary32) u2))

   1. Initial program 99.3%

      \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. clear-num 99.3%

         \[\leadsto \sqrt{\color{blue}{\frac{1}{\frac{1 - u1}{u1}}}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

      2. associate-/r/ 99.2%

         \[\leadsto \sqrt{\color{blue}{\frac{1}{1 - u1} \cdot u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

   4. Applied egg-rr 99.2%

      \[\leadsto \sqrt{\color{blue}{\frac{1}{1 - u1} \cdot u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

   5. Taylor expanded in u2 around 0 98.9%

      \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right)} \]
   6. Step-by-step derivation

      1. sub-neg 98.9%

         \[\leadsto \sqrt{\frac{u1}{\color{blue}{1 + \left(-u1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      2. lft-mult-inverse 98.8%

         \[\leadsto \sqrt{\frac{u1}{\color{blue}{\frac{1}{u1} \cdot u1} + \left(-u1\right)}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      3. mul-1-neg 98.8%

         \[\leadsto \sqrt{\frac{u1}{\frac{1}{u1} \cdot u1 + \color{blue}{-1 \cdot u1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      4. distribute-rgt-in 98.8%

         \[\leadsto \sqrt{\frac{u1}{\color{blue}{u1 \cdot \left(\frac{1}{u1} + -1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      5. associate-/r* 98.9%

         \[\leadsto \sqrt{\color{blue}{\frac{\frac{u1}{u1}}{\frac{1}{u1} + -1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      6. *-lft-identity 98.9%

         \[\leadsto \sqrt{\frac{\frac{\color{blue}{1 \cdot u1}}{u1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      7. associate-*l/ 98.9%

         \[\leadsto \sqrt{\frac{\color{blue}{\frac{1}{u1} \cdot u1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      8. lft-mult-inverse 98.9%

         \[\leadsto \sqrt{\frac{\color{blue}{1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      9. metadata-eval 98.9%

         \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} + \color{blue}{\left(-1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      10. sub-neg 98.9%

          \[\leadsto \sqrt{\frac{1}{\color{blue}{\frac{1}{u1} - 1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      11. *-commutative 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + -19.739208802181317 \cdot \color{blue}{\left({u2}^{2} \cdot \sqrt{\frac{u1}{1 - u1}}\right)} \]

      12. associate-*r* 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \color{blue}{\left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{1 - u1}}} \]

      13. sub-neg 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{1 + \left(-u1\right)}}} \]

      14. lft-mult-inverse 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{\frac{1}{u1} \cdot u1} + \left(-u1\right)}} \]

      15. mul-1-neg 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\frac{1}{u1} \cdot u1 + \color{blue}{-1 \cdot u1}}} \]

      16. distribute-rgt-in 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{u1 \cdot \left(\frac{1}{u1} + -1\right)}}} \]

      17. associate-/r* 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\color{blue}{\frac{\frac{u1}{u1}}{\frac{1}{u1} + -1}}} \]

      18. *-lft-identity 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{\frac{\color{blue}{1 \cdot u1}}{u1}}{\frac{1}{u1} + -1}} \]

      19. associate-*l/ 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{\color{blue}{\frac{1}{u1} \cdot u1}}{\frac{1}{u1} + -1}} \]

   7. Simplified 98.9%

      \[\leadsto \color{blue}{\left(-19.739208802181317 \cdot {u2}^{2} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}}} \]
   8. Step-by-step derivation

      1. unpow2 98.9%

         \[\leadsto \left(-19.739208802181317 \cdot \color{blue}{\left(u2 \cdot u2\right)} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}} \]

