Rust f32::acosh

Percentage Accurate: 51.9% → 99.1%

Time: 8.0s

Alternatives: 6

Speedup: 1.2×

Specification

\[x \geq 1\]

Math

\[\begin{array}{l} \\ \cosh^{-1} x \end{array} \]

FPCore

(FPCore (x) :precision binary32 (acosh x))

C

float code(float x) {
	return acoshf(x);
}

Julia

function code(x)
	return acosh(x)
end

MATLAB

function tmp = code(x)
	tmp = acosh(x);
end

Initial Program: 51.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary32 (log (+ x (sqrt (- (* x x) 1.0)))))

C

float code(float x) {
	return logf((x + sqrtf(((x * x) - 1.0f))));
}

Fortran

real(4) function code(x)
    real(4), intent (in) :: x
    code = log((x + sqrt(((x * x) - 1.0e0))))
end function

Julia

function code(x)
	return log(Float32(x + sqrt(Float32(Float32(x * x) - Float32(1.0)))))
end

MATLAB

function tmp = code(x)
	tmp = log((x + sqrt(((x * x) - single(1.0)))));
end

Alternative 1: 99.1% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \log \left(\mathsf{fma}\left(\sqrt{x - 1}, \sqrt{1 + x}, x\right)\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary32
 (log (fma (sqrt (- x 1.0)) (sqrt (+ 1.0 x)) x)))

C

float code(float x) {
	return logf(fmaf(sqrtf((x - 1.0f)), sqrtf((1.0f + x)), x));
}

Julia

function code(x)
	return log(fma(sqrt(Float32(x - Float32(1.0))), sqrt(Float32(Float32(1.0) + x)), x))
end

Derivation

1. Initial program 58.3%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-+.f32 N/A

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

   2. +-commutative N/A

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

   3. lift-sqrt.f32 N/A

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

   4. pow1/2 N/A

      \[\leadsto \log \left(\color{blue}{{\left(x \cdot x - 1\right)}^{\frac{1}{2}}} + x\right) \]

   5. lift--.f32 N/A

      \[\leadsto \log \left({\color{blue}{\left(x \cdot x - 1\right)}}^{\frac{1}{2}} + x\right) \]

   6. lift-*.f32 N/A

      \[\leadsto \log \left({\left(\color{blue}{x \cdot x} - 1\right)}^{\frac{1}{2}} + x\right) \]

   7. difference-of-sqr-1 N/A

      \[\leadsto \log \left({\color{blue}{\left(\left(x + 1\right) \cdot \left(x - 1\right)\right)}}^{\frac{1}{2}} + x\right) \]

   8. *-commutative N/A

      \[\leadsto \log \left({\color{blue}{\left(\left(x - 1\right) \cdot \left(x + 1\right)\right)}}^{\frac{1}{2}} + x\right) \]

   9. unpow-prod-down N/A

      \[\leadsto \log \left(\color{blue}{{\left(x - 1\right)}^{\frac{1}{2}} \cdot {\left(x + 1\right)}^{\frac{1}{2}}} + x\right) \]

   10. lower-fma.f32 N/A

       \[\leadsto \log \color{blue}{\left(\mathsf{fma}\left({\left(x - 1\right)}^{\frac{1}{2}}, {\left(x + 1\right)}^{\frac{1}{2}}, x\right)\right)} \]

   11. pow1/2 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\color{blue}{\sqrt{x - 1}}, {\left(x + 1\right)}^{\frac{1}{2}}, x\right)\right) \]

   12. lower-sqrt.f32 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\color{blue}{\sqrt{x - 1}}, {\left(x + 1\right)}^{\frac{1}{2}}, x\right)\right) \]

   13. lower--.f32 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{\color{blue}{x - 1}}, {\left(x + 1\right)}^{\frac{1}{2}}, x\right)\right) \]

   14. pow1/2 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x - 1}, \color{blue}{\sqrt{x + 1}}, x\right)\right) \]

   15. lower-sqrt.f32 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x - 1}, \color{blue}{\sqrt{x + 1}}, x\right)\right) \]

   16. +-commutative N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x - 1}, \sqrt{\color{blue}{1 + x}}, x\right)\right) \]

   17. lower-+.f32 99.6

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x - 1}, \sqrt{\color{blue}{1 + x}}, x\right)\right) \]

