Simplification of discriminant from scale-rotated-ellipse

Percentage Accurate: 24.5% → 77.9%

Time: 13.2s

Alternatives: 6

Speedup: 20.4×

• 33.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{angle}{180} \cdot \pi\\ t_1 := \sin t\_0\\ t_2 := \cos t\_0\\ t_3 := \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot t\_1\right) \cdot t\_2}{x-scale}}{y-scale}\\ t\_3 \cdot t\_3 - \left(4 \cdot \frac{\frac{{\left(a \cdot t\_1\right)}^{2} + {\left(b \cdot t\_2\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot t\_2\right)}^{2} + {\left(b \cdot t\_1\right)}^{2}}{y-scale}}{y-scale} \end{array} \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (let* ((t_0 (* (/ angle 180.0) PI))
        (t_1 (sin t_0))
        (t_2 (cos t_0))
        (t_3
         (/
          (/ (* (* (* 2.0 (- (pow b 2.0) (pow a 2.0))) t_1) t_2) x-scale)
          y-scale)))
   (-
    (* t_3 t_3)
    (*
     (*
      4.0
      (/ (/ (+ (pow (* a t_1) 2.0) (pow (* b t_2) 2.0)) x-scale) x-scale))
     (/ (/ (+ (pow (* a t_2) 2.0) (pow (* b t_1) 2.0)) y-scale) y-scale)))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (angle / 180.0) * ((double) M_PI);
	double t_1 = sin(t_0);
	double t_2 = cos(t_0);
	double t_3 = ((((2.0 * (pow(b, 2.0) - pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	return (t_3 * t_3) - ((4.0 * (((pow((a * t_1), 2.0) + pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((pow((a * t_2), 2.0) + pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale));
}

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (angle / 180.0) * Math.PI;
	double t_1 = Math.sin(t_0);
	double t_2 = Math.cos(t_0);
	double t_3 = ((((2.0 * (Math.pow(b, 2.0) - Math.pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	return (t_3 * t_3) - ((4.0 * (((Math.pow((a * t_1), 2.0) + Math.pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((Math.pow((a * t_2), 2.0) + Math.pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale));
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	t_0 = (angle / 180.0) * math.pi
	t_1 = math.sin(t_0)
	t_2 = math.cos(t_0)
	t_3 = ((((2.0 * (math.pow(b, 2.0) - math.pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale
	return (t_3 * t_3) - ((4.0 * (((math.pow((a * t_1), 2.0) + math.pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((math.pow((a * t_2), 2.0) + math.pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale))

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = Float64(Float64(angle / 180.0) * pi)
	t_1 = sin(t_0)
	t_2 = cos(t_0)
	t_3 = Float64(Float64(Float64(Float64(Float64(2.0 * Float64((b ^ 2.0) - (a ^ 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale)
	return Float64(Float64(t_3 * t_3) - Float64(Float64(4.0 * Float64(Float64(Float64((Float64(a * t_1) ^ 2.0) + (Float64(b * t_2) ^ 2.0)) / x_45_scale) / x_45_scale)) * Float64(Float64(Float64((Float64(a * t_2) ^ 2.0) + (Float64(b * t_1) ^ 2.0)) / y_45_scale) / y_45_scale)))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = (angle / 180.0) * pi;
	t_1 = sin(t_0);
	t_2 = cos(t_0);
	t_3 = ((((2.0 * ((b ^ 2.0) - (a ^ 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	tmp = (t_3 * t_3) - ((4.0 * (((((a * t_1) ^ 2.0) + ((b * t_2) ^ 2.0)) / x_45_scale) / x_45_scale)) * (((((a * t_2) ^ 2.0) + ((b * t_1) ^ 2.0)) / y_45_scale) / y_45_scale));
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := Block[{t$95$0 = N[(N[(angle / 180.0), $MachinePrecision] * Pi), $MachinePrecision]}, Block[{t$95$1 = N[Sin[t$95$0], $MachinePrecision]}, Block[{t$95$2 = N[Cos[t$95$0], $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[(N[(2.0 * N[(N[Power[b, 2.0], $MachinePrecision] - N[Power[a, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$2), $MachinePrecision] / x$45$scale), $MachinePrecision] / y$45$scale), $MachinePrecision]}, N[(N[(t$95$3 * t$95$3), $MachinePrecision] - N[(N[(4.0 * N[(N[(N[(N[Power[N[(a * t$95$1), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(b * t$95$2), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / x$45$scale), $MachinePrecision] / x$45$scale), $MachinePrecision]), $MachinePrecision] * N[(N[(N[(N[Power[N[(a * t$95$2), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(b * t$95$1), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / y$45$scale), $MachinePrecision] / y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Initial Program: 24.5% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{angle}{180} \cdot \pi\\ t_1 := \sin t\_0\\ t_2 := \cos t\_0\\ t_3 := \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot t\_1\right) \cdot t\_2}{x-scale}}{y-scale}\\ t\_3 \cdot t\_3 - \left(4 \cdot \frac{\frac{{\left(a \cdot t\_1\right)}^{2} + {\left(b \cdot t\_2\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot t\_2\right)}^{2} + {\left(b \cdot t\_1\right)}^{2}}{y-scale}}{y-scale} \end{array} \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (let* ((t_0 (* (/ angle 180.0) PI))
        (t_1 (sin t_0))
        (t_2 (cos t_0))
        (t_3
         (/
          (/ (* (* (* 2.0 (- (pow b 2.0) (pow a 2.0))) t_1) t_2) x-scale)
          y-scale)))
   (-
    (* t_3 t_3)
    (*
     (*
      4.0
      (/ (/ (+ (pow (* a t_1) 2.0) (pow (* b t_2) 2.0)) x-scale) x-scale))
     (/ (/ (+ (pow (* a t_2) 2.0) (pow (* b t_1) 2.0)) y-scale) y-scale)))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (angle / 180.0) * ((double) M_PI);
	double t_1 = sin(t_0);
	double t_2 = cos(t_0);
	double t_3 = ((((2.0 * (pow(b, 2.0) - pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	return (t_3 * t_3) - ((4.0 * (((pow((a * t_1), 2.0) + pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((pow((a * t_2), 2.0) + pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale));
}

