Linear.Quaternion:$clog from linear-1.19.1.3

Percentage Accurate: 68.8% → 98.9%

Time: 2.7s

Alternatives: 4

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \sqrt{x \cdot x + y} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 68.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{x \cdot x + y} \end{array} \]

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 98.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ \begin{array}{l} \mathbf{if}\;x \cdot x \leq 5 \cdot 10^{+170}:\\ \;\;\;\;\sqrt{x \cdot x + y}\\ \mathbf{else}:\\ \;\;\;\;x + 0.5 \cdot \frac{y}{x}\\ \end{array} \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x y)
 :precision binary64
 (if (<= (* x x) 5e+170) (sqrt (+ (* x x) y)) (+ x (* 0.5 (/ y x)))))

C

x = abs(x);
double code(double x, double y) {
	double tmp;
	if ((x * x) <= 5e+170) {
		tmp = sqrt(((x * x) + y));
	} else {
		tmp = x + (0.5 * (y / x));
	}
	return tmp;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if ((x * x) <= 5d+170) then
        tmp = sqrt(((x * x) + y))
    else
        tmp = x + (0.5d0 * (y / x))
    end if
    code = tmp
end function

Java

x = Math.abs(x);
public static double code(double x, double y) {
	double tmp;
	if ((x * x) <= 5e+170) {
		tmp = Math.sqrt(((x * x) + y));
	} else {
		tmp = x + (0.5 * (y / x));
	}
	return tmp;
}

Python

x = abs(x)
def code(x, y):
	tmp = 0
	if (x * x) <= 5e+170:
		tmp = math.sqrt(((x * x) + y))
	else:
		tmp = x + (0.5 * (y / x))
	return tmp

Julia

x = abs(x)
function code(x, y)
	tmp = 0.0
	if (Float64(x * x) <= 5e+170)
		tmp = sqrt(Float64(Float64(x * x) + y));
	else
		tmp = Float64(x + Float64(0.5 * Float64(y / x)));
	end
	return tmp
end

MATLAB

x = abs(x)
function tmp_2 = code(x, y)
	tmp = 0.0;
	if ((x * x) <= 5e+170)
		tmp = sqrt(((x * x) + y));
	else
		tmp = x + (0.5 * (y / x));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x should be positive before calling this function
code[x_, y_] := If[LessEqual[N[(x * x), $MachinePrecision], 5e+170], N[Sqrt[N[(N[(x * x), $MachinePrecision] + y), $MachinePrecision]], $MachinePrecision], N[(x + N[(0.5 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 x x) < 4.99999999999999977e170

   1. Initial program 100.0%

      \[\sqrt{x \cdot x + y} \]

   if 4.99999999999999977e170 < (*.f64 x x)

   1. Initial program 39.0%

      \[\sqrt{x \cdot x + y} \]

   2. Taylor expanded in x around inf 47.3%

      \[\leadsto \color{blue}{0.5 \cdot \frac{y}{x} + x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 75.5%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \cdot x \leq 5 \cdot 10^{+170}:\\ \;\;\;\;\sqrt{x \cdot x + y}\\ \mathbf{else}:\\ \;\;\;\;x + 0.5 \cdot \frac{y}{x}\\ \end{array} \]

Alternative 2: 87.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ \begin{array}{l} \mathbf{if}\;x \leq 3.5 \cdot 10^{-6}:\\ \;\;\;\;\sqrt{y}\\ \mathbf{else}:\\ \;\;\;\;x + 0.5 \cdot \frac{y}{x}\\ \end{array} \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x y)
 :precision binary64
 (if (<= x 3.5e-6) (sqrt y) (+ x (* 0.5 (/ y x)))))

C

x = abs(x);
double code(double x, double y) {
	double tmp;
	if (x <= 3.5e-6) {
		tmp = sqrt(y);
	} else {
		tmp = x + (0.5 * (y / x));
	}
	return tmp;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8) :: tmp
    if (x <= 3.5d-6) then
        tmp = sqrt(y)
    else
        tmp = x + (0.5d0 * (y / x))
    end if
    code = tmp
end function

Java

x = Math.abs(x);
public static double code(double x, double y) {
	double tmp;
	if (x <= 3.5e-6) {
		tmp = Math.sqrt(y);
	} else {
		tmp = x + (0.5 * (y / x));
	}
	return tmp;
}

Python

x = abs(x)
def code(x, y):
	tmp = 0
	if x <= 3.5e-6:
		tmp = math.sqrt(y)
	else:
		tmp = x + (0.5 * (y / x))
	return tmp

Julia

x = abs(x)
function code(x, y)
	tmp = 0.0
	if (x <= 3.5e-6)
		tmp = sqrt(y);
	else
		tmp = Float64(x + Float64(0.5 * Float64(y / x)));
	end
	return tmp
end

MATLAB

x = abs(x)
function tmp_2 = code(x, y)
	tmp = 0.0;
	if (x <= 3.5e-6)
		tmp = sqrt(y);
	else
		tmp = x + (0.5 * (y / x));
	end
	tmp_2 = tmp;
end

Wolfram

NOTE: x should be positive before calling this function
code[x_, y_] := If[LessEqual[x, 3.5e-6], N[Sqrt[y], $MachinePrecision], N[(x + N[(0.5 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 3.49999999999999995e-6

   1. Initial program 78.4%

      \[\sqrt{x \cdot x + y} \]

   2. Taylor expanded in x around 0 48.4%

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

   if 3.49999999999999995e-6 < x

   1. Initial program 56.5%

      \[\sqrt{x \cdot x + y} \]

   2. Taylor expanded in x around inf 93.1%

      \[\leadsto \color{blue}{0.5 \cdot \frac{y}{x} + x} \]
3. Recombined 2 regimes into one program.