4. Applied rewrites 99.6%

   \[\leadsto \log \color{blue}{\left(\mathsf{fma}\left(\sqrt{x - 1}, \sqrt{1 + x}, x\right)\right)} \]
5. Add Preprocessing

Alternative 2: 98.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \log \left(\left(x - \frac{0.5}{x}\right) + x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary32 (log (+ (- x (/ 0.5 x)) x)))

C

float code(float x) {
	return logf(((x - (0.5f / x)) + x));
}

Fortran

real(4) function code(x)
    real(4), intent (in) :: x
    code = log(((x - (0.5e0 / x)) + x))
end function

Julia

function code(x)
	return log(Float32(Float32(x - Float32(Float32(0.5) / x)) + x))
end

MATLAB

function tmp = code(x)
	tmp = log(((x - (single(0.5) / x)) + x));
end

Derivation

1. Initial program 58.3%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \log \left(x + \color{blue}{x \cdot \left(1 - \frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)}\right) \]
4. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto \log \left(x + x \cdot \color{blue}{\left(1 + \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}\right) \]

   2. distribute-lft-in N/A

      \[\leadsto \log \left(x + \color{blue}{\left(x \cdot 1 + x \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}\right) \]

   3. *-rgt-identity N/A

      \[\leadsto \log \left(x + \left(\color{blue}{x} + x \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right) \]

   4. distribute-rgt-neg-out N/A

      \[\leadsto \log \left(x + \left(x + \color{blue}{\left(\mathsf{neg}\left(x \cdot \left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}\right)\right) \]

   5. unsub-neg N/A

      \[\leadsto \log \left(x + \color{blue}{\left(x - x \cdot \left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)}\right) \]

   6. remove-double-neg N/A

      \[\leadsto \log \left(x + \left(x - \color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(x \cdot \left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)\right)}\right)\right) \]

   7. distribute-rgt-neg-out N/A

      \[\leadsto \log \left(x + \left(x - \left(\mathsf{neg}\left(\color{blue}{x \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)}\right)\right)\right)\right) \]

   8. distribute-lft-neg-out N/A

      \[\leadsto \log \left(x + \left(x - \color{blue}{\left(\mathsf{neg}\left(x\right)\right) \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)}\right)\right) \]

   9. mul-1-neg N/A

      \[\leadsto \log \left(x + \left(x - \color{blue}{\left(-1 \cdot x\right)} \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right) \]

   10. *-commutative N/A

       \[\leadsto \log \left(x + \left(x - \color{blue}{\left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot \left(-1 \cdot x\right)}\right)\right) \]

   11. lower--.f32 N/A

       \[\leadsto \log \left(x + \color{blue}{\left(x - \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right) \cdot \left(-1 \cdot x\right)\right)}\right) \]

   12. *-commutative N/A

       \[\leadsto \log \left(x + \left(x - \color{blue}{\left(-1 \cdot x\right) \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)}\right)\right) \]

   13. mul-1-neg N/A

       \[\leadsto \log \left(x + \left(x - \color{blue}{\left(\mathsf{neg}\left(x\right)\right)} \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right) \]

   14. distribute-lft-neg-out N/A

       \[\leadsto \log \left(x + \left(x - \color{blue}{\left(\mathsf{neg}\left(x \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)}\right)\right) \]

   15. *-commutative N/A

       \[\leadsto \log \left(x + \left(x - \left(\mathsf{neg}\left(x \cdot \left(\mathsf{neg}\left(\color{blue}{\frac{1}{{x}^{2}} \cdot \frac{1}{2}}\right)\right)\right)\right)\right)\right) \]

   16. distribute-rgt-neg-in N/A

       \[\leadsto \log \left(x + \left(x - \left(\mathsf{neg}\left(x \cdot \color{blue}{\left(\frac{1}{{x}^{2}} \cdot \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)\right)}\right)\right)\right)\right) \]

   17. metadata-eval N/A

       \[\leadsto \log \left(x + \left(x - \left(\mathsf{neg}\left(x \cdot \left(\frac{1}{{x}^{2}} \cdot \color{blue}{\frac{-1}{2}}\right)\right)\right)\right)\right) \]

5. Applied rewrites 98.0%

   \[\leadsto \log \left(x + \color{blue}{\left(x - \frac{0.5}{x}\right)}\right) \]

6. Final simplification 98.0%

   \[\leadsto \log \left(\left(x - \frac{0.5}{x}\right) + x\right) \]
7. Add Preprocessing

