Hyperbolic arc-(co)tangent

Percentage Accurate: 8.6% → 99.8%

Time: 10.8s

Alternatives: 6

Speedup: 134.0×

Specification

Math

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

FPCore

(FPCore (x) :precision binary64 (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))

C

double code(double x) {
	return (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 / 2.0d0) * log(((1.0d0 + x) / (1.0d0 - x)))
end function

Java

public static double code(double x) {
	return (1.0 / 2.0) * Math.log(((1.0 + x) / (1.0 - x)));
}

Python

def code(x):
	return (1.0 / 2.0) * math.log(((1.0 + x) / (1.0 - x)))

Julia

function code(x)
	return Float64(Float64(1.0 / 2.0) * log(Float64(Float64(1.0 + x) / Float64(1.0 - x))))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
end

Wolfram

code[x_] := N[(N[(1.0 / 2.0), $MachinePrecision] * N[Log[N[(N[(1.0 + x), $MachinePrecision] / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 8.6% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x) :precision binary64 (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))

C

double code(double x) {
	return (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (1.0d0 / 2.0d0) * log(((1.0d0 + x) / (1.0d0 - x)))
end function

Java

public static double code(double x) {
	return (1.0 / 2.0) * Math.log(((1.0 + x) / (1.0 - x)));
}

Python

def code(x):
	return (1.0 / 2.0) * math.log(((1.0 + x) / (1.0 - x)))

Julia

function code(x)
	return Float64(Float64(1.0 / 2.0) * log(Float64(Float64(1.0 + x) / Float64(1.0 - x))))
end

MATLAB

function tmp = code(x)
	tmp = (1.0 / 2.0) * log(((1.0 + x) / (1.0 - x)));
end

Wolfram

code[x_] := N[(N[(1.0 / 2.0), $MachinePrecision] * N[Log[N[(N[(1.0 + x), $MachinePrecision] / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x, x \cdot 0.14285714285714285, 0.2\right), 0.3333333333333333\right), 1\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (*
  x
  (fma
   (* x x)
   (fma x (* x (fma x (* x 0.14285714285714285) 0.2)) 0.3333333333333333)
   1.0)))

C

double code(double x) {
	return x * fma((x * x), fma(x, (x * fma(x, (x * 0.14285714285714285), 0.2)), 0.3333333333333333), 1.0);
}

Julia

function code(x)
	return Float64(x * fma(Float64(x * x), fma(x, Float64(x * fma(x, Float64(x * 0.14285714285714285), 0.2)), 0.3333333333333333), 1.0))
end

Wolfram

code[x_] := N[(x * N[(N[(x * x), $MachinePrecision] * N[(x * N[(x * N[(x * N[(x * 0.14285714285714285), $MachinePrecision] + 0.2), $MachinePrecision]), $MachinePrecision] + 0.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 8.4%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
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. *-lowering-*.f64 N/A

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

   2. +-commutative N/A

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

   3. accelerator-lowering-fma.f64 N/A

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

5. Simplified 100.0%

   \[\leadsto \color{blue}{x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x, x \cdot 0.14285714285714285, 0.2\right), 0.3333333333333333\right), 1\right)} \]
6. Step-by-step derivation

   1. +-rgt-identity N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x, x \cdot \frac{1}{7}, \frac{1}{5}\right), \frac{1}{3}\right), 1\right) \]

   2. *-lowering-*.f64 100.0

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x, x \cdot 0.14285714285714285, 0.2\right), 0.3333333333333333\right), 1\right) \]

7. Applied egg-rr 100.0%

   \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \mathsf{fma}\left(x, x \cdot \mathsf{fma}\left(x, x \cdot 0.14285714285714285, 0.2\right), 0.3333333333333333\right), 1\right) \]
8. Add Preprocessing

Alternative 2: 99.7% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x \cdot \left(x \cdot \mathsf{fma}\left(x, x \cdot 0.2, 0.3333333333333333\right)\right), x, x\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (fma (* x (* x (fma x (* x 0.2) 0.3333333333333333))) x x))

C

double code(double x) {
	return fma((x * (x * fma(x, (x * 0.2), 0.3333333333333333))), x, x);
}

