Diagrams.Solve.Polynomial:cubForm from diagrams-solve-0.1, B

Percentage Accurate: 99.8% → 99.8%

Time: 4.5s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \left(x \cdot 3\right) \cdot y - z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (* (* x 3.0) y) z))

C

double code(double x, double y, double z) {
	return ((x * 3.0) * y) - 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 = ((x * 3.0d0) * y) - z
end function

Java

public static double code(double x, double y, double z) {
	return ((x * 3.0) * y) - z;
}

Python

def code(x, y, z):
	return ((x * 3.0) * y) - z

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x * 3.0) * y) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x * 3.0) * y) - z;
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x * 3.0), $MachinePrecision] * y), $MachinePrecision] - z), $MachinePrecision]

Initial Program: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot 3\right) \cdot y - z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (* (* x 3.0) y) z))

C

double code(double x, double y, double z) {
	return ((x * 3.0) * y) - 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 = ((x * 3.0d0) * y) - z
end function

Java

public static double code(double x, double y, double z) {
	return ((x * 3.0) * y) - z;
}

Python

def code(x, y, z):
	return ((x * 3.0) * y) - z

Julia

function code(x, y, z)
	return Float64(Float64(Float64(x * 3.0) * y) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((x * 3.0) * y) - z;
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(x * 3.0), $MachinePrecision] * y), $MachinePrecision] - z), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(3 \cdot x\right) \cdot y - z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (* (* 3.0 x) y) z))

C

double code(double x, double y, double z) {
	return ((3.0 * x) * y) - 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 = ((3.0d0 * x) * y) - z
end function

Java

public static double code(double x, double y, double z) {
	return ((3.0 * x) * y) - z;
}

Python

def code(x, y, z):
	return ((3.0 * x) * y) - z

Julia

function code(x, y, z)
	return Float64(Float64(Float64(3.0 * x) * y) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((3.0 * x) * y) - z;
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(3.0 * x), $MachinePrecision] * y), $MachinePrecision] - z), $MachinePrecision]

Derivation

1. Initial program 99.9%

   \[\left(x \cdot 3\right) \cdot y - z \]
2. Add Preprocessing

3. Final simplification 99.9%

   \[\leadsto \left(3 \cdot x\right) \cdot y - z \]
4. Add Preprocessing

Alternative 2: 78.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(3 \cdot x\right) \cdot y\\ \mathbf{if}\;t\_0 \leq -5 \cdot 10^{-41}:\\ \;\;\;\;\left(y \cdot 3\right) \cdot x\\ \mathbf{elif}\;t\_0 \leq 2 \cdot 10^{-114}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (* 3.0 x) y)))
   (if (<= t_0 -5e-41) (* (* y 3.0) x) (if (<= t_0 2e-114) (- z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (3.0 * x) * y;
	double tmp;
	if (t_0 <= -5e-41) {
		tmp = (y * 3.0) * x;
	} else if (t_0 <= 2e-114) {
		tmp = -z;
	} else {
		tmp = t_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) :: t_0
    real(8) :: tmp
    t_0 = (3.0d0 * x) * y
    if (t_0 <= (-5d-41)) then
        tmp = (y * 3.0d0) * x
    else if (t_0 <= 2d-114) then
        tmp = -z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (3.0 * x) * y;
	double tmp;
	if (t_0 <= -5e-41) {
		tmp = (y * 3.0) * x;
	} else if (t_0 <= 2e-114) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (3.0 * x) * y
	tmp = 0
	if t_0 <= -5e-41:
		tmp = (y * 3.0) * x
	elif t_0 <= 2e-114:
		tmp = -z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(3.0 * x) * y)
	tmp = 0.0
	if (t_0 <= -5e-41)
		tmp = Float64(Float64(y * 3.0) * x);
	elseif (t_0 <= 2e-114)
		tmp = Float64(-z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (3.0 * x) * y;
	tmp = 0.0;
	if (t_0 <= -5e-41)
		tmp = (y * 3.0) * x;
	elseif (t_0 <= 2e-114)
		tmp = -z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(3.0 * x), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[t$95$0, -5e-41], N[(N[(y * 3.0), $MachinePrecision] * x), $MachinePrecision], If[LessEqual[t$95$0, 2e-114], (-z), t$95$0]]]

Derivation

1. Split input into 3 regimes

2. if (*.f64 (*.f64 x #s(literal 3 binary64)) y) < -4.9999999999999996e-41

   1. Initial program 99.8%

      \[\left(x \cdot 3\right) \cdot y - z \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y - z} \]

      2. sub-neg N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y + \left(\mathsf{neg}\left(z\right)\right)} \]

      3. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y} + \left(\mathsf{neg}\left(z\right)\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right)} \cdot y + \left(\mathsf{neg}\left(z\right)\right) \]

      5. associate-*l* N/A

         \[\leadsto \color{blue}{x \cdot \left(3 \cdot y\right)} + \left(\mathsf{neg}\left(z\right)\right) \]