4. Final simplification 62.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 3.5 \cdot 10^{-6}:\\ \;\;\;\;\sqrt{y}\\ \mathbf{else}:\\ \;\;\;\;x + 0.5 \cdot \frac{y}{x}\\ \end{array} \]

Alternative 3: 67.9% accurate, 15.0× speedup

Math

\[\begin{array}{l} x = |x|\\ \\ x + 0.5 \cdot \frac{y}{x} \end{array} \]

FPCore

NOTE: x should be positive before calling this function
(FPCore (x y) :precision binary64 (+ x (* 0.5 (/ y x))))

C

x = abs(x);
double code(double x, double y) {
	return x + (0.5 * (y / x));
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x + (0.5d0 * (y / x))
end function

Java

x = Math.abs(x);
public static double code(double x, double y) {
	return x + (0.5 * (y / x));
}

Python

x = abs(x)
def code(x, y):
	return x + (0.5 * (y / x))

Julia

x = abs(x)
function code(x, y)
	return Float64(x + Float64(0.5 * Float64(y / x)))
end

MATLAB

x = abs(x)
function tmp = code(x, y)
	tmp = x + (0.5 * (y / x));
end

Wolfram

NOTE: x should be positive before calling this function
code[x_, y_] := N[(x + N[(0.5 * N[(y / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 71.6%

   \[\sqrt{x \cdot x + y} \]

2. Taylor expanded in x around inf 33.4%

   \[\leadsto \color{blue}{0.5 \cdot \frac{y}{x} + x} \]

3. Final simplification 33.4%

   \[\leadsto x + 0.5 \cdot \frac{y}{x} \]

Alternative 4: 67.5% accurate, 105.0× speedup

Math

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

FPCore

NOTE: x should be positive before calling this function
(FPCore (x y) :precision binary64 x)

C

x = abs(x);
double code(double x, double y) {
	return x;
}

Fortran

NOTE: x should be positive before calling this function
real(8) function code(x, y)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    code = x
end function

Java

x = Math.abs(x);
public static double code(double x, double y) {
	return x;
}

Python

x = abs(x)
def code(x, y):
	return x

Julia

x = abs(x)
function code(x, y)
	return x
end

MATLAB

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

Wolfram

NOTE: x should be positive before calling this function
code[x_, y_] := x

Derivation

1. Initial program 71.6%

   \[\sqrt{x \cdot x + y} \]

2. Taylor expanded in x around inf 33.5%

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

3. Final simplification 33.5%

   \[\leadsto x \]

Developer target: 98.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := 0.5 \cdot \frac{y}{x} + x\\ \mathbf{if}\;x < -1.5097698010472593 \cdot 10^{+153}:\\ \;\;\;\;-t_0\\ \mathbf{elif}\;x < 5.582399551122541 \cdot 10^{+57}:\\ \;\;\;\;\sqrt{x \cdot x + y}\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y)
 :precision binary64
 (let* ((t_0 (+ (* 0.5 (/ y x)) x)))
   (if (< x -1.5097698010472593e+153)
     (- t_0)
     (if (< x 5.582399551122541e+57) (sqrt (+ (* x x) y)) t_0))))

C

double code(double x, double y) {
	double t_0 = (0.5 * (y / x)) + x;
	double tmp;
	if (x < -1.5097698010472593e+153) {
		tmp = -t_0;
	} else if (x < 5.582399551122541e+57) {
		tmp = sqrt(((x * x) + y));
	} else {
		tmp = t_0;
	}
	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 = (0.5d0 * (y / x)) + x
    if (x < (-1.5097698010472593d+153)) then
        tmp = -t_0
    else if (x < 5.582399551122541d+57) then
        tmp = sqrt(((x * x) + y))
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y) {
	double t_0 = (0.5 * (y / x)) + x;
	double tmp;
	if (x < -1.5097698010472593e+153) {
		tmp = -t_0;
	} else if (x < 5.582399551122541e+57) {
		tmp = Math.sqrt(((x * x) + y));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y):
	t_0 = (0.5 * (y / x)) + x
	tmp = 0
	if x < -1.5097698010472593e+153:
		tmp = -t_0
	elif x < 5.582399551122541e+57:
		tmp = math.sqrt(((x * x) + y))
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y)
	t_0 = Float64(Float64(0.5 * Float64(y / x)) + x)
	tmp = 0.0
	if (x < -1.5097698010472593e+153)
		tmp = Float64(-t_0);
	elseif (x < 5.582399551122541e+57)
		tmp = sqrt(Float64(Float64(x * x) + y));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y)
	t_0 = (0.5 * (y / x)) + x;
	tmp = 0.0;
	if (x < -1.5097698010472593e+153)
		tmp = -t_0;
	elseif (x < 5.582399551122541e+57)
		tmp = sqrt(((x * x) + y));
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_] := Block[{t$95$0 = N[(N[(0.5 * N[(y / x), $MachinePrecision]), $MachinePrecision] + x), $MachinePrecision]}, If[Less[x, -1.5097698010472593e+153], (-t$95$0), If[Less[x, 5.582399551122541e+57], N[Sqrt[N[(N[(x * x), $MachinePrecision] + y), $MachinePrecision]], $MachinePrecision], t$95$0]]]

Reproduce

herbie shell --seed 2023217 
(FPCore (x y)
  :name "Linear.Quaternion:$clog from linear-1.19.1.3"
  :precision binary64

  :herbie-target
  (if (< x -1.5097698010472593e+153) (- (+ (* 0.5 (/ y x)) x)) (if (< x 5.582399551122541e+57) (sqrt (+ (* x x) y)) (+ (* 0.5 (/ y x)) x)))

  (sqrt (+ (* x x) y)))