Julia

function code(x)
	return fma(Float64(x * Float64(x * fma(x, Float64(x * 0.2), 0.3333333333333333))), x, x)
end

Wolfram

code[x_] := N[(N[(x * N[(x * N[(x * N[(x * 0.2), $MachinePrecision] + 0.3333333333333333), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * x + x), $MachinePrecision]

Derivation

1. Initial program 8.4%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
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. *-lowering-*.f64 N/A

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

   2. +-commutative N/A

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

   3. accelerator-lowering-fma.f64 N/A

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

   4. remove-double-neg N/A

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

   5. +-rgt-identity N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{neg}\left(\color{blue}{\left(\left(\mathsf{neg}\left({x}^{2}\right)\right) + 0\right)}\right), \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   6. distribute-neg-in N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left({x}^{2}\right)\right)\right)\right) + \left(\mathsf{neg}\left(0\right)\right)}, \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   7. remove-double-neg N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{{x}^{2}} + \left(\mathsf{neg}\left(0\right)\right), \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   8. unpow2 N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x} + \left(\mathsf{neg}\left(0\right)\right), \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   9. metadata-eval N/A

      \[\leadsto x \cdot \mathsf{fma}\left(x \cdot x + \color{blue}{0}, \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   10. accelerator-lowering-fma.f64 N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(x, x, 0\right)}, \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   11. +-commutative N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{\frac{1}{5} \cdot {x}^{2} + \frac{1}{3}}, 1\right) \]

   12. unpow2 N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \frac{1}{5} \cdot \color{blue}{\left(x \cdot x\right)} + \frac{1}{3}, 1\right) \]

   13. associate-*r* N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{\left(\frac{1}{5} \cdot x\right) \cdot x} + \frac{1}{3}, 1\right) \]

   14. *-commutative N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{x \cdot \left(\frac{1}{5} \cdot x\right)} + \frac{1}{3}, 1\right) \]

   15. accelerator-lowering-fma.f64 N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{\mathsf{fma}\left(x, \frac{1}{5} \cdot x, \frac{1}{3}\right)}, 1\right) \]

   16. *-commutative N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \mathsf{fma}\left(x, \color{blue}{x \cdot \frac{1}{5}}, \frac{1}{3}\right), 1\right) \]

   17. *-lowering-*.f64 100.0

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \mathsf{fma}\left(x, \color{blue}{x \cdot 0.2}, 0.3333333333333333\right), 1\right) \]

5. Simplified 100.0%

   \[\leadsto \color{blue}{x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \mathsf{fma}\left(x, x \cdot 0.2, 0.3333333333333333\right), 1\right)} \]
6. Step-by-step derivation

   1. +-rgt-identity N/A

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

   2. *-lowering-*.f64 100.0

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \mathsf{fma}\left(x, x \cdot 0.2, 0.3333333333333333\right), 1\right) \]

7. Applied egg-rr 100.0%

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

   1. distribute-rgt-in N/A

      \[\leadsto \color{blue}{\left(\left(x \cdot x\right) \cdot \left(x \cdot \left(x \cdot \frac{1}{5}\right) + \frac{1}{3}\right)\right) \cdot x + 1 \cdot x} \]

   2. *-lft-identity N/A

      \[\leadsto \left(\left(x \cdot x\right) \cdot \left(x \cdot \left(x \cdot \frac{1}{5}\right) + \frac{1}{3}\right)\right) \cdot x + \color{blue}{x} \]

   3. accelerator-lowering-fma.f64 N/A

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

   4. associate-*l* N/A

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

   5. associate-*r* N/A

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

   6. +-rgt-identity N/A

      \[\leadsto \mathsf{fma}\left(x \cdot \left(x \cdot \left(\color{blue}{\left(x \cdot x + 0\right)} \cdot \frac{1}{5} + \frac{1}{3}\right)\right), x, x\right) \]

   7. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(x \cdot \color{blue}{\left(\left(\left(x \cdot x + 0\right) \cdot \frac{1}{5} + \frac{1}{3}\right) \cdot x\right)}, x, x\right) \]

