Equirectangular approximation to distance on a great circle

Percentage Accurate: 60.0% → 96.0%

Time: 6.7s

Alternatives: 14

Speedup: 1.8×

• 30.5% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\\ R \cdot \sqrt{t\_0 \cdot t\_0 + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (let* ((t_0 (* (- lambda1 lambda2) (cos (/ (+ phi1 phi2) 2.0)))))
   (* R (sqrt (+ (* t_0 t_0) (* (- phi1 phi2) (- phi1 phi2)))))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double t_0 = (lambda1 - lambda2) * cos(((phi1 + phi2) / 2.0));
	return R * sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))));
}

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(r, lambda1, lambda2, phi1, phi2)
use fmin_fmax_functions
    real(8), intent (in) :: r
    real(8), intent (in) :: lambda1
    real(8), intent (in) :: lambda2
    real(8), intent (in) :: phi1
    real(8), intent (in) :: phi2
    real(8) :: t_0
    t_0 = (lambda1 - lambda2) * cos(((phi1 + phi2) / 2.0d0))
    code = r * sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))))
end function

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double t_0 = (lambda1 - lambda2) * Math.cos(((phi1 + phi2) / 2.0));
	return R * Math.sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))));
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	t_0 = (lambda1 - lambda2) * math.cos(((phi1 + phi2) / 2.0))
	return R * math.sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))))

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	t_0 = Float64(Float64(lambda1 - lambda2) * cos(Float64(Float64(phi1 + phi2) / 2.0)))
	return Float64(R * sqrt(Float64(Float64(t_0 * t_0) + Float64(Float64(phi1 - phi2) * Float64(phi1 - phi2)))))
end

MATLAB

function tmp = code(R, lambda1, lambda2, phi1, phi2)
	t_0 = (lambda1 - lambda2) * cos(((phi1 + phi2) / 2.0));
	tmp = R * sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))));
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[(lambda1 - lambda2), $MachinePrecision] * N[Cos[N[(N[(phi1 + phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, N[(R * N[Sqrt[N[(N[(t$95$0 * t$95$0), $MachinePrecision] + N[(N[(phi1 - phi2), $MachinePrecision] * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Initial Program: 60.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\\ R \cdot \sqrt{t\_0 \cdot t\_0 + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (let* ((t_0 (* (- lambda1 lambda2) (cos (/ (+ phi1 phi2) 2.0)))))
   (* R (sqrt (+ (* t_0 t_0) (* (- phi1 phi2) (- phi1 phi2)))))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double t_0 = (lambda1 - lambda2) * cos(((phi1 + phi2) / 2.0));
	return R * sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))));
}

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(r, lambda1, lambda2, phi1, phi2)
use fmin_fmax_functions
    real(8), intent (in) :: r
    real(8), intent (in) :: lambda1
    real(8), intent (in) :: lambda2
    real(8), intent (in) :: phi1
    real(8), intent (in) :: phi2
    real(8) :: t_0
    t_0 = (lambda1 - lambda2) * cos(((phi1 + phi2) / 2.0d0))
    code = r * sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))))
end function

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double t_0 = (lambda1 - lambda2) * Math.cos(((phi1 + phi2) / 2.0));
	return R * Math.sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))));
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	t_0 = (lambda1 - lambda2) * math.cos(((phi1 + phi2) / 2.0))
	return R * math.sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))))

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	t_0 = Float64(Float64(lambda1 - lambda2) * cos(Float64(Float64(phi1 + phi2) / 2.0)))
	return Float64(R * sqrt(Float64(Float64(t_0 * t_0) + Float64(Float64(phi1 - phi2) * Float64(phi1 - phi2)))))
end

MATLAB

function tmp = code(R, lambda1, lambda2, phi1, phi2)
	t_0 = (lambda1 - lambda2) * cos(((phi1 + phi2) / 2.0));
	tmp = R * sqrt(((t_0 * t_0) + ((phi1 - phi2) * (phi1 - phi2))));
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := Block[{t$95$0 = N[(N[(lambda1 - lambda2), $MachinePrecision] * N[Cos[N[(N[(phi1 + phi2), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, N[(R * N[Sqrt[N[(N[(t$95$0 * t$95$0), $MachinePrecision] + N[(N[(phi1 - phi2), $MachinePrecision] * N[(phi1 - phi2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Alternative 1: 96.0% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(\frac{\phi_2 + \phi_1}{2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (*
  (hypot (- phi1 phi2) (* (cos (/ (+ phi2 phi1) 2.0)) (- lambda1 lambda2)))
  R))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	return hypot((phi1 - phi2), (cos(((phi2 + phi1) / 2.0)) * (lambda1 - lambda2))) * R;
}