Alternative 3: 98.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \log \left(\mathsf{fma}\left(2, x, \frac{-0.5}{x}\right)\right) \end{array} \]

FPCore

(FPCore (x) :precision binary32 (log (fma 2.0 x (/ -0.5 x))))

C

float code(float x) {
	return logf(fmaf(2.0f, x, (-0.5f / x)));
}

Julia

function code(x)
	return log(fma(Float32(2.0), x, Float32(Float32(-0.5) / x)))
end

Derivation

1. Initial program 58.3%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around inf

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

   1. sub-neg N/A

      \[\leadsto \log \left(x \cdot \color{blue}{\left(2 + \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)}\right) \]

   2. distribute-lft-in N/A

      \[\leadsto \log \color{blue}{\left(x \cdot 2 + x \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)} \]

   3. *-commutative N/A

      \[\leadsto \log \left(\color{blue}{2 \cdot x} + x \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right) \]

   4. lower-fma.f32 N/A

      \[\leadsto \log \color{blue}{\left(\mathsf{fma}\left(2, x, x \cdot \left(\mathsf{neg}\left(\frac{1}{2} \cdot \frac{1}{{x}^{2}}\right)\right)\right)\right)} \]

   5. *-commutative N/A

      \[\leadsto \log \left(\mathsf{fma}\left(2, x, x \cdot \left(\mathsf{neg}\left(\color{blue}{\frac{1}{{x}^{2}} \cdot \frac{1}{2}}\right)\right)\right)\right) \]

   6. distribute-rgt-neg-in N/A

      \[\leadsto \log \left(\mathsf{fma}\left(2, x, x \cdot \color{blue}{\left(\frac{1}{{x}^{2}} \cdot \left(\mathsf{neg}\left(\frac{1}{2}\right)\right)\right)}\right)\right) \]

   7. metadata-eval N/A

      \[\leadsto \log \left(\mathsf{fma}\left(2, x, x \cdot \left(\frac{1}{{x}^{2}} \cdot \color{blue}{\frac{-1}{2}}\right)\right)\right) \]

   8. associate-*r* N/A

      \[\leadsto \log \left(\mathsf{fma}\left(2, x, \color{blue}{\left(x \cdot \frac{1}{{x}^{2}}\right) \cdot \frac{-1}{2}}\right)\right) \]

   9. associate-/l* N/A

      \[\leadsto \log \left(\mathsf{fma}\left(2, x, \color{blue}{\frac{x \cdot 1}{{x}^{2}}} \cdot \frac{-1}{2}\right)\right) \]

   10. *-rgt-identity N/A

       \[\leadsto \log \left(\mathsf{fma}\left(2, x, \frac{\color{blue}{x}}{{x}^{2}} \cdot \frac{-1}{2}\right)\right) \]

   11. unpow2 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(2, x, \frac{x}{\color{blue}{x \cdot x}} \cdot \frac{-1}{2}\right)\right) \]

   12. associate-/r* N/A

       \[\leadsto \log \left(\mathsf{fma}\left(2, x, \color{blue}{\frac{\frac{x}{x}}{x}} \cdot \frac{-1}{2}\right)\right) \]

   13. *-inverses N/A

       \[\leadsto \log \left(\mathsf{fma}\left(2, x, \frac{\color{blue}{1}}{x} \cdot \frac{-1}{2}\right)\right) \]

   14. associate-*l/ N/A

       \[\leadsto \log \left(\mathsf{fma}\left(2, x, \color{blue}{\frac{1 \cdot \frac{-1}{2}}{x}}\right)\right) \]

   15. metadata-eval N/A

       \[\leadsto \log \left(\mathsf{fma}\left(2, x, \frac{\color{blue}{\frac{-1}{2}}}{x}\right)\right) \]

   16. lower-/.f32 98.0

       \[\leadsto \log \left(\mathsf{fma}\left(2, x, \color{blue}{\frac{-0.5}{x}}\right)\right) \]

5. Applied rewrites 98.0%

   \[\leadsto \log \color{blue}{\left(\mathsf{fma}\left(2, x, \frac{-0.5}{x}\right)\right)} \]
6. Add Preprocessing

Alternative 4: 97.7% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ -\log \left(\frac{0.5}{x}\right) \end{array} \]

FPCore

(FPCore (x) :precision binary32 (- (log (/ 0.5 x))))