   8. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot \left(\left(\left(x \cdot x + 0\right) \cdot \frac{1}{5} + \frac{1}{3}\right) \cdot x\right)}, x, x\right) \]

   9. *-commutative N/A

      \[\leadsto \mathsf{fma}\left(x \cdot \color{blue}{\left(x \cdot \left(\left(x \cdot x + 0\right) \cdot \frac{1}{5} + \frac{1}{3}\right)\right)}, x, x\right) \]

   10. *-lowering-*.f64 N/A

       \[\leadsto \mathsf{fma}\left(x \cdot \color{blue}{\left(x \cdot \left(\left(x \cdot x + 0\right) \cdot \frac{1}{5} + \frac{1}{3}\right)\right)}, x, x\right) \]

   11. +-rgt-identity N/A

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

   12. associate-*r* N/A

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

   13. accelerator-lowering-fma.f64 N/A

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

   14. *-lowering-*.f64 100.0

       \[\leadsto \mathsf{fma}\left(x \cdot \left(x \cdot \mathsf{fma}\left(x, \color{blue}{x \cdot 0.2}, 0.3333333333333333\right)\right), x, x\right) \]

9. Applied egg-rr 100.0%

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

Alternative 3: 99.7% accurate, 4.8× speedup

Math

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(x \cdot x, \mathsf{fma}\left(x, x \cdot 0.2, 0.3333333333333333\right), 1\right) \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (* x (fma (* x x) (fma x (* x 0.2) 0.3333333333333333) 1.0)))

C

double code(double x) {
	return x * fma((x * x), fma(x, (x * 0.2), 0.3333333333333333), 1.0);
}

Julia

function code(x)
	return Float64(x * fma(Float64(x * x), fma(x, Float64(x * 0.2), 0.3333333333333333), 1.0))
end

Wolfram

code[x_] := N[(x * N[(N[(x * x), $MachinePrecision] * N[(x * N[(x * 0.2), $MachinePrecision] + 0.3333333333333333), $MachinePrecision] + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 8.4%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
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. *-lowering-*.f64 N/A

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

   2. +-commutative N/A

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

   3. accelerator-lowering-fma.f64 N/A

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

   4. remove-double-neg N/A

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

   5. +-rgt-identity N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{neg}\left(\color{blue}{\left(\left(\mathsf{neg}\left({x}^{2}\right)\right) + 0\right)}\right), \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   6. distribute-neg-in N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left({x}^{2}\right)\right)\right)\right) + \left(\mathsf{neg}\left(0\right)\right)}, \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   7. remove-double-neg N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{{x}^{2}} + \left(\mathsf{neg}\left(0\right)\right), \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   8. unpow2 N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x} + \left(\mathsf{neg}\left(0\right)\right), \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   9. metadata-eval N/A

      \[\leadsto x \cdot \mathsf{fma}\left(x \cdot x + \color{blue}{0}, \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   10. accelerator-lowering-fma.f64 N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(x, x, 0\right)}, \frac{1}{3} + \frac{1}{5} \cdot {x}^{2}, 1\right) \]

   11. +-commutative N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{\frac{1}{5} \cdot {x}^{2} + \frac{1}{3}}, 1\right) \]

   12. unpow2 N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \frac{1}{5} \cdot \color{blue}{\left(x \cdot x\right)} + \frac{1}{3}, 1\right) \]

   13. associate-*r* N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{\left(\frac{1}{5} \cdot x\right) \cdot x} + \frac{1}{3}, 1\right) \]

   14. *-commutative N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{x \cdot \left(\frac{1}{5} \cdot x\right)} + \frac{1}{3}, 1\right) \]

   15. accelerator-lowering-fma.f64 N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{\mathsf{fma}\left(x, \frac{1}{5} \cdot x, \frac{1}{3}\right)}, 1\right) \]

   16. *-commutative N/A

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \mathsf{fma}\left(x, \color{blue}{x \cdot \frac{1}{5}}, \frac{1}{3}\right), 1\right) \]

   17. *-lowering-*.f64 100.0

       \[\leadsto x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \mathsf{fma}\left(x, \color{blue}{x \cdot 0.2}, 0.3333333333333333\right), 1\right) \]

