Graphics.Rasterific.QuadraticFormula:discriminant from Rasterific-0.6.1

Percentage Accurate: 98.2% → 99.6%

Time: 3.2s

Alternatives: 5

Speedup: 1.0×

• 34.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ x \cdot x - \left(y \cdot 4\right) \cdot z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (* x x) (* (* y 4.0) z)))

C

double code(double x, double y, double z) {
	return (x * x) - ((y * 4.0) * 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 * x) - ((y * 4.0d0) * z)
end function

Java

public static double code(double x, double y, double z) {
	return (x * x) - ((y * 4.0) * z);
}

Python

def code(x, y, z):
	return (x * x) - ((y * 4.0) * z)

Julia

function code(x, y, z)
	return Float64(Float64(x * x) - Float64(Float64(y * 4.0) * z))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * x) - ((y * 4.0) * z);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * x), $MachinePrecision] - N[(N[(y * 4.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]

Initial Program: 98.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x \cdot x - \left(y \cdot 4\right) \cdot z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (- (* x x) (* (* y 4.0) z)))

C

double code(double x, double y, double z) {
	return (x * x) - ((y * 4.0) * 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 * x) - ((y * 4.0d0) * z)
end function

Java

public static double code(double x, double y, double z) {
	return (x * x) - ((y * 4.0) * z);
}

Python

def code(x, y, z):
	return (x * x) - ((y * 4.0) * z)

Julia

function code(x, y, z)
	return Float64(Float64(x * x) - Float64(Float64(y * 4.0) * z))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * x) - ((y * 4.0) * z);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * x), $MachinePrecision] - N[(N[(y * 4.0), $MachinePrecision] * z), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 0.1× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 2 \cdot 10^{+197}:\\ \;\;\;\;\mathsf{fma}\left(x\_m, x\_m, y \cdot \left(z \cdot -4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;{x\_m}^{2}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m y z)
 :precision binary64
 (if (<= x_m 2e+197) (fma x_m x_m (* y (* z -4.0))) (pow x_m 2.0)))

C

x_m = fabs(x);
double code(double x_m, double y, double z) {
	double tmp;
	if (x_m <= 2e+197) {
		tmp = fma(x_m, x_m, (y * (z * -4.0)));
	} else {
		tmp = pow(x_m, 2.0);
	}
	return tmp;
}

Julia

x_m = abs(x)
function code(x_m, y, z)
	tmp = 0.0
	if (x_m <= 2e+197)
		tmp = fma(x_m, x_m, Float64(y * Float64(z * -4.0)));
	else
		tmp = x_m ^ 2.0;
	end
	return tmp
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y_, z_] := If[LessEqual[x$95$m, 2e+197], N[(x$95$m * x$95$m + N[(y * N[(z * -4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[Power[x$95$m, 2.0], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.9999999999999999e197

   1. Initial program 98.7%

      \[x \cdot x - \left(y \cdot 4\right) \cdot z \]
   2. Step-by-step derivation

      1. fma-neg 99.1%

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, x, -\left(y \cdot 4\right) \cdot z\right)} \]

      2. associate-*l* 99.1%

         \[\leadsto \mathsf{fma}\left(x, x, -\color{blue}{y \cdot \left(4 \cdot z\right)}\right) \]

      3. *-commutative 99.1%

         \[\leadsto \mathsf{fma}\left(x, x, -y \cdot \color{blue}{\left(z \cdot 4\right)}\right) \]

      4. distribute-rgt-neg-in 99.1%

         \[\leadsto \mathsf{fma}\left(x, x, \color{blue}{y \cdot \left(-z \cdot 4\right)}\right) \]

      5. distribute-rgt-neg-in 99.1%

         \[\leadsto \mathsf{fma}\left(x, x, y \cdot \color{blue}{\left(z \cdot \left(-4\right)\right)}\right) \]

      6. metadata-eval 99.1%

         \[\leadsto \mathsf{fma}\left(x, x, y \cdot \left(z \cdot \color{blue}{-4}\right)\right) \]

