Optimisation.CirclePacking:place from circle-packing-0.1.0.4, G

Percentage Accurate: 100.0% → 100.0%

Time: 6.0s

Alternatives: 9

Speedup: 1.0×

• 21.0% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

(FPCore (x y z) :precision binary64 (* (+ x y) (+ z 1.0)))

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return (x + y) * (z + 1.0)

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (* (+ x y) (+ z 1.0)))

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return (x + y) * (z + 1.0)

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (* (+ x y) (+ z 1.0)))

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return (x + y) * (z + 1.0)

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 50.9% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6 \cdot 10^{+104}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq -1:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq -5.1 \cdot 10^{-101}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -5.5 \cdot 10^{-143}:\\ \;\;\;\;y\\ \mathbf{elif}\;z \leq -1.62 \cdot 10^{-189}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 3.5 \cdot 10^{+14}:\\ \;\;\;\;y\\ \mathbf{elif}\;z \leq 1.16 \cdot 10^{+210} \lor \neg \left(z \leq 2.65 \cdot 10^{+272}\right):\\ \;\;\;\;x \cdot z\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -6e+104)
   (* x z)
   (if (<= z -1.0)
     (* y z)
     (if (<= z -5.1e-101)
       x
       (if (<= z -5.5e-143)
         y
         (if (<= z -1.62e-189)
           x
           (if (<= z 3.5e+14)
             y
             (if (or (<= z 1.16e+210) (not (<= z 2.65e+272)))
               (* x z)
               (* y z)))))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -6e+104) {
		tmp = x * z;
	} else if (z <= -1.0) {
		tmp = y * z;
	} else if (z <= -5.1e-101) {
		tmp = x;
	} else if (z <= -5.5e-143) {
		tmp = y;
	} else if (z <= -1.62e-189) {
		tmp = x;
	} else if (z <= 3.5e+14) {
		tmp = y;
	} else if ((z <= 1.16e+210) || !(z <= 2.65e+272)) {
		tmp = x * z;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-6d+104)) then
        tmp = x * z
    else if (z <= (-1.0d0)) then
        tmp = y * z
    else if (z <= (-5.1d-101)) then
        tmp = x
    else if (z <= (-5.5d-143)) then
        tmp = y
    else if (z <= (-1.62d-189)) then
        tmp = x
    else if (z <= 3.5d+14) then
        tmp = y
    else if ((z <= 1.16d+210) .or. (.not. (z <= 2.65d+272))) then
        tmp = x * z
    else
        tmp = y * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -6e+104) {
		tmp = x * z;
	} else if (z <= -1.0) {
		tmp = y * z;
	} else if (z <= -5.1e-101) {
		tmp = x;
	} else if (z <= -5.5e-143) {
		tmp = y;
	} else if (z <= -1.62e-189) {
		tmp = x;
	} else if (z <= 3.5e+14) {
		tmp = y;
	} else if ((z <= 1.16e+210) || !(z <= 2.65e+272)) {
		tmp = x * z;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -6e+104:
		tmp = x * z
	elif z <= -1.0:
		tmp = y * z
	elif z <= -5.1e-101:
		tmp = x
	elif z <= -5.5e-143:
		tmp = y
	elif z <= -1.62e-189:
		tmp = x
	elif z <= 3.5e+14:
		tmp = y
	elif (z <= 1.16e+210) or not (z <= 2.65e+272):
		tmp = x * z
	else:
		tmp = y * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -6e+104)
		tmp = Float64(x * z);
	elseif (z <= -1.0)
		tmp = Float64(y * z);
	elseif (z <= -5.1e-101)
		tmp = x;
	elseif (z <= -5.5e-143)
		tmp = y;
	elseif (z <= -1.62e-189)
		tmp = x;
	elseif (z <= 3.5e+14)
		tmp = y;
	elseif ((z <= 1.16e+210) || !(z <= 2.65e+272))
		tmp = Float64(x * z);
	else
		tmp = Float64(y * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -6e+104)
		tmp = x * z;
	elseif (z <= -1.0)
		tmp = y * z;
	elseif (z <= -5.1e-101)
		tmp = x;
	elseif (z <= -5.5e-143)
		tmp = y;
	elseif (z <= -1.62e-189)
		tmp = x;
	elseif (z <= 3.5e+14)
		tmp = y;
	elseif ((z <= 1.16e+210) || ~((z <= 2.65e+272)))
		tmp = x * z;
	else