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (angle / 180.0) * Math.PI;
	double t_1 = Math.sin(t_0);
	double t_2 = Math.cos(t_0);
	double t_3 = ((((2.0 * (Math.pow(b, 2.0) - Math.pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	return (t_3 * t_3) - ((4.0 * (((Math.pow((a * t_1), 2.0) + Math.pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((Math.pow((a * t_2), 2.0) + Math.pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale));
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	t_0 = (angle / 180.0) * math.pi
	t_1 = math.sin(t_0)
	t_2 = math.cos(t_0)
	t_3 = ((((2.0 * (math.pow(b, 2.0) - math.pow(a, 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale
	return (t_3 * t_3) - ((4.0 * (((math.pow((a * t_1), 2.0) + math.pow((b * t_2), 2.0)) / x_45_scale) / x_45_scale)) * (((math.pow((a * t_2), 2.0) + math.pow((b * t_1), 2.0)) / y_45_scale) / y_45_scale))

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = Float64(Float64(angle / 180.0) * pi)
	t_1 = sin(t_0)
	t_2 = cos(t_0)
	t_3 = Float64(Float64(Float64(Float64(Float64(2.0 * Float64((b ^ 2.0) - (a ^ 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale)
	return Float64(Float64(t_3 * t_3) - Float64(Float64(4.0 * Float64(Float64(Float64((Float64(a * t_1) ^ 2.0) + (Float64(b * t_2) ^ 2.0)) / x_45_scale) / x_45_scale)) * Float64(Float64(Float64((Float64(a * t_2) ^ 2.0) + (Float64(b * t_1) ^ 2.0)) / y_45_scale) / y_45_scale)))
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = (angle / 180.0) * pi;
	t_1 = sin(t_0);
	t_2 = cos(t_0);
	t_3 = ((((2.0 * ((b ^ 2.0) - (a ^ 2.0))) * t_1) * t_2) / x_45_scale) / y_45_scale;
	tmp = (t_3 * t_3) - ((4.0 * (((((a * t_1) ^ 2.0) + ((b * t_2) ^ 2.0)) / x_45_scale) / x_45_scale)) * (((((a * t_2) ^ 2.0) + ((b * t_1) ^ 2.0)) / y_45_scale) / y_45_scale));
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := Block[{t$95$0 = N[(N[(angle / 180.0), $MachinePrecision] * Pi), $MachinePrecision]}, Block[{t$95$1 = N[Sin[t$95$0], $MachinePrecision]}, Block[{t$95$2 = N[Cos[t$95$0], $MachinePrecision]}, Block[{t$95$3 = N[(N[(N[(N[(N[(2.0 * N[(N[Power[b, 2.0], $MachinePrecision] - N[Power[a, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * t$95$1), $MachinePrecision] * t$95$2), $MachinePrecision] / x$45$scale), $MachinePrecision] / y$45$scale), $MachinePrecision]}, N[(N[(t$95$3 * t$95$3), $MachinePrecision] - N[(N[(4.0 * N[(N[(N[(N[Power[N[(a * t$95$1), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(b * t$95$2), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / x$45$scale), $MachinePrecision] / x$45$scale), $MachinePrecision]), $MachinePrecision] * N[(N[(N[(N[Power[N[(a * t$95$2), $MachinePrecision], 2.0], $MachinePrecision] + N[Power[N[(b * t$95$1), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] / y$45$scale), $MachinePrecision] / y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]

Alternative 1: 77.9% accurate, 14.6× speedup

Math

\[\begin{array}{l} \\ \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (* (/ (* (* a b) (* a b)) (pow (* x-scale y-scale) 2.0)) -4.0))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return (((a * b) * (a * b)) / pow((x_45_scale * y_45_scale), 2.0)) * -4.0;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, angle, x_45scale, y_45scale)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = (((a * b) * (a * b)) / ((x_45scale * y_45scale) ** 2.0d0)) * (-4.0d0)
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return (((a * b) * (a * b)) / Math.pow((x_45_scale * y_45_scale), 2.0)) * -4.0;
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return (((a * b) * (a * b)) / math.pow((x_45_scale * y_45_scale), 2.0)) * -4.0

