Sample trimmed logistic on [-pi, pi]

Percentage Accurate: 98.9% → 98.9%

Time: 15.7s

Alternatives: 10

Speedup: 1.3×

Specification

\[\left(2.328306437 \cdot 10^{-10} \leq u \land u \leq 1\right) \land \left(0 \leq s \land s \leq 1.0651631\right)\]

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{1 + e^{\frac{\pi}{s}}}\\ \left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - t\_0\right) + t\_0} - 1\right) \end{array} \end{array} \]

FPCore

(FPCore (u s)
 :precision binary32
 (let* ((t_0 (/ 1.0 (+ 1.0 (exp (/ PI s))))))
   (*
    (- s)
    (log
     (-
      (/ 1.0 (+ (* u (- (/ 1.0 (+ 1.0 (exp (/ (- PI) s)))) t_0)) t_0))
      1.0)))))

C

float code(float u, float s) {
	float t_0 = 1.0f / (1.0f + expf((((float) M_PI) / s)));
	return -s * logf(((1.0f / ((u * ((1.0f / (1.0f + expf((-((float) M_PI) / s)))) - t_0)) + t_0)) - 1.0f));
}

Julia

function code(u, s)
	t_0 = Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(pi) / s))))
	return Float32(Float32(-s) * log(Float32(Float32(Float32(1.0) / Float32(Float32(u * Float32(Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(-Float32(pi)) / s)))) - t_0)) + t_0)) - Float32(1.0))))
end

MATLAB

function tmp = code(u, s)
	t_0 = single(1.0) / (single(1.0) + exp((single(pi) / s)));
	tmp = -s * log(((single(1.0) / ((u * ((single(1.0) / (single(1.0) + exp((-single(pi) / s)))) - t_0)) + t_0)) - single(1.0)));
end

Initial Program: 98.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{1}{1 + e^{\frac{\pi}{s}}}\\ \left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - t\_0\right) + t\_0} - 1\right) \end{array} \end{array} \]

FPCore

(FPCore (u s)
 :precision binary32
 (let* ((t_0 (/ 1.0 (+ 1.0 (exp (/ PI s))))))
   (*
    (- s)
    (log
     (-
      (/ 1.0 (+ (* u (- (/ 1.0 (+ 1.0 (exp (/ (- PI) s)))) t_0)) t_0))
      1.0)))))

C

float code(float u, float s) {
	float t_0 = 1.0f / (1.0f + expf((((float) M_PI) / s)));
	return -s * logf(((1.0f / ((u * ((1.0f / (1.0f + expf((-((float) M_PI) / s)))) - t_0)) + t_0)) - 1.0f));
}

Julia

function code(u, s)
	t_0 = Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(pi) / s))))
	return Float32(Float32(-s) * log(Float32(Float32(Float32(1.0) / Float32(Float32(u * Float32(Float32(Float32(1.0) / Float32(Float32(1.0) + exp(Float32(Float32(-Float32(pi)) / s)))) - t_0)) + t_0)) - Float32(1.0))))
end

MATLAB

function tmp = code(u, s)
	t_0 = single(1.0) / (single(1.0) + exp((single(pi) / s)));
	tmp = -s * log(((single(1.0) / ((u * ((single(1.0) / (single(1.0) + exp((-single(pi) / s)))) - t_0)) + t_0)) - single(1.0)));
end

Alternative 1: 98.9% accurate, 1.3× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(-\log \left(\frac{1}{\frac{u}{1 + e^{\frac{-\pi}{s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)\right) \end{array} \]

FPCore

(FPCore (u s)
 :precision binary32
 (*
  s
  (-
   (log
    (+
     (/
      1.0
      (+
       (/ u (+ 1.0 (exp (/ (- PI) s))))
       (/ (- 1.0 u) (+ 1.0 (exp (/ PI s))))))
     -1.0)))))

C

float code(float u, float s) {
	return s * -logf(((1.0f / ((u / (1.0f + expf((-((float) M_PI) / s)))) + ((1.0f - u) / (1.0f + expf((((float) M_PI) / s)))))) + -1.0f));
}

Julia

function code(u, s)
	return Float32(s * Float32(-log(Float32(Float32(Float32(1.0) / Float32(Float32(u / Float32(Float32(1.0) + exp(Float32(Float32(-Float32(pi)) / s)))) + Float32(Float32(Float32(1.0) - u) / Float32(Float32(1.0) + exp(Float32(Float32(pi) / s)))))) + Float32(-1.0)))))
end

MATLAB

function tmp = code(u, s)
	tmp = s * -log(((single(1.0) / ((u / (single(1.0) + exp((-single(pi) / s)))) + ((single(1.0) - u) / (single(1.0) + exp((single(pi) / s)))))) + single(-1.0)));
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Final simplification 99.1%

   \[\leadsto s \cdot \left(-\log \left(\frac{1}{\frac{u}{1 + e^{\frac{-\pi}{s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)\right) \]
6. Add Preprocessing