		tmp = y * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -6e+104], N[(x * z), $MachinePrecision], If[LessEqual[z, -1.0], N[(y * z), $MachinePrecision], If[LessEqual[z, -5.1e-101], x, If[LessEqual[z, -5.5e-143], y, If[LessEqual[z, -1.62e-189], x, If[LessEqual[z, 3.5e+14], y, If[Or[LessEqual[z, 1.16e+210], N[Not[LessEqual[z, 2.65e+272]], $MachinePrecision]], N[(x * z), $MachinePrecision], N[(y * z), $MachinePrecision]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 74.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -9 \cdot 10^{+105}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq -1:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq 3.5 \cdot 10^{+14}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 1.9 \cdot 10^{+211} \lor \neg \left(z \leq 7.5 \cdot 10^{+274}\right):\\ \;\;\;\;x \cdot z\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -9e+105)
   (* x z)
   (if (<= z -1.0)
     (* y z)
     (if (<= z 3.5e+14)
       (+ x y)
       (if (or (<= z 1.9e+211) (not (<= z 7.5e+274))) (* x z) (* y z))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -9e+105) {
		tmp = x * z;
	} else if (z <= -1.0) {
		tmp = y * z;
	} else if (z <= 3.5e+14) {
		tmp = x + y;
	} else if ((z <= 1.9e+211) || !(z <= 7.5e+274)) {
		tmp = x * z;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-9d+105)) then
        tmp = x * z
    else if (z <= (-1.0d0)) then
        tmp = y * z
    else if (z <= 3.5d+14) then
        tmp = x + y
    else if ((z <= 1.9d+211) .or. (.not. (z <= 7.5d+274))) then
        tmp = x * z
    else
        tmp = y * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -9e+105) {
		tmp = x * z;
	} else if (z <= -1.0) {
		tmp = y * z;
	} else if (z <= 3.5e+14) {
		tmp = x + y;
	} else if ((z <= 1.9e+211) || !(z <= 7.5e+274)) {
		tmp = x * z;
	} else {
		tmp = y * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -9e+105:
		tmp = x * z
	elif z <= -1.0:
		tmp = y * z
	elif z <= 3.5e+14:
		tmp = x + y
	elif (z <= 1.9e+211) or not (z <= 7.5e+274):
		tmp = x * z
	else:
		tmp = y * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -9e+105)
		tmp = Float64(x * z);
	elseif (z <= -1.0)
		tmp = Float64(y * z);
	elseif (z <= 3.5e+14)
		tmp = Float64(x + y);
	elseif ((z <= 1.9e+211) || !(z <= 7.5e+274))
		tmp = Float64(x * z);
	else
		tmp = Float64(y * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -9e+105)
		tmp = x * z;
	elseif (z <= -1.0)
		tmp = y * z;
	elseif (z <= 3.5e+14)
		tmp = x + y;
	elseif ((z <= 1.9e+211) || ~((z <= 7.5e+274)))
		tmp = x * z;
	else
		tmp = y * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -9e+105], N[(x * z), $MachinePrecision], If[LessEqual[z, -1.0], N[(y * z), $MachinePrecision], If[LessEqual[z, 3.5e+14], N[(x + y), $MachinePrecision], If[Or[LessEqual[z, 1.9e+211], N[Not[LessEqual[z, 7.5e+274]], $MachinePrecision]], N[(x * z), $MachinePrecision], N[(y * z), $MachinePrecision]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 50.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -300000000000:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq -1.62 \cdot 10^{-108}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq -2.25 \cdot 10^{-144}:\\ \;\;\;\;y\\ \mathbf{elif}\;z \leq -1.62 \cdot 10^{-189}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 3.5 \cdot 10^{+14}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -300000000000.0)
   (* x z)
   (if (<= z -1.62e-108)
     x
     (if (<= z -2.25e-144)
       y
       (if (<= z -1.62e-189) x (if (<= z 3.5e+14) y (* x z)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -300000000000.0) {
		tmp = x * z;
	} else if (z <= -1.62e-108) {
		tmp = x;
	} else if (z <= -2.25e-144) {
		tmp = y;
	} else if (z <= -1.62e-189) {
		tmp = x;
	} else if (z <= 3.5e+14) {
		tmp = y;
	} else {
		tmp = x * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-300000000000.0d0)) then
        tmp = x * z
    else if (z <= (-1.62d-108)) then
        tmp = x
    else if (z <= (-2.25d-144)) then
        tmp = y
    else if (z <= (-1.62d-189)) then
        tmp = x
    else if (z <= 3.5d+14) then
        tmp = y
    else
        tmp = x * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -300000000000.0) {
		tmp = x * z;
	} else if (z <= -1.62e-108) {
		tmp = x;
	} else if (z <= -2.25e-144) {
		tmp = y;
	} else if (z <= -1.62e-189) {
		tmp = x;
	} else if (z <= 3.5e+14) {
