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

Percentage Accurate: 99.9% → 99.9%

Time: 7.1s

Alternatives: 11

Speedup: 1.0×

Specification

Math

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

FPCore

(FPCore (x y) :precision binary64 (+ (- 1.0 x) (* y (sqrt x))))

C

double code(double x, double y) {
	return (1.0 - x) + (y * sqrt(x));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - x) + (y * sqrt(x))
end function

Java

public static double code(double x, double y) {
	return (1.0 - x) + (y * Math.sqrt(x));
}

Python

def code(x, y):
	return (1.0 - x) + (y * math.sqrt(x))

Julia

function code(x, y)
	return Float64(Float64(1.0 - x) + Float64(y * sqrt(x)))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - x) + (y * sqrt(x));
end

Wolfram

code[x_, y_] := N[(N[(1.0 - x), $MachinePrecision] + N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (+ (- 1.0 x) (* y (sqrt x))))

C

double code(double x, double y) {
	return (1.0 - x) + (y * sqrt(x));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - x) + (y * sqrt(x))
end function

Java

public static double code(double x, double y) {
	return (1.0 - x) + (y * Math.sqrt(x));
}

Python

def code(x, y):
	return (1.0 - x) + (y * math.sqrt(x))

Julia

function code(x, y)
	return Float64(Float64(1.0 - x) + Float64(y * sqrt(x)))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - x) + (y * sqrt(x));
end

Wolfram

code[x_, y_] := N[(N[(1.0 - x), $MachinePrecision] + N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (+ (- (* y (sqrt x)) x) 1.0))

C

double code(double x, double y) {
	return ((y * sqrt(x)) - x) + 1.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = ((y * sqrt(x)) - x) + 1.0d0
end function

Java

public static double code(double x, double y) {
	return ((y * Math.sqrt(x)) - x) + 1.0;
}

Python

def code(x, y):
	return ((y * math.sqrt(x)) - x) + 1.0

Julia

function code(x, y)
	return Float64(Float64(Float64(y * sqrt(x)) - x) + 1.0)
end

MATLAB

function tmp = code(x, y)
	tmp = ((y * sqrt(x)) - x) + 1.0;
end

Wolfram

code[x_, y_] := N[(N[(N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] - x), $MachinePrecision] + 1.0), $MachinePrecision]

Derivation

1. Initial program 99.9%

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

   1. associate-+l- 99.9%

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

4. Applied egg-rr 99.9%

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

5. Final simplification 99.9%

   \[\leadsto \left(y \cdot \sqrt{x} - x\right) + 1 \]
6. Add Preprocessing

Alternative 2: 95.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -3.15 \cdot 10^{+60} \lor \neg \left(y \leq 1.9 \cdot 10^{+62}\right):\\ \;\;\;\;y \cdot \sqrt{x} + 1\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -3.15e+60) (not (<= y 1.9e+62)))
   (+ (* y (sqrt x)) 1.0)
   (- 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -3.15e+60) || !(y <= 1.9e+62)) {
		tmp = (y * sqrt(x)) + 1.0;
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-3.15d+60)) .or. (.not. (y <= 1.9d+62))) then
        tmp = (y * sqrt(x)) + 1.0d0
    else
        tmp = 1.0d0 - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -3.15e+60) || !(y <= 1.9e+62)) {
		tmp = (y * Math.sqrt(x)) + 1.0;
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -3.15e+60) or not (y <= 1.9e+62):
		tmp = (y * math.sqrt(x)) + 1.0
	else:
		tmp = 1.0 - x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -3.15e+60) || !(y <= 1.9e+62))
		tmp = Float64(Float64(y * sqrt(x)) + 1.0);
	else
		tmp = Float64(1.0 - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -3.15e+60) || ~((y <= 1.9e+62)))
		tmp = (y * sqrt(x)) + 1.0;
	else
		tmp = 1.0 - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -3.15e+60], N[Not[LessEqual[y, 1.9e+62]], $MachinePrecision]], N[(N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision], N[(1.0 - x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -3.1500000000000002e60 or 1.89999999999999992e62 < y