Alternative 2: 37.5% accurate, 1.3× speedup

Math

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

FPCore

(FPCore (u s)
 :precision binary32
 (*
  (- s)
  (log
   (+
    -1.0
    (/ 1.0 (+ (/ u 2.0) (/ (- 1.0 u) (+ 1.0 (exp (pow (/ s PI) -1.0))))))))))

C

float code(float u, float s) {
	return -s * logf((-1.0f + (1.0f / ((u / 2.0f) + ((1.0f - u) / (1.0f + expf(powf((s / ((float) M_PI)), -1.0f))))))));
}

Julia

function code(u, s)
	return Float32(Float32(-s) * log(Float32(Float32(-1.0) + Float32(Float32(1.0) / Float32(Float32(u / Float32(2.0)) + Float32(Float32(Float32(1.0) - u) / Float32(Float32(1.0) + exp((Float32(s / Float32(pi)) ^ Float32(-1.0))))))))))
end

MATLAB

function tmp = code(u, s)
	tmp = -s * log((single(-1.0) + (single(1.0) / ((u / single(2.0)) + ((single(1.0) - u) / (single(1.0) + exp(((s / single(pi)) ^ single(-1.0)))))))));
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Taylor expanded in s around inf 38.8%

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

   1. clear-num 99.1%

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

   2. inv-pow 99.1%

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

7. Applied egg-rr 38.8%

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

8. Final simplification 38.8%

   \[\leadsto \left(-s\right) \cdot \log \left(-1 + \frac{1}{\frac{u}{2} + \frac{1 - u}{1 + e^{{\left(\frac{s}{\pi}\right)}^{-1}}}}\right) \]
9. Add Preprocessing

Alternative 3: 37.5% accurate, 2.0× speedup

Math

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

FPCore

(FPCore (u s)
 :precision binary32
 (*
  s
  (-
   (log (+ -1.0 (/ 1.0 (+ (/ (- 1.0 u) (+ 1.0 (exp (/ PI s)))) (/ u 2.0))))))))

C

float code(float u, float s) {
	return s * -logf((-1.0f + (1.0f / (((1.0f - u) / (1.0f + expf((((float) M_PI) / s)))) + (u / 2.0f)))));
}

Julia

function code(u, s)
	return Float32(s * Float32(-log(Float32(Float32(-1.0) + Float32(Float32(1.0) / Float32(Float32(Float32(Float32(1.0) - u) / Float32(Float32(1.0) + exp(Float32(Float32(pi) / s)))) + Float32(u / Float32(2.0))))))))
end

MATLAB

function tmp = code(u, s)
	tmp = s * -log((single(-1.0) + (single(1.0) / (((single(1.0) - u) / (single(1.0) + exp((single(pi) / s)))) + (u / single(2.0))))));
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Taylor expanded in s around inf 38.8%

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

6. Final simplification 38.8%

   \[\leadsto s \cdot \left(-\log \left(-1 + \frac{1}{\frac{1 - u}{1 + e^{\frac{\pi}{s}}} + \frac{u}{2}}\right)\right) \]
7. Add Preprocessing

Alternative 4: 37.0% accurate, 2.0× speedup

Math

\[\begin{array}{l} \\ s \cdot \left(-\mathsf{log1p}\left(\frac{1}{u \cdot \left(0.5 + \frac{-1}{1 + e^{\frac{\pi}{s}}}\right)} - 2\right)\right) \end{array} \]

FPCore

(FPCore (u s)
 :precision binary32
 (*
  s
  (- (log1p (- (/ 1.0 (* u (+ 0.5 (/ -1.0 (+ 1.0 (exp (/ PI s))))))) 2.0)))))

C

float code(float u, float s) {
	return s * -log1pf(((1.0f / (u * (0.5f + (-1.0f / (1.0f + expf((((float) M_PI) / s))))))) - 2.0f));
}

Julia

function code(u, s)
	return Float32(s * Float32(-log1p(Float32(Float32(Float32(1.0) / Float32(u * Float32(Float32(0.5) + Float32(Float32(-1.0) / Float32(Float32(1.0) + exp(Float32(Float32(pi) / s))))))) - Float32(2.0)))))
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Taylor expanded in s around inf 38.8%

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

   1. log1p-expm1-u 38.8%

      \[\leadsto \left(-s\right) \cdot \color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\log \left(\frac{1}{\frac{u}{1 + 1} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)\right)\right)} \]

   2. expm1-undefine 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{e^{\log \left(\frac{1}{\frac{u}{1 + 1} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} - 1}\right) \]

   3. add-exp-log 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{\left(\frac{1}{\frac{u}{1 + 1} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} - 1\right) \]

   4. +-commutative 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{\left(-1 + \frac{1}{\frac{u}{1 + 1} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}}\right)} - 1\right) \]

   5. metadata-eval 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\left(-1 + \frac{1}{\frac{u}{\color{blue}{2}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}}\right) - 1\right) \]