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return Float64(Float64(Float64(Float64(a * b) * Float64(a * b)) / (Float64(x_45_scale * y_45_scale) ^ 2.0)) * -4.0)
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = (((a * b) * (a * b)) / ((x_45_scale * y_45_scale) ^ 2.0)) * -4.0;
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := N[(N[(N[(N[(a * b), $MachinePrecision] * N[(a * b), $MachinePrecision]), $MachinePrecision] / N[Power[N[(x$45$scale * y$45$scale), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision]

Derivation

1. Initial program 24.5%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

2. Taylor expanded in angle around 0

   \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

4. Applied rewrites 48.3%

   \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

5. Taylor expanded in a around 0

   \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
6. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   3. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   4. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   5. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   6. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   7. lower-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   8. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

   9. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

   10. pow2 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   11. lift-*.f64 47.9

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

7. Applied rewrites 47.9%

   \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
8. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   4. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   5. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   6. pow-prod-down N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   7. lower-pow.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   8. lift-*.f64 60.2

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

9. Applied rewrites 60.2%

   \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
10. Step-by-step derivation

    1. lift-*.f64 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

    2. lift-*.f64 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

    3. lift-*.f64 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

    4. unswap-sqr N/A

       \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

    5. lower-*.f64 N/A

       \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

    6. lower-*.f64 N/A

       \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

    7. lower-*.f64 77.9

       \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

11. Applied rewrites 77.9%

    \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
12. Add Preprocessing

Alternative 2: 63.6% accurate, 13.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4\\ \mathbf{if}\;a \leq 8.5 \cdot 10^{-158}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;a \leq 2.1 \cdot 10^{-43}:\\ \;\;\;\;\frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4\\ \mathbf{elif}\;a \leq 1.2 \cdot 10^{+115}:\\ \;\;\;\;\left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \left(\frac{b}{y-scale} \cdot \frac{b}{y-scale}\right)\right) \cdot -4\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (let* ((t_0
         (*
          (/ (* (* a b) (* a b)) (* (* x-scale x-scale) (* y-scale y-scale)))
          -4.0)))
   (if (<= a 8.5e-158)
     t_0
     (if (<= a 2.1e-43)
       (*
        (/ (* (* a a) (* b b)) (* (* x-scale y-scale) (* x-scale y-scale)))
        -4.0)
       (if (<= a 1.2e+115)
         (*
          (* (/ (* a a) (* x-scale x-scale)) (* (/ b y-scale) (/ b y-scale)))
          -4.0)
         t_0)))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	double tmp;
	if (a <= 8.5e-158) {
		tmp = t_0;
	} else if (a <= 2.1e-43) {
		tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	} else if (a <= 1.2e+115) {
		tmp = (((a * a) / (x_45_scale * x_45_scale)) * ((b / y_45_scale) * (b / y_45_scale))) * -4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, angle, x_45scale, y_45scale)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (((a * b) * (a * b)) / ((x_45scale * x_45scale) * (y_45scale * y_45scale))) * (-4.0d0)
    if (a <= 8.5d-158) then
        tmp = t_0
    else if (a <= 2.1d-43) then
        tmp = (((a * a) * (b * b)) / ((x_45scale * y_45scale) * (x_45scale * y_45scale))) * (-4.0d0)
    else if (a <= 1.2d+115) then
        tmp = (((a * a) / (x_45scale * x_45scale)) * ((b / y_45scale) * (b / y_45scale))) * (-4.0d0)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	double tmp;
	if (a <= 8.5e-158) {
		tmp = t_0;
	} else if (a <= 2.1e-43) {
		tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	} else if (a <= 1.2e+115) {
		tmp = (((a * a) / (x_45_scale * x_45_scale)) * ((b / y_45_scale) * (b / y_45_scale))) * -4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	t_0 = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0
	tmp = 0
	if a <= 8.5e-158:
		tmp = t_0
	elif a <= 2.1e-43:
		tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0
	elif a <= 1.2e+115:
		tmp = (((a * a) / (x_45_scale * x_45_scale)) * ((b / y_45_scale) * (b / y_45_scale))) * -4.0
	else:
		tmp = t_0
	return tmp

