FRP.Yampa.Vector3:vector3Rho from Yampa-0.10.2

Percentage Accurate: 45.6% → 100.0%

Time: 6.8s

Alternatives: 6

Speedup: 1.5×

Specification

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 45.6% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ y_m = \left|y\right| \\ x_m = \left|x\right| \\ [x_m, y_m, z_m] = \mathsf{sort}([x_m, y_m, z_m])\\ \\ \mathsf{hypot}\left(z\_m, y\_m\right) \end{array} \]

FPCore

z_m = (fabs.f64 z)
y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
(FPCore (x_m y_m z_m) :precision binary64 (hypot z_m y_m))

C

z_m = fabs(z);
y_m = fabs(y);
x_m = fabs(x);
assert(x_m < y_m && y_m < z_m);
double code(double x_m, double y_m, double z_m) {
	return hypot(z_m, y_m);
}

Java

z_m = Math.abs(z);
y_m = Math.abs(y);
x_m = Math.abs(x);
assert x_m < y_m && y_m < z_m;
public static double code(double x_m, double y_m, double z_m) {
	return Math.hypot(z_m, y_m);
}

Python

z_m = math.fabs(z)
y_m = math.fabs(y)
x_m = math.fabs(x)
[x_m, y_m, z_m] = sort([x_m, y_m, z_m])
def code(x_m, y_m, z_m):
	return math.hypot(z_m, y_m)

Julia

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
x_m, y_m, z_m = sort([x_m, y_m, z_m])
function code(x_m, y_m, z_m)
	return hypot(z_m, y_m)
end

MATLAB

z_m = abs(z);
y_m = abs(y);
x_m = abs(x);
x_m, y_m, z_m = num2cell(sort([x_m, y_m, z_m])){:}
function tmp = code(x_m, y_m, z_m)
	tmp = hypot(z_m, y_m);
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
code[x$95$m_, y$95$m_, z$95$m_] := N[Sqrt[z$95$m ^ 2 + y$95$m ^ 2], $MachinePrecision]

Derivation

1. Initial program 44.2%

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

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\sqrt{{y}^{2} + {z}^{2}}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \sqrt{\color{blue}{{z}^{2} + {y}^{2}}} \]

   2. unpow2 N/A

      \[\leadsto \sqrt{\color{blue}{z \cdot z} + {y}^{2}} \]

   3. unpow2 N/A

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

   4. lower-hypot.f64 69.8

      \[\leadsto \color{blue}{\mathsf{hypot}\left(z, y\right)} \]

5. Applied rewrites 69.8%

   \[\leadsto \color{blue}{\mathsf{hypot}\left(z, y\right)} \]
6. Add Preprocessing

Alternative 2: 98.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ y_m = \left|y\right| \\ x_m = \left|x\right| \\ [x_m, y_m, z_m] = \mathsf{sort}([x_m, y_m, z_m])\\ \\ \mathsf{fma}\left(\frac{x\_m}{z\_m} \cdot x\_m, 0.5, z\_m\right) \end{array} \]

FPCore

z_m = (fabs.f64 z)
y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
(FPCore (x_m y_m z_m) :precision binary64 (fma (* (/ x_m z_m) x_m) 0.5 z_m))

C

z_m = fabs(z);
y_m = fabs(y);
x_m = fabs(x);
assert(x_m < y_m && y_m < z_m);
double code(double x_m, double y_m, double z_m) {
	return fma(((x_m / z_m) * x_m), 0.5, z_m);
}

Julia

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
x_m, y_m, z_m = sort([x_m, y_m, z_m])
function code(x_m, y_m, z_m)
	return fma(Float64(Float64(x_m / z_m) * x_m), 0.5, z_m)
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
code[x$95$m_, y$95$m_, z$95$m_] := N[(N[(N[(x$95$m / z$95$m), $MachinePrecision] * x$95$m), $MachinePrecision] * 0.5 + z$95$m), $MachinePrecision]