5. Simplified 100.0%

   \[\leadsto \color{blue}{x \cdot \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \mathsf{fma}\left(x, x \cdot 0.2, 0.3333333333333333\right), 1\right)} \]
6. Step-by-step derivation

   1. +-rgt-identity N/A

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

   2. *-lowering-*.f64 100.0

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{x \cdot x}, \mathsf{fma}\left(x, x \cdot 0.2, 0.3333333333333333\right), 1\right) \]

7. Applied egg-rr 100.0%

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

Alternative 4: 99.5% accurate, 7.9× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x \cdot x, x \cdot 0.3333333333333333, x\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (fma (* x x) (* x 0.3333333333333333) x))

C

double code(double x) {
	return fma((x * x), (x * 0.3333333333333333), x);
}

Julia

function code(x)
	return fma(Float64(x * x), Float64(x * 0.3333333333333333), x)
end

Wolfram

code[x_] := N[(N[(x * x), $MachinePrecision] * N[(x * 0.3333333333333333), $MachinePrecision] + x), $MachinePrecision]

Derivation

1. Initial program 8.4%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
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. *-lowering-*.f64 N/A

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

   2. +-commutative N/A

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

   3. accelerator-lowering-fma.f64 N/A

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

   4. remove-double-neg N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \color{blue}{\mathsf{neg}\left(\left(\mathsf{neg}\left({x}^{2}\right)\right)\right)}, 1\right) \]

   5. +-rgt-identity N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \mathsf{neg}\left(\color{blue}{\left(\left(\mathsf{neg}\left({x}^{2}\right)\right) + 0\right)}\right), 1\right) \]

   6. distribute-neg-in N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left({x}^{2}\right)\right)\right)\right) + \left(\mathsf{neg}\left(0\right)\right)}, 1\right) \]

   7. remove-double-neg N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \color{blue}{{x}^{2}} + \left(\mathsf{neg}\left(0\right)\right), 1\right) \]

   8. unpow2 N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \color{blue}{x \cdot x} + \left(\mathsf{neg}\left(0\right)\right), 1\right) \]

   9. metadata-eval N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, x \cdot x + \color{blue}{0}, 1\right) \]

   10. accelerator-lowering-fma.f64 99.8

       \[\leadsto x \cdot \mathsf{fma}\left(0.3333333333333333, \color{blue}{\mathsf{fma}\left(x, x, 0\right)}, 1\right) \]

5. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \mathsf{fma}\left(0.3333333333333333, \mathsf{fma}\left(x, x, 0\right), 1\right)} \]
6. Step-by-step derivation

   1. distribute-rgt-in N/A

      \[\leadsto \color{blue}{\left(\frac{1}{3} \cdot \left(x \cdot x + 0\right)\right) \cdot x + 1 \cdot x} \]

   2. +-rgt-identity N/A

      \[\leadsto \left(\frac{1}{3} \cdot \color{blue}{\left(x \cdot x\right)}\right) \cdot x + 1 \cdot x \]

   3. *-commutative N/A

      \[\leadsto \color{blue}{\left(\left(x \cdot x\right) \cdot \frac{1}{3}\right)} \cdot x + 1 \cdot x \]

   4. associate-*l* N/A

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot \left(\frac{1}{3} \cdot x\right)} + 1 \cdot x \]

   5. *-lft-identity N/A

      \[\leadsto \left(x \cdot x\right) \cdot \left(\frac{1}{3} \cdot x\right) + \color{blue}{x} \]

   6. accelerator-lowering-fma.f64 N/A

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

   7. +-rgt-identity N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{x \cdot x + 0}, \frac{1}{3} \cdot x, x\right) \]

   8. accelerator-lowering-fma.f64 N/A

      \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(x, x, 0\right)}, \frac{1}{3} \cdot x, x\right) \]

   9. *-lowering-*.f64 99.8

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), \color{blue}{0.3333333333333333 \cdot x}, x\right) \]

7. Applied egg-rr 99.8%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\mathsf{fma}\left(x, x, 0\right), 0.3333333333333333 \cdot x, x\right)} \]
8. Step-by-step derivation

