Rust f32::atanh

Percentage Accurate: 99.8% → 99.9%
Time: 9.5s
Alternatives: 8
Speedup: 1.0×

Specification

?
\[\begin{array}{l} \\ \tanh^{-1} x \end{array} \]
(FPCore (x) :precision binary32 (atanh x))
float code(float x) {
	return atanhf(x);
}
function code(x)
	return atanh(x)
end
function tmp = code(x)
	tmp = atanh(x);
end
\begin{array}{l}

\\
\tanh^{-1} x
\end{array}

Sampling outcomes in binary32 precision:

Local Percentage Accuracy vs ?

The average percentage accuracy by input value. Horizontal axis shows value of an input variable; the variable is choosen in the title. Vertical axis is accuracy; higher is better. Red represent the original program, while blue represents Herbie's suggestion. These can be toggled with buttons below the plot. The line is an average while dots represent individual samples.

Accuracy vs Speed?

Herbie found 8 alternatives:

AlternativeAccuracySpeedup
The accuracy (vertical axis) and speed (horizontal axis) of each alternatives. Up and to the right is better. The red square shows the initial program, and each blue circle shows an alternative.The line shows the best available speed-accuracy tradeoffs.

Initial Program: 99.8% accurate, 1.0× speedup?

\[\begin{array}{l} \\ 0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \end{array} \]
(FPCore (x) :precision binary32 (* 0.5 (log1p (/ (* 2.0 x) (- 1.0 x)))))
float code(float x) {
	return 0.5f * log1pf(((2.0f * x) / (1.0f - x)));
}
function code(x)
	return Float32(Float32(0.5) * log1p(Float32(Float32(Float32(2.0) * x) / Float32(Float32(1.0) - x))))
end
\begin{array}{l}

\\
0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right)
\end{array}

Alternative 1: 99.9% accurate, 0.9× speedup?

\[\begin{array}{l} \\ 0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\mathsf{fma}\left(x, -x, 1\right)}\right) \end{array} \]
(FPCore (x)
 :precision binary32
 (* 0.5 (log1p (/ (* 2.0 (fma x x x)) (fma x (- x) 1.0)))))
float code(float x) {
	return 0.5f * log1pf(((2.0f * fmaf(x, x, x)) / fmaf(x, -x, 1.0f)));
}
function code(x)
	return Float32(Float32(0.5) * log1p(Float32(Float32(Float32(2.0) * fma(x, x, x)) / fma(x, Float32(-x), Float32(1.0)))))
end
\begin{array}{l}

\\
0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\mathsf{fma}\left(x, -x, 1\right)}\right)
\end{array}
Derivation
  1. Initial program 99.7%