   9. Applied egg-rr 98.9%

      \[\leadsto \left(-19.739208802181317 \cdot \color{blue}{\left(u2 \cdot u2\right)} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 95.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\cos \left(6.28318530718 \cdot u2\right) \leq 0.9929999709129333:\\ \;\;\;\;\cos \left(6.28318530718 \cdot u2\right) \cdot \sqrt{u1}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{\frac{u1}{1 - u1}} \cdot \left(1 + -19.739208802181317 \cdot \left(u2 \cdot u2\right)\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 96.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;6.28318530718 \cdot u2 \leq 0.11599999666213989:\\ \;\;\;\;\sqrt{\frac{u1}{1 - u1}} \cdot \left(1 + -19.739208802181317 \cdot \left(u2 \cdot u2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\cos \left(6.28318530718 \cdot u2\right) \cdot \sqrt{u1 \cdot \left(u1 + 1\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (if (<= (* 6.28318530718 u2) 0.11599999666213989)
   (* (sqrt (/ u1 (- 1.0 u1))) (+ 1.0 (* -19.739208802181317 (* u2 u2))))
   (* (cos (* 6.28318530718 u2)) (sqrt (* u1 (+ u1 1.0))))))

C

float code(float cosTheta_i, float u1, float u2) {
	float tmp;
	if ((6.28318530718f * u2) <= 0.11599999666213989f) {
		tmp = sqrtf((u1 / (1.0f - u1))) * (1.0f + (-19.739208802181317f * (u2 * u2)));
	} else {
		tmp = cosf((6.28318530718f * u2)) * sqrtf((u1 * (u1 + 1.0f)));
	}
	return tmp;
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    real(4) :: tmp
    if ((6.28318530718e0 * u2) <= 0.11599999666213989e0) then
        tmp = sqrt((u1 / (1.0e0 - u1))) * (1.0e0 + ((-19.739208802181317e0) * (u2 * u2)))
    else
        tmp = cos((6.28318530718e0 * u2)) * sqrt((u1 * (u1 + 1.0e0)))
    end if
    code = tmp
end function

Julia

function code(cosTheta_i, u1, u2)
	tmp = Float32(0.0)
	if (Float32(Float32(6.28318530718) * u2) <= Float32(0.11599999666213989))
		tmp = Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * Float32(Float32(1.0) + Float32(Float32(-19.739208802181317) * Float32(u2 * u2))));
	else
		tmp = Float32(cos(Float32(Float32(6.28318530718) * u2)) * sqrt(Float32(u1 * Float32(u1 + Float32(1.0)))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(cosTheta_i, u1, u2)
	tmp = single(0.0);
	if ((single(6.28318530718) * u2) <= single(0.11599999666213989))
		tmp = sqrt((u1 / (single(1.0) - u1))) * (single(1.0) + (single(-19.739208802181317) * (u2 * u2)));
	else
		tmp = cos((single(6.28318530718) * u2)) * sqrt((u1 * (u1 + single(1.0))));
	end
	tmp_2 = tmp;
end

Derivation

1. Split input into 2 regimes

2. if (*.f32 #s(literal 314159265359/50000000000 binary32) u2) < 0.115999997

   1. Initial program 99.3%

      \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. clear-num 99.3%

         \[\leadsto \sqrt{\color{blue}{\frac{1}{\frac{1 - u1}{u1}}}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

      2. associate-/r/ 99.2%

         \[\leadsto \sqrt{\color{blue}{\frac{1}{1 - u1} \cdot u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

   4. Applied egg-rr 99.2%

      \[\leadsto \sqrt{\color{blue}{\frac{1}{1 - u1} \cdot u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

   5. Taylor expanded in u2 around 0 98.9%

      \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right)} \]
   6. Step-by-step derivation