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	return Math.hypot((phi1 - phi2), (Math.cos(((phi2 + phi1) / 2.0)) * (lambda1 - lambda2))) * R;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	return math.hypot((phi1 - phi2), (math.cos(((phi2 + phi1) / 2.0)) * (lambda1 - lambda2))) * R

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	return Float64(hypot(Float64(phi1 - phi2), Float64(cos(Float64(Float64(phi2 + phi1) / 2.0)) * Float64(lambda1 - lambda2))) * R)
end

MATLAB

function tmp = code(R, lambda1, lambda2, phi1, phi2)
	tmp = hypot((phi1 - phi2), (cos(((phi2 + phi1) / 2.0)) * (lambda1 - lambda2))) * R;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(N[Sqrt[N[(phi1 - phi2), $MachinePrecision] ^ 2 + N[(N[Cos[N[(N[(phi2 + phi1), $MachinePrecision] / 2.0), $MachinePrecision]], $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision] * R), $MachinePrecision]

Derivation

1. Initial program 64.4%

   \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   2. lift-sqrt.f64 N/A

      \[\leadsto R \cdot \color{blue}{\sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   3. lift-+.f64 N/A

      \[\leadsto R \cdot \sqrt{\color{blue}{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

4. Applied rewrites 96.7%

   \[\leadsto \color{blue}{\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(\frac{\phi_2 + \phi_1}{2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R} \]
5. Add Preprocessing

Alternative 2: 92.9% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_1 \leq -4.3 \cdot 10^{-8}:\\ \;\;\;\;\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \phi_1\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R\\ \mathbf{else}:\\ \;\;\;\;\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \phi_2\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi1 -4.3e-8)
   (* (hypot (- phi1 phi2) (* (cos (* 0.5 phi1)) (- lambda1 lambda2))) R)
   (* (hypot (- phi1 phi2) (* (cos (* 0.5 phi2)) (- lambda1 lambda2))) R)))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi1 <= -4.3e-8) {
		tmp = hypot((phi1 - phi2), (cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	} else {
		tmp = hypot((phi1 - phi2), (cos((0.5 * phi2)) * (lambda1 - lambda2))) * R;
	}
	return tmp;
}

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi1 <= -4.3e-8) {
		tmp = Math.hypot((phi1 - phi2), (Math.cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	} else {
		tmp = Math.hypot((phi1 - phi2), (Math.cos((0.5 * phi2)) * (lambda1 - lambda2))) * R;
	}
	return tmp;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	tmp = 0
	if phi1 <= -4.3e-8:
		tmp = math.hypot((phi1 - phi2), (math.cos((0.5 * phi1)) * (lambda1 - lambda2))) * R
	else:
		tmp = math.hypot((phi1 - phi2), (math.cos((0.5 * phi2)) * (lambda1 - lambda2))) * R
	return tmp

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi1 <= -4.3e-8)
		tmp = Float64(hypot(Float64(phi1 - phi2), Float64(cos(Float64(0.5 * phi1)) * Float64(lambda1 - lambda2))) * R);
	else
		tmp = Float64(hypot(Float64(phi1 - phi2), Float64(cos(Float64(0.5 * phi2)) * Float64(lambda1 - lambda2))) * R);
	end
	return tmp
end

MATLAB

function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0;
	if (phi1 <= -4.3e-8)
		tmp = hypot((phi1 - phi2), (cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	else
		tmp = hypot((phi1 - phi2), (cos((0.5 * phi2)) * (lambda1 - lambda2))) * R;
	end
	tmp_2 = tmp;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi1, -4.3e-8], N[(N[Sqrt[N[(phi1 - phi2), $MachinePrecision] ^ 2 + N[(N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision] * R), $MachinePrecision], N[(N[Sqrt[N[(phi1 - phi2), $MachinePrecision] ^ 2 + N[(N[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision] * R), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if phi1 < -4.3000000000000001e-8

   1. Initial program 52.2%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      2. lift-sqrt.f64 N/A

         \[\leadsto R \cdot \color{blue}{\sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\color{blue}{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   4. Applied rewrites 91.4%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(\frac{\phi_2 + \phi_1}{2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R} \]