    \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
  2. Add Preprocessing
  3. Step-by-step derivation
    1. lift-*.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{\color{blue}{2 \cdot x}}{1 - x}\right) \]
    2. flip--N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{\color{blue}{\frac{1 \cdot 1 - x \cdot x}{1 + x}}}\right) \]
    3. associate-/r/N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\color{blue}{\frac{2 \cdot x}{1 \cdot 1 - x \cdot x} \cdot \left(1 + x\right)}\right) \]
    4. associate-*l/N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\color{blue}{\frac{\left(2 \cdot x\right) \cdot \left(1 + x\right)}{1 \cdot 1 - x \cdot x}}\right) \]
    5. lower-/.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\color{blue}{\frac{\left(2 \cdot x\right) \cdot \left(1 + x\right)}{1 \cdot 1 - x \cdot x}}\right) \]
    6. lift-*.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{\color{blue}{\left(2 \cdot x\right)} \cdot \left(1 + x\right)}{1 \cdot 1 - x \cdot x}\right) \]
    7. associate-*l*N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{\color{blue}{2 \cdot \left(x \cdot \left(1 + x\right)\right)}}{1 \cdot 1 - x \cdot x}\right) \]
    8. +-commutativeN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \left(x \cdot \color{blue}{\left(x + 1\right)}\right)}{1 \cdot 1 - x \cdot x}\right) \]
    9. distribute-rgt-outN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \color{blue}{\left(x \cdot x + 1 \cdot x\right)}}{1 \cdot 1 - x \cdot x}\right) \]
    10. lower-*.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{\color{blue}{2 \cdot \left(x \cdot x + 1 \cdot x\right)}}{1 \cdot 1 - x \cdot x}\right) \]
    11. *-lft-identityN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \left(x \cdot x + \color{blue}{x}\right)}{1 \cdot 1 - x \cdot x}\right) \]
    12. lower-fma.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \color{blue}{\mathsf{fma}\left(x, x, x\right)}}{1 \cdot 1 - x \cdot x}\right) \]
    13. metadata-evalN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{1} - x \cdot x}\right) \]
    14. sub-negN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{1 + \left(\mathsf{neg}\left(x \cdot x\right)\right)}}\right) \]
    15. +-commutativeN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{\left(\mathsf{neg}\left(x \cdot x\right)\right) + 1}}\right) \]
    16. distribute-rgt-neg-inN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{x \cdot \left(\mathsf{neg}\left(x\right)\right)} + 1}\right) \]
    17. lower-fma.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{\mathsf{fma}\left(x, \mathsf{neg}\left(x\right), 1\right)}}\right) \]
    18. lower-neg.f3299.9

      \[\leadsto 0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\mathsf{fma}\left(x, \color{blue}{-x}, 1\right)}\right) \]
  4. Applied egg-rr99.9%

    \[\leadsto 0.5 \cdot \mathsf{log1p}\left(\color{blue}{\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\mathsf{fma}\left(x, -x, 1\right)}}\right) \]
  5. Add Preprocessing

Alternative 2: 99.8% accurate, 1.0× speedup?

\[\begin{array}{l} \\ 0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \end{array} \]
(FPCore (x) :precision binary32 (* 0.5 (log1p (/ (* 2.0 x) (- 1.0 x)))))
float code(float x) {
	return 0.5f * log1pf(((2.0f * x) / (1.0f - x)));
}
function code(x)
	return Float32(Float32(0.5) * log1p(Float32(Float32(Float32(2.0) * x) / Float32(Float32(1.0) - x))))
end
\begin{array}{l}

\\
0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right)
\end{array}
Derivation
  1. Initial program 99.7%

    \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
  2. Add Preprocessing
  3. Add Preprocessing

Alternative 3: 99.2% accurate, 3.2× speedup?

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot 0.14285714285714285, 0.2\right), 0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right) \end{array} \]
(FPCore (x)
 :precision binary32
 (fma
  (fma (* x x) (fma x (* x 0.14285714285714285) 0.2) 0.3333333333333333)
  (* x (* x x))
  x))
float code(float x) {
	return fmaf(fmaf((x * x), fmaf(x, (x * 0.14285714285714285f), 0.2f), 0.3333333333333333f), (x * (x * x)), x);
}
function code(x)
	return fma(fma(Float32(x * x), fma(x, Float32(x * Float32(0.14285714285714285)), Float32(0.2)), Float32(0.3333333333333333)), Float32(x * Float32(x * x)), x)
end
\begin{array}{l}

\\
\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot 0.14285714285714285, 0.2\right), 0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)
\end{array}
Derivation
  1. Initial program 99.7%

    \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in x around 0

    \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right)\right)\right)} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \color{blue}{\left(1 + {x}^{2} \cdot \left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right)\right)\right) \cdot x} \]
    2. +-commutativeN/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right)\right) + 1\right)} \cdot x \]
    3. distribute-lft1-inN/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right)\right)\right) \cdot x + x} \]
    4. *-commutativeN/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right)\right) \cdot {x}^{2}\right)} \cdot x + x \]
    5. associate-*l*N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right)\right) \cdot \left({x}^{2} \cdot x\right)} + x \]
    6. unpow2N/A

      \[\leadsto \left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right)\right) \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot x\right) + x \]
    7. unpow3N/A

      \[\leadsto \left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right)\right) \cdot \color{blue}{{x}^{3}} + x \]
    8. lower-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{3} + {x}^{2} \cdot \left(\frac{1}{5} + \frac{1}{7} \cdot {x}^{2}\right), {x}^{3}, x\right)} \]
  5. Simplified99.1%

