Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, D

Percentage Accurate: 100.0% → 100.0%

Time: 10.2s

Alternatives: 7

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (- x (/ (+ 2.30753 (* x 0.27061)) (+ 1.0 (* (+ 0.99229 (* x 0.04481)) x)))))

C

double code(double x) {
	return x - ((2.30753 + (x * 0.27061)) / (1.0 + ((0.99229 + (x * 0.04481)) * x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x - ((2.30753d0 + (x * 0.27061d0)) / (1.0d0 + ((0.99229d0 + (x * 0.04481d0)) * x)))
end function

Java

public static double code(double x) {
	return x - ((2.30753 + (x * 0.27061)) / (1.0 + ((0.99229 + (x * 0.04481)) * x)));
}

Python

def code(x):
	return x - ((2.30753 + (x * 0.27061)) / (1.0 + ((0.99229 + (x * 0.04481)) * x)))

Julia

function code(x)
	return Float64(x - Float64(Float64(2.30753 + Float64(x * 0.27061)) / Float64(1.0 + Float64(Float64(0.99229 + Float64(x * 0.04481)) * x))))
end

MATLAB

function tmp = code(x)
	tmp = x - ((2.30753 + (x * 0.27061)) / (1.0 + ((0.99229 + (x * 0.04481)) * x)));
end

Wolfram

code[x_] := N[(x - N[(N[(2.30753 + N[(x * 0.27061), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[(0.99229 + N[(x * 0.04481), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (- x (/ (+ 2.30753 (* x 0.27061)) (+ 1.0 (* (+ 0.99229 (* x 0.04481)) x)))))

C

double code(double x) {
	return x - ((2.30753 + (x * 0.27061)) / (1.0 + ((0.99229 + (x * 0.04481)) * x)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x - ((2.30753d0 + (x * 0.27061d0)) / (1.0d0 + ((0.99229d0 + (x * 0.04481d0)) * x)))
end function

Java

public static double code(double x) {
	return x - ((2.30753 + (x * 0.27061)) / (1.0 + ((0.99229 + (x * 0.04481)) * x)));
}

Python

def code(x):
	return x - ((2.30753 + (x * 0.27061)) / (1.0 + ((0.99229 + (x * 0.04481)) * x)))

Julia

function code(x)
	return Float64(x - Float64(Float64(2.30753 + Float64(x * 0.27061)) / Float64(1.0 + Float64(Float64(0.99229 + Float64(x * 0.04481)) * x))))
end

MATLAB

function tmp = code(x)
	tmp = x - ((2.30753 + (x * 0.27061)) / (1.0 + ((0.99229 + (x * 0.04481)) * x)));
end

Wolfram

code[x_] := N[(x - N[(N[(2.30753 + N[(x * 0.27061), $MachinePrecision]), $MachinePrecision] / N[(1.0 + N[(N[(0.99229 + N[(x * 0.04481), $MachinePrecision]), $MachinePrecision] * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ x + \frac{-1}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{x \cdot 0.27061 + 2.30753}} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+
  x
  (/ -1.0 (/ (fma x (fma x 0.04481 0.99229) 1.0) (+ (* x 0.27061) 2.30753)))))

C

double code(double x) {
	return x + (-1.0 / (fma(x, fma(x, 0.04481, 0.99229), 1.0) / ((x * 0.27061) + 2.30753)));
}

Julia

function code(x)
	return Float64(x + Float64(-1.0 / Float64(fma(x, fma(x, 0.04481, 0.99229), 1.0) / Float64(Float64(x * 0.27061) + 2.30753))))
end

Wolfram

code[x_] := N[(x + N[(-1.0 / N[(N[(x * N[(x * 0.04481 + 0.99229), $MachinePrecision] + 1.0), $MachinePrecision] / N[(N[(x * 0.27061), $MachinePrecision] + 2.30753), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. clear-num 100.0%

      \[\leadsto x - \color{blue}{\frac{1}{\frac{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x}{2.30753 + x \cdot 0.27061}}} \]