C

float code(float x) {
	return -logf((0.5f / x));
}

Fortran

real(4) function code(x)
    real(4), intent (in) :: x
    code = -log((0.5e0 / x))
end function

Julia

function code(x)
	return Float32(-log(Float32(Float32(0.5) / x)))
end

MATLAB

function tmp = code(x)
	tmp = -log((single(0.5) / x));
end

Derivation

1. Initial program 58.3%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing

3. Taylor expanded in x around inf

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

   1. +-commutative N/A

      \[\leadsto \color{blue}{-1 \cdot \log \left(\frac{1}{x}\right) + \log 2} \]

   2. lower-+.f32 N/A

      \[\leadsto \color{blue}{-1 \cdot \log \left(\frac{1}{x}\right) + \log 2} \]

   3. mul-1-neg N/A

      \[\leadsto \color{blue}{\left(\mathsf{neg}\left(\log \left(\frac{1}{x}\right)\right)\right)} + \log 2 \]

   4. log-rec N/A

      \[\leadsto \left(\mathsf{neg}\left(\color{blue}{\left(\mathsf{neg}\left(\log x\right)\right)}\right)\right) + \log 2 \]

   5. remove-double-neg N/A

      \[\leadsto \color{blue}{\log x} + \log 2 \]

   6. lower-log.f32 N/A

      \[\leadsto \color{blue}{\log x} + \log 2 \]

   7. lower-log.f32 96.0

      \[\leadsto \log x + \color{blue}{\log 2} \]

5. Applied rewrites 96.0%

   \[\leadsto \color{blue}{\log x + \log 2} \]
6. Step-by-step derivation

7. Applied rewrites 96.8%

   \[\leadsto -\log \left(\frac{0.5}{x}\right) \]
8. Add Preprocessing

Alternative 5: 96.8% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \log \left(x + x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary32 (log (+ x x)))

C

float code(float x) {
	return logf((x + x));
}

Fortran

real(4) function code(x)
    real(4), intent (in) :: x
    code = log((x + x))
end function

Julia

function code(x)
	return log(Float32(x + x))
end

MATLAB

function tmp = code(x)
	tmp = log((x + x));
end

Derivation

1. Initial program 58.3%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift--.f32 N/A

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

   2. lift-*.f32 N/A

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

   3. difference-of-sqr-1 N/A

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

   4. *-commutative N/A

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

   5. lower-*.f32 N/A

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

   6. lower--.f32 N/A

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

   7. +-commutative N/A

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

   8. lower-+.f32 58.3

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

4. Applied rewrites 58.3%

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

5. Taylor expanded in x around -inf

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

   1. *-commutative N/A

      \[\leadsto \log \left(x + -1 \cdot \color{blue}{\left({\left(\sqrt{-1}\right)}^{2} \cdot x\right)}\right) \]

   2. unpow2 N/A

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

   3. rem-square-sqrt N/A

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

   4. associate-*r* N/A

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

   5. metadata-eval N/A

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

   6. *-lft-identity 96.4

      \[\leadsto \log \left(x + \color{blue}{x}\right) \]

7. Applied rewrites 96.4%

   \[\leadsto \log \left(x + \color{blue}{x}\right) \]

8. Final simplification 96.4%

   \[\leadsto \log \left(x + x\right) \]
9. Add Preprocessing

Alternative 6: 1.3% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \log 0 \end{array} \]

FPCore

(FPCore (x) :precision binary32 (log 0.0))

C

float code(float x) {
	return logf(0.0f);
}

Fortran

real(4) function code(x)
    real(4), intent (in) :: x
    code = log(0.0e0)
end function

Julia

function code(x)
	return log(Float32(0.0))
end

MATLAB

function tmp = code(x)
	tmp = log(single(0.0));
end

Derivation

1. Initial program 58.3%

   \[\log \left(x + \sqrt{x \cdot x - 1}\right) \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-+.f32 N/A

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

   2. unpow1 N/A

      \[\leadsto \log \left(\color{blue}{{x}^{1}} + \sqrt{x \cdot x - 1}\right) \]

   3. sqr-pow N/A

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

   4. lower-fma.f32 N/A

      \[\leadsto \log \color{blue}{\left(\mathsf{fma}\left({x}^{\left(\frac{1}{2}\right)}, {x}^{\left(\frac{1}{2}\right)}, \sqrt{x \cdot x - 1}\right)\right)} \]