   6. div-inv 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\left(-1 + \frac{1}{\color{blue}{u \cdot \frac{1}{2}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}}\right) - 1\right) \]

   7. metadata-eval 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\left(-1 + \frac{1}{u \cdot \color{blue}{0.5} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}}\right) - 1\right) \]

   8. fma-define 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\left(-1 + \frac{1}{\color{blue}{\mathsf{fma}\left(u, 0.5, \frac{1 - u}{1 + e^{\frac{\pi}{s}}}\right)}}\right) - 1\right) \]

7. Applied egg-rr 38.8%

   \[\leadsto \left(-s\right) \cdot \color{blue}{\mathsf{log1p}\left(\left(-1 + \frac{1}{\mathsf{fma}\left(u, 0.5, \frac{1 - u}{1 + e^{\frac{\pi}{s}}}\right)}\right) - 1\right)} \]

8. Taylor expanded in u around inf 37.4%

   \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{\frac{1}{u \cdot \left(0.5 - \frac{1}{1 + e^{\frac{\pi}{s}}}\right)} - 2}\right) \]

9. Final simplification 37.4%

   \[\leadsto s \cdot \left(-\mathsf{log1p}\left(\frac{1}{u \cdot \left(0.5 + \frac{-1}{1 + e^{\frac{\pi}{s}}}\right)} - 2\right)\right) \]
10. Add Preprocessing

Alternative 5: 36.2% accurate, 3.5× speedup

Math

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

FPCore

(FPCore (u s)
 :precision binary32
 (*
  s
  (-
   (log
    (+ -1.0 (/ 1.0 (+ (/ u 2.0) (/ (- 1.0 u) (+ 1.0 (+ 1.0 (/ PI s)))))))))))

C

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

Julia

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

MATLAB

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

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Taylor expanded in s around inf 38.8%

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

6. Taylor expanded in s around inf 36.3%

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

7. Final simplification 36.3%

   \[\leadsto s \cdot \left(-\log \left(-1 + \frac{1}{\frac{u}{2} + \frac{1 - u}{1 + \left(1 + \frac{\pi}{s}\right)}}\right)\right) \]
8. Add Preprocessing

Alternative 6: 25.0% accurate, 3.8× speedup

Math

\[\begin{array}{l} \\ \left(-s\right) \cdot \mathsf{log1p}\left(-4 \cdot \frac{0.25 \cdot \left(u \cdot \pi - \pi\right)}{s}\right) \end{array} \]

FPCore

(FPCore (u s)
 :precision binary32
 (* (- s) (log1p (* -4.0 (/ (* 0.25 (- (* u PI) PI)) s)))))

C

float code(float u, float s) {
	return -s * log1pf((-4.0f * ((0.25f * ((u * ((float) M_PI)) - ((float) M_PI))) / s)));
}

Julia

function code(u, s)
	return Float32(Float32(-s) * log1p(Float32(Float32(-4.0) * Float32(Float32(Float32(0.25) * Float32(Float32(u * Float32(pi)) - Float32(pi))) / s))))
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Taylor expanded in s around inf 38.8%

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

   1. log1p-expm1-u 38.8%

      \[\leadsto \left(-s\right) \cdot \color{blue}{\mathsf{log1p}\left(\mathsf{expm1}\left(\log \left(\frac{1}{\frac{u}{1 + 1} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)\right)\right)} \]

   2. expm1-undefine 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{e^{\log \left(\frac{1}{\frac{u}{1 + 1} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} - 1}\right) \]

   3. add-exp-log 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{\left(\frac{1}{\frac{u}{1 + 1} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} - 1\right) \]

   4. +-commutative 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{\left(-1 + \frac{1}{\frac{u}{1 + 1} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}}\right)} - 1\right) \]

   5. metadata-eval 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\left(-1 + \frac{1}{\frac{u}{\color{blue}{2}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}}\right) - 1\right) \]

   6. div-inv 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\left(-1 + \frac{1}{\color{blue}{u \cdot \frac{1}{2}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}}\right) - 1\right) \]

   7. metadata-eval 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\left(-1 + \frac{1}{u \cdot \color{blue}{0.5} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}}\right) - 1\right) \]

   8. fma-define 38.8%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\left(-1 + \frac{1}{\color{blue}{\mathsf{fma}\left(u, 0.5, \frac{1 - u}{1 + e^{\frac{\pi}{s}}}\right)}}\right) - 1\right) \]

7. Applied egg-rr 38.8%

   \[\leadsto \left(-s\right) \cdot \color{blue}{\mathsf{log1p}\left(\left(-1 + \frac{1}{\mathsf{fma}\left(u, 0.5, \frac{1 - u}{1 + e^{\frac{\pi}{s}}}\right)}\right) - 1\right)} \]

8. Taylor expanded in s around inf 25.4%

   \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{-4 \cdot \frac{0.25 \cdot \left(u \cdot \pi\right) - 0.25 \cdot \pi}{s}}\right) \]
9. Step-by-step derivation