   2. inv-pow 100.0%

      \[\leadsto x - \color{blue}{{\left(\frac{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x}{2.30753 + x \cdot 0.27061}\right)}^{-1}} \]

   3. +-commutative 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{\left(0.99229 + x \cdot 0.04481\right) \cdot x + 1}}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   4. +-commutative 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{\left(x \cdot 0.04481 + 0.99229\right)} \cdot x + 1}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   5. fma-udef 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{\mathsf{fma}\left(x, 0.04481, 0.99229\right)} \cdot x + 1}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   6. *-commutative 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{x \cdot \mathsf{fma}\left(x, 0.04481, 0.99229\right)} + 1}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   7. fma-udef 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   8. +-commutative 100.0%

      \[\leadsto x - {\left(\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\color{blue}{x \cdot 0.27061 + 2.30753}}\right)}^{-1} \]

   9. fma-udef 100.0%

      \[\leadsto x - {\left(\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\color{blue}{\mathsf{fma}\left(x, 0.27061, 2.30753\right)}}\right)}^{-1} \]

4. Applied egg-rr 100.0%

   \[\leadsto x - \color{blue}{{\left(\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\mathsf{fma}\left(x, 0.27061, 2.30753\right)}\right)}^{-1}} \]
5. Step-by-step derivation

   1. unpow-1 100.0%

      \[\leadsto x - \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\mathsf{fma}\left(x, 0.27061, 2.30753\right)}}} \]

6. Applied egg-rr 100.0%

   \[\leadsto x - \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\mathsf{fma}\left(x, 0.27061, 2.30753\right)}}} \]
7. Step-by-step derivation

   1. fma-udef 100.0%

      \[\leadsto x - \frac{1}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\color{blue}{x \cdot 0.27061 + 2.30753}}} \]

8. Applied egg-rr 100.0%

   \[\leadsto x - \frac{1}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\color{blue}{x \cdot 0.27061 + 2.30753}}} \]

9. Final simplification 100.0%

   \[\leadsto x + \frac{-1}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{x \cdot 0.27061 + 2.30753}} \]
10. Add Preprocessing

Alternative 2: 100.0% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ x + \frac{\left(1 + \left(-1 - x \cdot 0.27061\right)\right) - 2.30753}{1 + x \cdot \left(0.99229 + x \cdot 0.04481\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+
  x
  (/
   (- (+ 1.0 (- -1.0 (* x 0.27061))) 2.30753)
   (+ 1.0 (* x (+ 0.99229 (* x 0.04481)))))))

C

double code(double x) {
	return x + (((1.0 + (-1.0 - (x * 0.27061))) - 2.30753) / (1.0 + (x * (0.99229 + (x * 0.04481)))));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x + (((1.0d0 + ((-1.0d0) - (x * 0.27061d0))) - 2.30753d0) / (1.0d0 + (x * (0.99229d0 + (x * 0.04481d0)))))
end function

Java

public static double code(double x) {
	return x + (((1.0 + (-1.0 - (x * 0.27061))) - 2.30753) / (1.0 + (x * (0.99229 + (x * 0.04481)))));
}

Python

def code(x):
	return x + (((1.0 + (-1.0 - (x * 0.27061))) - 2.30753) / (1.0 + (x * (0.99229 + (x * 0.04481)))))

Julia

function code(x)
	return Float64(x + Float64(Float64(Float64(1.0 + Float64(-1.0 - Float64(x * 0.27061))) - 2.30753) / Float64(1.0 + Float64(x * Float64(0.99229 + Float64(x * 0.04481))))))
end

MATLAB

function tmp = code(x)
	tmp = x + (((1.0 + (-1.0 - (x * 0.27061))) - 2.30753) / (1.0 + (x * (0.99229 + (x * 0.04481)))));
end