   1. Initial program 99.8%

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

   3. Taylor expanded in x around 0 95.0%

      \[\leadsto \color{blue}{1 + \sqrt{x} \cdot y} \]

   if -3.1500000000000002e60 < y < 1.89999999999999992e62

   1. Initial program 100.0%

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

      1. associate-+l- 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in y around 0 97.4%

      \[\leadsto 1 - \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 96.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -3.15 \cdot 10^{+60} \lor \neg \left(y \leq 1.9 \cdot 10^{+62}\right):\\ \;\;\;\;y \cdot \sqrt{x} + 1\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 93.4% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -1.75 \cdot 10^{+59} \lor \neg \left(y \leq 3.8 \cdot 10^{+79}\right):\\ \;\;\;\;y \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (or (<= y -1.75e+59) (not (<= y 3.8e+79))) (* y (sqrt x)) (- 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if ((y <= -1.75e+59) || !(y <= 3.8e+79)) {
		tmp = y * sqrt(x);
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((y <= (-1.75d+59)) .or. (.not. (y <= 3.8d+79))) then
        tmp = y * sqrt(x)
    else
        tmp = 1.0d0 - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if ((y <= -1.75e+59) || !(y <= 3.8e+79)) {
		tmp = y * Math.sqrt(x);
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if (y <= -1.75e+59) or not (y <= 3.8e+79):
		tmp = y * math.sqrt(x)
	else:
		tmp = 1.0 - x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if ((y <= -1.75e+59) || !(y <= 3.8e+79))
		tmp = Float64(y * sqrt(x));
	else
		tmp = Float64(1.0 - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((y <= -1.75e+59) || ~((y <= 3.8e+79)))
		tmp = y * sqrt(x);
	else
		tmp = 1.0 - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[Or[LessEqual[y, -1.75e+59], N[Not[LessEqual[y, 3.8e+79]], $MachinePrecision]], N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision], N[(1.0 - x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -1.75e59 or 3.8000000000000002e79 < y

   1. Initial program 99.8%

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

      1. associate-+l- 99.8%

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

   4. Applied egg-rr 99.8%

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

   5. Taylor expanded in y around inf 92.9%

      \[\leadsto \color{blue}{\sqrt{x} \cdot y} \]

   if -1.75e59 < y < 3.8000000000000002e79

   1. Initial program 100.0%

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

      1. associate-+l- 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in y around 0 95.7%

      \[\leadsto 1 - \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 94.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.75 \cdot 10^{+59} \lor \neg \left(y \leq 3.8 \cdot 10^{+79}\right):\\ \;\;\;\;y \cdot \sqrt{x}\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 98.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \sqrt{x}\\ \mathbf{if}\;x \leq 1:\\ \;\;\;\;t\_0 + 1\\ \mathbf{else}:\\ \;\;\;\;t\_0 - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (* y (sqrt x)))) (if (<= x 1.0) (+ t_0 1.0) (- t_0 x))))

C

double code(double x, double y) {
	double t_0 = y * sqrt(x);
	double tmp;
	if (x <= 1.0) {
		tmp = t_0 + 1.0;
	} else {
		tmp = t_0 - x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = y * sqrt(x)
    if (x <= 1.0d0) then
        tmp = t_0 + 1.0d0
    else
        tmp = t_0 - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = y * Math.sqrt(x);
	double tmp;
	if (x <= 1.0) {
		tmp = t_0 + 1.0;
	} else {
		tmp = t_0 - x;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = y * math.sqrt(x)
	tmp = 0
	if x <= 1.0:
		tmp = t_0 + 1.0
	else:
		tmp = t_0 - x
	return tmp

Julia

function code(x, y)
	t_0 = Float64(y * sqrt(x))
	tmp = 0.0
	if (x <= 1.0)
		tmp = Float64(t_0 + 1.0);
	else
		tmp = Float64(t_0 - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = y * sqrt(x);
	tmp = 0.0;
	if (x <= 1.0)
		tmp = t_0 + 1.0;
	else
		tmp = t_0 - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, 1.0], N[(t$95$0 + 1.0), $MachinePrecision], N[(t$95$0 - x), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < 1