Derivation

1. Initial program 44.2%

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

3. Taylor expanded in z around inf

   \[\leadsto \color{blue}{z \cdot \left(1 + \frac{1}{2} \cdot \frac{{x}^{2} + {y}^{2}}{{z}^{2}}\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto z \cdot \color{blue}{\left(\frac{1}{2} \cdot \frac{{x}^{2} + {y}^{2}}{{z}^{2}} + 1\right)} \]

   2. distribute-lft-in N/A

      \[\leadsto \color{blue}{z \cdot \left(\frac{1}{2} \cdot \frac{{x}^{2} + {y}^{2}}{{z}^{2}}\right) + z \cdot 1} \]

   3. *-commutative N/A

      \[\leadsto z \cdot \color{blue}{\left(\frac{{x}^{2} + {y}^{2}}{{z}^{2}} \cdot \frac{1}{2}\right)} + z \cdot 1 \]

   4. associate-*r* N/A

      \[\leadsto \color{blue}{\left(z \cdot \frac{{x}^{2} + {y}^{2}}{{z}^{2}}\right) \cdot \frac{1}{2}} + z \cdot 1 \]

   5. associate-*r/ N/A

      \[\leadsto \color{blue}{\frac{z \cdot \left({x}^{2} + {y}^{2}\right)}{{z}^{2}}} \cdot \frac{1}{2} + z \cdot 1 \]

   6. unpow2 N/A

      \[\leadsto \frac{z \cdot \left({x}^{2} + {y}^{2}\right)}{\color{blue}{z \cdot z}} \cdot \frac{1}{2} + z \cdot 1 \]

   7. times-frac N/A

      \[\leadsto \color{blue}{\left(\frac{z}{z} \cdot \frac{{x}^{2} + {y}^{2}}{z}\right)} \cdot \frac{1}{2} + z \cdot 1 \]

   8. *-inverses N/A

      \[\leadsto \left(\color{blue}{1} \cdot \frac{{x}^{2} + {y}^{2}}{z}\right) \cdot \frac{1}{2} + z \cdot 1 \]

   9. associate-/l* N/A

      \[\leadsto \color{blue}{\frac{1 \cdot \left({x}^{2} + {y}^{2}\right)}{z}} \cdot \frac{1}{2} + z \cdot 1 \]

   10. *-lft-identity N/A

       \[\leadsto \frac{\color{blue}{{x}^{2} + {y}^{2}}}{z} \cdot \frac{1}{2} + z \cdot 1 \]

   11. *-rgt-identity N/A

       \[\leadsto \frac{{x}^{2} + {y}^{2}}{z} \cdot \frac{1}{2} + \color{blue}{z} \]

   12. lower-fma.f64 N/A

       \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{{x}^{2} + {y}^{2}}{z}, \frac{1}{2}, z\right)} \]

5. Applied rewrites 18.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(y, y, x \cdot x\right)}{z}, 0.5, z\right)} \]

6. Taylor expanded in x around inf

   \[\leadsto \mathsf{fma}\left(\frac{{x}^{2}}{z}, \frac{1}{2}, z\right) \]
7. Step-by-step derivation

8. Applied rewrites 19.2%

   \[\leadsto \mathsf{fma}\left(\frac{x \cdot x}{z}, 0.5, z\right) \]
9. Step-by-step derivation

10. Applied rewrites 19.9%

    \[\leadsto \mathsf{fma}\left(\frac{x}{z} \cdot x, 0.5, z\right) \]
11. Add Preprocessing

Alternative 3: 98.2% accurate, 1.4× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ y_m = \left|y\right| \\ x_m = \left|x\right| \\ [x_m, y_m, z_m] = \mathsf{sort}([x_m, y_m, z_m])\\ \\ \frac{1}{\frac{1}{z\_m}} \end{array} \]