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = Float64(Float64(Float64(Float64(a * b) * Float64(a * b)) / Float64(Float64(x_45_scale * x_45_scale) * Float64(y_45_scale * y_45_scale))) * -4.0)
	tmp = 0.0
	if (a <= 8.5e-158)
		tmp = t_0;
	elseif (a <= 2.1e-43)
		tmp = Float64(Float64(Float64(Float64(a * a) * Float64(b * b)) / Float64(Float64(x_45_scale * y_45_scale) * Float64(x_45_scale * y_45_scale))) * -4.0);
	elseif (a <= 1.2e+115)
		tmp = Float64(Float64(Float64(Float64(a * a) / Float64(x_45_scale * x_45_scale)) * Float64(Float64(b / y_45_scale) * Float64(b / y_45_scale))) * -4.0);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	tmp = 0.0;
	if (a <= 8.5e-158)
		tmp = t_0;
	elseif (a <= 2.1e-43)
		tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	elseif (a <= 1.2e+115)
		tmp = (((a * a) / (x_45_scale * x_45_scale)) * ((b / y_45_scale) * (b / y_45_scale))) * -4.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := Block[{t$95$0 = N[(N[(N[(N[(a * b), $MachinePrecision] * N[(a * b), $MachinePrecision]), $MachinePrecision] / N[(N[(x$45$scale * x$45$scale), $MachinePrecision] * N[(y$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision]}, If[LessEqual[a, 8.5e-158], t$95$0, If[LessEqual[a, 2.1e-43], N[(N[(N[(N[(a * a), $MachinePrecision] * N[(b * b), $MachinePrecision]), $MachinePrecision] / N[(N[(x$45$scale * y$45$scale), $MachinePrecision] * N[(x$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision], If[LessEqual[a, 1.2e+115], N[(N[(N[(N[(a * a), $MachinePrecision] / N[(x$45$scale * x$45$scale), $MachinePrecision]), $MachinePrecision] * N[(N[(b / y$45$scale), $MachinePrecision] * N[(b / y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision], t$95$0]]]]

Derivation

1. Split input into 3 regimes

2. if a < 8.49999999999999944e-158 or 1.2e115 < a

   1. Initial program 22.6%

      \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

   2. Taylor expanded in angle around 0

      \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   4. Applied rewrites 45.2%

      \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      3. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      8. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. lift-*.f64 45.0

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   7. Applied rewrites 45.0%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      4. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. pow-prod-down N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      7. lower-pow.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      8. lift-*.f64 56.7

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   9. Applied rewrites 56.7%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       2. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       3. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       4. unswap-sqr N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       5. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       6. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       7. lower-*.f64 77.8

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       8. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       9. lift-pow.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       10. unpow2 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       11. unswap-sqr N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       12. lift-*.f64 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       13. lift-*.f64 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       14. lift-*.f64 60.7

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   11. Applied rewrites 60.7%

       \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   if 8.49999999999999944e-158 < a < 2.1000000000000001e-43

   1. Initial program 39.0%

      \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

   2. Taylor expanded in angle around 0

      \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   4. Applied rewrites 58.4%

      \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      3. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      8. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. lift-*.f64 56.9

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   7. Applied rewrites 56.9%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      4. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. pow-prod-down N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      7. lower-pow.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      8. lift-*.f64 70.9

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   9. Applied rewrites 70.9%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       2. lift-pow.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       3. unpow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       4. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       5. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       6. lift-*.f64 70.9

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

   11. Applied rewrites 70.9%

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

   if 2.1000000000000001e-43 < a < 1.2e115

   1. Initial program 25.8%

      \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

   2. Taylor expanded in angle around 0

      \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   4. Applied rewrites 59.7%

      \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      3. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      8. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. lift-*.f64 59.3

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   7. Applied rewrites 59.3%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
   8. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{{a}^{2} \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      6. pow2 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. pow2 N/A

          \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      12. times-frac N/A

          \[\leadsto \left(\frac{{a}^{2}}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\frac{{a}^{2}}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      14. lower-/.f64 N/A

          \[\leadsto \left(\frac{{a}^{2}}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      15. pow2 N/A

          \[\leadsto \left(\frac{a \cdot a}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      16. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      17. pow2 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      18. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      19. lower-/.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      20. pow2 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{{y-scale}^{2}}\right) \cdot -4 \]

      21. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{{y-scale}^{2}}\right) \cdot -4 \]

      22. pow2 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

      23. lift-*.f64 59.3

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

   9. Applied rewrites 59.3%

      \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       2. lift-/.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       3. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       4. times-frac N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \left(\frac{b}{y-scale} \cdot \frac{b}{y-scale}\right)\right) \cdot -4 \]

       5. lower-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \left(\frac{b}{y-scale} \cdot \frac{b}{y-scale}\right)\right) \cdot -4 \]

       6. lower-/.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \left(\frac{b}{y-scale} \cdot \frac{b}{y-scale}\right)\right) \cdot -4 \]

       7. lower-/.f64 76.1

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \left(\frac{b}{y-scale} \cdot \frac{b}{y-scale}\right)\right) \cdot -4 \]