    \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot 0.14285714285714285, 0.2\right), 0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]
  6. Add Preprocessing

Alternative 4: 99.0% accurate, 4.5× speedup?

\[\begin{array}{l} \\ \mathsf{fma}\left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right)\right), x, x\right) \end{array} \]
(FPCore (x)
 :precision binary32
 (fma (* x (* x (fma (* x x) 0.2 0.3333333333333333))) x x))
float code(float x) {
	return fmaf((x * (x * fmaf((x * x), 0.2f, 0.3333333333333333f))), x, x);
}
function code(x)
	return fma(Float32(x * Float32(x * fma(Float32(x * x), Float32(0.2), Float32(0.3333333333333333)))), x, x)
end
\begin{array}{l}

\\
\mathsf{fma}\left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right)\right), x, x\right)
\end{array}
Derivation
  1. Initial program 99.7%

    \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in x around 0

    \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right)\right)} \]
  4. Step-by-step derivation
    1. distribute-rgt-inN/A

      \[\leadsto \color{blue}{1 \cdot x + \left({x}^{2} \cdot \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right)\right) \cdot x} \]
    2. *-lft-identityN/A

      \[\leadsto \color{blue}{x} + \left({x}^{2} \cdot \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right)\right) \cdot x \]
    3. +-commutativeN/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right)\right) \cdot x + x} \]
    4. *-commutativeN/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right) \cdot {x}^{2}\right)} \cdot x + x \]
    5. associate-*l*N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right) \cdot \left({x}^{2} \cdot x\right)} + x \]
    6. unpow2N/A

      \[\leadsto \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right) \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot x\right) + x \]
    7. unpow3N/A

      \[\leadsto \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right) \cdot \color{blue}{{x}^{3}} + x \]
    8. lower-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, {x}^{3}, x\right)} \]
    9. +-commutativeN/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{1}{5} \cdot {x}^{2} + \frac{1}{3}}, {x}^{3}, x\right) \]
    10. *-commutativeN/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{1}{5}} + \frac{1}{3}, {x}^{3}, x\right) \]
    11. lower-fma.f32N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{5}, \frac{1}{3}\right)}, {x}^{3}, x\right) \]
    12. unpow2N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{5}, \frac{1}{3}\right), {x}^{3}, x\right) \]
    13. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{5}, \frac{1}{3}\right), {x}^{3}, x\right) \]
    14. cube-multN/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right), \color{blue}{x \cdot \left(x \cdot x\right)}, x\right) \]
    15. unpow2N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right), x \cdot \color{blue}{{x}^{2}}, x\right) \]
    16. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right), \color{blue}{x \cdot {x}^{2}}, x\right) \]
    17. unpow2N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]
    18. lower-*.f3298.6

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]
  5. Simplified98.6%

    \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]
  6. Step-by-step derivation
    1. lift-*.f32N/A

      \[\leadsto \left(\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{5} + \frac{1}{3}\right) \cdot \left(x \cdot \left(x \cdot x\right)\right) + x \]
    2. lift-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)} \cdot \left(x \cdot \left(x \cdot x\right)\right) + x \]
    3. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) + x \]
    4. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} + x \]
    5. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} + x \]
    6. associate-*r*N/A

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot \left(x \cdot x\right)} + x \]
    7. lift-*.f32N/A

      \[\leadsto \left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot \color{blue}{\left(x \cdot x\right)} + x \]
    8. associate-*r*N/A

      \[\leadsto \color{blue}{\left(\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot x\right) \cdot x} + x \]
    9. lower-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot x, x, x\right)} \]
    10. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot x}, x, x\right) \]
    11. *-commutativeN/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(x \cdot \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)\right)} \cdot x, x, x\right) \]
    12. lower-*.f3298.6