   1. distribute-lft-out-- 25.4%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(-4 \cdot \frac{\color{blue}{0.25 \cdot \left(u \cdot \pi - \pi\right)}}{s}\right) \]

   2. *-commutative 25.4%

      \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(-4 \cdot \frac{0.25 \cdot \left(\color{blue}{\pi \cdot u} - \pi\right)}{s}\right) \]

10. Simplified 25.4%

    \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(\color{blue}{-4 \cdot \frac{0.25 \cdot \left(\pi \cdot u - \pi\right)}{s}}\right) \]

11. Final simplification 25.4%

    \[\leadsto \left(-s\right) \cdot \mathsf{log1p}\left(-4 \cdot \frac{0.25 \cdot \left(u \cdot \pi - \pi\right)}{s}\right) \]
12. Add Preprocessing

Alternative 7: 11.5% accurate, 22.8× speedup

Math

\[\begin{array}{l} \\ -4 \cdot \left(-1 + u \cdot \left(\pi \cdot -0.5 + \left(0.25 \cdot \frac{\pi}{u} + \frac{1}{u}\right)\right)\right) \end{array} \]

FPCore

(FPCore (u s)
 :precision binary32
 (* -4.0 (+ -1.0 (* u (+ (* PI -0.5) (+ (* 0.25 (/ PI u)) (/ 1.0 u)))))))

C

float code(float u, float s) {
	return -4.0f * (-1.0f + (u * ((((float) M_PI) * -0.5f) + ((0.25f * (((float) M_PI) / u)) + (1.0f / u)))));
}

Julia

function code(u, s)
	return Float32(Float32(-4.0) * Float32(Float32(-1.0) + Float32(u * Float32(Float32(Float32(pi) * Float32(-0.5)) + Float32(Float32(Float32(0.25) * Float32(Float32(pi) / u)) + Float32(Float32(1.0) / u))))))
end

MATLAB

function tmp = code(u, s)
	tmp = single(-4.0) * (single(-1.0) + (u * ((single(pi) * single(-0.5)) + ((single(0.25) * (single(pi) / u)) + (single(1.0) / u)))));
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Taylor expanded in s around -inf 12.6%

   \[\leadsto \color{blue}{-4 \cdot \left(-0.25 \cdot \left(u \cdot \pi\right) - \left(-0.25 \cdot \pi + 0.25 \cdot \left(u \cdot \pi\right)\right)\right)} \]
6. Step-by-step derivation

   1. associate--r+ 12.6%

      \[\leadsto -4 \cdot \color{blue}{\left(\left(-0.25 \cdot \left(u \cdot \pi\right) - -0.25 \cdot \pi\right) - 0.25 \cdot \left(u \cdot \pi\right)\right)} \]

   2. cancel-sign-sub-inv 12.6%

      \[\leadsto -4 \cdot \color{blue}{\left(\left(-0.25 \cdot \left(u \cdot \pi\right) - -0.25 \cdot \pi\right) + \left(-0.25\right) \cdot \left(u \cdot \pi\right)\right)} \]

   3. cancel-sign-sub-inv 12.6%

      \[\leadsto -4 \cdot \left(\color{blue}{\left(-0.25 \cdot \left(u \cdot \pi\right) + \left(--0.25\right) \cdot \pi\right)} + \left(-0.25\right) \cdot \left(u \cdot \pi\right)\right) \]

   4. metadata-eval 12.6%

      \[\leadsto -4 \cdot \left(\left(-0.25 \cdot \left(u \cdot \pi\right) + \color{blue}{0.25} \cdot \pi\right) + \left(-0.25\right) \cdot \left(u \cdot \pi\right)\right) \]

   5. associate-*r* 12.6%

      \[\leadsto -4 \cdot \left(\left(\color{blue}{\left(-0.25 \cdot u\right) \cdot \pi} + 0.25 \cdot \pi\right) + \left(-0.25\right) \cdot \left(u \cdot \pi\right)\right) \]

   6. distribute-rgt-out 12.6%

      \[\leadsto -4 \cdot \left(\color{blue}{\pi \cdot \left(-0.25 \cdot u + 0.25\right)} + \left(-0.25\right) \cdot \left(u \cdot \pi\right)\right) \]

   7. *-commutative 12.6%

      \[\leadsto -4 \cdot \left(\pi \cdot \left(\color{blue}{u \cdot -0.25} + 0.25\right) + \left(-0.25\right) \cdot \left(u \cdot \pi\right)\right) \]

   8. metadata-eval 12.6%

      \[\leadsto -4 \cdot \left(\pi \cdot \left(u \cdot -0.25 + 0.25\right) + \color{blue}{-0.25} \cdot \left(u \cdot \pi\right)\right) \]

   9. *-commutative 12.6%

      \[\leadsto -4 \cdot \left(\pi \cdot \left(u \cdot -0.25 + 0.25\right) + \color{blue}{\left(u \cdot \pi\right) \cdot -0.25}\right) \]

