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

Percentage Accurate: 99.9% → 99.8%

Time: 3.9s

Alternatives: 8

Speedup: 1.0×

• 24.6% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \frac{x \cdot x - 3}{6} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (- (* x x) 3.0) 6.0))

C

double code(double x) {
	return ((x * x) - 3.0) / 6.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((x * x) - 3.0d0) / 6.0d0
end function

Java

public static double code(double x) {
	return ((x * x) - 3.0) / 6.0;
}

Python

def code(x):
	return ((x * x) - 3.0) / 6.0

Julia

function code(x)
	return Float64(Float64(Float64(x * x) - 3.0) / 6.0)
end

MATLAB

function tmp = code(x)
	tmp = ((x * x) - 3.0) / 6.0;
end

Wolfram

code[x_] := N[(N[(N[(x * x), $MachinePrecision] - 3.0), $MachinePrecision] / 6.0), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot x - 3}{6} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (- (* x x) 3.0) 6.0))

C

double code(double x) {
	return ((x * x) - 3.0) / 6.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((x * x) - 3.0d0) / 6.0d0
end function

Java

public static double code(double x) {
	return ((x * x) - 3.0) / 6.0;
}

Python

def code(x):
	return ((x * x) - 3.0) / 6.0

Julia

function code(x)
	return Float64(Float64(Float64(x * x) - 3.0) / 6.0)
end

MATLAB

function tmp = code(x)
	tmp = ((x * x) - 3.0) / 6.0;
end

Wolfram

code[x_] := N[(N[(N[(x * x), $MachinePrecision] - 3.0), $MachinePrecision] / 6.0), $MachinePrecision]

Alternative 1: 99.8% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ {\left(\frac{6}{\mathsf{fma}\left(x, x, -3\right)}\right)}^{-1} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (pow (/ 6.0 (fma x x -3.0)) -1.0))

C

double code(double x) {
	return pow((6.0 / fma(x, x, -3.0)), -1.0);
}

Julia

function code(x)
	return Float64(6.0 / fma(x, x, -3.0)) ^ -1.0
end

Wolfram

code[x_] := N[Power[N[(6.0 / N[(x * x + -3.0), $MachinePrecision]), $MachinePrecision], -1.0], $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{x \cdot x - 3}{6} \]
2. Step-by-step derivation

   1. clear-num 99.9%

      \[\leadsto \color{blue}{\frac{1}{\frac{6}{x \cdot x - 3}}} \]

   2. inv-pow 99.9%

      \[\leadsto \color{blue}{{\left(\frac{6}{x \cdot x - 3}\right)}^{-1}} \]

   3. fma-neg 99.9%

      \[\leadsto {\left(\frac{6}{\color{blue}{\mathsf{fma}\left(x, x, -3\right)}}\right)}^{-1} \]

   4. metadata-eval 99.9%

      \[\leadsto {\left(\frac{6}{\mathsf{fma}\left(x, x, \color{blue}{-3}\right)}\right)}^{-1} \]

3. Applied egg-rr 99.9%

   \[\leadsto \color{blue}{{\left(\frac{6}{\mathsf{fma}\left(x, x, -3\right)}\right)}^{-1}} \]

4. Final simplification 99.9%

   \[\leadsto {\left(\frac{6}{\mathsf{fma}\left(x, x, -3\right)}\right)}^{-1} \]

Alternative 2: 98.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot x \leq 3:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot x\right) \cdot 0.16666666666666666\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (* x x) 3.0) -0.5 (* (* x x) 0.16666666666666666)))