      6. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left(y \cdot 3\right)} + \left(\mathsf{neg}\left(z\right)\right) \]

      7. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot y\right) \cdot 3} + \left(\mathsf{neg}\left(z\right)\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot y, 3, \mathsf{neg}\left(z\right)\right)} \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot x}, 3, \mathsf{neg}\left(z\right)\right) \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot x}, 3, \mathsf{neg}\left(z\right)\right) \]

      11. lower-neg.f64 99.7

          \[\leadsto \mathsf{fma}\left(y \cdot x, 3, \color{blue}{-z}\right) \]

   4. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y \cdot x, 3, -z\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{3 \cdot \left(x \cdot y\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto 3 \cdot \color{blue}{\left(y \cdot x\right)} \]

      2. associate-*r* N/A

         \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]

      4. lower-*.f64 77.9

         \[\leadsto \color{blue}{\left(3 \cdot y\right)} \cdot x \]

   7. Applied rewrites 77.9%

      \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]

   if -4.9999999999999996e-41 < (*.f64 (*.f64 x #s(literal 3 binary64)) y) < 2.0000000000000001e-114

   1. Initial program 100.0%

      \[\left(x \cdot 3\right) \cdot y - z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 85.7

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

   5. Applied rewrites 85.7%

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

   if 2.0000000000000001e-114 < (*.f64 (*.f64 x #s(literal 3 binary64)) y)

   1. Initial program 99.8%

      \[\left(x \cdot 3\right) \cdot y - z \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y - z} \]

      2. sub-neg N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y + \left(\mathsf{neg}\left(z\right)\right)} \]

      3. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y} + \left(\mathsf{neg}\left(z\right)\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right)} \cdot y + \left(\mathsf{neg}\left(z\right)\right) \]

      5. associate-*l* N/A

         \[\leadsto \color{blue}{x \cdot \left(3 \cdot y\right)} + \left(\mathsf{neg}\left(z\right)\right) \]

      6. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left(y \cdot 3\right)} + \left(\mathsf{neg}\left(z\right)\right) \]

      7. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot y\right) \cdot 3} + \left(\mathsf{neg}\left(z\right)\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot y, 3, \mathsf{neg}\left(z\right)\right)} \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot x}, 3, \mathsf{neg}\left(z\right)\right) \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot x}, 3, \mathsf{neg}\left(z\right)\right) \]

      11. lower-neg.f64 99.7

          \[\leadsto \mathsf{fma}\left(y \cdot x, 3, \color{blue}{-z}\right) \]

   4. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y \cdot x, 3, -z\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{3 \cdot \left(x \cdot y\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto 3 \cdot \color{blue}{\left(y \cdot x\right)} \]

      2. associate-*r* N/A

         \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]

      4. lower-*.f64 74.2

         \[\leadsto \color{blue}{\left(3 \cdot y\right)} \cdot x \]

   7. Applied rewrites 74.2%

      \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]
   8. Step-by-step derivation

   9. Applied rewrites 74.4%

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

4. Final simplification 80.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(3 \cdot x\right) \cdot y \leq -5 \cdot 10^{-41}:\\ \;\;\;\;\left(y \cdot 3\right) \cdot x\\ \mathbf{elif}\;\left(3 \cdot x\right) \cdot y \leq 2 \cdot 10^{-114}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot x\right) \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 78.2% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(3 \cdot x\right) \cdot y\\ \mathbf{if}\;t\_0 \leq -5 \cdot 10^{-41}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;t\_0 \leq 2 \cdot 10^{-114}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* (* 3.0 x) y)))
   (if (<= t_0 -5e-41) t_0 (if (<= t_0 2e-114) (- z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = (3.0 * x) * y;
	double tmp;
	if (t_0 <= -5e-41) {
		tmp = t_0;
	} else if (t_0 <= 2e-114) {
		tmp = -z;
	} else {
		tmp = t_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) :: t_0
    real(8) :: tmp
    t_0 = (3.0d0 * x) * y
    if (t_0 <= (-5d-41)) then
        tmp = t_0
    else if (t_0 <= 2d-114) then
        tmp = -z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = (3.0 * x) * y;
	double tmp;
	if (t_0 <= -5e-41) {
		tmp = t_0;
	} else if (t_0 <= 2e-114) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = (3.0 * x) * y
	tmp = 0
	if t_0 <= -5e-41:
		tmp = t_0
	elif t_0 <= 2e-114:
		tmp = -z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(Float64(3.0 * x) * y)
	tmp = 0.0
	if (t_0 <= -5e-41)
		tmp = t_0;
	elseif (t_0 <= 2e-114)
		tmp = Float64(-z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = (3.0 * x) * y;
	tmp = 0.0;
	if (t_0 <= -5e-41)
		tmp = t_0;
	elseif (t_0 <= 2e-114)
		tmp = -z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(3.0 * x), $MachinePrecision] * y), $MachinePrecision]}, If[LessEqual[t$95$0, -5e-41], t$95$0, If[LessEqual[t$95$0, 2e-114], (-z), t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 (*.f64 x #s(literal 3 binary64)) y) < -4.9999999999999996e-41 or 2.0000000000000001e-114 < (*.f64 (*.f64 x #s(literal 3 binary64)) y)