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right)\right)} \cdot x, x, x\right) \]
  7. Applied egg-rr98.6%

    \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right)\right) \cdot x, x, x\right)} \]
  8. Final simplification98.6%

    \[\leadsto \mathsf{fma}\left(x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right)\right), x, x\right) \]
  9. Add Preprocessing

Alternative 5: 98.9% accurate, 4.5× speedup?

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), 1\right) \end{array} \]
(FPCore (x)
 :precision binary32
 (* x (fma x (* x (fma (* x x) 0.2 0.3333333333333333)) 1.0)))
float code(float x) {
	return x * fmaf(x, (x * fmaf((x * x), 0.2f, 0.3333333333333333f)), 1.0f);
}
function code(x)
	return Float32(x * fma(x, Float32(x * fma(Float32(x * x), Float32(0.2), Float32(0.3333333333333333))), Float32(1.0)))
end
\begin{array}{l}

\\
x \cdot \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), 1\right)
\end{array}
Derivation
  1. Initial program 99.7%

    \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in x around 0

    \[\leadsto \color{blue}{x \cdot \left(1 + {x}^{2} \cdot \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right)\right)} \]
  4. Step-by-step derivation
    1. distribute-rgt-inN/A

      \[\leadsto \color{blue}{1 \cdot x + \left({x}^{2} \cdot \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right)\right) \cdot x} \]
    2. *-lft-identityN/A

      \[\leadsto \color{blue}{x} + \left({x}^{2} \cdot \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right)\right) \cdot x \]
    3. +-commutativeN/A

      \[\leadsto \color{blue}{\left({x}^{2} \cdot \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right)\right) \cdot x + x} \]
    4. *-commutativeN/A

      \[\leadsto \color{blue}{\left(\left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right) \cdot {x}^{2}\right)} \cdot x + x \]
    5. associate-*l*N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right) \cdot \left({x}^{2} \cdot x\right)} + x \]
    6. unpow2N/A

      \[\leadsto \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right) \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot x\right) + x \]
    7. unpow3N/A

      \[\leadsto \left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}\right) \cdot \color{blue}{{x}^{3}} + x \]
    8. lower-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, {x}^{3}, x\right)} \]
    9. +-commutativeN/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\frac{1}{5} \cdot {x}^{2} + \frac{1}{3}}, {x}^{3}, x\right) \]
    10. *-commutativeN/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{{x}^{2} \cdot \frac{1}{5}} + \frac{1}{3}, {x}^{3}, x\right) \]
    11. lower-fma.f32N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left({x}^{2}, \frac{1}{5}, \frac{1}{3}\right)}, {x}^{3}, x\right) \]
    12. unpow2N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{5}, \frac{1}{3}\right), {x}^{3}, x\right) \]
    13. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(\color{blue}{x \cdot x}, \frac{1}{5}, \frac{1}{3}\right), {x}^{3}, x\right) \]
    14. cube-multN/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right), \color{blue}{x \cdot \left(x \cdot x\right)}, x\right) \]
    15. unpow2N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right), x \cdot \color{blue}{{x}^{2}}, x\right) \]
    16. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right), \color{blue}{x \cdot {x}^{2}}, x\right) \]
    17. unpow2N/A