   11. Applied rewrites 76.1%

       \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \left(\frac{b}{y-scale} \cdot \frac{b}{y-scale}\right)\right) \cdot -4 \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 63.1% accurate, 15.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x-scale \leq 3.3 \cdot 10^{-164}:\\ \;\;\;\;\left(\left(\frac{a}{x-scale} \cdot \frac{a}{x-scale}\right) \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4\\ \mathbf{elif}\;x-scale \leq 2.65 \cdot 10^{+101}:\\ \;\;\;\;\frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4\\ \mathbf{else}:\\ \;\;\;\;\frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4\\ \end{array} \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (if (<= x-scale 3.3e-164)
   (* (* (* (/ a x-scale) (/ a x-scale)) (/ (* b b) (* y-scale y-scale))) -4.0)
   (if (<= x-scale 2.65e+101)
     (*
      (/ (* (* a b) (* a b)) (* (* x-scale x-scale) (* y-scale y-scale)))
      -4.0)
     (*
      (/ (* (* a a) (* b b)) (* (* x-scale y-scale) (* x-scale y-scale)))
      -4.0))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double tmp;
	if (x_45_scale <= 3.3e-164) {
		tmp = (((a / x_45_scale) * (a / x_45_scale)) * ((b * b) / (y_45_scale * y_45_scale))) * -4.0;
	} else if (x_45_scale <= 2.65e+101) {
		tmp = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	} else {
		tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, angle, x_45scale, y_45scale)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    real(8) :: tmp
    if (x_45scale <= 3.3d-164) then
        tmp = (((a / x_45scale) * (a / x_45scale)) * ((b * b) / (y_45scale * y_45scale))) * (-4.0d0)
    else if (x_45scale <= 2.65d+101) then
        tmp = (((a * b) * (a * b)) / ((x_45scale * x_45scale) * (y_45scale * y_45scale))) * (-4.0d0)
    else
        tmp = (((a * a) * (b * b)) / ((x_45scale * y_45scale) * (x_45scale * y_45scale))) * (-4.0d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double tmp;
	if (x_45_scale <= 3.3e-164) {
		tmp = (((a / x_45_scale) * (a / x_45_scale)) * ((b * b) / (y_45_scale * y_45_scale))) * -4.0;
	} else if (x_45_scale <= 2.65e+101) {
		tmp = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	} else {
		tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	}
	return tmp;
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	tmp = 0
	if x_45_scale <= 3.3e-164:
		tmp = (((a / x_45_scale) * (a / x_45_scale)) * ((b * b) / (y_45_scale * y_45_scale))) * -4.0
	elif x_45_scale <= 2.65e+101:
		tmp = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0
	else:
		tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0
	return tmp

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	tmp = 0.0
	if (x_45_scale <= 3.3e-164)
		tmp = Float64(Float64(Float64(Float64(a / x_45_scale) * Float64(a / x_45_scale)) * Float64(Float64(b * b) / Float64(y_45_scale * y_45_scale))) * -4.0);
	elseif (x_45_scale <= 2.65e+101)
		tmp = Float64(Float64(Float64(Float64(a * b) * Float64(a * b)) / Float64(Float64(x_45_scale * x_45_scale) * Float64(y_45_scale * y_45_scale))) * -4.0);
	else
		tmp = Float64(Float64(Float64(Float64(a * a) * Float64(b * b)) / Float64(Float64(x_45_scale * y_45_scale) * Float64(x_45_scale * y_45_scale))) * -4.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = 0.0;
	if (x_45_scale <= 3.3e-164)
		tmp = (((a / x_45_scale) * (a / x_45_scale)) * ((b * b) / (y_45_scale * y_45_scale))) * -4.0;
	elseif (x_45_scale <= 2.65e+101)
		tmp = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	else
		tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := If[LessEqual[x$45$scale, 3.3e-164], N[(N[(N[(N[(a / x$45$scale), $MachinePrecision] * N[(a / x$45$scale), $MachinePrecision]), $MachinePrecision] * N[(N[(b * b), $MachinePrecision] / N[(y$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision], If[LessEqual[x$45$scale, 2.65e+101], N[(N[(N[(N[(a * b), $MachinePrecision] * N[(a * b), $MachinePrecision]), $MachinePrecision] / N[(N[(x$45$scale * x$45$scale), $MachinePrecision] * N[(y$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision], N[(N[(N[(N[(a * a), $MachinePrecision] * N[(b * b), $MachinePrecision]), $MachinePrecision] / N[(N[(x$45$scale * y$45$scale), $MachinePrecision] * N[(x$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if x-scale < 3.3e-164

   1. Initial program 21.2%

      \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

   2. Taylor expanded in angle around 0

      \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   4. Applied rewrites 46.7%

      \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      3. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      8. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. lift-*.f64 46.2

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   7. Applied rewrites 46.2%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
   8. Step-by-step derivation

      1. lift-/.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{{a}^{2} \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      6. pow2 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      7. lift-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      8. lift-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. pow2 N/A

          \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      12. times-frac N/A

          \[\leadsto \left(\frac{{a}^{2}}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      13. lower-*.f64 N/A

          \[\leadsto \left(\frac{{a}^{2}}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      14. lower-/.f64 N/A

          \[\leadsto \left(\frac{{a}^{2}}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      15. pow2 N/A

          \[\leadsto \left(\frac{a \cdot a}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      16. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{{x-scale}^{2}} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      17. pow2 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      18. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      19. lower-/.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{{b}^{2}}{{y-scale}^{2}}\right) \cdot -4 \]

      20. pow2 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{{y-scale}^{2}}\right) \cdot -4 \]

      21. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{{y-scale}^{2}}\right) \cdot -4 \]

      22. pow2 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

      23. lift-*.f64 46.1

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

   9. Applied rewrites 46.1%

      \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]
   10. Step-by-step derivation