   1. +-rgt-identity N/A

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

   2. *-lowering-*.f64 99.8

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

9. Applied egg-rr 99.8%

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

10. Final simplification 99.8%

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

Alternative 5: 99.5% accurate, 7.9× speedup

Math

\[\begin{array}{l} \\ x \cdot \mathsf{fma}\left(x \cdot 0.3333333333333333, x, 1\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (* x (fma (* x 0.3333333333333333) x 1.0)))

C

double code(double x) {
	return x * fma((x * 0.3333333333333333), x, 1.0);
}

Julia

function code(x)
	return Float64(x * fma(Float64(x * 0.3333333333333333), x, 1.0))
end

Wolfram

code[x_] := N[(x * N[(N[(x * 0.3333333333333333), $MachinePrecision] * x + 1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 8.4%

   \[\frac{1}{2} \cdot \log \left(\frac{1 + x}{1 - x}\right) \]
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. *-lowering-*.f64 N/A

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

   2. +-commutative N/A

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

   3. accelerator-lowering-fma.f64 N/A

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

   4. remove-double-neg N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \color{blue}{\mathsf{neg}\left(\left(\mathsf{neg}\left({x}^{2}\right)\right)\right)}, 1\right) \]

   5. +-rgt-identity N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \mathsf{neg}\left(\color{blue}{\left(\left(\mathsf{neg}\left({x}^{2}\right)\right) + 0\right)}\right), 1\right) \]

   6. distribute-neg-in N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left({x}^{2}\right)\right)\right)\right) + \left(\mathsf{neg}\left(0\right)\right)}, 1\right) \]

   7. remove-double-neg N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \color{blue}{{x}^{2}} + \left(\mathsf{neg}\left(0\right)\right), 1\right) \]

   8. unpow2 N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, \color{blue}{x \cdot x} + \left(\mathsf{neg}\left(0\right)\right), 1\right) \]

   9. metadata-eval N/A

      \[\leadsto x \cdot \mathsf{fma}\left(\frac{1}{3}, x \cdot x + \color{blue}{0}, 1\right) \]

   10. accelerator-lowering-fma.f64 99.8

       \[\leadsto x \cdot \mathsf{fma}\left(0.3333333333333333, \color{blue}{\mathsf{fma}\left(x, x, 0\right)}, 1\right) \]

5. Simplified 99.8%

   \[\leadsto \color{blue}{x \cdot \mathsf{fma}\left(0.3333333333333333, \mathsf{fma}\left(x, x, 0\right), 1\right)} \]
6. Step-by-step derivation

   1. +-rgt-identity N/A

      \[\leadsto x \cdot \left(\frac{1}{3} \cdot \color{blue}{\left(x \cdot x\right)} + 1\right) \]

   2. associate-*r* N/A

      \[\leadsto x \cdot \left(\color{blue}{\left(\frac{1}{3} \cdot x\right) \cdot x} + 1\right) \]

   3. accelerator-lowering-fma.f64 N/A

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

   4. *-lowering-*.f64 99.8

      \[\leadsto x \cdot \mathsf{fma}\left(\color{blue}{0.3333333333333333 \cdot x}, x, 1\right) \]

7. Applied egg-rr 99.8%

   \[\leadsto x \cdot \color{blue}{\mathsf{fma}\left(0.3333333333333333 \cdot x, x, 1\right)} \]

8. Final simplification 99.8%

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

Alternative 6: 99.0% accurate, 134.0× speedup

Math

\[\begin{array}{l} \\ x \end{array} \]

FPCore

(FPCore (x) :precision binary64 x)

C

double code(double x) {
	return x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x
end function

Java

public static double code(double x) {
	return x;
}

Python

def code(x):
	return x

Julia

function code(x)
	return x
end

MATLAB

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

Wolfram

code[x_] := x

Derivation

1. Initial program 8.4%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{x} \]
4. Step-by-step derivation

5. Simplified 99.4%

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

Reproduce

herbie shell --seed 2024198 
(FPCore (x)
  :name "Hyperbolic arc-(co)tangent"
  :precision binary64
  (* (/ 1.0 2.0) (log (/ (+ 1.0 x) (- 1.0 x)))))