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0 99.5%

      \[\leadsto \color{blue}{1 + \sqrt{x} \cdot y} \]

   if 1 < x

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf 99.9%

      \[\leadsto \color{blue}{-1 \cdot x} + y \cdot \sqrt{x} \]
   4. Step-by-step derivation

      1. neg-mul-1 99.9%

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

   5. Simplified 99.9%

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

   6. Taylor expanded in y around 0 99.9%

      \[\leadsto \color{blue}{\sqrt{x} \cdot y - x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 99.7%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 1:\\ \;\;\;\;y \cdot \sqrt{x} + 1\\ \mathbf{else}:\\ \;\;\;\;y \cdot \sqrt{x} - x\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.9% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (+ (- 1.0 x) (* y (sqrt x))))

C

double code(double x, double y) {
	return (1.0 - x) + (y * sqrt(x));
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = (1.0d0 - x) + (y * sqrt(x))
end function

Java

public static double code(double x, double y) {
	return (1.0 - x) + (y * Math.sqrt(x));
}

Python

def code(x, y):
	return (1.0 - x) + (y * math.sqrt(x))

Julia

function code(x, y)
	return Float64(Float64(1.0 - x) + Float64(y * sqrt(x)))
end

MATLAB

function tmp = code(x, y)
	tmp = (1.0 - x) + (y * sqrt(x));
end

Wolfram

code[x_, y_] := N[(N[(1.0 - x), $MachinePrecision] + N[(y * N[Sqrt[x], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.9%

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

Alternative 6: 65.9% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - x \cdot x\\ \mathbf{if}\;y \leq -1.35 \cdot 10^{+154}:\\ \;\;\;\;\frac{t\_0}{x + 1}\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+63}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;\frac{t\_0}{1 - x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (* x x))))
   (if (<= y -1.35e+154)
     (/ t_0 (+ x 1.0))
     (if (<= y 7e+63) (- 1.0 x) (/ t_0 (- 1.0 x))))))

C

double code(double x, double y) {
	double t_0 = 1.0 - (x * x);
	double tmp;
	if (y <= -1.35e+154) {
		tmp = t_0 / (x + 1.0);
	} else if (y <= 7e+63) {
		tmp = 1.0 - x;
	} else {
		tmp = t_0 / (1.0 - x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - (x * x)
    if (y <= (-1.35d+154)) then
        tmp = t_0 / (x + 1.0d0)
    else if (y <= 7d+63) then
        tmp = 1.0d0 - x
    else
        tmp = t_0 / (1.0d0 - x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - (x * x);
	double tmp;
	if (y <= -1.35e+154) {
		tmp = t_0 / (x + 1.0);
	} else if (y <= 7e+63) {
		tmp = 1.0 - x;
	} else {
		tmp = t_0 / (1.0 - x);
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - (x * x)
	tmp = 0
	if y <= -1.35e+154:
		tmp = t_0 / (x + 1.0)
	elif y <= 7e+63:
		tmp = 1.0 - x
	else:
		tmp = t_0 / (1.0 - x)
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(x * x))
	tmp = 0.0
	if (y <= -1.35e+154)
		tmp = Float64(t_0 / Float64(x + 1.0));
	elseif (y <= 7e+63)
		tmp = Float64(1.0 - x);
	else
		tmp = Float64(t_0 / Float64(1.0 - x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 - (x * x);
	tmp = 0.0;
	if (y <= -1.35e+154)
		tmp = t_0 / (x + 1.0);
	elseif (y <= 7e+63)
		tmp = 1.0 - x;
	else
		tmp = t_0 / (1.0 - x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -1.35e+154], N[(t$95$0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[y, 7e+63], N[(1.0 - x), $MachinePrecision], N[(t$95$0 / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -1.35000000000000003e154