   10. associate-*l* 12.6%

       \[\leadsto -4 \cdot \left(\pi \cdot \left(u \cdot -0.25 + 0.25\right) + \color{blue}{u \cdot \left(\pi \cdot -0.25\right)}\right) \]

7. Simplified 12.6%

   \[\leadsto \color{blue}{-4 \cdot \left(\pi \cdot \left(u \cdot -0.25 + 0.25\right) + u \cdot \left(\pi \cdot -0.25\right)\right)} \]
8. Step-by-step derivation

   1. expm1-log1p-u 12.6%

      \[\leadsto -4 \cdot \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\pi \cdot \left(u \cdot -0.25 + 0.25\right) + u \cdot \left(\pi \cdot -0.25\right)\right)\right)} \]

   2. expm1-undefine 12.6%

      \[\leadsto -4 \cdot \color{blue}{\left(e^{\mathsf{log1p}\left(\pi \cdot \left(u \cdot -0.25 + 0.25\right) + u \cdot \left(\pi \cdot -0.25\right)\right)} - 1\right)} \]

   3. fma-define 12.6%

      \[\leadsto -4 \cdot \left(e^{\mathsf{log1p}\left(\color{blue}{\mathsf{fma}\left(\pi, u \cdot -0.25 + 0.25, u \cdot \left(\pi \cdot -0.25\right)\right)}\right)} - 1\right) \]

   4. fma-define 12.6%

      \[\leadsto -4 \cdot \left(e^{\mathsf{log1p}\left(\mathsf{fma}\left(\pi, \color{blue}{\mathsf{fma}\left(u, -0.25, 0.25\right)}, u \cdot \left(\pi \cdot -0.25\right)\right)\right)} - 1\right) \]

   5. associate-*r* 12.6%

      \[\leadsto -4 \cdot \left(e^{\mathsf{log1p}\left(\mathsf{fma}\left(\pi, \mathsf{fma}\left(u, -0.25, 0.25\right), \color{blue}{\left(u \cdot \pi\right) \cdot -0.25}\right)\right)} - 1\right) \]

   6. *-commutative 12.6%

      \[\leadsto -4 \cdot \left(e^{\mathsf{log1p}\left(\mathsf{fma}\left(\pi, \mathsf{fma}\left(u, -0.25, 0.25\right), \color{blue}{-0.25 \cdot \left(u \cdot \pi\right)}\right)\right)} - 1\right) \]

   7. *-commutative 12.6%

      \[\leadsto -4 \cdot \left(e^{\mathsf{log1p}\left(\mathsf{fma}\left(\pi, \mathsf{fma}\left(u, -0.25, 0.25\right), -0.25 \cdot \color{blue}{\left(\pi \cdot u\right)}\right)\right)} - 1\right) \]

9. Applied egg-rr 12.6%

   \[\leadsto -4 \cdot \color{blue}{\left(e^{\mathsf{log1p}\left(\mathsf{fma}\left(\pi, \mathsf{fma}\left(u, -0.25, 0.25\right), -0.25 \cdot \left(\pi \cdot u\right)\right)\right)} - 1\right)} \]
10. Step-by-step derivation

    1. expm1-define 12.6%

       \[\leadsto -4 \cdot \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\mathsf{fma}\left(\pi, \mathsf{fma}\left(u, -0.25, 0.25\right), -0.25 \cdot \left(\pi \cdot u\right)\right)\right)\right)} \]

    2. fma-undefine 12.6%

       \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(\color{blue}{\pi \cdot \mathsf{fma}\left(u, -0.25, 0.25\right) + -0.25 \cdot \left(\pi \cdot u\right)}\right)\right) \]

    3. fma-undefine 12.6%

       \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(\pi \cdot \color{blue}{\left(u \cdot -0.25 + 0.25\right)} + -0.25 \cdot \left(\pi \cdot u\right)\right)\right) \]

    4. distribute-lft-out 12.6%

       \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(\color{blue}{\left(\pi \cdot \left(u \cdot -0.25\right) + \pi \cdot 0.25\right)} + -0.25 \cdot \left(\pi \cdot u\right)\right)\right) \]

    5. associate-*r* 12.6%

       \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(\left(\color{blue}{\left(\pi \cdot u\right) \cdot -0.25} + \pi \cdot 0.25\right) + -0.25 \cdot \left(\pi \cdot u\right)\right)\right) \]

    6. *-commutative 12.6%

       \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(\left(\color{blue}{-0.25 \cdot \left(\pi \cdot u\right)} + \pi \cdot 0.25\right) + -0.25 \cdot \left(\pi \cdot u\right)\right)\right) \]

    7. +-commutative 12.6%

       \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(\color{blue}{\left(\pi \cdot 0.25 + -0.25 \cdot \left(\pi \cdot u\right)\right)} + -0.25 \cdot \left(\pi \cdot u\right)\right)\right) \]