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]
    18. lower-*.f3298.6

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]
  5. Simplified98.6%

    \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), x \cdot \left(x \cdot x\right), x\right)} \]
  6. Step-by-step derivation
    1. lift-*.f32N/A

      \[\leadsto \left(\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{5} + \frac{1}{3}\right) \cdot \left(x \cdot \left(x \cdot x\right)\right) + x \]
    2. lift-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)} \cdot \left(x \cdot \left(x \cdot x\right)\right) + x \]
    3. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot \left(x \cdot \color{blue}{\left(x \cdot x\right)}\right) + x \]
    4. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} + x \]
    5. lift-*.f32N/A

      \[\leadsto \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)} + x \]
    6. associate-*r*N/A

      \[\leadsto \color{blue}{\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot \left(x \cdot x\right)} + x \]
    7. lift-*.f32N/A

      \[\leadsto \left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot \color{blue}{\left(x \cdot x\right)} + x \]
    8. associate-*r*N/A

      \[\leadsto \color{blue}{\left(\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot x\right) \cdot x} + x \]
    9. lower-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot x, x, x\right)} \]
    10. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right) \cdot x\right) \cdot x}, x, x\right) \]
    11. *-commutativeN/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(x \cdot \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)\right)} \cdot x, x, x\right) \]
    12. lower-*.f3298.6

      \[\leadsto \mathsf{fma}\left(\color{blue}{\left(x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right)\right)} \cdot x, x, x\right) \]
  7. Applied egg-rr98.6%

    \[\leadsto \color{blue}{\mathsf{fma}\left(\left(x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right)\right) \cdot x, x, x\right)} \]
  8. Step-by-step derivation
    1. lift-*.f32N/A

      \[\leadsto \left(\left(x \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{5} + \frac{1}{3}\right)\right) \cdot x\right) \cdot x + x \]
    2. lift-fma.f32N/A

      \[\leadsto \left(\left(x \cdot \color{blue}{\mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)}\right) \cdot x\right) \cdot x + x \]
    3. lift-*.f32N/A

      \[\leadsto \left(\color{blue}{\left(x \cdot \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)\right)} \cdot x\right) \cdot x + x \]
    4. lift-*.f32N/A

      \[\leadsto \color{blue}{\left(\left(x \cdot \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)\right) \cdot x\right)} \cdot x + x \]
    5. distribute-lft1-inN/A

      \[\leadsto \color{blue}{\left(\left(x \cdot \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)\right) \cdot x + 1\right) \cdot x} \]
    6. lower-*.f32N/A

      \[\leadsto \color{blue}{\left(\left(x \cdot \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)\right) \cdot x + 1\right) \cdot x} \]
    7. lift-*.f32N/A

      \[\leadsto \left(\color{blue}{\left(x \cdot \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)\right) \cdot x} + 1\right) \cdot x \]
    8. *-commutativeN/A

      \[\leadsto \left(\color{blue}{x \cdot \left(x \cdot \mathsf{fma}\left(x \cdot x, \frac{1}{5}, \frac{1}{3}\right)\right)} + 1\right) \cdot x \]
    9. lower-fma.f3298.5

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), 1\right)} \cdot x \]
  9. Applied egg-rr98.5%

    \[\leadsto \color{blue}{\mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), 1\right) \cdot x} \]
  10. Final simplification98.5%

    \[\leadsto x \cdot \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x \cdot x, 0.2, 0.3333333333333333\right), 1\right) \]
  11. Add Preprocessing

Alternative 6: 98.4% accurate, 7.4× speedup?

\[\begin{array}{l} \\ \mathsf{fma}\left(0.3333333333333333, x \cdot \left(x \cdot x\right), x\right) \end{array} \]
(FPCore (x) :precision binary32 (fma 0.3333333333333333 (* x (* x x)) x))
float code(float x) {
	return fmaf(0.3333333333333333f, (x * (x * x)), x);
}
function code(x)
	return fma(Float32(0.3333333333333333), Float32(x * Float32(x * x)), x)
end
\begin{array}{l}

\\
\mathsf{fma}\left(0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)
\end{array}
Derivation
  1. Initial program 99.7%