Wolfram

code[x_] := N[(x + N[(N[(N[(1.0 + N[(-1.0 - N[(x * 0.27061), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] - 2.30753), $MachinePrecision] / N[(1.0 + N[(x * N[(0.99229 + N[(x * 0.04481), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. expm1-log1p-u 80.0%

      \[\leadsto x - \frac{2.30753 + \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left(x \cdot 0.27061\right)\right)}}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]

   2. expm1-udef 80.1%

      \[\leadsto x - \frac{2.30753 + \color{blue}{\left(e^{\mathsf{log1p}\left(x \cdot 0.27061\right)} - 1\right)}}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]

4. Applied egg-rr 80.1%

   \[\leadsto x - \frac{2.30753 + \color{blue}{\left(e^{\mathsf{log1p}\left(x \cdot 0.27061\right)} - 1\right)}}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
5. Step-by-step derivation

   1. log1p-udef 80.1%

      \[\leadsto x - \frac{2.30753 + \left(e^{\color{blue}{\log \left(1 + x \cdot 0.27061\right)}} - 1\right)}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]

   2. rem-exp-log 100.0%

      \[\leadsto x - \frac{2.30753 + \left(\color{blue}{\left(1 + x \cdot 0.27061\right)} - 1\right)}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]

   3. +-commutative 100.0%

      \[\leadsto x - \frac{2.30753 + \left(\color{blue}{\left(x \cdot 0.27061 + 1\right)} - 1\right)}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]

6. Applied egg-rr 100.0%

   \[\leadsto x - \frac{2.30753 + \left(\color{blue}{\left(x \cdot 0.27061 + 1\right)} - 1\right)}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]

7. Final simplification 100.0%

   \[\leadsto x + \frac{\left(1 + \left(-1 - x \cdot 0.27061\right)\right) - 2.30753}{1 + x \cdot \left(0.99229 + x \cdot 0.04481\right)} \]
8. Add Preprocessing

Alternative 3: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \frac{x \cdot 0.27061 + 2.30753}{1 + x \cdot \left(0.99229 + x \cdot 0.04481\right)} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (- x (/ (+ (* x 0.27061) 2.30753) (+ 1.0 (* x (+ 0.99229 (* x 0.04481)))))))

C

double code(double x) {
	return x - (((x * 0.27061) + 2.30753) / (1.0 + (x * (0.99229 + (x * 0.04481)))));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x - (((x * 0.27061d0) + 2.30753d0) / (1.0d0 + (x * (0.99229d0 + (x * 0.04481d0)))))
end function

Java

public static double code(double x) {
	return x - (((x * 0.27061) + 2.30753) / (1.0 + (x * (0.99229 + (x * 0.04481)))));
}

Python

def code(x):
	return x - (((x * 0.27061) + 2.30753) / (1.0 + (x * (0.99229 + (x * 0.04481)))))

Julia

function code(x)
	return Float64(x - Float64(Float64(Float64(x * 0.27061) + 2.30753) / Float64(1.0 + Float64(x * Float64(0.99229 + Float64(x * 0.04481))))))
end

MATLAB

function tmp = code(x)
	tmp = x - (((x * 0.27061) + 2.30753) / (1.0 + (x * (0.99229 + (x * 0.04481)))));
end

Wolfram

code[x_] := N[(x - N[(N[(N[(x * 0.27061), $MachinePrecision] + 2.30753), $MachinePrecision] / N[(1.0 + N[(x * N[(0.99229 + N[(x * 0.04481), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
2. Add Preprocessing

3. Final simplification 100.0%

   \[\leadsto x - \frac{x \cdot 0.27061 + 2.30753}{1 + x \cdot \left(0.99229 + x \cdot 0.04481\right)} \]
4. Add Preprocessing

Alternative 4: 98.4% accurate, 1.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.05:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.2:\\ \;\;\;\;-2.30753\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (if (<= x -1.05) x (if (<= x 1.2) -2.30753 x)))