C

double code(double x) {
	double tmp;
	if ((x * x) <= 3.0) {
		tmp = -0.5;
	} else {
		tmp = (x * x) * 0.16666666666666666;
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x * x) <= 3.0d0) then
        tmp = -0.5d0
    else
        tmp = (x * x) * 0.16666666666666666d0
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x * x) <= 3.0) {
		tmp = -0.5;
	} else {
		tmp = (x * x) * 0.16666666666666666;
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x * x) <= 3.0:
		tmp = -0.5
	else:
		tmp = (x * x) * 0.16666666666666666
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (Float64(x * x) <= 3.0)
		tmp = -0.5;
	else
		tmp = Float64(Float64(x * x) * 0.16666666666666666);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x * x) <= 3.0)
		tmp = -0.5;
	else
		tmp = (x * x) * 0.16666666666666666;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[N[(x * x), $MachinePrecision], 3.0], -0.5, N[(N[(x * x), $MachinePrecision] * 0.16666666666666666), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x x) < 3

   1. Initial program 100.0%

      \[\frac{x \cdot x - 3}{6} \]

   2. Taylor expanded in x around 0 99.3%

      \[\leadsto \color{blue}{-0.5} \]

   if 3 < (*.f64 x x)

   1. Initial program 99.7%

      \[\frac{x \cdot x - 3}{6} \]

   2. Taylor expanded in x around inf 98.2%

      \[\leadsto \color{blue}{0.16666666666666666 \cdot {x}^{2}} \]
   3. Step-by-step derivation

      1. unpow2 98.2%

         \[\leadsto 0.16666666666666666 \cdot \color{blue}{\left(x \cdot x\right)} \]

   4. Simplified 98.2%

      \[\leadsto \color{blue}{0.16666666666666666 \cdot \left(x \cdot x\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot x \leq 3:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;\left(x \cdot x\right) \cdot 0.16666666666666666\\ \end{array} \]

Alternative 3: 98.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot x \leq 0.01:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot 0.16666666666666666\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= (* x x) 0.01) -0.5 (* x (* x 0.16666666666666666))))

C

double code(double x) {
	double tmp;
	if ((x * x) <= 0.01) {
		tmp = -0.5;
	} else {
		tmp = x * (x * 0.16666666666666666);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x * x) <= 0.01d0) then
        tmp = -0.5d0
    else
        tmp = x * (x * 0.16666666666666666d0)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x * x) <= 0.01) {
		tmp = -0.5;
	} else {
		tmp = x * (x * 0.16666666666666666);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x * x) <= 0.01:
		tmp = -0.5
	else:
		tmp = x * (x * 0.16666666666666666)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (Float64(x * x) <= 0.01)
		tmp = -0.5;
	else
		tmp = Float64(x * Float64(x * 0.16666666666666666));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x * x) <= 0.01)
		tmp = -0.5;
	else
		tmp = x * (x * 0.16666666666666666);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[N[(x * x), $MachinePrecision], 0.01], -0.5, N[(x * N[(x * 0.16666666666666666), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x x) < 0.0100000000000000002

   1. Initial program 100.0%

      \[\frac{x \cdot x - 3}{6} \]

   2. Taylor expanded in x around 0 99.3%

      \[\leadsto \color{blue}{-0.5} \]

   if 0.0100000000000000002 < (*.f64 x x)

   1. Initial program 99.7%

      \[\frac{x \cdot x - 3}{6} \]
   2. Step-by-step derivation

      1. clear-num 99.7%

         \[\leadsto \color{blue}{\frac{1}{\frac{6}{x \cdot x - 3}}} \]

      2. inv-pow 99.7%

         \[\leadsto \color{blue}{{\left(\frac{6}{x \cdot x - 3}\right)}^{-1}} \]

      3. fma-neg 99.7%

         \[\leadsto {\left(\frac{6}{\color{blue}{\mathsf{fma}\left(x, x, -3\right)}}\right)}^{-1} \]

      4. metadata-eval 99.7%

         \[\leadsto {\left(\frac{6}{\mathsf{fma}\left(x, x, \color{blue}{-3}\right)}\right)}^{-1} \]

   3. Applied egg-rr 99.7%

      \[\leadsto \color{blue}{{\left(\frac{6}{\mathsf{fma}\left(x, x, -3\right)}\right)}^{-1}} \]

