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

Percentage Accurate: 99.8% → 99.9%

Time: 4.1s

Alternatives: 5

Speedup: 1.0×

• 24.1% 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: 99.8% 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: 99.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(x, \frac{x}{6}, -0.5\right) \end{array} \]

FPCore

(FPCore (x) :precision binary64 (fma x (/ x 6.0) -0.5))

C

double code(double x) {
	return fma(x, (x / 6.0), -0.5);
}

Julia

function code(x)
	return fma(x, Float64(x / 6.0), -0.5)
end

Wolfram

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

Derivation

1. Initial program 99.9%

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

   1. sqr-neg 99.9%

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

   2. *-rgt-identity 99.9%

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

   3. metadata-eval 99.9%

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

   4. associate-*l/ 99.9%

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

   5. associate-/l* 99.9%

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

   6. sqr-neg 99.9%

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

   7. fma-neg 99.9%

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

   8. metadata-eval 99.9%

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

   9. metadata-eval 99.9%

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

   10. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x, x, -3\right) \cdot \color{blue}{0.16666666666666666} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -3\right) \cdot 0.16666666666666666} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. metadata-eval 99.9%

      \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{-3}\right) \cdot 0.16666666666666666 \]

   2. fma-neg 99.9%

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

   3. metadata-eval 99.9%

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

   4. div-inv 99.9%

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

   5. div-sub 99.9%

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

   6. associate-/l* 99.9%

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

   7. fma-neg 99.9%

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

   8. metadata-eval 99.9%

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

   9. metadata-eval 99.9%

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

6. Applied egg-rr 99.9%

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

Alternative 2: 74.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.72:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{x}{6}\\ \end{array} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (if (<= x 1.72) -0.5 (* x (/ x 6.0))))

C

double code(double x) {
	double tmp;
	if (x <= 1.72) {
		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 <= 1.72d0) 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 <= 1.72) {
		tmp = -0.5;
	} else {
		tmp = x * (x / 6.0);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 1.72:
		tmp = -0.5
	else:
		tmp = x * (x / 6.0)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 1.72)
		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 <= 1.72)
		tmp = -0.5;
	else
		tmp = x * (x / 6.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 1.72], -0.5, N[(x * N[(x / 6.0), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.71999999999999997

   1. Initial program 99.9%

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

      1. sqr-neg 99.9%

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

      2. *-rgt-identity 99.9%

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

      3. metadata-eval 99.9%

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

      4. associate-*l/ 99.9%

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

      5. associate-/l* 99.9%

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

      6. sqr-neg 99.9%

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

      7. fma-neg 99.9%

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

      8. metadata-eval 99.9%

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

      9. metadata-eval 99.9%

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

      10. metadata-eval 99.9%

          \[\leadsto \mathsf{fma}\left(x, x, -3\right) \cdot \color{blue}{0.16666666666666666} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -3\right) \cdot 0.16666666666666666} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 66.8%

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

   if 1.71999999999999997 < x

   1. Initial program 99.8%

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

      1. sqr-neg 99.8%

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

      2. *-rgt-identity 99.8%

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

      3. metadata-eval 99.8%

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

      4. associate-*l/ 99.8%

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

      5. associate-/l* 99.7%

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

      6. sqr-neg 99.7%

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

      7. fma-neg 99.7%

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

      8. metadata-eval 99.7%

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

      9. metadata-eval 99.7%

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

      10. metadata-eval 99.7%

          \[\leadsto \mathsf{fma}\left(x, x, -3\right) \cdot \color{blue}{0.16666666666666666} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -3\right) \cdot 0.16666666666666666} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 99.7%

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

      1. *-commutative 99.7%

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

      2. unpow2 99.7%

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

      3. associate-*r* 99.6%

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

      4. metadata-eval 99.6%

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

      5. div-inv 99.8%

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

      6. clear-num 99.6%

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

      7. un-div-inv 99.7%

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

   7. Applied egg-rr 99.7%

      \[\leadsto \color{blue}{\frac{x}{\frac{6}{x}}} \]
   8. Step-by-step derivation

      1. associate-/r/ 99.8%

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

   9. Simplified 99.8%

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

4. Final simplification 75.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.72:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \frac{x}{6}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 74.2% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.72:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot 0.16666666666666666\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (if (<= x 1.72) -0.5 (* x (* x 0.16666666666666666))))