    8. associate-+l+ 12.6%

       \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(\color{blue}{\pi \cdot 0.25 + \left(-0.25 \cdot \left(\pi \cdot u\right) + -0.25 \cdot \left(\pi \cdot u\right)\right)}\right)\right) \]

    9. *-commutative 12.6%

       \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(\color{blue}{0.25 \cdot \pi} + \left(-0.25 \cdot \left(\pi \cdot u\right) + -0.25 \cdot \left(\pi \cdot u\right)\right)\right)\right) \]

    10. associate-*r* 12.6%

        \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(0.25 \cdot \pi + \left(\color{blue}{\left(-0.25 \cdot \pi\right) \cdot u} + -0.25 \cdot \left(\pi \cdot u\right)\right)\right)\right) \]

    11. *-commutative 12.6%

        \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(0.25 \cdot \pi + \left(\color{blue}{\left(\pi \cdot -0.25\right)} \cdot u + -0.25 \cdot \left(\pi \cdot u\right)\right)\right)\right) \]

    12. associate-*r* 12.6%

        \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(0.25 \cdot \pi + \left(\left(\pi \cdot -0.25\right) \cdot u + \color{blue}{\left(-0.25 \cdot \pi\right) \cdot u}\right)\right)\right) \]

    13. *-commutative 12.6%

        \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(0.25 \cdot \pi + \left(\left(\pi \cdot -0.25\right) \cdot u + \color{blue}{\left(\pi \cdot -0.25\right)} \cdot u\right)\right)\right) \]

    14. distribute-rgt-in 12.6%

        \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(0.25 \cdot \pi + \color{blue}{u \cdot \left(\pi \cdot -0.25 + \pi \cdot -0.25\right)}\right)\right) \]

    15. *-commutative 12.6%

        \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(0.25 \cdot \pi + u \cdot \left(\color{blue}{-0.25 \cdot \pi} + \pi \cdot -0.25\right)\right)\right) \]

    16. *-commutative 12.6%

        \[\leadsto -4 \cdot \mathsf{expm1}\left(\mathsf{log1p}\left(0.25 \cdot \pi + u \cdot \left(-0.25 \cdot \pi + \color{blue}{-0.25 \cdot \pi}\right)\right)\right) \]

11. Simplified 12.6%

    \[\leadsto -4 \cdot \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(\pi \cdot \left(-0.5 \cdot u + 0.25\right)\right)\right)} \]
12. Step-by-step derivation

    1. expm1-undefine 12.6%

       \[\leadsto -4 \cdot \color{blue}{\left(e^{\mathsf{log1p}\left(\pi \cdot \left(-0.5 \cdot u + 0.25\right)\right)} - 1\right)} \]

    2. log1p-undefine 12.6%

       \[\leadsto -4 \cdot \left(e^{\color{blue}{\log \left(1 + \pi \cdot \left(-0.5 \cdot u + 0.25\right)\right)}} - 1\right) \]

    3. rem-exp-log 12.6%

       \[\leadsto -4 \cdot \left(\color{blue}{\left(1 + \pi \cdot \left(-0.5 \cdot u + 0.25\right)\right)} - 1\right) \]

    4. *-commutative 12.6%

       \[\leadsto -4 \cdot \left(\left(1 + \pi \cdot \left(\color{blue}{u \cdot -0.5} + 0.25\right)\right) - 1\right) \]

    5. fma-define 12.6%

       \[\leadsto -4 \cdot \left(\left(1 + \pi \cdot \color{blue}{\mathsf{fma}\left(u, -0.5, 0.25\right)}\right) - 1\right) \]

13. Applied egg-rr 12.6%

    \[\leadsto -4 \cdot \color{blue}{\left(\left(1 + \pi \cdot \mathsf{fma}\left(u, -0.5, 0.25\right)\right) - 1\right)} \]

14. Taylor expanded in u around inf 12.6%

    \[\leadsto -4 \cdot \left(\color{blue}{u \cdot \left(-0.5 \cdot \pi + \left(0.25 \cdot \frac{\pi}{u} + \frac{1}{u}\right)\right)} - 1\right) \]

15. Final simplification 12.6%

    \[\leadsto -4 \cdot \left(-1 + u \cdot \left(\pi \cdot -0.5 + \left(0.25 \cdot \frac{\pi}{u} + \frac{1}{u}\right)\right)\right) \]
16. Add Preprocessing

Alternative 8: 11.5% accurate, 33.3× speedup

Math

\[\begin{array}{l} \\ 4 \cdot \left(u \cdot \left(\frac{\pi}{u} \cdot -0.25 + \pi \cdot 0.5\right)\right) \end{array} \]

FPCore

(FPCore (u s)
 :precision binary32
 (* 4.0 (* u (+ (* (/ PI u) -0.25) (* PI 0.5)))))

C

float code(float u, float s) {
	return 4.0f * (u * (((((float) M_PI) / u) * -0.25f) + (((float) M_PI) * 0.5f)));
}