   4. Taylor expanded in x around inf 98.2%

      \[\leadsto \color{blue}{0.16666666666666666 \cdot {x}^{2}} \]
   5. Step-by-step derivation

      1. *-commutative 98.2%

         \[\leadsto \color{blue}{{x}^{2} \cdot 0.16666666666666666} \]

      2. unpow2 98.2%

         \[\leadsto \color{blue}{\left(x \cdot x\right)} \cdot 0.16666666666666666 \]

      3. associate-*r* 98.3%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.16666666666666666\right)} \]

   6. Simplified 98.3%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.16666666666666666\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot x \leq 0.01:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot 0.16666666666666666\right)\\ \end{array} \]

Alternative 4: 98.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot x \leq 0.01:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{x}{6}\\ \end{array} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (if (<= (* x x) 0.01) -0.5 (* x (/ x 6.0))))

C

double code(double x) {
	double tmp;
	if ((x * x) <= 0.01) {
		tmp = -0.5;
	} else {
		tmp = x * (x / 6.0);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x * x) <= 0.01d0) then
        tmp = -0.5d0
    else
        tmp = x * (x / 6.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x * x) <= 0.01) {
		tmp = -0.5;
	} else {
		tmp = x * (x / 6.0);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x * x) <= 0.01:
		tmp = -0.5
	else:
		tmp = x * (x / 6.0)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (Float64(x * x) <= 0.01)
		tmp = -0.5;
	else
		tmp = Float64(x * Float64(x / 6.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x * x) <= 0.01)
		tmp = -0.5;
	else
		tmp = x * (x / 6.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[N[(x * x), $MachinePrecision], 0.01], -0.5, N[(x * N[(x / 6.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x x) < 0.0100000000000000002

   1. Initial program 100.0%

      \[\frac{x \cdot x - 3}{6} \]

   2. Taylor expanded in x around 0 99.3%

      \[\leadsto \color{blue}{-0.5} \]

   if 0.0100000000000000002 < (*.f64 x x)

   1. Initial program 99.7%

      \[\frac{x \cdot x - 3}{6} \]
   2. Step-by-step derivation

      1. clear-num 99.7%

         \[\leadsto \color{blue}{\frac{1}{\frac{6}{x \cdot x - 3}}} \]

      2. inv-pow 99.7%

         \[\leadsto \color{blue}{{\left(\frac{6}{x \cdot x - 3}\right)}^{-1}} \]

      3. fma-neg 99.7%

         \[\leadsto {\left(\frac{6}{\color{blue}{\mathsf{fma}\left(x, x, -3\right)}}\right)}^{-1} \]

      4. metadata-eval 99.7%

         \[\leadsto {\left(\frac{6}{\mathsf{fma}\left(x, x, \color{blue}{-3}\right)}\right)}^{-1} \]

   3. Applied egg-rr 99.7%

      \[\leadsto \color{blue}{{\left(\frac{6}{\mathsf{fma}\left(x, x, -3\right)}\right)}^{-1}} \]

   4. Taylor expanded in x around inf 98.2%

      \[\leadsto \color{blue}{0.16666666666666666 \cdot {x}^{2}} \]
   5. Step-by-step derivation

      1. *-commutative 98.2%

         \[\leadsto \color{blue}{{x}^{2} \cdot 0.16666666666666666} \]

      2. unpow2 98.2%

         \[\leadsto \color{blue}{\left(x \cdot x\right)} \cdot 0.16666666666666666 \]

      3. associate-*r* 98.3%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.16666666666666666\right)} \]

   6. Simplified 98.3%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.16666666666666666\right)} \]
   7. Step-by-step derivation

      1. metadata-eval 98.3%

         \[\leadsto x \cdot \left(x \cdot \color{blue}{\frac{1}{6}}\right) \]

      2. div-inv 98.4%

         \[\leadsto x \cdot \color{blue}{\frac{x}{6}} \]