		tmp = y;
	} else {
		tmp = x * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -300000000000.0:
		tmp = x * z
	elif z <= -1.62e-108:
		tmp = x
	elif z <= -2.25e-144:
		tmp = y
	elif z <= -1.62e-189:
		tmp = x
	elif z <= 3.5e+14:
		tmp = y
	else:
		tmp = x * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -300000000000.0)
		tmp = Float64(x * z);
	elseif (z <= -1.62e-108)
		tmp = x;
	elseif (z <= -2.25e-144)
		tmp = y;
	elseif (z <= -1.62e-189)
		tmp = x;
	elseif (z <= 3.5e+14)
		tmp = y;
	else
		tmp = Float64(x * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -300000000000.0)
		tmp = x * z;
	elseif (z <= -1.62e-108)
		tmp = x;
	elseif (z <= -2.25e-144)
		tmp = y;
	elseif (z <= -1.62e-189)
		tmp = x;
	elseif (z <= 3.5e+14)
		tmp = y;
	else
		tmp = x * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -300000000000.0], N[(x * z), $MachinePrecision], If[LessEqual[z, -1.62e-108], x, If[LessEqual[z, -2.25e-144], y, If[LessEqual[z, -1.62e-189], x, If[LessEqual[z, 3.5e+14], y, N[(x * z), $MachinePrecision]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 75.1% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2.6 \cdot 10^{+104}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;z \leq -1:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{-8}:\\ \;\;\;\;x + y\\ \mathbf{elif}\;z \leq 2.4 \cdot 10^{+213}:\\ \;\;\;\;x \cdot \left(z + 1\right)\\ \mathbf{elif}\;z \leq 2.6 \cdot 10^{+269}:\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -2.6e+104)
   (* x z)
   (if (<= z -1.0)
     (* y z)
     (if (<= z 2.6e-8)
       (+ x y)
       (if (<= z 2.4e+213)
         (* x (+ z 1.0))
         (if (<= z 2.6e+269) (* y z) (* x z)))))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.6e+104) {
		tmp = x * z;
	} else if (z <= -1.0) {
		tmp = y * z;
	} else if (z <= 2.6e-8) {
		tmp = x + y;
	} else if (z <= 2.4e+213) {
		tmp = x * (z + 1.0);
	} else if (z <= 2.6e+269) {
		tmp = y * z;
	} else {
		tmp = x * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (z <= (-2.6d+104)) then
        tmp = x * z
    else if (z <= (-1.0d0)) then
        tmp = y * z
    else if (z <= 2.6d-8) then
        tmp = x + y
    else if (z <= 2.4d+213) then
        tmp = x * (z + 1.0d0)
    else if (z <= 2.6d+269) then
        tmp = y * z
    else
        tmp = x * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -2.6e+104) {
		tmp = x * z;
	} else if (z <= -1.0) {
		tmp = y * z;
	} else if (z <= 2.6e-8) {
		tmp = x + y;
	} else if (z <= 2.4e+213) {
		tmp = x * (z + 1.0);
	} else if (z <= 2.6e+269) {
		tmp = y * z;
	} else {
		tmp = x * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -2.6e+104:
		tmp = x * z
	elif z <= -1.0:
		tmp = y * z
	elif z <= 2.6e-8:
		tmp = x + y
	elif z <= 2.4e+213:
		tmp = x * (z + 1.0)
	elif z <= 2.6e+269:
		tmp = y * z
	else:
		tmp = x * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -2.6e+104)
		tmp = Float64(x * z);
	elseif (z <= -1.0)
		tmp = Float64(y * z);
	elseif (z <= 2.6e-8)
		tmp = Float64(x + y);
	elseif (z <= 2.4e+213)
		tmp = Float64(x * Float64(z + 1.0));
	elseif (z <= 2.6e+269)
		tmp = Float64(y * z);
	else
		tmp = Float64(x * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -2.6e+104)
		tmp = x * z;
	elseif (z <= -1.0)
		tmp = y * z;
	elseif (z <= 2.6e-8)
		tmp = x + y;
	elseif (z <= 2.4e+213)
		tmp = x * (z + 1.0);
	elseif (z <= 2.6e+269)
		tmp = y * z;
	else
		tmp = x * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -2.6e+104], N[(x * z), $MachinePrecision], If[LessEqual[z, -1.0], N[(y * z), $MachinePrecision], If[LessEqual[z, 2.6e-8], N[(x + y), $MachinePrecision], If[LessEqual[z, 2.4e+213], N[(x * N[(z + 1.0), $MachinePrecision]), $MachinePrecision], If[LessEqual[z, 2.6e+269], N[(y * z), $MachinePrecision], N[(x * z), $MachinePrecision]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 97.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1 \lor \neg \left(z \leq 1\right):\\ \;\;\;\;z \cdot \left(x + y\right)\\ \mathbf{else}:\\ \;\;\;\;x + y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -1.0) (not (<= z 1.0))) (* z (+ x y)) (+ x y)))