    \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in x around 0

    \[\leadsto \color{blue}{x \cdot \left(1 + \frac{1}{3} \cdot {x}^{2}\right)} \]
  4. Step-by-step derivation
    1. distribute-rgt-inN/A

      \[\leadsto \color{blue}{1 \cdot x + \left(\frac{1}{3} \cdot {x}^{2}\right) \cdot x} \]
    2. *-lft-identityN/A

      \[\leadsto \color{blue}{x} + \left(\frac{1}{3} \cdot {x}^{2}\right) \cdot x \]
    3. +-commutativeN/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} \cdot {x}^{2}\right) \cdot x + x} \]
    4. associate-*l*N/A

      \[\leadsto \color{blue}{\frac{1}{3} \cdot \left({x}^{2} \cdot x\right)} + x \]
    5. unpow2N/A

      \[\leadsto \frac{1}{3} \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot x\right) + x \]
    6. unpow3N/A

      \[\leadsto \frac{1}{3} \cdot \color{blue}{{x}^{3}} + x \]
    7. lower-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{1}{3}, {x}^{3}, x\right)} \]
    8. cube-multN/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{3}, \color{blue}{x \cdot \left(x \cdot x\right)}, x\right) \]
    9. unpow2N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{3}, x \cdot \color{blue}{{x}^{2}}, x\right) \]
    10. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{3}, \color{blue}{x \cdot {x}^{2}}, x\right) \]
    11. unpow2N/A

      \[\leadsto \mathsf{fma}\left(\frac{1}{3}, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]
    12. lower-*.f3297.7

      \[\leadsto \mathsf{fma}\left(0.3333333333333333, x \cdot \color{blue}{\left(x \cdot x\right)}, x\right) \]
  5. Simplified97.7%

    \[\leadsto \color{blue}{\mathsf{fma}\left(0.3333333333333333, x \cdot \left(x \cdot x\right), x\right)} \]
  6. Add Preprocessing

Alternative 7: 98.4% accurate, 7.4× speedup?

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(x \cdot 0.3333333333333333, x, 1\right) \end{array} \]
(FPCore (x) :precision binary32 (* x (fma (* x 0.3333333333333333) x 1.0)))
float code(float x) {
	return x * fmaf((x * 0.3333333333333333f), x, 1.0f);
}
function code(x)
	return Float32(x * fma(Float32(x * Float32(0.3333333333333333)), x, Float32(1.0)))
end
\begin{array}{l}

\\
x \cdot \mathsf{fma}\left(x \cdot 0.3333333333333333, x, 1\right)
\end{array}
Derivation
  1. Initial program 99.7%

    \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
  2. Add Preprocessing
  3. Step-by-step derivation
    1. lift-*.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{\color{blue}{2 \cdot x}}{1 - x}\right) \]
    2. flip--N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{\color{blue}{\frac{1 \cdot 1 - x \cdot x}{1 + x}}}\right) \]
    3. associate-/r/N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\color{blue}{\frac{2 \cdot x}{1 \cdot 1 - x \cdot x} \cdot \left(1 + x\right)}\right) \]
    4. associate-*l/N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\color{blue}{\frac{\left(2 \cdot x\right) \cdot \left(1 + x\right)}{1 \cdot 1 - x \cdot x}}\right) \]
    5. lower-/.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\color{blue}{\frac{\left(2 \cdot x\right) \cdot \left(1 + x\right)}{1 \cdot 1 - x \cdot x}}\right) \]
    6. lift-*.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{\color{blue}{\left(2 \cdot x\right)} \cdot \left(1 + x\right)}{1 \cdot 1 - x \cdot x}\right) \]
    7. associate-*l*N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{\color{blue}{2 \cdot \left(x \cdot \left(1 + x\right)\right)}}{1 \cdot 1 - x \cdot x}\right) \]
    8. +-commutativeN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \left(x \cdot \color{blue}{\left(x + 1\right)}\right)}{1 \cdot 1 - x \cdot x}\right) \]
    9. distribute-rgt-outN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \color{blue}{\left(x \cdot x + 1 \cdot x\right)}}{1 \cdot 1 - x \cdot x}\right) \]
    10. lower-*.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{\color{blue}{2 \cdot \left(x \cdot x + 1 \cdot x\right)}}{1 \cdot 1 - x \cdot x}\right) \]
    11. *-lft-identityN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \left(x \cdot x + \color{blue}{x}\right)}{1 \cdot 1 - x \cdot x}\right) \]
    12. lower-fma.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \color{blue}{\mathsf{fma}\left(x, x, x\right)}}{1 \cdot 1 - x \cdot x}\right) \]
    13. metadata-evalN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{1} - x \cdot x}\right) \]
    14. sub-negN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{1 + \left(\mathsf{neg}\left(x \cdot x\right)\right)}}\right) \]
    15. +-commutativeN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{\left(\mathsf{neg}\left(x \cdot x\right)\right) + 1}}\right) \]
    16. distribute-rgt-neg-inN/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{x \cdot \left(\mathsf{neg}\left(x\right)\right)} + 1}\right) \]
    17. lower-fma.f32N/A