   8. Applied egg-rr 98.4%

      \[\leadsto x \cdot \color{blue}{\frac{x}{6}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot x \leq 0.01:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{x}{6}\\ \end{array} \]

Alternative 5: 98.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \cdot x \leq 0.01:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{6}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (if (<= (* x x) 0.01) -0.5 (/ x (/ 6.0 x))))

C

double code(double x) {
	double tmp;
	if ((x * x) <= 0.01) {
		tmp = -0.5;
	} else {
		tmp = x / (6.0 / x);
	}
	return tmp;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    real(8) :: tmp
    if ((x * x) <= 0.01d0) then
        tmp = -0.5d0
    else
        tmp = x / (6.0d0 / x)
    end if
    code = tmp
end function

Java

public static double code(double x) {
	double tmp;
	if ((x * x) <= 0.01) {
		tmp = -0.5;
	} else {
		tmp = x / (6.0 / x);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if (x * x) <= 0.01:
		tmp = -0.5
	else:
		tmp = x / (6.0 / x)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (Float64(x * x) <= 0.01)
		tmp = -0.5;
	else
		tmp = Float64(x / Float64(6.0 / x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x)
	tmp = 0.0;
	if ((x * x) <= 0.01)
		tmp = -0.5;
	else
		tmp = x / (6.0 / x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[N[(x * x), $MachinePrecision], 0.01], -0.5, N[(x / N[(6.0 / x), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x x) < 0.0100000000000000002

   1. Initial program 100.0%

      \[\frac{x \cdot x - 3}{6} \]

   2. Taylor expanded in x around 0 99.3%

      \[\leadsto \color{blue}{-0.5} \]

   if 0.0100000000000000002 < (*.f64 x x)

   1. Initial program 99.7%

      \[\frac{x \cdot x - 3}{6} \]
   2. Step-by-step derivation

      1. clear-num 99.7%

         \[\leadsto \color{blue}{\frac{1}{\frac{6}{x \cdot x - 3}}} \]

      2. inv-pow 99.7%

         \[\leadsto \color{blue}{{\left(\frac{6}{x \cdot x - 3}\right)}^{-1}} \]

      3. fma-neg 99.7%

         \[\leadsto {\left(\frac{6}{\color{blue}{\mathsf{fma}\left(x, x, -3\right)}}\right)}^{-1} \]

      4. metadata-eval 99.7%

         \[\leadsto {\left(\frac{6}{\mathsf{fma}\left(x, x, \color{blue}{-3}\right)}\right)}^{-1} \]

   3. Applied egg-rr 99.7%

      \[\leadsto \color{blue}{{\left(\frac{6}{\mathsf{fma}\left(x, x, -3\right)}\right)}^{-1}} \]

   4. Taylor expanded in x around inf 98.2%

      \[\leadsto \color{blue}{0.16666666666666666 \cdot {x}^{2}} \]
   5. Step-by-step derivation

      1. *-commutative 98.2%

         \[\leadsto \color{blue}{{x}^{2} \cdot 0.16666666666666666} \]

      2. unpow2 98.2%

         \[\leadsto \color{blue}{\left(x \cdot x\right)} \cdot 0.16666666666666666 \]

      3. associate-*r* 98.3%

         \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.16666666666666666\right)} \]

   6. Simplified 98.3%

      \[\leadsto \color{blue}{x \cdot \left(x \cdot 0.16666666666666666\right)} \]
   7. Step-by-step derivation

      1. associate-*r* 98.2%

         \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 0.16666666666666666} \]

      2. metadata-eval 98.2%

         \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{\frac{1}{6}} \]

      3. div-inv 98.3%

         \[\leadsto \color{blue}{\frac{x \cdot x}{6}} \]

      4. associate-/l* 98.5%

         \[\leadsto \color{blue}{\frac{x}{\frac{6}{x}}} \]

   8. Applied egg-rr 98.5%

      \[\leadsto \color{blue}{\frac{x}{\frac{6}{x}}} \]
3. Recombined 2 regimes into one program.

