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

Percentage Accurate: 99.8% → 99.8%

Time: 4.3s

Alternatives: 4

Speedup: N/A×

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

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

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(3.0 * Float64(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[(3.0 * N[(x * y), $MachinePrecision]), $MachinePrecision] - z), $MachinePrecision]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 65.0% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -1.55 \cdot 10^{+169} \lor \neg \left(x \leq -2.8 \cdot 10^{+134}\right) \land \left(x \leq -2.25 \cdot 10^{+105} \lor \neg \left(x \leq -4 \cdot 10^{+93} \lor \neg \left(x \leq -1.1 \cdot 10^{+47}\right) \land \left(x \leq 5 \cdot 10^{-266} \lor \neg \left(x \leq 6 \cdot 10^{-155}\right) \land x \leq 4.6 \cdot 10^{-26}\right)\right)\right):\\ \;\;\;\;3 \cdot \left(x \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;-z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -1.55e+169)
         (and (not (<= x -2.8e+134))
              (or (<= x -2.25e+105)
                  (not
                   (or (<= x -4e+93)
                       (and (not (<= x -1.1e+47))
                            (or (<= x 5e-266)
                                (and (not (<= x 6e-155)) (<= x 4.6e-26)))))))))
   (* 3.0 (* x y))
   (- z)))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.55e+169) || (!(x <= -2.8e+134) && ((x <= -2.25e+105) || !((x <= -4e+93) || (!(x <= -1.1e+47) && ((x <= 5e-266) || (!(x <= 6e-155) && (x <= 4.6e-26)))))))) {
		tmp = 3.0 * (x * 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 ((x <= (-1.55d+169)) .or. (.not. (x <= (-2.8d+134))) .and. (x <= (-2.25d+105)) .or. (.not. (x <= (-4d+93)) .or. (.not. (x <= (-1.1d+47))) .and. (x <= 5d-266) .or. (.not. (x <= 6d-155)) .and. (x <= 4.6d-26))) then
        tmp = 3.0d0 * (x * y)
    else
        tmp = -z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -1.55e+169) || (!(x <= -2.8e+134) && ((x <= -2.25e+105) || !((x <= -4e+93) || (!(x <= -1.1e+47) && ((x <= 5e-266) || (!(x <= 6e-155) && (x <= 4.6e-26)))))))) {
		tmp = 3.0 * (x * y);
	} else {
		tmp = -z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -1.55e+169) or (not (x <= -2.8e+134) and ((x <= -2.25e+105) or not ((x <= -4e+93) or (not (x <= -1.1e+47) and ((x <= 5e-266) or (not (x <= 6e-155) and (x <= 4.6e-26))))))):
		tmp = 3.0 * (x * y)
	else:
		tmp = -z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -1.55e+169) || (!(x <= -2.8e+134) && ((x <= -2.25e+105) || !((x <= -4e+93) || (!(x <= -1.1e+47) && ((x <= 5e-266) || (!(x <= 6e-155) && (x <= 4.6e-26))))))))
		tmp = Float64(3.0 * Float64(x * y));
	else
		tmp = Float64(-z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -1.55e+169) || (~((x <= -2.8e+134)) && ((x <= -2.25e+105) || ~(((x <= -4e+93) || (~((x <= -1.1e+47)) && ((x <= 5e-266) || (~((x <= 6e-155)) && (x <= 4.6e-26)))))))))
		tmp = 3.0 * (x * y);
	else
		tmp = -z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -1.55e+169], And[N[Not[LessEqual[x, -2.8e+134]], $MachinePrecision], Or[LessEqual[x, -2.25e+105], N[Not[Or[LessEqual[x, -4e+93], And[N[Not[LessEqual[x, -1.1e+47]], $MachinePrecision], Or[LessEqual[x, 5e-266], And[N[Not[LessEqual[x, 6e-155]], $MachinePrecision], LessEqual[x, 4.6e-26]]]]]], $MachinePrecision]]]], N[(3.0 * N[(x * y), $MachinePrecision]), $MachinePrecision], (-z)]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 78.4% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := y \cdot \left(3 \cdot x\right)\\ \mathbf{if}\;t_0 \leq -1 \cdot 10^{-22}:\\ \;\;\;\;3 \cdot \left(x \cdot y\right)\\ \mathbf{elif}\;t_0 \leq 5 \cdot 10^{+76}:\\ \;\;\;\;-z\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* y (* 3.0 x))))
   (if (<= t_0 -1e-22) (* 3.0 (* x y)) (if (<= t_0 5e+76) (- z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = y * (3.0 * x);
	double tmp;
	if (t_0 <= -1e-22) {
		tmp = 3.0 * (x * y);
	} else if (t_0 <= 5e+76) {
		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 = y * (3.0d0 * x)
    if (t_0 <= (-1d-22)) then
        tmp = 3.0d0 * (x * y)
    else if (t_0 <= 5d+76) 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 = y * (3.0 * x);
	double tmp;
	if (t_0 <= -1e-22) {
		tmp = 3.0 * (x * y);
	} else if (t_0 <= 5e+76) {
		tmp = -z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = y * (3.0 * x)
	tmp = 0
	if t_0 <= -1e-22:
		tmp = 3.0 * (x * y)
	elif t_0 <= 5e+76:
		tmp = -z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(y * Float64(3.0 * x))
	tmp = 0.0
	if (t_0 <= -1e-22)
		tmp = Float64(3.0 * Float64(x * y));
	elseif (t_0 <= 5e+76)
		tmp = Float64(-z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = y * (3.0 * x);
	tmp = 0.0;
	if (t_0 <= -1e-22)
		tmp = 3.0 * (x * y);
	elseif (t_0 <= 5e+76)
		tmp = -z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(y * N[(3.0 * x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -1e-22], N[(3.0 * N[(x * y), $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, 5e+76], (-z), t$95$0]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 50.3% accurate, 3.5× 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

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Developer target: 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 2023348 
(FPCore (x y z)
  :name "Diagrams.Solve.Polynomial:cubForm  from diagrams-solve-0.1, B"
  :precision binary64

  :herbie-target
  (- (* x (* 3.0 y)) z)

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