Julia

function code(u, s)
	return Float32(Float32(4.0) * Float32(u * Float32(Float32(Float32(Float32(pi) / u) * Float32(-0.25)) + Float32(Float32(pi) * Float32(0.5)))))
end

MATLAB

function tmp = code(u, s)
	tmp = single(4.0) * (u * (((single(pi) / u) * single(-0.25)) + (single(pi) * single(0.5))));
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Taylor expanded in s around inf 12.6%

   \[\leadsto \color{blue}{4 \cdot \left(0.25 \cdot \left(u \cdot \pi\right) - \left(-0.25 \cdot \left(u \cdot \pi\right) + 0.25 \cdot \pi\right)\right)} \]
6. Step-by-step derivation

   1. associate--r+ 12.6%

      \[\leadsto 4 \cdot \color{blue}{\left(\left(0.25 \cdot \left(u \cdot \pi\right) - -0.25 \cdot \left(u \cdot \pi\right)\right) - 0.25 \cdot \pi\right)} \]

   2. cancel-sign-sub-inv 12.6%

      \[\leadsto 4 \cdot \color{blue}{\left(\left(0.25 \cdot \left(u \cdot \pi\right) - -0.25 \cdot \left(u \cdot \pi\right)\right) + \left(-0.25\right) \cdot \pi\right)} \]

   3. distribute-rgt-out-- 12.6%

      \[\leadsto 4 \cdot \left(\color{blue}{\left(u \cdot \pi\right) \cdot \left(0.25 - -0.25\right)} + \left(-0.25\right) \cdot \pi\right) \]

   4. metadata-eval 12.6%

      \[\leadsto 4 \cdot \left(\left(u \cdot \pi\right) \cdot \color{blue}{0.5} + \left(-0.25\right) \cdot \pi\right) \]

   5. *-commutative 12.6%

      \[\leadsto 4 \cdot \left(\color{blue}{\left(\pi \cdot u\right)} \cdot 0.5 + \left(-0.25\right) \cdot \pi\right) \]

   6. metadata-eval 12.6%

      \[\leadsto 4 \cdot \left(\left(\pi \cdot u\right) \cdot 0.5 + \color{blue}{-0.25} \cdot \pi\right) \]

   7. *-commutative 12.6%

      \[\leadsto 4 \cdot \left(\left(\pi \cdot u\right) \cdot 0.5 + \color{blue}{\pi \cdot -0.25}\right) \]

7. Simplified 12.6%

   \[\leadsto \color{blue}{4 \cdot \left(\left(\pi \cdot u\right) \cdot 0.5 + \pi \cdot -0.25\right)} \]

8. Taylor expanded in u around inf 12.6%

   \[\leadsto 4 \cdot \color{blue}{\left(u \cdot \left(-0.25 \cdot \frac{\pi}{u} + 0.5 \cdot \pi\right)\right)} \]

9. Final simplification 12.6%

   \[\leadsto 4 \cdot \left(u \cdot \left(\frac{\pi}{u} \cdot -0.25 + \pi \cdot 0.5\right)\right) \]
10. Add Preprocessing

Alternative 9: 11.5% accurate, 48.1× speedup

Math

\[\begin{array}{l} \\ 4 \cdot \left(\pi \cdot \left(-0.25 + u \cdot 0.5\right)\right) \end{array} \]

FPCore

(FPCore (u s) :precision binary32 (* 4.0 (* PI (+ -0.25 (* u 0.5)))))

C

float code(float u, float s) {
	return 4.0f * (((float) M_PI) * (-0.25f + (u * 0.5f)));
}

Julia

function code(u, s)
	return Float32(Float32(4.0) * Float32(Float32(pi) * Float32(Float32(-0.25) + Float32(u * Float32(0.5)))))
end

MATLAB

function tmp = code(u, s)
	tmp = single(4.0) * (single(pi) * (single(-0.25) + (u * single(0.5))));
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. clear-num 99.1%

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

   2. inv-pow 99.1%

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

6. Applied egg-rr 99.1%

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

7. Taylor expanded in s around inf 12.6%

   \[\leadsto \color{blue}{4 \cdot \left(0.25 \cdot \left(u \cdot \pi\right) - \left(-0.25 \cdot \left(u \cdot \pi\right) + 0.25 \cdot \pi\right)\right)} \]
8. Step-by-step derivation

   1. associate--r+ 12.6%

      \[\leadsto 4 \cdot \color{blue}{\left(\left(0.25 \cdot \left(u \cdot \pi\right) - -0.25 \cdot \left(u \cdot \pi\right)\right) - 0.25 \cdot \pi\right)} \]

   2. cancel-sign-sub-inv 12.6%

      \[\leadsto 4 \cdot \color{blue}{\left(\left(0.25 \cdot \left(u \cdot \pi\right) - -0.25 \cdot \left(u \cdot \pi\right)\right) + \left(-0.25\right) \cdot \pi\right)} \]