   5. metadata-eval N/A

      \[\leadsto \log \left(\mathsf{fma}\left({x}^{\color{blue}{\frac{1}{2}}}, {x}^{\left(\frac{1}{2}\right)}, \sqrt{x \cdot x - 1}\right)\right) \]

   6. unpow1/2 N/A

      \[\leadsto \log \left(\mathsf{fma}\left(\color{blue}{\sqrt{x}}, {x}^{\left(\frac{1}{2}\right)}, \sqrt{x \cdot x - 1}\right)\right) \]

   7. lower-sqrt.f32 N/A

      \[\leadsto \log \left(\mathsf{fma}\left(\color{blue}{\sqrt{x}}, {x}^{\left(\frac{1}{2}\right)}, \sqrt{x \cdot x - 1}\right)\right) \]

   8. metadata-eval N/A

      \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x}, {x}^{\color{blue}{\frac{1}{2}}}, \sqrt{x \cdot x - 1}\right)\right) \]

   9. unpow1/2 N/A

      \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x}, \color{blue}{\sqrt{x}}, \sqrt{x \cdot x - 1}\right)\right) \]

   10. lower-sqrt.f32 58.3

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x}, \color{blue}{\sqrt{x}}, \sqrt{x \cdot x - 1}\right)\right) \]

   11. lift--.f32 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x}, \sqrt{x}, \sqrt{\color{blue}{x \cdot x - 1}}\right)\right) \]

   12. sub-neg N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x}, \sqrt{x}, \sqrt{\color{blue}{x \cdot x + \left(\mathsf{neg}\left(1\right)\right)}}\right)\right) \]

   13. lift-*.f32 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x}, \sqrt{x}, \sqrt{\color{blue}{x \cdot x} + \left(\mathsf{neg}\left(1\right)\right)}\right)\right) \]

   14. lower-fma.f32 N/A

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x}, \sqrt{x}, \sqrt{\color{blue}{\mathsf{fma}\left(x, x, \mathsf{neg}\left(1\right)\right)}}\right)\right) \]

   15. metadata-eval 58.3

       \[\leadsto \log \left(\mathsf{fma}\left(\sqrt{x}, \sqrt{x}, \sqrt{\mathsf{fma}\left(x, x, \color{blue}{-1}\right)}\right)\right) \]

4. Applied rewrites 58.3%

   \[\leadsto \log \color{blue}{\left(\mathsf{fma}\left(\sqrt{x}, \sqrt{x}, \sqrt{\mathsf{fma}\left(x, x, -1\right)}\right)\right)} \]

5. Taylor expanded in x around -inf

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

   1. associate-*r* N/A

      \[\leadsto \log \color{blue}{\left(\left(-1 \cdot x\right) \cdot \left(1 + {\left(\sqrt{-1}\right)}^{2}\right)\right)} \]

   2. unpow2 N/A

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

   3. rem-square-sqrt N/A

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

   4. metadata-eval N/A

      \[\leadsto \log \left(\left(-1 \cdot x\right) \cdot \color{blue}{0}\right) \]

   5. mul0-rgt 1.3

      \[\leadsto \log \color{blue}{0} \]

7. Applied rewrites 1.3%

   \[\leadsto \log \color{blue}{0} \]
8. Add Preprocessing

Developer Target 1: 99.0% accurate, 0.9× speedup

Math

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

FPCore

(FPCore (x)
 :precision binary32
 (log (+ x (* (sqrt (- x 1.0)) (sqrt (+ x 1.0))))))

C

float code(float x) {
	return logf((x + (sqrtf((x - 1.0f)) * sqrtf((x + 1.0f)))));
}

Fortran

real(4) function code(x)
    real(4), intent (in) :: x
    code = log((x + (sqrt((x - 1.0e0)) * sqrt((x + 1.0e0)))))
end function

Julia

function code(x)
	return log(Float32(x + Float32(sqrt(Float32(x - Float32(1.0))) * sqrt(Float32(x + Float32(1.0))))))
end

MATLAB

function tmp = code(x)
	tmp = log((x + (sqrt((x - single(1.0))) * sqrt((x + single(1.0))))));
end

Reproduce

herbie shell --seed 2024249 
(FPCore (x)
  :name "Rust f32::acosh"
  :precision binary32
  :pre (>= x 1.0)

  :alt
  (! :herbie-platform default (log (+ x (* (sqrt (- x 1)) (sqrt (+ x 1))))))

  (log (+ x (sqrt (- (* x x) 1.0)))))