      \[\leadsto \frac{1}{2} \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\color{blue}{\mathsf{fma}\left(x, \mathsf{neg}\left(x\right), 1\right)}}\right) \]
    18. lower-neg.f3299.9

      \[\leadsto 0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\mathsf{fma}\left(x, \color{blue}{-x}, 1\right)}\right) \]
  4. Applied egg-rr99.9%

    \[\leadsto 0.5 \cdot \mathsf{log1p}\left(\color{blue}{\frac{2 \cdot \mathsf{fma}\left(x, x, x\right)}{\mathsf{fma}\left(x, -x, 1\right)}}\right) \]
  5. Taylor expanded in x around 0

    \[\leadsto \color{blue}{x \cdot \left(1 + \frac{1}{3} \cdot {x}^{2}\right)} \]
  6. Step-by-step derivation
    1. +-commutativeN/A

      \[\leadsto x \cdot \color{blue}{\left(\frac{1}{3} \cdot {x}^{2} + 1\right)} \]
    2. distribute-lft-inN/A

      \[\leadsto \color{blue}{x \cdot \left(\frac{1}{3} \cdot {x}^{2}\right) + x \cdot 1} \]
    3. *-rgt-identityN/A

      \[\leadsto x \cdot \left(\frac{1}{3} \cdot {x}^{2}\right) + \color{blue}{x} \]
    4. lower-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, \frac{1}{3} \cdot {x}^{2}, x\right)} \]
    5. *-commutativeN/A

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{{x}^{2} \cdot \frac{1}{3}}, x\right) \]
    6. lower-*.f32N/A

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{{x}^{2} \cdot \frac{1}{3}}, x\right) \]
    7. unpow2N/A

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{3}, x\right) \]
    8. lower-*.f3297.7

      \[\leadsto \mathsf{fma}\left(x, \color{blue}{\left(x \cdot x\right)} \cdot 0.3333333333333333, x\right) \]
  7. Simplified97.7%

    \[\leadsto \color{blue}{\mathsf{fma}\left(x, \left(x \cdot x\right) \cdot 0.3333333333333333, x\right)} \]
  8. Step-by-step derivation
    1. lift-*.f32N/A

      \[\leadsto x \cdot \left(\color{blue}{\left(x \cdot x\right)} \cdot \frac{1}{3}\right) + x \]
    2. lift-*.f32N/A

      \[\leadsto x \cdot \color{blue}{\left(\left(x \cdot x\right) \cdot \frac{1}{3}\right)} + x \]
    3. *-commutativeN/A

      \[\leadsto \color{blue}{\left(\left(x \cdot x\right) \cdot \frac{1}{3}\right) \cdot x} + x \]
    4. distribute-lft1-inN/A

      \[\leadsto \color{blue}{\left(\left(x \cdot x\right) \cdot \frac{1}{3} + 1\right) \cdot x} \]
    5. lower-*.f32N/A

      \[\leadsto \color{blue}{\left(\left(x \cdot x\right) \cdot \frac{1}{3} + 1\right) \cdot x} \]
    6. lift-*.f32N/A

      \[\leadsto \left(\color{blue}{\left(x \cdot x\right) \cdot \frac{1}{3}} + 1\right) \cdot x \]
    7. lower-fma.f3297.6