FPCore

z_m = (fabs.f64 z)
y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
(FPCore (x_m y_m z_m) :precision binary64 (/ 1.0 (/ 1.0 z_m)))

C

z_m = fabs(z);
y_m = fabs(y);
x_m = fabs(x);
assert(x_m < y_m && y_m < z_m);
double code(double x_m, double y_m, double z_m) {
	return 1.0 / (1.0 / z_m);
}

Fortran

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
real(8) function code(x_m, y_m, z_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z_m
    code = 1.0d0 / (1.0d0 / z_m)
end function

Java

z_m = Math.abs(z);
y_m = Math.abs(y);
x_m = Math.abs(x);
assert x_m < y_m && y_m < z_m;
public static double code(double x_m, double y_m, double z_m) {
	return 1.0 / (1.0 / z_m);
}

Python

z_m = math.fabs(z)
y_m = math.fabs(y)
x_m = math.fabs(x)
[x_m, y_m, z_m] = sort([x_m, y_m, z_m])
def code(x_m, y_m, z_m):
	return 1.0 / (1.0 / z_m)

Julia

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
x_m, y_m, z_m = sort([x_m, y_m, z_m])
function code(x_m, y_m, z_m)
	return Float64(1.0 / Float64(1.0 / z_m))
end

MATLAB

z_m = abs(z);
y_m = abs(y);
x_m = abs(x);
x_m, y_m, z_m = num2cell(sort([x_m, y_m, z_m])){:}
function tmp = code(x_m, y_m, z_m)
	tmp = 1.0 / (1.0 / z_m);
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
code[x$95$m_, y$95$m_, z$95$m_] := N[(1.0 / N[(1.0 / z$95$m), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 44.2%

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

3. Taylor expanded in z around inf

   \[\leadsto \color{blue}{z \cdot \left(1 + \frac{1}{2} \cdot \frac{{x}^{2} + {y}^{2}}{{z}^{2}}\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto z \cdot \color{blue}{\left(\frac{1}{2} \cdot \frac{{x}^{2} + {y}^{2}}{{z}^{2}} + 1\right)} \]

   2. distribute-lft-in N/A

      \[\leadsto \color{blue}{z \cdot \left(\frac{1}{2} \cdot \frac{{x}^{2} + {y}^{2}}{{z}^{2}}\right) + z \cdot 1} \]

   3. *-commutative N/A

      \[\leadsto z \cdot \color{blue}{\left(\frac{{x}^{2} + {y}^{2}}{{z}^{2}} \cdot \frac{1}{2}\right)} + z \cdot 1 \]

   4. associate-*r* N/A

      \[\leadsto \color{blue}{\left(z \cdot \frac{{x}^{2} + {y}^{2}}{{z}^{2}}\right) \cdot \frac{1}{2}} + z \cdot 1 \]

   5. associate-*r/ N/A

      \[\leadsto \color{blue}{\frac{z \cdot \left({x}^{2} + {y}^{2}\right)}{{z}^{2}}} \cdot \frac{1}{2} + z \cdot 1 \]

   6. unpow2 N/A

      \[\leadsto \frac{z \cdot \left({x}^{2} + {y}^{2}\right)}{\color{blue}{z \cdot z}} \cdot \frac{1}{2} + z \cdot 1 \]

   7. times-frac N/A

      \[\leadsto \color{blue}{\left(\frac{z}{z} \cdot \frac{{x}^{2} + {y}^{2}}{z}\right)} \cdot \frac{1}{2} + z \cdot 1 \]

   8. *-inverses N/A

      \[\leadsto \left(\color{blue}{1} \cdot \frac{{x}^{2} + {y}^{2}}{z}\right) \cdot \frac{1}{2} + z \cdot 1 \]

   9. associate-/l* N/A

      \[\leadsto \color{blue}{\frac{1 \cdot \left({x}^{2} + {y}^{2}\right)}{z}} \cdot \frac{1}{2} + z \cdot 1 \]