C

double code(double x) {
	double tmp;
	if (x <= -1.05) {
		tmp = x;
	} else if (x <= 1.2) {
		tmp = -2.30753;
	} else {
		tmp = x;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if (x <= (-1.05d0)) then
        tmp = x
    else if (x <= 1.2d0) then
        tmp = -2.30753d0
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if (x <= -1.05) {
		tmp = x;
	} else if (x <= 1.2) {
		tmp = -2.30753;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= -1.05:
		tmp = x
	elif x <= 1.2:
		tmp = -2.30753
	else:
		tmp = x
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= -1.05)
		tmp = x;
	elseif (x <= 1.2)
		tmp = -2.30753;
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if (x <= -1.05)
		tmp = x;
	elseif (x <= 1.2)
		tmp = -2.30753;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, -1.05], x, If[LessEqual[x, 1.2], -2.30753, x]]

Derivation

1. Split input into 2 regimes

2. if x < -1.05000000000000004 or 1.19999999999999996 < x

   1. Initial program 100.0%

      \[x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 98.2%

      \[\leadsto x - \color{blue}{2.30753} \]

   4. Taylor expanded in x around inf 99.3%

      \[\leadsto \color{blue}{x} \]

   if -1.05000000000000004 < x < 1.19999999999999996

   1. Initial program 99.9%

      \[x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 96.3%

      \[\leadsto x - \color{blue}{2.30753} \]

   4. Taylor expanded in x around 0 96.2%

      \[\leadsto \color{blue}{-2.30753} \]
3. Recombined 2 regimes into one program.

4. Final simplification 97.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.05:\\ \;\;\;\;x\\ \mathbf{elif}\;x \leq 1.2:\\ \;\;\;\;-2.30753\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.0% accurate, 1.9× speedup

Math

\[\begin{array}{l} \\ x + \frac{-1}{0.4333638132548656 + x \cdot 0.37920088514346545} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+ x (/ -1.0 (+ 0.4333638132548656 (* x 0.37920088514346545)))))

C

double code(double x) {
	return x + (-1.0 / (0.4333638132548656 + (x * 0.37920088514346545)));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x + ((-1.0d0) / (0.4333638132548656d0 + (x * 0.37920088514346545d0)))
end function

Java

public static double code(double x) {
	return x + (-1.0 / (0.4333638132548656 + (x * 0.37920088514346545)));
}

Python

def code(x):
	return x + (-1.0 / (0.4333638132548656 + (x * 0.37920088514346545)))

Julia

function code(x)
	return Float64(x + Float64(-1.0 / Float64(0.4333638132548656 + Float64(x * 0.37920088514346545))))
end

MATLAB

function tmp = code(x)
	tmp = x + (-1.0 / (0.4333638132548656 + (x * 0.37920088514346545)));
end

Wolfram

code[x_] := N[(x + N[(-1.0 / N[(0.4333638132548656 + N[(x * 0.37920088514346545), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. clear-num 100.0%

      \[\leadsto x - \color{blue}{\frac{1}{\frac{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x}{2.30753 + x \cdot 0.27061}}} \]

   2. inv-pow 100.0%

      \[\leadsto x - \color{blue}{{\left(\frac{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x}{2.30753 + x \cdot 0.27061}\right)}^{-1}} \]

   3. +-commutative 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{\left(0.99229 + x \cdot 0.04481\right) \cdot x + 1}}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   4. +-commutative 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{\left(x \cdot 0.04481 + 0.99229\right)} \cdot x + 1}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   5. fma-udef 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{\mathsf{fma}\left(x, 0.04481, 0.99229\right)} \cdot x + 1}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   6. *-commutative 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{x \cdot \mathsf{fma}\left(x, 0.04481, 0.99229\right)} + 1}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   7. fma-udef 100.0%

      \[\leadsto x - {\left(\frac{\color{blue}{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}}{2.30753 + x \cdot 0.27061}\right)}^{-1} \]