   1. Initial program 99.8%

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

      1. associate-+l- 99.8%

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

   4. Applied egg-rr 99.8%

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

   5. Taylor expanded in y around 0 3.4%

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

      1. sub-neg 3.4%

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

      2. flip-+ 22.6%

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

      3. metadata-eval 22.6%

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

   7. Applied egg-rr 22.6%

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

   if -1.35000000000000003e154 < y < 7.00000000000000059e63

   1. Initial program 100.0%

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

      1. associate-+l- 100.0%

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

   4. Applied egg-rr 100.0%

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

   5. Taylor expanded in y around 0 88.7%

      \[\leadsto 1 - \color{blue}{x} \]

   if 7.00000000000000059e63 < y

   1. Initial program 99.8%

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

      1. associate-+l- 99.8%

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

   4. Applied egg-rr 99.8%

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

   5. Taylor expanded in y around 0 10.5%

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

      1. sub-neg 10.5%

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

      2. flip-+ 10.5%

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

      3. metadata-eval 10.5%

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

   7. Applied egg-rr 10.5%

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

      1. neg-sub0 10.5%

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

      2. sub-neg 10.5%

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

      3. add-sqr-sqrt 0.0%

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

      4. sqrt-unprod 11.8%

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

      5. sqr-neg 11.8%

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

      6. sqrt-unprod 23.9%

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

      7. add-sqr-sqrt 23.9%

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

   9. Applied egg-rr 23.9%

      \[\leadsto \frac{1 - \left(-x\right) \cdot \left(-x\right)}{1 - \color{blue}{\left(0 + x\right)}} \]
   10. Step-by-step derivation

       1. +-lft-identity 23.9%

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

   11. Simplified 23.9%

       \[\leadsto \frac{1 - \left(-x\right) \cdot \left(-x\right)}{1 - \color{blue}{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 67.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{+154}:\\ \;\;\;\;\frac{1 - x \cdot x}{x + 1}\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+63}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - x \cdot x}{1 - x}\\ \end{array} \]
5. Add Preprocessing

Alternative 7: 65.6% accurate, 5.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 1 - x \cdot x\\ \mathbf{if}\;y \leq -4 \cdot 10^{+195}:\\ \;\;\;\;\frac{t\_0}{x}\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+63}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;\frac{t\_0}{1 - x}\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (- 1.0 (* x x))))
   (if (<= y -4e+195)
     (/ t_0 x)
     (if (<= y 7e+63) (- 1.0 x) (/ t_0 (- 1.0 x))))))

C

double code(double x, double y) {
	double t_0 = 1.0 - (x * x);
	double tmp;
	if (y <= -4e+195) {
		tmp = t_0 / x;
	} else if (y <= 7e+63) {
		tmp = 1.0 - x;
	} else {
		tmp = t_0 / (1.0 - x);
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: t_0
    real(8) :: tmp
    t_0 = 1.0d0 - (x * x)
    if (y <= (-4d+195)) then
        tmp = t_0 / x
    else if (y <= 7d+63) then
        tmp = 1.0d0 - x
    else
        tmp = t_0 / (1.0d0 - x)
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = 1.0 - (x * x);
	double tmp;
	if (y <= -4e+195) {
		tmp = t_0 / x;
	} else if (y <= 7e+63) {
		tmp = 1.0 - x;
	} else {
		tmp = t_0 / (1.0 - x);
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = 1.0 - (x * x)
	tmp = 0
	if y <= -4e+195:
		tmp = t_0 / x
	elif y <= 7e+63:
		tmp = 1.0 - x
	else:
		tmp = t_0 / (1.0 - x)
	return tmp

Julia

function code(x, y)
	t_0 = Float64(1.0 - Float64(x * x))
	tmp = 0.0
	if (y <= -4e+195)
		tmp = Float64(t_0 / x);
	elseif (y <= 7e+63)
		tmp = Float64(1.0 - x);
	else
		tmp = Float64(t_0 / Float64(1.0 - x));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = 1.0 - (x * x);
	tmp = 0.0;
	if (y <= -4e+195)
		tmp = t_0 / x;
	elseif (y <= 7e+63)
		tmp = 1.0 - x;
	else
		tmp = t_0 / (1.0 - x);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[y, -4e+195], N[(t$95$0 / x), $MachinePrecision], If[LessEqual[y, 7e+63], N[(1.0 - x), $MachinePrecision], N[(t$95$0 / N[(1.0 - x), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if y < -3.99999999999999991e195