   3. Simplified 99.1%

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

   if 1.9999999999999999e197 < x

   1. Initial program 86.7%

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

   3. Taylor expanded in x around inf 100.0%

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

4. Final simplification 99.2%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 2 \cdot 10^{+197}:\\ \;\;\;\;\mathsf{fma}\left(x, x, y \cdot \left(z \cdot -4\right)\right)\\ \mathbf{else}:\\ \;\;\;\;{x}^{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 2: 98.9% accurate, 0.1× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ \begin{array}{l} \mathbf{if}\;x\_m \leq 4.5 \cdot 10^{+133}:\\ \;\;\;\;x\_m \cdot x\_m - z \cdot \left(y \cdot 4\right)\\ \mathbf{else}:\\ \;\;\;\;{x\_m}^{2}\\ \end{array} \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m y z)
 :precision binary64
 (if (<= x_m 4.5e+133) (- (* x_m x_m) (* z (* y 4.0))) (pow x_m 2.0)))

C

x_m = fabs(x);
double code(double x_m, double y, double z) {
	double tmp;
	if (x_m <= 4.5e+133) {
		tmp = (x_m * x_m) - (z * (y * 4.0));
	} else {
		tmp = pow(x_m, 2.0);
	}
	return tmp;
}

Fortran

x_m = abs(x)
real(8) function code(x_m, y, z)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x_m <= 4.5d+133) then
        tmp = (x_m * x_m) - (z * (y * 4.0d0))
    else
        tmp = x_m ** 2.0d0
    end if
    code = tmp
end function

Java

x_m = Math.abs(x);
public static double code(double x_m, double y, double z) {
	double tmp;
	if (x_m <= 4.5e+133) {
		tmp = (x_m * x_m) - (z * (y * 4.0));
	} else {
		tmp = Math.pow(x_m, 2.0);
	}
	return tmp;
}

Python

x_m = math.fabs(x)
def code(x_m, y, z):
	tmp = 0
	if x_m <= 4.5e+133:
		tmp = (x_m * x_m) - (z * (y * 4.0))
	else:
		tmp = math.pow(x_m, 2.0)
	return tmp

Julia

x_m = abs(x)
function code(x_m, y, z)
	tmp = 0.0
	if (x_m <= 4.5e+133)
		tmp = Float64(Float64(x_m * x_m) - Float64(z * Float64(y * 4.0)));
	else
		tmp = x_m ^ 2.0;
	end
	return tmp
end

MATLAB

x_m = abs(x);
function tmp_2 = code(x_m, y, z)
	tmp = 0.0;
	if (x_m <= 4.5e+133)
		tmp = (x_m * x_m) - (z * (y * 4.0));
	else
		tmp = x_m ^ 2.0;
	end
	tmp_2 = tmp;
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y_, z_] := If[LessEqual[x$95$m, 4.5e+133], N[(N[(x$95$m * x$95$m), $MachinePrecision] - N[(z * N[(y * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[Power[x$95$m, 2.0], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 4.49999999999999985e133

   1. Initial program 99.1%

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

   if 4.49999999999999985e133 < x

   1. Initial program 88.6%

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

   3. Taylor expanded in x around inf 97.7%

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

4. Final simplification 98.8%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq 4.5 \cdot 10^{+133}:\\ \;\;\;\;x \cdot x - z \cdot \left(y \cdot 4\right)\\ \mathbf{else}:\\ \;\;\;\;{x}^{2}\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 98.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ x\_m \cdot x\_m - z \cdot \left(y \cdot 4\right) \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m y z) :precision binary64 (- (* x_m x_m) (* z (* y 4.0))))

C

x_m = fabs(x);
double code(double x_m, double y, double z) {
	return (x_m * x_m) - (z * (y * 4.0));
}

Fortran

x_m = abs(x)
real(8) function code(x_m, y, z)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x_m * x_m) - (z * (y * 4.0d0))
end function

Java

x_m = Math.abs(x);
public static double code(double x_m, double y, double z) {
	return (x_m * x_m) - (z * (y * 4.0));
}