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 0.3333333333333333, 1\right)} \cdot x \]
  9. Applied egg-rr97.6%

    \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot x, 0.3333333333333333, 1\right) \cdot x} \]
  10. Step-by-step derivation
    1. associate-*l*N/A

      \[\leadsto \left(\color{blue}{x \cdot \left(x \cdot \frac{1}{3}\right)} + 1\right) \cdot x \]
    2. *-commutativeN/A

      \[\leadsto \left(\color{blue}{\left(x \cdot \frac{1}{3}\right) \cdot x} + 1\right) \cdot x \]
    3. lower-fma.f32N/A

      \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot \frac{1}{3}, x, 1\right)} \cdot x \]
    4. lower-*.f3297.6

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot 0.3333333333333333}, x, 1\right) \cdot x \]
  11. Applied egg-rr97.6%

    \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot 0.3333333333333333, x, 1\right)} \cdot x \]
  12. Final simplification97.6%

    \[\leadsto x \cdot \mathsf{fma}\left(x \cdot 0.3333333333333333, x, 1\right) \]
  13. Add Preprocessing

Alternative 8: 96.8% accurate, 125.0× speedup?

\[\begin{array}{l} \\ x \end{array} \]
(FPCore (x) :precision binary32 x)
float code(float x) {
	return x;
}
real(4) function code(x)
    real(4), intent (in) :: x
    code = x
end function
function code(x)
	return x
end
function tmp = code(x)
	tmp = x;
end
\begin{array}{l}

\\
x
\end{array}
Derivation
  1. Initial program 99.7%

    \[0.5 \cdot \mathsf{log1p}\left(\frac{2 \cdot x}{1 - x}\right) \]
  2. Add Preprocessing
  3. Taylor expanded in x around 0

    \[\leadsto \frac{1}{2} \cdot \color{blue}{\left(2 \cdot x\right)} \]
  4. Step-by-step derivation
    1. *-commutativeN/A

      \[\leadsto \frac{1}{2} \cdot \color{blue}{\left(x \cdot 2\right)} \]
    2. lower-*.f3295.8

      \[\leadsto 0.5 \cdot \color{blue}{\left(x \cdot 2\right)} \]
  5. Simplified95.8%

    \[\leadsto 0.5 \cdot \color{blue}{\left(x \cdot 2\right)} \]
  6. Step-by-step derivation
    1. lift-*.f32N/A

      \[\leadsto \frac{1}{2} \cdot \color{blue}{\left(x \cdot 2\right)} \]
    2. *-commutativeN/A

      \[\leadsto \color{blue}{\left(x \cdot 2\right) \cdot \frac{1}{2}} \]
    3. lift-*.f32N/A

      \[\leadsto \color{blue}{\left(x \cdot 2\right)} \cdot \frac{1}{2} \]
    4. associate-*l*N/A

      \[\leadsto \color{blue}{x \cdot \left(2 \cdot \frac{1}{2}\right)} \]
    5. metadata-evalN/A

      \[\leadsto x \cdot \color{blue}{1} \]
    6. lower-*.f3295.8

      \[\leadsto \color{blue}{x \cdot 1} \]
  7. Applied egg-rr95.8%

    \[\leadsto \color{blue}{x \cdot 1} \]
  8. Step-by-step derivation
    1. *-rgt-identity95.8

      \[\leadsto \color{blue}{x} \]
  9. Applied egg-rr95.8%

    \[\leadsto \color{blue}{x} \]
  10. Add Preprocessing

Reproduce

?
herbie shell --seed 2024207 
(FPCore (x)
  :name "Rust f32::atanh"
  :precision binary32
  (* 0.5 (log1p (/ (* 2.0 x) (- 1.0 x)))))