   10. *-lft-identity N/A

       \[\leadsto \frac{\color{blue}{{x}^{2} + {y}^{2}}}{z} \cdot \frac{1}{2} + z \cdot 1 \]

   11. *-rgt-identity N/A

       \[\leadsto \frac{{x}^{2} + {y}^{2}}{z} \cdot \frac{1}{2} + \color{blue}{z} \]

   12. lower-fma.f64 N/A

       \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{{x}^{2} + {y}^{2}}{z}, \frac{1}{2}, z\right)} \]

5. Applied rewrites 18.3%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{\mathsf{fma}\left(y, y, x \cdot x\right)}{z}, 0.5, z\right)} \]
6. Step-by-step derivation

7. Applied rewrites 18.3%

   \[\leadsto \frac{1}{\color{blue}{\frac{1}{\mathsf{fma}\left(0.5, \frac{\mathsf{fma}\left(y, y, x \cdot x\right)}{z}, z\right)}}} \]

8. Taylor expanded in y around 0

   \[\leadsto \frac{1}{\frac{1}{\color{blue}{z + \frac{1}{2} \cdot \frac{{x}^{2}}{z}}}} \]
9. Step-by-step derivation

10. Applied rewrites 19.1%

    \[\leadsto \frac{1}{\frac{1}{\color{blue}{\mathsf{fma}\left(\frac{x \cdot x}{z}, 0.5, z\right)}}} \]

11. Taylor expanded in x around 0

    \[\leadsto \frac{1}{\frac{1}{z}} \]
12. Step-by-step derivation

13. Applied rewrites 19.3%

    \[\leadsto \frac{1}{\frac{1}{z}} \]
14. Add Preprocessing

Alternative 4: 45.6% accurate, 1.5× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ y_m = \left|y\right| \\ x_m = \left|x\right| \\ [x_m, y_m, z_m] = \mathsf{sort}([x_m, y_m, z_m])\\ \\ \sqrt{\mathsf{fma}\left(z\_m, z\_m, y\_m \cdot y\_m\right)} \end{array} \]

FPCore

z_m = (fabs.f64 z)
y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
(FPCore (x_m y_m z_m) :precision binary64 (sqrt (fma z_m z_m (* y_m y_m))))

C

z_m = fabs(z);
y_m = fabs(y);
x_m = fabs(x);
assert(x_m < y_m && y_m < z_m);
double code(double x_m, double y_m, double z_m) {
	return sqrt(fma(z_m, z_m, (y_m * y_m)));
}

Julia

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
x_m, y_m, z_m = sort([x_m, y_m, z_m])
function code(x_m, y_m, z_m)
	return sqrt(fma(z_m, z_m, Float64(y_m * y_m)))
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
code[x$95$m_, y$95$m_, z$95$m_] := N[Sqrt[N[(z$95$m * z$95$m + N[(y$95$m * y$95$m), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 44.2%

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

3. Taylor expanded in x around 0

   \[\leadsto \sqrt{\color{blue}{{y}^{2} + {z}^{2}}} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \sqrt{\color{blue}{{z}^{2} + {y}^{2}}} \]

   2. unpow2 N/A

      \[\leadsto \sqrt{\color{blue}{z \cdot z} + {y}^{2}} \]

   3. lower-fma.f64 N/A

      \[\leadsto \sqrt{\color{blue}{\mathsf{fma}\left(z, z, {y}^{2}\right)}} \]

   4. unpow2 N/A

      \[\leadsto \sqrt{\mathsf{fma}\left(z, z, \color{blue}{y \cdot y}\right)} \]

   5. lower-*.f64 32.4

      \[\leadsto \sqrt{\mathsf{fma}\left(z, z, \color{blue}{y \cdot y}\right)} \]

5. Applied rewrites 32.4%

   \[\leadsto \sqrt{\color{blue}{\mathsf{fma}\left(z, z, y \cdot y\right)}} \]
6. Add Preprocessing