      1. sub-neg 98.9%

         \[\leadsto \sqrt{\frac{u1}{\color{blue}{1 + \left(-u1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      2. lft-mult-inverse 98.8%

         \[\leadsto \sqrt{\frac{u1}{\color{blue}{\frac{1}{u1} \cdot u1} + \left(-u1\right)}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      3. mul-1-neg 98.8%

         \[\leadsto \sqrt{\frac{u1}{\frac{1}{u1} \cdot u1 + \color{blue}{-1 \cdot u1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      4. distribute-rgt-in 98.8%

         \[\leadsto \sqrt{\frac{u1}{\color{blue}{u1 \cdot \left(\frac{1}{u1} + -1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      5. associate-/r* 98.9%

         \[\leadsto \sqrt{\color{blue}{\frac{\frac{u1}{u1}}{\frac{1}{u1} + -1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      6. *-lft-identity 98.9%

         \[\leadsto \sqrt{\frac{\frac{\color{blue}{1 \cdot u1}}{u1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      7. associate-*l/ 98.9%

         \[\leadsto \sqrt{\frac{\color{blue}{\frac{1}{u1} \cdot u1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      8. lft-mult-inverse 98.9%

         \[\leadsto \sqrt{\frac{\color{blue}{1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      9. metadata-eval 98.9%

         \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} + \color{blue}{\left(-1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      10. sub-neg 98.9%

          \[\leadsto \sqrt{\frac{1}{\color{blue}{\frac{1}{u1} - 1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

      11. *-commutative 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + -19.739208802181317 \cdot \color{blue}{\left({u2}^{2} \cdot \sqrt{\frac{u1}{1 - u1}}\right)} \]

      12. associate-*r* 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \color{blue}{\left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{1 - u1}}} \]

      13. sub-neg 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{1 + \left(-u1\right)}}} \]

      14. lft-mult-inverse 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{\frac{1}{u1} \cdot u1} + \left(-u1\right)}} \]

      15. mul-1-neg 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\frac{1}{u1} \cdot u1 + \color{blue}{-1 \cdot u1}}} \]

      16. distribute-rgt-in 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{u1 \cdot \left(\frac{1}{u1} + -1\right)}}} \]

      17. associate-/r* 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\color{blue}{\frac{\frac{u1}{u1}}{\frac{1}{u1} + -1}}} \]

      18. *-lft-identity 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{\frac{\color{blue}{1 \cdot u1}}{u1}}{\frac{1}{u1} + -1}} \]

      19. associate-*l/ 98.9%

          \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{\color{blue}{\frac{1}{u1} \cdot u1}}{\frac{1}{u1} + -1}} \]

   7. Simplified 98.9%

      \[\leadsto \color{blue}{\left(-19.739208802181317 \cdot {u2}^{2} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}}} \]
   8. Step-by-step derivation

      1. unpow2 98.9%

         \[\leadsto \left(-19.739208802181317 \cdot \color{blue}{\left(u2 \cdot u2\right)} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}} \]

   9. Applied egg-rr 98.9%

      \[\leadsto \left(-19.739208802181317 \cdot \color{blue}{\left(u2 \cdot u2\right)} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}} \]

   if 0.115999997 < (*.f32 #s(literal 314159265359/50000000000 binary32) u2)

   1. Initial program 96.6%

      \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   2. Add Preprocessing

   3. Taylor expanded in u1 around 0 85.9%

      \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(1 + u1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
   4. Step-by-step derivation

      1. +-commutative 85.9%

         \[\leadsto \sqrt{u1 \cdot \color{blue}{\left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

   5. Simplified 85.9%

      \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
3. Recombined 2 regimes into one program.

4. Final simplification 96.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;6.28318530718 \cdot u2 \leq 0.11599999666213989:\\ \;\;\;\;\sqrt{\frac{u1}{1 - u1}} \cdot \left(1 + -19.739208802181317 \cdot \left(u2 \cdot u2\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\cos \left(6.28318530718 \cdot u2\right) \cdot \sqrt{u1 \cdot \left(u1 + 1\right)}\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 88.8% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \cdot \left(1 + -19.739208802181317 \cdot \left(u2 \cdot u2\right)\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2)
 :precision binary32
 (* (sqrt (/ u1 (- 1.0 u1))) (+ 1.0 (* -19.739208802181317 (* u2 u2)))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1))) * (1.0f + (-19.739208802181317f * (u2 * u2)));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1))) * (1.0e0 + ((-19.739208802181317e0) * (u2 * u2)))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(sqrt(Float32(u1 / Float32(Float32(1.0) - u1))) * Float32(Float32(1.0) + Float32(Float32(-19.739208802181317) * Float32(u2 * u2))))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1))) * (single(1.0) + (single(-19.739208802181317) * (u2 * u2)));
end