   1. Initial program 99.8%

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

      1. associate-+l- 99.8%

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

   4. Applied egg-rr 99.8%

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

   5. Taylor expanded in y around 0 3.8%

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

      1. sub-neg 3.8%

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

      2. flip-+ 31.5%

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

      3. metadata-eval 31.5%

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

   7. Applied egg-rr 31.5%

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

   8. Taylor expanded in x around inf 31.4%

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

   if -3.99999999999999991e195 < y < 7.00000000000000059e63

   1. Initial program 99.9%

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

      1. associate-+l- 99.9%

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

   4. Applied egg-rr 99.9%

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

   5. Taylor expanded in y around 0 83.1%

      \[\leadsto 1 - \color{blue}{x} \]

   if 7.00000000000000059e63 < y

   1. Initial program 99.8%

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

      1. associate-+l- 99.8%

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

   4. Applied egg-rr 99.8%

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

   5. Taylor expanded in y around 0 10.5%

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

      1. sub-neg 10.5%

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

      2. flip-+ 10.5%

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

      3. metadata-eval 10.5%

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

   7. Applied egg-rr 10.5%

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

      1. neg-sub0 10.5%

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

      2. sub-neg 10.5%

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

      3. add-sqr-sqrt 0.0%

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

      4. sqrt-unprod 11.8%

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

      5. sqr-neg 11.8%

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

      6. sqrt-unprod 23.9%

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

      7. add-sqr-sqrt 23.9%

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

   9. Applied egg-rr 23.9%

      \[\leadsto \frac{1 - \left(-x\right) \cdot \left(-x\right)}{1 - \color{blue}{\left(0 + x\right)}} \]
   10. Step-by-step derivation

       1. +-lft-identity 23.9%

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

   11. Simplified 23.9%

       \[\leadsto \frac{1 - \left(-x\right) \cdot \left(-x\right)}{1 - \color{blue}{x}} \]
3. Recombined 3 regimes into one program.

4. Final simplification 67.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -4 \cdot 10^{+195}:\\ \;\;\;\;\frac{1 - x \cdot x}{x}\\ \mathbf{elif}\;y \leq 7 \cdot 10^{+63}:\\ \;\;\;\;1 - x\\ \mathbf{else}:\\ \;\;\;\;\frac{1 - x \cdot x}{1 - x}\\ \end{array} \]
5. Add Preprocessing

Alternative 8: 63.8% accurate, 8.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -7.2 \cdot 10^{+194}:\\ \;\;\;\;\frac{1 - x \cdot x}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (if (<= y -7.2e+194) (/ (- 1.0 (* x x)) x) (- 1.0 x)))

C

double code(double x, double y) {
	double tmp;
	if (y <= -7.2e+194) {
		tmp = (1.0 - (x * x)) / x;
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (y <= (-7.2d+194)) then
        tmp = (1.0d0 - (x * x)) / x
    else
        tmp = 1.0d0 - x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (y <= -7.2e+194) {
		tmp = (1.0 - (x * x)) / x;
	} else {
		tmp = 1.0 - x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if y <= -7.2e+194:
		tmp = (1.0 - (x * x)) / x
	else:
		tmp = 1.0 - x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (y <= -7.2e+194)
		tmp = Float64(Float64(1.0 - Float64(x * x)) / x);
	else
		tmp = Float64(1.0 - x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (y <= -7.2e+194)
		tmp = (1.0 - (x * x)) / x;
	else
		tmp = 1.0 - x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[y, -7.2e+194], N[(N[(1.0 - N[(x * x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision], N[(1.0 - x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -7.19999999999999999e194