Alternative 5: 44.8% accurate, 2.0× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ y_m = \left|y\right| \\ x_m = \left|x\right| \\ [x_m, y_m, z_m] = \mathsf{sort}([x_m, y_m, z_m])\\ \\ \sqrt{z\_m \cdot z\_m} \end{array} \]

FPCore

z_m = (fabs.f64 z)
y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
(FPCore (x_m y_m z_m) :precision binary64 (sqrt (* z_m z_m)))

C

z_m = fabs(z);
y_m = fabs(y);
x_m = fabs(x);
assert(x_m < y_m && y_m < z_m);
double code(double x_m, double y_m, double z_m) {
	return sqrt((z_m * z_m));
}

Fortran

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
real(8) function code(x_m, y_m, z_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z_m
    code = sqrt((z_m * z_m))
end function

Java

z_m = Math.abs(z);
y_m = Math.abs(y);
x_m = Math.abs(x);
assert x_m < y_m && y_m < z_m;
public static double code(double x_m, double y_m, double z_m) {
	return Math.sqrt((z_m * z_m));
}

Python

z_m = math.fabs(z)
y_m = math.fabs(y)
x_m = math.fabs(x)
[x_m, y_m, z_m] = sort([x_m, y_m, z_m])
def code(x_m, y_m, z_m):
	return math.sqrt((z_m * z_m))

Julia

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
x_m, y_m, z_m = sort([x_m, y_m, z_m])
function code(x_m, y_m, z_m)
	return sqrt(Float64(z_m * z_m))
end

MATLAB

z_m = abs(z);
y_m = abs(y);
x_m = abs(x);
x_m, y_m, z_m = num2cell(sort([x_m, y_m, z_m])){:}
function tmp = code(x_m, y_m, z_m)
	tmp = sqrt((z_m * z_m));
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
code[x$95$m_, y$95$m_, z$95$m_] := N[Sqrt[N[(z$95$m * z$95$m), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 44.2%

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

3. Taylor expanded in z around inf

   \[\leadsto \sqrt{\color{blue}{{z}^{2}}} \]
4. Step-by-step derivation

   1. unpow2 N/A

      \[\leadsto \sqrt{\color{blue}{z \cdot z}} \]

   2. lower-*.f64 17.0

      \[\leadsto \sqrt{\color{blue}{z \cdot z}} \]

5. Applied rewrites 17.0%

   \[\leadsto \sqrt{\color{blue}{z \cdot z}} \]
6. Add Preprocessing

Alternative 6: 1.7% accurate, 10.7× speedup

Math

\[\begin{array}{l} z_m = \left|z\right| \\ y_m = \left|y\right| \\ x_m = \left|x\right| \\ [x_m, y_m, z_m] = \mathsf{sort}([x_m, y_m, z_m])\\ \\ -x\_m \end{array} \]

FPCore

z_m = (fabs.f64 z)
y_m = (fabs.f64 y)
x_m = (fabs.f64 x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
(FPCore (x_m y_m z_m) :precision binary64 (- x_m))

C

z_m = fabs(z);
y_m = fabs(y);
x_m = fabs(x);
assert(x_m < y_m && y_m < z_m);
double code(double x_m, double y_m, double z_m) {
	return -x_m;
}

Fortran

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
real(8) function code(x_m, y_m, z_m)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y_m
    real(8), intent (in) :: z_m
    code = -x_m
end function

Java

z_m = Math.abs(z);
y_m = Math.abs(y);
x_m = Math.abs(x);
assert x_m < y_m && y_m < z_m;
public static double code(double x_m, double y_m, double z_m) {
	return -x_m;
}

Python

z_m = math.fabs(z)
y_m = math.fabs(y)
x_m = math.fabs(x)
[x_m, y_m, z_m] = sort([x_m, y_m, z_m])
def code(x_m, y_m, z_m):
	return -x_m