Derivation

1. Initial program 98.9%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. clear-num 98.9%

      \[\leadsto \sqrt{\color{blue}{\frac{1}{\frac{1 - u1}{u1}}}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

   2. associate-/r/ 98.8%

      \[\leadsto \sqrt{\color{blue}{\frac{1}{1 - u1} \cdot u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

4. Applied egg-rr 98.8%

   \[\leadsto \sqrt{\color{blue}{\frac{1}{1 - u1} \cdot u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

5. Taylor expanded in u2 around 0 90.4%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right)} \]
6. Step-by-step derivation

   1. sub-neg 90.4%

      \[\leadsto \sqrt{\frac{u1}{\color{blue}{1 + \left(-u1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   2. lft-mult-inverse 90.3%

      \[\leadsto \sqrt{\frac{u1}{\color{blue}{\frac{1}{u1} \cdot u1} + \left(-u1\right)}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   3. mul-1-neg 90.3%

      \[\leadsto \sqrt{\frac{u1}{\frac{1}{u1} \cdot u1 + \color{blue}{-1 \cdot u1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   4. distribute-rgt-in 90.3%

      \[\leadsto \sqrt{\frac{u1}{\color{blue}{u1 \cdot \left(\frac{1}{u1} + -1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   5. associate-/r* 90.4%

      \[\leadsto \sqrt{\color{blue}{\frac{\frac{u1}{u1}}{\frac{1}{u1} + -1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   6. *-lft-identity 90.4%

      \[\leadsto \sqrt{\frac{\frac{\color{blue}{1 \cdot u1}}{u1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   7. associate-*l/ 90.4%

      \[\leadsto \sqrt{\frac{\color{blue}{\frac{1}{u1} \cdot u1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   8. lft-mult-inverse 90.4%

      \[\leadsto \sqrt{\frac{\color{blue}{1}}{\frac{1}{u1} + -1}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   9. metadata-eval 90.4%

      \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} + \color{blue}{\left(-1\right)}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   10. sub-neg 90.4%

       \[\leadsto \sqrt{\frac{1}{\color{blue}{\frac{1}{u1} - 1}}} + -19.739208802181317 \cdot \left(\sqrt{\frac{u1}{1 - u1}} \cdot {u2}^{2}\right) \]

   11. *-commutative 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + -19.739208802181317 \cdot \color{blue}{\left({u2}^{2} \cdot \sqrt{\frac{u1}{1 - u1}}\right)} \]

   12. associate-*r* 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \color{blue}{\left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{1 - u1}}} \]

   13. sub-neg 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{1 + \left(-u1\right)}}} \]

   14. lft-mult-inverse 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{\frac{1}{u1} \cdot u1} + \left(-u1\right)}} \]

   15. mul-1-neg 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\frac{1}{u1} \cdot u1 + \color{blue}{-1 \cdot u1}}} \]

   16. distribute-rgt-in 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{u1}{\color{blue}{u1 \cdot \left(\frac{1}{u1} + -1\right)}}} \]

   17. associate-/r* 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\color{blue}{\frac{\frac{u1}{u1}}{\frac{1}{u1} + -1}}} \]

   18. *-lft-identity 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{\frac{\color{blue}{1 \cdot u1}}{u1}}{\frac{1}{u1} + -1}} \]