   1. Initial program 99.8%

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

      1. associate-+l- 99.8%

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

   4. Applied egg-rr 99.8%

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

   5. Taylor expanded in y around 0 3.8%

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

      1. sub-neg 3.8%

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

      2. flip-+ 31.5%

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

      3. metadata-eval 31.5%

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

   7. Applied egg-rr 31.5%

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

   8. Taylor expanded in x around inf 31.4%

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

   if -7.19999999999999999e194 < y

   1. Initial program 99.9%

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

      1. associate-+l- 99.9%

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

   4. Applied egg-rr 99.9%

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

   5. Taylor expanded in y around 0 69.2%

      \[\leadsto 1 - \color{blue}{x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 65.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -7.2 \cdot 10^{+194}:\\ \;\;\;\;\frac{1 - x \cdot x}{x}\\ \mathbf{else}:\\ \;\;\;\;1 - x\\ \end{array} \]
5. Add Preprocessing

Alternative 9: 61.0% accurate, 15.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;-x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y) :precision binary64 (if (<= x 1.0) 1.0 (- x)))

C

double code(double x, double y) {
	double tmp;
	if (x <= 1.0) {
		tmp = 1.0;
	} else {
		tmp = -x;
	}
	return tmp;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= 1.0d0) then
        tmp = 1.0d0
    else
        tmp = -x
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double tmp;
	if (x <= 1.0) {
		tmp = 1.0;
	} else {
		tmp = -x;
	}
	return tmp;
}

Python

def code(x, y):
	tmp = 0
	if x <= 1.0:
		tmp = 1.0
	else:
		tmp = -x
	return tmp

Julia

function code(x, y)
	tmp = 0.0
	if (x <= 1.0)
		tmp = 1.0;
	else
		tmp = Float64(-x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= 1.0)
		tmp = 1.0;
	else
		tmp = -x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := If[LessEqual[x, 1.0], 1.0, (-x)]

Derivation

1. Split input into 2 regimes

2. if x < 1

   1. Initial program 99.9%

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

   3. Taylor expanded in x around 0 99.5%

      \[\leadsto \color{blue}{1 + \sqrt{x} \cdot y} \]

   4. Taylor expanded in y around 0 63.2%

      \[\leadsto \color{blue}{1} \]

   if 1 < x

   1. Initial program 99.9%

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

   3. Taylor expanded in x around inf 99.8%

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

   4. Taylor expanded in y around 0 60.8%

      \[\leadsto \color{blue}{-1 \cdot x} \]
   5. Step-by-step derivation

      1. neg-mul-1 60.8%

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

   6. Simplified 60.8%

      \[\leadsto \color{blue}{-x} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 61.9% accurate, 35.7× speedup

Math

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

FPCore

(FPCore (x y) :precision binary64 (- 1.0 x))

C

double code(double x, double y) {
	return 1.0 - x;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0 - x
end function

Java

public static double code(double x, double y) {
	return 1.0 - x;
}

Python

def code(x, y):
	return 1.0 - x

Julia

function code(x, y)
	return Float64(1.0 - x)
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0 - x;
end

Wolfram

code[x_, y_] := N[(1.0 - x), $MachinePrecision]

Derivation

1. Initial program 99.9%

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

   1. associate-+l- 99.9%

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

4. Applied egg-rr 99.9%

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

5. Taylor expanded in y around 0 62.3%

   \[\leadsto 1 - \color{blue}{x} \]
6. Add Preprocessing

Alternative 11: 30.3% accurate, 107.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (x y) :precision binary64 1.0)

C

double code(double x, double y) {
	return 1.0;
}

Fortran

real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = 1.0d0
end function

Java

public static double code(double x, double y) {
	return 1.0;
}

Python

def code(x, y):
	return 1.0

Julia

function code(x, y)
	return 1.0
end

MATLAB

function tmp = code(x, y)
	tmp = 1.0;
end

Wolfram

code[x_, y_] := 1.0

Derivation

1. Initial program 99.9%

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

3. Taylor expanded in x around 0 71.3%

   \[\leadsto \color{blue}{1 + \sqrt{x} \cdot y} \]

4. Taylor expanded in y around 0 34.5%

   \[\leadsto \color{blue}{1} \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2024185 
(FPCore (x y)
  :name "Numeric.SpecFunctions:invIncompleteBetaWorker from math-functions-0.1.5.2, E"
  :precision binary64
  (+ (- 1.0 x) (* y (sqrt x))))