Julia

z_m = abs(z)
y_m = abs(y)
x_m = abs(x)
x_m, y_m, z_m = sort([x_m, y_m, z_m])
function code(x_m, y_m, z_m)
	return Float64(-x_m)
end

MATLAB

z_m = abs(z);
y_m = abs(y);
x_m = abs(x);
x_m, y_m, z_m = num2cell(sort([x_m, y_m, z_m])){:}
function tmp = code(x_m, y_m, z_m)
	tmp = -x_m;
end

Wolfram

z_m = N[Abs[z], $MachinePrecision]
y_m = N[Abs[y], $MachinePrecision]
x_m = N[Abs[x], $MachinePrecision]
NOTE: x_m, y_m, and z_m should be sorted in increasing order before calling this function.
code[x$95$m_, y$95$m_, z$95$m_] := (-x$95$m)

Derivation

1. Initial program 44.2%

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

3. Taylor expanded in x around -inf

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

   1. mul-1-neg N/A

      \[\leadsto \color{blue}{\mathsf{neg}\left(x\right)} \]

   2. lower-neg.f64 17.4

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

5. Applied rewrites 17.4%

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

Developer Target 1: 97.9% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z < -6.396479394109776 \cdot 10^{+136}:\\ \;\;\;\;-z\\ \mathbf{elif}\;z < 7.320293694404182 \cdot 10^{+117}:\\ \;\;\;\;\sqrt{\left(z \cdot z + x \cdot x\right) + y \cdot y}\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (< z -6.396479394109776e+136)
   (- z)
   (if (< z 7.320293694404182e+117) (sqrt (+ (+ (* z z) (* x x)) (* y y))) z)))

C

double code(double x, double y, double z) {
	double tmp;
	if (z < -6.396479394109776e+136) {
		tmp = -z;
	} else if (z < 7.320293694404182e+117) {
		tmp = sqrt((((z * z) + (x * x)) + (y * y)));
	} else {
		tmp = 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 < (-6.396479394109776d+136)) then
        tmp = -z
    else if (z < 7.320293694404182d+117) then
        tmp = sqrt((((z * z) + (x * x)) + (y * y)))
    else
        tmp = z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z < -6.396479394109776e+136) {
		tmp = -z;
	} else if (z < 7.320293694404182e+117) {
		tmp = Math.sqrt((((z * z) + (x * x)) + (y * y)));
	} else {
		tmp = z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z < -6.396479394109776e+136:
		tmp = -z
	elif z < 7.320293694404182e+117:
		tmp = math.sqrt((((z * z) + (x * x)) + (y * y)))
	else:
		tmp = z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z < -6.396479394109776e+136)
		tmp = Float64(-z);
	elseif (z < 7.320293694404182e+117)
		tmp = sqrt(Float64(Float64(Float64(z * z) + Float64(x * x)) + Float64(y * y)));
	else
		tmp = z;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z < -6.396479394109776e+136)
		tmp = -z;
	elseif (z < 7.320293694404182e+117)
		tmp = sqrt((((z * z) + (x * x)) + (y * y)));
	else
		tmp = z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Less[z, -6.396479394109776e+136], (-z), If[Less[z, 7.320293694404182e+117], N[Sqrt[N[(N[(N[(z * z), $MachinePrecision] + N[(x * x), $MachinePrecision]), $MachinePrecision] + N[(y * y), $MachinePrecision]), $MachinePrecision]], $MachinePrecision], z]]

Reproduce

herbie shell --seed 2024284 
(FPCore (x y z)
  :name "FRP.Yampa.Vector3:vector3Rho from Yampa-0.10.2"
  :precision binary64

  :alt
  (! :herbie-platform default (if (< z -63964793941097760000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (- z) (if (< z 7320293694404182000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000) (sqrt (+ (+ (* z z) (* x x)) (* y y))) z)))

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