   8. +-commutative 100.0%

      \[\leadsto x - {\left(\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\color{blue}{x \cdot 0.27061 + 2.30753}}\right)}^{-1} \]

   9. fma-udef 100.0%

      \[\leadsto x - {\left(\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\color{blue}{\mathsf{fma}\left(x, 0.27061, 2.30753\right)}}\right)}^{-1} \]

4. Applied egg-rr 100.0%

   \[\leadsto x - \color{blue}{{\left(\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\mathsf{fma}\left(x, 0.27061, 2.30753\right)}\right)}^{-1}} \]
5. Step-by-step derivation

   1. unpow-1 100.0%

      \[\leadsto x - \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\mathsf{fma}\left(x, 0.27061, 2.30753\right)}}} \]

6. Applied egg-rr 100.0%

   \[\leadsto x - \color{blue}{\frac{1}{\frac{\mathsf{fma}\left(x, \mathsf{fma}\left(x, 0.04481, 0.99229\right), 1\right)}{\mathsf{fma}\left(x, 0.27061, 2.30753\right)}}} \]

7. Taylor expanded in x around 0 98.6%

   \[\leadsto x - \frac{1}{\color{blue}{0.4333638132548656 + 0.37920088514346545 \cdot x}} \]
8. Step-by-step derivation

   1. *-commutative 98.6%

      \[\leadsto x - \frac{1}{0.4333638132548656 + \color{blue}{x \cdot 0.37920088514346545}} \]

9. Simplified 98.6%

   \[\leadsto x - \frac{1}{\color{blue}{0.4333638132548656 + x \cdot 0.37920088514346545}} \]

10. Final simplification 98.6%

    \[\leadsto x + \frac{-1}{0.4333638132548656 + x \cdot 0.37920088514346545} \]
11. Add Preprocessing

Alternative 6: 98.0% accurate, 5.7× speedup

Math

\[\begin{array}{l} \\ x - 2.30753 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- x 2.30753))

C

double code(double x) {
	return x - 2.30753;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = x - 2.30753d0
end function

Java

public static double code(double x) {
	return x - 2.30753;
}

Python

def code(x):
	return x - 2.30753

Julia

function code(x)
	return Float64(x - 2.30753)
end

MATLAB

function tmp = code(x)
	tmp = x - 2.30753;
end

Wolfram

code[x_] := N[(x - 2.30753), $MachinePrecision]

Derivation

1. Initial program 100.0%

   \[x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 97.3%

   \[\leadsto x - \color{blue}{2.30753} \]

4. Final simplification 97.3%

   \[\leadsto x - 2.30753 \]
5. Add Preprocessing

Alternative 7: 50.6% accurate, 17.0× speedup

Math

\[\begin{array}{l} \\ -2.30753 \end{array} \]

FPCore

(FPCore (x) :precision binary64 -2.30753)

C

double code(double x) {
	return -2.30753;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = -2.30753d0
end function

Java

public static double code(double x) {
	return -2.30753;
}

Python

def code(x):
	return -2.30753

Julia

function code(x)
	return -2.30753
end

MATLAB

function tmp = code(x)
	tmp = -2.30753;
end

Wolfram

code[x_] := -2.30753

Derivation

1. Initial program 100.0%

   \[x - \frac{2.30753 + x \cdot 0.27061}{1 + \left(0.99229 + x \cdot 0.04481\right) \cdot x} \]
2. Add Preprocessing

3. Taylor expanded in x around 0 97.3%

   \[\leadsto x - \color{blue}{2.30753} \]

4. Taylor expanded in x around 0 50.2%

   \[\leadsto \color{blue}{-2.30753} \]

5. Final simplification 50.2%

   \[\leadsto -2.30753 \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024031 
(FPCore (x)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, D"
  :precision binary64
  (- x (/ (+ 2.30753 (* x 0.27061)) (+ 1.0 (* (+ 0.99229 (* x 0.04481)) x)))))