   19. associate-*l/ 90.4%

       \[\leadsto \sqrt{\frac{1}{\frac{1}{u1} - 1}} + \left(-19.739208802181317 \cdot {u2}^{2}\right) \cdot \sqrt{\frac{\color{blue}{\frac{1}{u1} \cdot u1}}{\frac{1}{u1} + -1}} \]

7. Simplified 90.4%

   \[\leadsto \color{blue}{\left(-19.739208802181317 \cdot {u2}^{2} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}}} \]
8. Step-by-step derivation

   1. unpow2 90.4%

      \[\leadsto \left(-19.739208802181317 \cdot \color{blue}{\left(u2 \cdot u2\right)} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}} \]

9. Applied egg-rr 90.4%

   \[\leadsto \left(-19.739208802181317 \cdot \color{blue}{\left(u2 \cdot u2\right)} + 1\right) \cdot \sqrt{\frac{u1}{1 - u1}} \]

10. Final simplification 90.4%

    \[\leadsto \sqrt{\frac{u1}{1 - u1}} \cdot \left(1 + -19.739208802181317 \cdot \left(u2 \cdot u2\right)\right) \]
11. Add Preprocessing

Alternative 5: 80.4% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\frac{u1}{1 - u1}} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (sqrt (/ u1 (- 1.0 u1))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 / (1.0f - u1)));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 / (1.0e0 - u1)))
end function

Julia

function code(cosTheta_i, u1, u2)
	return sqrt(Float32(u1 / Float32(Float32(1.0) - u1)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 / (single(1.0) - u1)));
end

Derivation

1. Initial program 98.9%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 81.7%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]
4. Add Preprocessing

Alternative 6: 72.0% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{u1 \cdot \left(u1 + 1\right)} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (sqrt (* u1 (+ u1 1.0))))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf((u1 * (u1 + 1.0f)));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt((u1 * (u1 + 1.0e0)))
end function

Julia

function code(cosTheta_i, u1, u2)
	return sqrt(Float32(u1 * Float32(u1 + Float32(1.0))))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt((u1 * (u1 + single(1.0))));
end

Derivation

1. Initial program 98.9%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 81.7%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

4. Taylor expanded in u1 around 0 74.5%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(1 + u1\right)}} \]
5. Step-by-step derivation

   1. +-commutative 88.3%

      \[\leadsto \sqrt{u1 \cdot \color{blue}{\left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

6. Simplified 74.5%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(u1 + 1\right)}} \]
7. Add Preprocessing

Alternative 7: 63.7% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \sqrt{u1} \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (sqrt u1))

C

float code(float cosTheta_i, float u1, float u2) {
	return sqrtf(u1);
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = sqrt(u1)
end function

Julia

function code(cosTheta_i, u1, u2)
	return sqrt(u1)
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = sqrt(u1);
end

Derivation

1. Initial program 98.9%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 81.7%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

4. Taylor expanded in u1 around 0 65.8%

   \[\leadsto \color{blue}{\sqrt{u1}} \]
5. Add Preprocessing

Alternative 8: 20.2% accurate, 29.9× speedup

Math

\[\begin{array}{l} \\ u1 \cdot \left(1 + \frac{0.5}{u1}\right) \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (* u1 (+ 1.0 (/ 0.5 u1))))

C

float code(float cosTheta_i, float u1, float u2) {
	return u1 * (1.0f + (0.5f / u1));
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = u1 * (1.0e0 + (0.5e0 / u1))
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(u1 * Float32(Float32(1.0) + Float32(Float32(0.5) / u1)))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = u1 * (single(1.0) + (single(0.5) / u1));
end

Derivation

1. Initial program 98.9%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 81.7%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

4. Taylor expanded in u1 around 0 74.5%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(1 + u1\right)}} \]
5. Step-by-step derivation

   1. +-commutative 88.3%

      \[\leadsto \sqrt{u1 \cdot \color{blue}{\left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

6. Simplified 74.5%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(u1 + 1\right)}} \]

7. Taylor expanded in u1 around inf 19.6%

   \[\leadsto \color{blue}{u1 \cdot \left(1 + 0.5 \cdot \frac{1}{u1}\right)} \]
8. Step-by-step derivation