C

double code(double x) {
	double tmp;
	if (x <= 1.72) {
		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 <= 1.72d0) 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 <= 1.72) {
		tmp = -0.5;
	} else {
		tmp = x * (x * 0.16666666666666666);
	}
	return tmp;
}

Python

def code(x):
	tmp = 0
	if x <= 1.72:
		tmp = -0.5
	else:
		tmp = x * (x * 0.16666666666666666)
	return tmp

Julia

function code(x)
	tmp = 0.0
	if (x <= 1.72)
		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 <= 1.72)
		tmp = -0.5;
	else
		tmp = x * (x * 0.16666666666666666);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_] := If[LessEqual[x, 1.72], -0.5, N[(x * N[(x * 0.16666666666666666), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.71999999999999997

   1. Initial program 99.9%

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

      1. sqr-neg 99.9%

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

      2. *-rgt-identity 99.9%

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

      3. metadata-eval 99.9%

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

      4. associate-*l/ 99.9%

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

      5. associate-/l* 99.9%

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

      6. sqr-neg 99.9%

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

      7. fma-neg 99.9%

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

      8. metadata-eval 99.9%

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

      9. metadata-eval 99.9%

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

      10. metadata-eval 99.9%

          \[\leadsto \mathsf{fma}\left(x, x, -3\right) \cdot \color{blue}{0.16666666666666666} \]

   3. Simplified 99.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -3\right) \cdot 0.16666666666666666} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 66.8%

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

   if 1.71999999999999997 < x

   1. Initial program 99.8%

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

      1. sqr-neg 99.8%

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

      2. *-rgt-identity 99.8%

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

      3. metadata-eval 99.8%

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

      4. associate-*l/ 99.8%

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

      5. associate-/l* 99.7%

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

      6. sqr-neg 99.7%

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

      7. fma-neg 99.7%

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

      8. metadata-eval 99.7%

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

      9. metadata-eval 99.7%

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

      10. metadata-eval 99.7%

          \[\leadsto \mathsf{fma}\left(x, x, -3\right) \cdot \color{blue}{0.16666666666666666} \]

   3. Simplified 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -3\right) \cdot 0.16666666666666666} \]
   4. Add Preprocessing
   5. Step-by-step derivation

      1. metadata-eval 99.7%

         \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{-3}\right) \cdot 0.16666666666666666 \]

      2. fma-neg 99.7%

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

      3. metadata-eval 99.7%

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

      4. div-inv 99.8%

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

      5. clear-num 99.7%

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

      6. fma-neg 99.7%

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

      7. metadata-eval 99.7%

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

   6. Applied egg-rr 99.7%

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

   7. Taylor expanded in x around inf 99.7%

      \[\leadsto \frac{1}{\color{blue}{\frac{6}{{x}^{2}}}} \]
   8. Step-by-step derivation

      1. clear-num 99.8%

         \[\leadsto \color{blue}{\frac{{x}^{2}}{6}} \]

      2. unpow2 99.8%

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

      3. associate-*l/ 99.8%

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

      4. div-inv 99.6%

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

      5. metadata-eval 99.6%

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

   9. Applied egg-rr 99.6%

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

4. Final simplification 75.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1.72:\\ \;\;\;\;-0.5\\ \mathbf{else}:\\ \;\;\;\;x \cdot \left(x \cdot 0.16666666666666666\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 99.8% 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 99.9%

   \[\frac{x \cdot x - 3}{6} \]
2. Add Preprocessing
3. Add Preprocessing

Alternative 5: 50.2% 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 99.9%

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

   1. sqr-neg 99.9%

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

   2. *-rgt-identity 99.9%

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

   3. metadata-eval 99.9%

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

   4. associate-*l/ 99.9%

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

   5. associate-/l* 99.9%

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

   6. sqr-neg 99.9%

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

   7. fma-neg 99.9%

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

   8. metadata-eval 99.9%

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

   9. metadata-eval 99.9%

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

   10. metadata-eval 99.9%

       \[\leadsto \mathsf{fma}\left(x, x, -3\right) \cdot \color{blue}{0.16666666666666666} \]

3. Simplified 99.9%

   \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -3\right) \cdot 0.16666666666666666} \]
4. Add Preprocessing

5. Taylor expanded in x around 0 48.5%

   \[\leadsto \color{blue}{-0.5} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024100 
(FPCore (x)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, H"
  :precision binary64
  (/ (- (* x x) 3.0) 6.0))