       1. lift-/.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       2. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       3. lift-*.f64 N/A

          \[\leadsto \left(\frac{a \cdot a}{x-scale \cdot x-scale} \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       4. times-frac N/A

          \[\leadsto \left(\left(\frac{a}{x-scale} \cdot \frac{a}{x-scale}\right) \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       5. lower-*.f64 N/A

          \[\leadsto \left(\left(\frac{a}{x-scale} \cdot \frac{a}{x-scale}\right) \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       6. lower-/.f64 N/A

          \[\leadsto \left(\left(\frac{a}{x-scale} \cdot \frac{a}{x-scale}\right) \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

       7. lower-/.f64 60.6

          \[\leadsto \left(\left(\frac{a}{x-scale} \cdot \frac{a}{x-scale}\right) \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

   11. Applied rewrites 60.6%

       \[\leadsto \left(\left(\frac{a}{x-scale} \cdot \frac{a}{x-scale}\right) \cdot \frac{b \cdot b}{y-scale \cdot y-scale}\right) \cdot -4 \]

   if 3.3e-164 < x-scale < 2.65000000000000003e101

   1. Initial program 24.1%

      \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

   2. Taylor expanded in angle around 0

      \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   4. Applied rewrites 56.6%

      \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      3. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      8. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. lift-*.f64 56.4

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   7. Applied rewrites 56.4%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      4. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. pow-prod-down N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      7. lower-pow.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      8. lift-*.f64 60.2

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   9. Applied rewrites 60.2%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       2. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       3. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       4. unswap-sqr N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       5. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       6. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       7. lower-*.f64 77.3

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       8. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       9. lift-pow.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       10. unpow2 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       11. unswap-sqr N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       12. lift-*.f64 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       13. lift-*.f64 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       14. lift-*.f64 71.5

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   11. Applied rewrites 71.5%

       \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   if 2.65000000000000003e101 < x-scale

   1. Initial program 38.0%

      \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

   2. Taylor expanded in angle around 0

      \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   4. Applied rewrites 43.3%

      \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      3. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      8. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. lift-*.f64 42.9

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   7. Applied rewrites 42.9%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      4. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. pow-prod-down N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      7. lower-pow.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      8. lift-*.f64 61.1

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   9. Applied rewrites 61.1%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       2. lift-pow.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       3. unpow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       4. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       5. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       6. lift-*.f64 61.1

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

   11. Applied rewrites 61.1%

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 62.5% accurate, 15.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4\\ \mathbf{if}\;x-scale \leq 3.2 \cdot 10^{-189}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x-scale \leq 2.65 \cdot 10^{+101}:\\ \;\;\;\;\frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (let* ((t_0
         (*
          (/ (* (* a a) (* b b)) (* (* x-scale y-scale) (* x-scale y-scale)))
          -4.0)))
   (if (<= x-scale 3.2e-189)
     t_0
     (if (<= x-scale 2.65e+101)
       (*
        (/ (* (* a b) (* a b)) (* (* x-scale x-scale) (* y-scale y-scale)))
        -4.0)
       t_0))))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	double tmp;
	if (x_45_scale <= 3.2e-189) {
		tmp = t_0;
	} else if (x_45_scale <= 2.65e+101) {
		tmp = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, angle, x_45scale, y_45scale)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    real(8) :: t_0
    real(8) :: tmp
    t_0 = (((a * a) * (b * b)) / ((x_45scale * y_45scale) * (x_45scale * y_45scale))) * (-4.0d0)
    if (x_45scale <= 3.2d-189) then
        tmp = t_0
    else if (x_45scale <= 2.65d+101) then
        tmp = (((a * b) * (a * b)) / ((x_45scale * x_45scale) * (y_45scale * y_45scale))) * (-4.0d0)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	double t_0 = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	double tmp;
	if (x_45_scale <= 3.2e-189) {
		tmp = t_0;
	} else if (x_45_scale <= 2.65e+101) {
		tmp = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	t_0 = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0
	tmp = 0
	if x_45_scale <= 3.2e-189:
		tmp = t_0
	elif x_45_scale <= 2.65e+101:
		tmp = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0
	else:
		tmp = t_0
	return tmp

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = Float64(Float64(Float64(Float64(a * a) * Float64(b * b)) / Float64(Float64(x_45_scale * y_45_scale) * Float64(x_45_scale * y_45_scale))) * -4.0)
	tmp = 0.0
	if (x_45_scale <= 3.2e-189)
		tmp = t_0;
	elseif (x_45_scale <= 2.65e+101)
		tmp = Float64(Float64(Float64(Float64(a * b) * Float64(a * b)) / Float64(Float64(x_45_scale * x_45_scale) * Float64(y_45_scale * y_45_scale))) * -4.0);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b, angle, x_45_scale, y_45_scale)
	t_0 = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
	tmp = 0.0;
	if (x_45_scale <= 3.2e-189)
		tmp = t_0;
	elseif (x_45_scale <= 2.65e+101)
		tmp = (((a * b) * (a * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := Block[{t$95$0 = N[(N[(N[(N[(a * a), $MachinePrecision] * N[(b * b), $MachinePrecision]), $MachinePrecision] / N[(N[(x$45$scale * y$45$scale), $MachinePrecision] * N[(x$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision]}, If[LessEqual[x$45$scale, 3.2e-189], t$95$0, If[LessEqual[x$45$scale, 2.65e+101], N[(N[(N[(N[(a * b), $MachinePrecision] * N[(a * b), $MachinePrecision]), $MachinePrecision] / N[(N[(x$45$scale * x$45$scale), $MachinePrecision] * N[(y$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if x-scale < 3.2000000000000001e-189 or 2.65000000000000003e101 < x-scale

