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

Percentage Accurate: 63.6% → 100.0%

Time: 5.1s

Alternatives: 3

Speedup: 3.0×

• 20.9% 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: 63.6% 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 65.2%

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

   1. cancel-sign-sub-inv 65.2%

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

   2. *-commutative 65.2%

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

   3. associate-+l+ 65.2%

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

   4. +-commutative 65.2%

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

   5. associate-+r+ 65.2%

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

   6. associate--l+ 75.8%

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

   7. +-inverses 98.4%

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

   8. +-rgt-identity 98.4%

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

   9. cancel-sign-sub-inv 98.4%

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

   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: 76.5% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.22 \cdot 10^{+73} \lor \neg \left(x \leq -5.2 \cdot 10^{-10}\right) \land \left(x \leq -2.3 \cdot 10^{-70} \lor \neg \left(x \leq 1.4 \cdot 10^{-70}\right)\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(-z\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -1.22e+73)
         (and (not (<= x -5.2e-10)) (or (<= x -2.3e-70) (not (<= x 1.4e-70)))))
   (* y x)
   (* y (- z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.22e+73) || (!(x <= -5.2e-10) && ((x <= -2.3e-70) || !(x <= 1.4e-70)))) {
		tmp = y * x;
	} 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 ((x <= (-1.22d+73)) .or. (.not. (x <= (-5.2d-10))) .and. (x <= (-2.3d-70)) .or. (.not. (x <= 1.4d-70))) then
        tmp = y * x
    else
        tmp = y * -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.22e+73) || (!(x <= -5.2e-10) && ((x <= -2.3e-70) || !(x <= 1.4e-70)))) {
		tmp = y * x;
	} else {
		tmp = y * -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -1.22e+73) or (not (x <= -5.2e-10) and ((x <= -2.3e-70) or not (x <= 1.4e-70))):
		tmp = y * x
	else:
		tmp = y * -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -1.22e+73) || (!(x <= -5.2e-10) && ((x <= -2.3e-70) || !(x <= 1.4e-70))))
		tmp = Float64(y * x);
	else
		tmp = Float64(y * Float64(-z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -1.22e+73) || (~((x <= -5.2e-10)) && ((x <= -2.3e-70) || ~((x <= 1.4e-70)))))
		tmp = y * x;
	else
		tmp = y * -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -1.22e+73], And[N[Not[LessEqual[x, -5.2e-10]], $MachinePrecision], Or[LessEqual[x, -2.3e-70], N[Not[LessEqual[x, 1.4e-70]], $MachinePrecision]]]], N[(y * x), $MachinePrecision], N[(y * (-z)), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -1.21999999999999998e73 or -5.19999999999999962e-10 < x < -2.30000000000000001e-70 or 1.4e-70 < x

   1. Initial program 66.4%

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

      1. cancel-sign-sub-inv 66.4%

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

      2. *-commutative 66.4%

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

      3. associate-+l+ 66.4%

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

      4. +-commutative 66.4%

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

      5. associate-+r+ 66.4%

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

      6. associate--l+ 74.3%

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

      7. +-inverses 98.5%

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

      8. +-rgt-identity 98.5%

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

      9. cancel-sign-sub-inv 98.5%

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

      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. Taylor expanded in x around inf 83.1%

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

      1. *-commutative 83.1%

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

   7. Simplified 83.1%

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

   if -1.21999999999999998e73 < x < -5.19999999999999962e-10 or -2.30000000000000001e-70 < x < 1.4e-70

   1. Initial program 63.8%

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

      1. cancel-sign-sub-inv 63.8%

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

      2. *-commutative 63.8%

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

      3. associate-+l+ 63.8%

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

      4. +-commutative 63.8%

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

      5. associate-+r+ 63.8%

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

      6. associate--l+ 77.6%

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

      7. +-inverses 98.2%

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

      8. +-rgt-identity 98.2%

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

      9. cancel-sign-sub-inv 98.2%

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

      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 0 84.8%

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

      1. mul-1-neg 84.8%

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

      2. distribute-rgt-neg-out 84.8%

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

   7. Simplified 84.8%

      \[\leadsto \color{blue}{y \cdot \left(-z\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 83.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -1.22 \cdot 10^{+73} \lor \neg \left(x \leq -5.2 \cdot 10^{-10}\right) \land \left(x \leq -2.3 \cdot 10^{-70} \lor \neg \left(x \leq 1.4 \cdot 10^{-70}\right)\right):\\ \;\;\;\;y \cdot x\\ \mathbf{else}:\\ \;\;\;\;y \cdot \left(-z\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 54.7% 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 65.2%

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

   1. cancel-sign-sub-inv 65.2%

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

   2. *-commutative 65.2%

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

   3. associate-+l+ 65.2%

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

   4. +-commutative 65.2%

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

   5. associate-+r+ 65.2%

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

   6. associate--l+ 75.8%

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

   7. +-inverses 98.4%

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

   8. +-rgt-identity 98.4%

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

   9. cancel-sign-sub-inv 98.4%

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

   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 55.6%

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

   1. *-commutative 55.6%

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

7. Simplified 55.6%

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

8. Final simplification 55.6%

   \[\leadsto y \cdot x \]
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 2024115 
(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)))