Python

x_m = math.fabs(x)
def code(x_m, y, z):
	return (x_m * x_m) - (z * (y * 4.0))

Julia

x_m = abs(x)
function code(x_m, y, z)
	return Float64(Float64(x_m * x_m) - Float64(z * Float64(y * 4.0)))
end

MATLAB

x_m = abs(x);
function tmp = code(x_m, y, z)
	tmp = (x_m * x_m) - (z * (y * 4.0));
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y_, z_] := N[(N[(x$95$m * x$95$m), $MachinePrecision] - N[(z * N[(y * 4.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.3%

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

3. Final simplification 97.3%

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

Alternative 4: 52.7% accurate, 1.8× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ -4 \cdot \left(y \cdot z\right) \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m y z) :precision binary64 (* -4.0 (* y z)))

C

x_m = fabs(x);
double code(double x_m, double y, double z) {
	return -4.0 * (y * z);
}

Fortran

x_m = abs(x)
real(8) function code(x_m, y, z)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (-4.0d0) * (y * z)
end function

Java

x_m = Math.abs(x);
public static double code(double x_m, double y, double z) {
	return -4.0 * (y * z);
}

Python

x_m = math.fabs(x)
def code(x_m, y, z):
	return -4.0 * (y * z)

Julia

x_m = abs(x)
function code(x_m, y, z)
	return Float64(-4.0 * Float64(y * z))
end

MATLAB

x_m = abs(x);
function tmp = code(x_m, y, z)
	tmp = -4.0 * (y * z);
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y_, z_] := N[(-4.0 * N[(y * z), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.3%

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

3. Taylor expanded in x around 0 50.3%

   \[\leadsto \color{blue}{-4 \cdot \left(y \cdot z\right)} \]

4. Final simplification 50.3%

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

Alternative 5: 52.7% accurate, 1.8× speedup

Math

\[\begin{array}{l} x_m = \left|x\right| \\ y \cdot \left(z \cdot -4\right) \end{array} \]

FPCore

x_m = (fabs.f64 x)
(FPCore (x_m y z) :precision binary64 (* y (* z -4.0)))

C

x_m = fabs(x);
double code(double x_m, double y, double z) {
	return y * (z * -4.0);
}

Fortran

x_m = abs(x)
real(8) function code(x_m, y, z)
    real(8), intent (in) :: x_m
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = y * (z * (-4.0d0))
end function

Java

x_m = Math.abs(x);
public static double code(double x_m, double y, double z) {
	return y * (z * -4.0);
}

Python

x_m = math.fabs(x)
def code(x_m, y, z):
	return y * (z * -4.0)

Julia

x_m = abs(x)
function code(x_m, y, z)
	return Float64(y * Float64(z * -4.0))
end

MATLAB

x_m = abs(x);
function tmp = code(x_m, y, z)
	tmp = y * (z * -4.0);
end

Wolfram

x_m = N[Abs[x], $MachinePrecision]
code[x$95$m_, y_, z_] := N[(y * N[(z * -4.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 97.3%

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

3. Taylor expanded in x around 0 50.3%

   \[\leadsto \color{blue}{-4 \cdot \left(y \cdot z\right)} \]
4. Step-by-step derivation

   1. *-commutative 50.3%

      \[\leadsto \color{blue}{\left(y \cdot z\right) \cdot -4} \]

   2. associate-*r* 50.3%

      \[\leadsto \color{blue}{y \cdot \left(z \cdot -4\right)} \]

   3. *-commutative 50.3%

      \[\leadsto y \cdot \color{blue}{\left(-4 \cdot z\right)} \]

5. Simplified 50.3%

   \[\leadsto \color{blue}{y \cdot \left(-4 \cdot z\right)} \]

6. Final simplification 50.3%

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

Reproduce

herbie shell --seed 2024051 
(FPCore (x y z)
  :name "Graphics.Rasterific.QuadraticFormula:discriminant from Rasterific-0.6.1"
  :precision binary64
  (- (* x x) (* (* y 4.0) z)))