   3. *-commutative 12.6%

      \[\leadsto 4 \cdot \left(\left(0.25 \cdot \color{blue}{\left(\pi \cdot u\right)} - -0.25 \cdot \left(u \cdot \pi\right)\right) + \left(-0.25\right) \cdot \pi\right) \]

   4. *-commutative 12.6%

      \[\leadsto 4 \cdot \left(\left(\color{blue}{\left(\pi \cdot u\right) \cdot 0.25} - -0.25 \cdot \left(u \cdot \pi\right)\right) + \left(-0.25\right) \cdot \pi\right) \]

   5. *-commutative 12.6%

      \[\leadsto 4 \cdot \left(\left(\left(\pi \cdot u\right) \cdot 0.25 - -0.25 \cdot \color{blue}{\left(\pi \cdot u\right)}\right) + \left(-0.25\right) \cdot \pi\right) \]

   6. *-commutative 12.6%

      \[\leadsto 4 \cdot \left(\left(\left(\pi \cdot u\right) \cdot 0.25 - \color{blue}{\left(\pi \cdot u\right) \cdot -0.25}\right) + \left(-0.25\right) \cdot \pi\right) \]

   7. distribute-lft-out-- 12.6%

      \[\leadsto 4 \cdot \left(\color{blue}{\left(\pi \cdot u\right) \cdot \left(0.25 - -0.25\right)} + \left(-0.25\right) \cdot \pi\right) \]

   8. metadata-eval 12.6%

      \[\leadsto 4 \cdot \left(\left(\pi \cdot u\right) \cdot \color{blue}{0.5} + \left(-0.25\right) \cdot \pi\right) \]

   9. associate-*r* 12.6%

      \[\leadsto 4 \cdot \left(\color{blue}{\pi \cdot \left(u \cdot 0.5\right)} + \left(-0.25\right) \cdot \pi\right) \]

   10. metadata-eval 12.6%

       \[\leadsto 4 \cdot \left(\pi \cdot \left(u \cdot 0.5\right) + \color{blue}{-0.25} \cdot \pi\right) \]

   11. *-commutative 12.6%

       \[\leadsto 4 \cdot \left(\pi \cdot \left(u \cdot 0.5\right) + \color{blue}{\pi \cdot -0.25}\right) \]

   12. distribute-lft-out 12.6%

       \[\leadsto 4 \cdot \color{blue}{\left(\pi \cdot \left(u \cdot 0.5 + -0.25\right)\right)} \]

9. Simplified 12.6%

   \[\leadsto \color{blue}{4 \cdot \left(\pi \cdot \left(u \cdot 0.5 + -0.25\right)\right)} \]

10. Final simplification 12.6%

    \[\leadsto 4 \cdot \left(\pi \cdot \left(-0.25 + u \cdot 0.5\right)\right) \]
11. Add Preprocessing

Alternative 10: 11.2% accurate, 216.5× speedup

Math

\[\begin{array}{l} \\ -\pi \end{array} \]

FPCore

(FPCore (u s) :precision binary32 (- PI))

C

float code(float u, float s) {
	return -((float) M_PI);
}

Julia

function code(u, s)
	return Float32(-Float32(pi))
end

MATLAB

function tmp = code(u, s)
	tmp = -single(pi);
end

Derivation

1. Initial program 99.1%

   \[\left(-s\right) \cdot \log \left(\frac{1}{u \cdot \left(\frac{1}{1 + e^{\frac{-\pi}{s}}} - \frac{1}{1 + e^{\frac{\pi}{s}}}\right) + \frac{1}{1 + e^{\frac{\pi}{s}}}} - 1\right) \]

3. Simplified 99.1%

   \[\leadsto \color{blue}{\left(-s\right) \cdot \log \left(\frac{1}{\frac{u}{1 + e^{\frac{\pi}{-s}}} + \frac{1 - u}{1 + e^{\frac{\pi}{s}}}} + -1\right)} \]
4. Add Preprocessing

5. Taylor expanded in u around 0 12.5%

   \[\leadsto \color{blue}{-1 \cdot \pi} \]
6. Step-by-step derivation

   1. neg-mul-1 12.5%

      \[\leadsto \color{blue}{-\pi} \]

7. Simplified 12.5%

   \[\leadsto \color{blue}{-\pi} \]

8. Final simplification 12.5%

   \[\leadsto -\pi \]
9. Add Preprocessing

Reproduce

herbie shell --seed 2024071 
(FPCore (u s)
  :name "Sample trimmed logistic on [-pi, pi]"
  :precision binary32
  :pre (and (and (<= 2.328306437e-10 u) (<= u 1.0)) (and (<= 0.0 s) (<= s 1.0651631)))
  (* (- s) (log (- (/ 1.0 (+ (* u (- (/ 1.0 (+ 1.0 (exp (/ (- PI) s)))) (/ 1.0 (+ 1.0 (exp (/ PI s)))))) (/ 1.0 (+ 1.0 (exp (/ PI s)))))) 1.0))))