   1. associate-*r/ 19.6%

      \[\leadsto u1 \cdot \left(1 + \color{blue}{\frac{0.5 \cdot 1}{u1}}\right) \]

   2. metadata-eval 19.6%

      \[\leadsto u1 \cdot \left(1 + \frac{\color{blue}{0.5}}{u1}\right) \]

9. Simplified 19.6%

   \[\leadsto \color{blue}{u1 \cdot \left(1 + \frac{0.5}{u1}\right)} \]
10. Add Preprocessing

Alternative 9: 20.2% accurate, 69.7× speedup

Math

\[\begin{array}{l} \\ u1 + 0.5 \end{array} \]

FPCore

(FPCore (cosTheta_i u1 u2) :precision binary32 (+ u1 0.5))

C

float code(float cosTheta_i, float u1, float u2) {
	return u1 + 0.5f;
}

Fortran

real(4) function code(costheta_i, u1, u2)
    real(4), intent (in) :: costheta_i
    real(4), intent (in) :: u1
    real(4), intent (in) :: u2
    code = u1 + 0.5e0
end function

Julia

function code(cosTheta_i, u1, u2)
	return Float32(u1 + Float32(0.5))
end

MATLAB

function tmp = code(cosTheta_i, u1, u2)
	tmp = u1 + single(0.5);
end

Derivation

1. Initial program 98.9%

   \[\sqrt{\frac{u1}{1 - u1}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]
2. Add Preprocessing

3. Taylor expanded in u2 around 0 81.7%

   \[\leadsto \color{blue}{\sqrt{\frac{u1}{1 - u1}}} \]

4. Taylor expanded in u1 around 0 74.5%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(1 + u1\right)}} \]
5. Step-by-step derivation

   1. +-commutative 88.3%

      \[\leadsto \sqrt{u1 \cdot \color{blue}{\left(u1 + 1\right)}} \cdot \cos \left(6.28318530718 \cdot u2\right) \]

6. Simplified 74.5%

   \[\leadsto \sqrt{\color{blue}{u1 \cdot \left(u1 + 1\right)}} \]

7. Taylor expanded in u1 around inf 19.6%

   \[\leadsto \color{blue}{u1 \cdot \left(1 + 0.5 \cdot \frac{1}{u1}\right)} \]
8. Step-by-step derivation

   1. distribute-rgt-in 19.6%

      \[\leadsto \color{blue}{1 \cdot u1 + \left(0.5 \cdot \frac{1}{u1}\right) \cdot u1} \]

   2. *-lft-identity 19.6%

      \[\leadsto \color{blue}{u1} + \left(0.5 \cdot \frac{1}{u1}\right) \cdot u1 \]

   3. associate-*l* 19.6%

      \[\leadsto u1 + \color{blue}{0.5 \cdot \left(\frac{1}{u1} \cdot u1\right)} \]

   4. lft-mult-inverse 19.6%

      \[\leadsto u1 + 0.5 \cdot \color{blue}{1} \]

   5. metadata-eval 19.6%

      \[\leadsto u1 + \color{blue}{0.5} \]

9. Simplified 19.6%

   \[\leadsto \color{blue}{u1 + 0.5} \]
10. Add Preprocessing

Reproduce

herbie shell --seed 2024137 
(FPCore (cosTheta_i u1 u2)
  :name "Trowbridge-Reitz Sample, near normal, slope_x"
  :precision binary32
  :pre (and (and (and (> cosTheta_i 0.9999) (<= cosTheta_i 1.0)) (and (<= 2.328306437e-10 u1) (<= u1 1.0))) (and (<= 2.328306437e-10 u2) (<= u2 1.0)))
  (* (sqrt (/ u1 (- 1.0 u1))) (cos (* 6.28318530718 u2))))