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

Percentage Accurate: 63.4% → 100.0%

Time: 7.7s

Alternatives: 3

Speedup: 3.0×

• 20.5% 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: 63.4% 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 68.7%

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

   1. cancel-sign-sub-inv 68.7%

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

   2. +-commutative 68.7%

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

   3. *-commutative 68.7%

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

   4. associate-+r+ 68.7%

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

   5. +-commutative 68.7%

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

   6. associate--l+ 80.5%

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

   7. +-inverses 97.6%

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

   8. +-rgt-identity 97.6%

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

   9. cancel-sign-sub-inv 97.6%

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

   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. Final simplification 100.0%

   \[\leadsto y \cdot \left(x - z\right) \]
6. Add Preprocessing

Alternative 2: 78.0% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.2 \cdot 10^{-14} \lor \neg \left(z \leq 1.8 \cdot 10^{+39}\right):\\ \;\;\;\;z \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -3.2e-14) (not (<= z 1.8e+39))) (* z (- y)) (* y x)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -3.2e-14) || !(z <= 1.8e+39)) {
		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 <= (-3.2d-14)) .or. (.not. (z <= 1.8d+39))) 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 <= -3.2e-14) || !(z <= 1.8e+39)) {
		tmp = z * -y;
	} else {
		tmp = y * x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -3.2e-14) or not (z <= 1.8e+39):
		tmp = z * -y
	else:
		tmp = y * x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -3.2e-14) || !(z <= 1.8e+39))
		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 <= -3.2e-14) || ~((z <= 1.8e+39)))
		tmp = z * -y;
	else
		tmp = y * x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -3.2e-14], N[Not[LessEqual[z, 1.8e+39]], $MachinePrecision]], N[(z * (-y)), $MachinePrecision], N[(y * x), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -3.2000000000000002e-14 or 1.79999999999999992e39 < z

   1. Initial program 76.2%

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

      1. cancel-sign-sub-inv 76.2%

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

      2. +-commutative 76.2%

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

      3. *-commutative 76.2%

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

      4. associate-+r+ 76.2%

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

      5. +-commutative 76.2%

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

      6. associate--l+ 80.6%

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

      7. +-inverses 96.3%

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

      8. +-rgt-identity 96.3%

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

      9. cancel-sign-sub-inv 96.3%

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

      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. Taylor expanded in x around 0 84.4%

      \[\leadsto \color{blue}{-1 \cdot \left(y \cdot z\right)} \]
   6. Step-by-step derivation

      1. mul-1-neg 84.4%

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

      2. distribute-rgt-neg-out 84.4%

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

   7. Simplified 84.4%

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

   if -3.2000000000000002e-14 < z < 1.79999999999999992e39

   1. Initial program 60.4%

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

      1. cancel-sign-sub-inv 60.4%

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

      2. +-commutative 60.4%

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

      3. *-commutative 60.4%

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

      4. associate-+r+ 60.4%

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

      5. +-commutative 60.4%

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

      6. associate--l+ 80.3%

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

      7. +-inverses 99.2%

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

      8. +-rgt-identity 99.2%

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

      9. cancel-sign-sub-inv 99.2%

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

      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. Taylor expanded in x around inf 85.8%

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

      1. *-commutative 85.8%

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

   7. Simplified 85.8%

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

4. Final simplification 85.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -3.2 \cdot 10^{-14} \lor \neg \left(z \leq 1.8 \cdot 10^{+39}\right):\\ \;\;\;\;z \cdot \left(-y\right)\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 53.8% 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 68.7%

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

   1. cancel-sign-sub-inv 68.7%

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

   2. +-commutative 68.7%

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

   3. *-commutative 68.7%

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

   4. associate-+r+ 68.7%

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

   5. +-commutative 68.7%

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

   6. associate--l+ 80.5%

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

   7. +-inverses 97.6%

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

   8. +-rgt-identity 97.6%

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

   9. cancel-sign-sub-inv 97.6%

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

   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. Taylor expanded in x around inf 50.6%

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

   1. *-commutative 50.6%

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

7. Simplified 50.6%

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

8. Final simplification 50.6%

   \[\leadsto y \cdot x \]
9. 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 2024040 
(FPCore (x y z)
  :name "Linear.Quaternion:$c/ from linear-1.19.1.3, C"
  :precision binary64

  :herbie-target
  (* (- x z) y)

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