C

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

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if ((z <= (-1.0d0)) .or. (.not. (z <= 1.0d0))) then
        tmp = z * (x + y)
    else
        tmp = x + y
    end if
    code = tmp
end function

Java

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

Python

def code(x, y, z):
	tmp = 0
	if (z <= -1.0) or not (z <= 1.0):
		tmp = z * (x + y)
	else:
		tmp = x + y
	return tmp

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -1.0], N[Not[LessEqual[z, 1.0]], $MachinePrecision]], N[(z * N[(x + y), $MachinePrecision]), $MachinePrecision], N[(x + y), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 63.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.5 \cdot 10^{-46}:\\ \;\;\;\;x \cdot \left(z + 1\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(z + 1\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -1.5e-46) (* x (+ z 1.0)) (* y (+ z 1.0))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.5e-46) {
		tmp = x * (z + 1.0);
	} else {
		tmp = y * (z + 1.0);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-1.5d-46)) then
        tmp = x * (z + 1.0d0)
    else
        tmp = y * (z + 1.0d0)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -1.5e-46) {
		tmp = x * (z + 1.0);
	} else {
		tmp = y * (z + 1.0);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -1.5e-46:
		tmp = x * (z + 1.0)
	else:
		tmp = y * (z + 1.0)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -1.5e-46)
		tmp = Float64(x * Float64(z + 1.0));
	else
		tmp = Float64(y * Float64(z + 1.0));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -1.5e-46)
		tmp = x * (z + 1.0);
	else
		tmp = y * (z + 1.0);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -1.5e-46], N[(x * N[(z + 1.0), $MachinePrecision]), $MachinePrecision], N[(y * N[(z + 1.0), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 31.2% accurate, 2.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.8 \cdot 10^{-111}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (if (<= x -3.8e-111) x y))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -3.8e-111) {
		tmp = x;
	} else {
		tmp = y;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-3.8d-111)) then
        tmp = x
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -3.8e-111) {
		tmp = x;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -3.8e-111:
		tmp = x
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -3.8e-111)
		tmp = x;
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -3.8e-111)
		tmp = x;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -3.8e-111], x, y]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 9: 25.7% accurate, 7.0× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 x)

C

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

Fortran

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

Java

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

Python

def code(x, y, z):
	return x

Julia

function code(x, y, z)
	return x
end

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Reproduce

herbie shell --seed 2024010 
(FPCore (x y z)
  :name "Optimisation.CirclePacking:place from circle-packing-0.1.0.4, G"
  :precision binary64
  (* (+ x y) (+ z 1.0)))