   1. Initial program 24.9%

      \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

   2. Taylor expanded in angle around 0

      \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   4. Applied rewrites 46.2%

      \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      3. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      8. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. lift-*.f64 45.7

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   7. Applied rewrites 45.7%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      4. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. pow-prod-down N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      7. lower-pow.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      8. lift-*.f64 60.2

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   9. Applied rewrites 60.2%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       2. lift-pow.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       3. unpow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       4. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       5. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       6. lift-*.f64 60.2

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

   11. Applied rewrites 60.2%

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

   if 3.2000000000000001e-189 < x-scale < 2.65000000000000003e101

   1. Initial program 23.5%

      \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

   2. Taylor expanded in angle around 0

      \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   4. Applied rewrites 55.2%

      \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
   6. Step-by-step derivation

      1. lower-/.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      3. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      4. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      8. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      9. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

      10. pow2 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      11. lift-*.f64 54.9

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   7. Applied rewrites 54.9%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
   8. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      2. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      3. lift-*.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      4. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

      5. pow2 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

      6. pow-prod-down N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      7. lower-pow.f64 N/A

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

      8. lift-*.f64 60.3

         \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   9. Applied rewrites 60.3%

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
   10. Step-by-step derivation

       1. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       2. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       3. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       4. unswap-sqr N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       5. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       6. lower-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       7. lower-*.f64 77.6

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       8. lift-*.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       9. lift-pow.f64 N/A

          \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

       10. unpow2 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

       11. unswap-sqr N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       12. lift-*.f64 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       13. lift-*.f64 N/A

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

       14. lift-*.f64 69.6

           \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   11. Applied rewrites 69.6%

       \[\leadsto \frac{\left(a \cdot b\right) \cdot \left(a \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 60.2% accurate, 20.4× speedup

Math

\[\begin{array}{l} \\ \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (* (/ (* (* a a) (* b b)) (* (* x-scale y-scale) (* x-scale y-scale))) -4.0))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, angle, x_45scale, y_45scale)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = (((a * a) * (b * b)) / ((x_45scale * y_45scale) * (x_45scale * y_45scale))) * (-4.0d0)
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return Float64(Float64(Float64(Float64(a * a) * Float64(b * b)) / Float64(Float64(x_45_scale * y_45_scale) * Float64(x_45_scale * y_45_scale))) * -4.0)
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = (((a * a) * (b * b)) / ((x_45_scale * y_45_scale) * (x_45_scale * y_45_scale))) * -4.0;
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := N[(N[(N[(N[(a * a), $MachinePrecision] * N[(b * b), $MachinePrecision]), $MachinePrecision] / N[(N[(x$45$scale * y$45$scale), $MachinePrecision] * N[(x$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision]

Derivation

1. Initial program 24.5%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

2. Taylor expanded in angle around 0

   \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

4. Applied rewrites 48.3%

   \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

5. Taylor expanded in a around 0

   \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
6. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   3. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   4. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   5. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   6. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   7. lower-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   8. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

   9. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

   10. pow2 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   11. lift-*.f64 47.9

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

7. Applied rewrites 47.9%

   \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
8. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   2. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   4. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   5. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   6. pow-prod-down N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   7. lower-pow.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

   8. lift-*.f64 60.2

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

9. Applied rewrites 60.2%

   \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]
10. Step-by-step derivation

    1. lift-*.f64 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

    2. lift-pow.f64 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{\left(x-scale \cdot y-scale\right)}^{2}} \cdot -4 \]

    3. unpow2 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

    4. lower-*.f64 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

    5. lift-*.f64 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

    6. lift-*.f64 60.2

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]

11. Applied rewrites 60.2%

    \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot y-scale\right) \cdot \left(x-scale \cdot y-scale\right)} \cdot -4 \]
12. Add Preprocessing

Alternative 6: 47.9% accurate, 20.4× speedup

Math

\[\begin{array}{l} \\ \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \end{array} \]

FPCore

(FPCore (a b angle x-scale y-scale)
 :precision binary64
 (* (/ (* (* a a) (* b b)) (* (* x-scale x-scale) (* y-scale y-scale))) -4.0))

