Linear.Quaternion:$c/ from linear-1.19.1.3, C

Percentage Accurate: 63.0% → 100.0%

Time: 5.8s

Alternatives: 3

Speedup: 3.0×

• 21.2% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

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) + (y * y)) - (y * z)) - (y * y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 63.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

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) + (y * y)) - (y * z)) - (y * y)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 3.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return y * (x - 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 = y * (x - z)
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 58.4%

   \[\left(\left(x \cdot y + y \cdot y\right) - y \cdot z\right) - y \cdot y \]
2. Step-by-step derivation

   1. cancel-sign-sub-inv 58.4%

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

   2. +-commutative 58.4%

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

   3. *-commutative 58.4%

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

   4. associate-+r+ 58.4%

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

   5. +-commutative 58.4%

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

   6. associate--l+ 71.1%

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

   7. +-inverses 96.5%

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

   8. +-rgt-identity 96.5%

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

   9. cancel-sign-sub-inv 96.5%

      \[\leadsto \color{blue}{y \cdot x - y \cdot z} \]

   10. distribute-lft-out-- 100.0%

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

3. Simplified 100.0%

   \[\leadsto \color{blue}{y \cdot \left(x - z\right)} \]
4. Add Preprocessing
5. Add Preprocessing

Alternative 2: 78.6% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{-19} \lor \neg \left(z \leq 390000000\right):\\ \;\;\;\;z \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -4.2e-19) (not (<= z 390000000.0))) (* z (- y)) (* y x)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -4.2e-19) || !(z <= 390000000.0)) {
		tmp = z * -y;
	} else {
		tmp = y * x;
	}
	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 <= (-4.2d-19)) .or. (.not. (z <= 390000000.0d0))) then
        tmp = z * -y
    else
        tmp = y * x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -4.2e-19) || !(z <= 390000000.0)) {
		tmp = z * -y;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -4.2e-19) or not (z <= 390000000.0):
		tmp = z * -y
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -4.2e-19) || !(z <= 390000000.0))
		tmp = Float64(z * Float64(-y));
	else
		tmp = Float64(y * x);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -4.2e-19) || ~((z <= 390000000.0)))
		tmp = z * -y;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -4.2e-19], N[Not[LessEqual[z, 390000000.0]], $MachinePrecision]], N[(z * (-y)), $MachinePrecision], N[(y * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -4.1999999999999998e-19 or 3.9e8 < z

   1. Initial program 64.3%

      \[\left(\left(x \cdot y + y \cdot y\right) - y \cdot z\right) - y \cdot y \]
   2. Step-by-step derivation

      1. associate--l- 64.3%

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

      2. *-commutative 64.3%

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

      3. distribute-rgt-out 64.3%

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

      4. distribute-rgt-out 66.5%

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

   3. Simplified 66.5%

      \[\leadsto \color{blue}{y \cdot \left(x + y\right) - y \cdot \left(z + y\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around 0 51.1%

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

      1. distribute-lft-in 48.1%

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

      2. unpow2 48.1%

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

      3. associate--r+ 52.8%

         \[\leadsto \color{blue}{\left({y}^{2} - {y}^{2}\right) - y \cdot z} \]

      4. +-inverses 78.3%

         \[\leadsto \color{blue}{0} - y \cdot z \]

      5. neg-sub0 78.3%

         \[\leadsto \color{blue}{-y \cdot z} \]

      6. distribute-lft-neg-in 78.3%

         \[\leadsto \color{blue}{\left(-y\right) \cdot z} \]

      7. *-commutative 78.3%

         \[\leadsto \color{blue}{z \cdot \left(-y\right)} \]

   7. Simplified 78.3%

      \[\leadsto \color{blue}{z \cdot \left(-y\right)} \]

   if -4.1999999999999998e-19 < z < 3.9e8

   1. Initial program 52.1%

      \[\left(\left(x \cdot y + y \cdot y\right) - y \cdot z\right) - y \cdot y \]
   2. Step-by-step derivation

      1. associate--l- 52.1%

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

      2. *-commutative 52.1%

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

      3. distribute-rgt-out 54.5%

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

      4. distribute-rgt-out 54.4%

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

   3. Simplified 54.4%

      \[\leadsto \color{blue}{y \cdot \left(x + y\right) - y \cdot \left(z + y\right)} \]
   4. Add Preprocessing

   5. Taylor expanded in x around inf 77.9%

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

      1. *-commutative 77.9%

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

   7. Simplified 77.9%

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

4. Final simplification 78.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -4.2 \cdot 10^{-19} \lor \neg \left(z \leq 390000000\right):\\ \;\;\;\;z \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 54.4% accurate, 5.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return y * 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 = y * x
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 58.4%

   \[\left(\left(x \cdot y + y \cdot y\right) - y \cdot z\right) - y \cdot y \]
2. Step-by-step derivation

   1. associate--l- 58.4%

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

   2. *-commutative 58.4%

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

   3. distribute-rgt-out 59.6%

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

   4. distribute-rgt-out 60.7%

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

3. Simplified 60.7%

   \[\leadsto \color{blue}{y \cdot \left(x + y\right) - y \cdot \left(z + y\right)} \]
4. Add Preprocessing

5. Taylor expanded in x around inf 51.6%

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

   1. *-commutative 51.6%

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

7. Simplified 51.6%

   \[\leadsto \color{blue}{y \cdot x} \]
8. Add Preprocessing

Developer target: 100.0% accurate, 3.0× speedup

Math

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

FPCore

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

C

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

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 - z) * y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024111 
(FPCore (x y z)
  :name "Linear.Quaternion:$c/ from linear-1.19.1.3, C"
  :precision binary64

  :alt
  (* (- x z) y)

  (- (- (+ (* x y) (* y y)) (* y z)) (* y y)))