   1. Initial program 99.8%

      \[\left(x \cdot 3\right) \cdot y - z \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift--.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y - z} \]

      2. sub-neg N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y + \left(\mathsf{neg}\left(z\right)\right)} \]

      3. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right) \cdot y} + \left(\mathsf{neg}\left(z\right)\right) \]

      4. lift-*.f64 N/A

         \[\leadsto \color{blue}{\left(x \cdot 3\right)} \cdot y + \left(\mathsf{neg}\left(z\right)\right) \]

      5. associate-*l* N/A

         \[\leadsto \color{blue}{x \cdot \left(3 \cdot y\right)} + \left(\mathsf{neg}\left(z\right)\right) \]

      6. *-commutative N/A

         \[\leadsto x \cdot \color{blue}{\left(y \cdot 3\right)} + \left(\mathsf{neg}\left(z\right)\right) \]

      7. associate-*r* N/A

         \[\leadsto \color{blue}{\left(x \cdot y\right) \cdot 3} + \left(\mathsf{neg}\left(z\right)\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \color{blue}{\mathsf{fma}\left(x \cdot y, 3, \mathsf{neg}\left(z\right)\right)} \]

      9. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot x}, 3, \mathsf{neg}\left(z\right)\right) \]

      10. lower-*.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{y \cdot x}, 3, \mathsf{neg}\left(z\right)\right) \]

      11. lower-neg.f64 99.7

          \[\leadsto \mathsf{fma}\left(y \cdot x, 3, \color{blue}{-z}\right) \]

   4. Applied rewrites 99.7%

      \[\leadsto \color{blue}{\mathsf{fma}\left(y \cdot x, 3, -z\right)} \]

   5. Taylor expanded in x around inf

      \[\leadsto \color{blue}{3 \cdot \left(x \cdot y\right)} \]
   6. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto 3 \cdot \color{blue}{\left(y \cdot x\right)} \]

      2. associate-*r* N/A

         \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]

      3. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]

      4. lower-*.f64 76.0

         \[\leadsto \color{blue}{\left(3 \cdot y\right)} \cdot x \]

   7. Applied rewrites 76.0%

      \[\leadsto \color{blue}{\left(3 \cdot y\right) \cdot x} \]
   8. Step-by-step derivation

   9. Applied rewrites 76.1%

      \[\leadsto \left(3 \cdot x\right) \cdot \color{blue}{y} \]

   if -4.9999999999999996e-41 < (*.f64 (*.f64 x #s(literal 3 binary64)) y) < 2.0000000000000001e-114

   1. Initial program 100.0%

      \[\left(x \cdot 3\right) \cdot y - z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

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

      1. mul-1-neg N/A

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

      2. lower-neg.f64 85.7

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

   5. Applied rewrites 85.7%

      \[\leadsto \color{blue}{-z} \]
3. Recombined 2 regimes into one program.

4. Final simplification 80.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;\left(3 \cdot x\right) \cdot y \leq -5 \cdot 10^{-41}:\\ \;\;\;\;\left(3 \cdot x\right) \cdot y\\ \mathbf{elif}\;\left(3 \cdot x\right) \cdot y \leq 2 \cdot 10^{-114}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;\left(3 \cdot x\right) \cdot y\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 50.8% accurate, 4.7× speedup

Math

\[\begin{array}{l} \\ -z \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := (-z)

Derivation

1. Initial program 99.9%

   \[\left(x \cdot 3\right) \cdot y - z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 51.2

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

5. Applied rewrites 51.2%

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

Alternative 5: 2.2% accurate, 14.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := z

Derivation

1. Initial program 99.9%

   \[\left(x \cdot 3\right) \cdot y - z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 51.2

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

5. Applied rewrites 51.2%

   \[\leadsto \color{blue}{-z} \]
6. Step-by-step derivation

7. Applied rewrites 2.4%

   \[\leadsto z \]
8. Add Preprocessing

Developer Target 1: 99.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot \left(3 \cdot y\right) - z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (* x (* 3.0 y)) z))

C

double code(double x, double y, double z) {
	return (x * (3.0 * y)) - 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 = (x * (3.0d0 * y)) - z
end function

Java

public static double code(double x, double y, double z) {
	return (x * (3.0 * y)) - z;
}

Python

def code(x, y, z):
	return (x * (3.0 * y)) - z

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(3.0 * y)) - z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * (3.0 * y)) - z;
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(3.0 * y), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]

Reproduce

herbie shell --seed 2024298 
(FPCore (x y z)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, B"
  :precision binary64

  :alt
  (! :herbie-platform default (- (* x (* 3 y)) z))

  (- (* (* x 3.0) y) z))