C

double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return (((a * a) * (b * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

    interface fmax
        module procedure fmax88
        module procedure fmax44
        module procedure fmax84
        module procedure fmax48
    end interface
    interface fmin
        module procedure fmin88
        module procedure fmin44
        module procedure fmin84
        module procedure fmin48
    end interface
contains
    real(8) function fmax88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(4) function fmax44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, max(x, y), y /= y), x /= x)
    end function
    real(8) function fmax84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, max(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmax48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), max(dble(x), y), y /= y), x /= x)
    end function
    real(8) function fmin88(x, y) result (res)
        real(8), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(4) function fmin44(x, y) result (res)
        real(4), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(y, merge(x, min(x, y), y /= y), x /= x)
    end function
    real(8) function fmin84(x, y) result(res)
        real(8), intent (in) :: x
        real(4), intent (in) :: y
        res = merge(dble(y), merge(x, min(x, dble(y)), y /= y), x /= x)
    end function
    real(8) function fmin48(x, y) result(res)
        real(4), intent (in) :: x
        real(8), intent (in) :: y
        res = merge(y, merge(dble(x), min(dble(x), y), y /= y), x /= x)
    end function
end module

real(8) function code(a, b, angle, x_45scale, y_45scale)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    real(8), intent (in) :: angle
    real(8), intent (in) :: x_45scale
    real(8), intent (in) :: y_45scale
    code = (((a * a) * (b * b)) / ((x_45scale * x_45scale) * (y_45scale * y_45scale))) * (-4.0d0)
end function

Java

public static double code(double a, double b, double angle, double x_45_scale, double y_45_scale) {
	return (((a * a) * (b * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
}

Python

def code(a, b, angle, x_45_scale, y_45_scale):
	return (((a * a) * (b * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0

Julia

function code(a, b, angle, x_45_scale, y_45_scale)
	return Float64(Float64(Float64(Float64(a * a) * Float64(b * b)) / Float64(Float64(x_45_scale * x_45_scale) * Float64(y_45_scale * y_45_scale))) * -4.0)
end

MATLAB

function tmp = code(a, b, angle, x_45_scale, y_45_scale)
	tmp = (((a * a) * (b * b)) / ((x_45_scale * x_45_scale) * (y_45_scale * y_45_scale))) * -4.0;
end

Wolfram

code[a_, b_, angle_, x$45$scale_, y$45$scale_] := N[(N[(N[(N[(a * a), $MachinePrecision] * N[(b * b), $MachinePrecision]), $MachinePrecision] / N[(N[(x$45$scale * x$45$scale), $MachinePrecision] * N[(y$45$scale * y$45$scale), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -4.0), $MachinePrecision]

Derivation

1. Initial program 24.5%

   \[\frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} \cdot \frac{\frac{\left(\left(2 \cdot \left({b}^{2} - {a}^{2}\right)\right) \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right) \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)}{x-scale}}{y-scale} - \left(4 \cdot \frac{\frac{{\left(a \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{x-scale}}{x-scale}\right) \cdot \frac{\frac{{\left(a \cdot \cos \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2} + {\left(b \cdot \sin \left(\frac{angle}{180} \cdot \pi\right)\right)}^{2}}{y-scale}}{y-scale} \]

2. Taylor expanded in angle around 0

   \[\leadsto \color{blue}{-4 \cdot \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}}} \]
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot \color{blue}{-4} \]

4. Applied rewrites 48.3%

   \[\leadsto \color{blue}{\left(\left(a \cdot a\right) \cdot \frac{b \cdot b}{\left(y-scale \cdot y-scale\right) \cdot \left(x-scale \cdot x-scale\right)}\right) \cdot -4} \]

5. Taylor expanded in a around 0

   \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]
6. Step-by-step derivation

   1. lower-/.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{{a}^{2} \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   3. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   4. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot {b}^{2}}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   5. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   6. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   7. lower-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{{x-scale}^{2} \cdot {y-scale}^{2}} \cdot -4 \]

   8. pow2 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

   9. lift-*.f64 N/A

      \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot {y-scale}^{2}} \cdot -4 \]

   10. pow2 N/A

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

   11. lift-*.f64 47.9

       \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]

7. Applied rewrites 47.9%

   \[\leadsto \frac{\left(a \cdot a\right) \cdot \left(b \cdot b\right)}{\left(x-scale \cdot x-scale\right) \cdot \left(y-scale \cdot y-scale\right)} \cdot -4 \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2025116 
(FPCore (a b angle x-scale y-scale)
  :name "Simplification of discriminant from scale-rotated-ellipse"
  :precision binary64
  (- (* (/ (/ (* (* (* 2.0 (- (pow b 2.0) (pow a 2.0))) (sin (* (/ angle 180.0) PI))) (cos (* (/ angle 180.0) PI))) x-scale) y-scale) (/ (/ (* (* (* 2.0 (- (pow b 2.0) (pow a 2.0))) (sin (* (/ angle 180.0) PI))) (cos (* (/ angle 180.0) PI))) x-scale) y-scale)) (* (* 4.0 (/ (/ (+ (pow (* a (sin (* (/ angle 180.0) PI))) 2.0) (pow (* b (cos (* (/ angle 180.0) PI))) 2.0)) x-scale) x-scale)) (/ (/ (+ (pow (* a (cos (* (/ angle 180.0) PI))) 2.0) (pow (* b (sin (* (/ angle 180.0) PI))) 2.0)) y-scale) y-scale))))