4. Final simplification 98.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot x \leq 0.01:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;\frac{x}{\frac{6}{x}}\\ \end{array} \]

Alternative 6: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot x\right) \cdot 0.16666666666666666 - 0.5 \end{array} \]

FPCore

(FPCore (x) :precision binary64 (- (* (* x x) 0.16666666666666666) 0.5))

C

double code(double x) {
	return ((x * x) * 0.16666666666666666) - 0.5;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((x * x) * 0.16666666666666666d0) - 0.5d0
end function

Java

public static double code(double x) {
	return ((x * x) * 0.16666666666666666) - 0.5;
}

Python

def code(x):
	return ((x * x) * 0.16666666666666666) - 0.5

Julia

function code(x)
	return Float64(Float64(Float64(x * x) * 0.16666666666666666) - 0.5)
end

MATLAB

function tmp = code(x)
	tmp = ((x * x) * 0.16666666666666666) - 0.5;
end

Wolfram

code[x_] := N[(N[(N[(x * x), $MachinePrecision] * 0.16666666666666666), $MachinePrecision] - 0.5), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{x \cdot x - 3}{6} \]
2. Step-by-step derivation

   1. div-sub 99.9%

      \[\leadsto \color{blue}{\frac{x \cdot x}{6} - \frac{3}{6}} \]

   2. div-inv 99.8%

      \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot \frac{1}{6}} - \frac{3}{6} \]

   3. metadata-eval 99.8%

      \[\leadsto \left(x \cdot x\right) \cdot \color{blue}{0.16666666666666666} - \frac{3}{6} \]

   4. metadata-eval 99.8%

      \[\leadsto \left(x \cdot x\right) \cdot 0.16666666666666666 - \color{blue}{0.5} \]

3. Applied egg-rr 99.8%

   \[\leadsto \color{blue}{\left(x \cdot x\right) \cdot 0.16666666666666666 - 0.5} \]

4. Final simplification 99.8%

   \[\leadsto \left(x \cdot x\right) \cdot 0.16666666666666666 - 0.5 \]

Alternative 7: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{x \cdot x - 3}{6} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (- (* x x) 3.0) 6.0))

C

double code(double x) {
	return ((x * x) - 3.0) / 6.0;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((x * x) - 3.0d0) / 6.0d0
end function

Java

public static double code(double x) {
	return ((x * x) - 3.0) / 6.0;
}

Python

def code(x):
	return ((x * x) - 3.0) / 6.0

Julia

function code(x)
	return Float64(Float64(Float64(x * x) - 3.0) / 6.0)
end

MATLAB

function tmp = code(x)
	tmp = ((x * x) - 3.0) / 6.0;
end

Wolfram

code[x_] := N[(N[(N[(x * x), $MachinePrecision] - 3.0), $MachinePrecision] / 6.0), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[\frac{x \cdot x - 3}{6} \]

2. Final simplification 99.8%

   \[\leadsto \frac{x \cdot x - 3}{6} \]

Alternative 8: 50.4% accurate, 7.0× speedup

Math

\[\begin{array}{l} \\ -0.5 \end{array} \]

FPCore

(FPCore (x) :precision binary64 -0.5)

C

double code(double x) {
	return -0.5;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = -0.5d0
end function

Java

public static double code(double x) {
	return -0.5;
}

Python

def code(x):
	return -0.5

Julia

function code(x)
	return -0.5
end

MATLAB

function tmp = code(x)
	tmp = -0.5;
end

Wolfram

code[x_] := -0.5

Derivation

1. Initial program 99.8%

   \[\frac{x \cdot x - 3}{6} \]

2. Taylor expanded in x around 0 55.4%

   \[\leadsto \color{blue}{-0.5} \]

3. Final simplification 55.4%

   \[\leadsto -0.5 \]

Reproduce

herbie shell --seed 2023185 
(FPCore (x)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, H"
  :precision binary64
  (/ (- (* x x) 3.0) 6.0))