   5. Taylor expanded in phi1 around inf

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(\frac{1}{2} \cdot \phi_1\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
   6. Step-by-step derivation

      1. lift-*.f64 91.4

         \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \color{blue}{\phi_1}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   7. Applied rewrites 91.4%

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(0.5 \cdot \phi_1\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   if -4.3000000000000001e-8 < phi1

   1. Initial program 68.8%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      2. lift-sqrt.f64 N/A

         \[\leadsto R \cdot \color{blue}{\sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\color{blue}{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   4. Applied rewrites 98.6%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(\frac{\phi_2 + \phi_1}{2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R} \]

   5. Taylor expanded in phi1 around 0

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(\frac{1}{2} \cdot \phi_2\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
   6. Step-by-step derivation

      1. lower-*.f64 94.9

         \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \color{blue}{\phi_2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   7. Applied rewrites 94.9%

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(0.5 \cdot \phi_2\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 3: 90.4% accurate, 1.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \phi_1\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (* (hypot (- phi1 phi2) (* (cos (* 0.5 phi1)) (- lambda1 lambda2))) R))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	return hypot((phi1 - phi2), (cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
}

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	return Math.hypot((phi1 - phi2), (Math.cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	return math.hypot((phi1 - phi2), (math.cos((0.5 * phi1)) * (lambda1 - lambda2))) * R

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	return Float64(hypot(Float64(phi1 - phi2), Float64(cos(Float64(0.5 * phi1)) * Float64(lambda1 - lambda2))) * R)
end

MATLAB

function tmp = code(R, lambda1, lambda2, phi1, phi2)
	tmp = hypot((phi1 - phi2), (cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(N[Sqrt[N[(phi1 - phi2), $MachinePrecision] ^ 2 + N[(N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision] * R), $MachinePrecision]

Derivation

1. Initial program 64.4%

   \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \color{blue}{R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   2. lift-sqrt.f64 N/A

      \[\leadsto R \cdot \color{blue}{\sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   3. lift-+.f64 N/A

      \[\leadsto R \cdot \sqrt{\color{blue}{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

4. Applied rewrites 96.7%

   \[\leadsto \color{blue}{\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(\frac{\phi_2 + \phi_1}{2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R} \]

5. Taylor expanded in phi1 around inf

   \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(\frac{1}{2} \cdot \phi_1\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
6. Step-by-step derivation

   1. lift-*.f64 91.0

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \color{blue}{\phi_1}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

7. Applied rewrites 91.0%

   \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(0.5 \cdot \phi_1\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
8. Add Preprocessing

Alternative 4: 76.5% accurate, 1.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_2 \leq 5 \cdot 10^{-50}:\\ \;\;\;\;\mathsf{hypot}\left(\phi_1, \cos \left(0.5 \cdot \phi_1\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R\\ \mathbf{elif}\;\phi_2 \leq 2.3 \cdot 10^{+54}:\\ \;\;\;\;\mathsf{hypot}\left(\phi_1, \cos \left(0.5 \cdot \phi_2\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R\\ \mathbf{else}:\\ \;\;\;\;R \cdot \left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi2 5e-50)
   (* (hypot phi1 (* (cos (* 0.5 phi1)) (- lambda1 lambda2))) R)
   (if (<= phi2 2.3e+54)
     (* (hypot phi1 (* (cos (* 0.5 phi2)) (- lambda1 lambda2))) R)
     (* R (* (+ (/ (- phi1) phi2) 1.0) phi2)))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 5e-50) {
		tmp = hypot(phi1, (cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	} else if (phi2 <= 2.3e+54) {
		tmp = hypot(phi1, (cos((0.5 * phi2)) * (lambda1 - lambda2))) * R;
	} else {
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	}
	return tmp;
}

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 5e-50) {
		tmp = Math.hypot(phi1, (Math.cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	} else if (phi2 <= 2.3e+54) {
		tmp = Math.hypot(phi1, (Math.cos((0.5 * phi2)) * (lambda1 - lambda2))) * R;
	} else {
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	}
	return tmp;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	tmp = 0
	if phi2 <= 5e-50:
		tmp = math.hypot(phi1, (math.cos((0.5 * phi1)) * (lambda1 - lambda2))) * R
	elif phi2 <= 2.3e+54:
		tmp = math.hypot(phi1, (math.cos((0.5 * phi2)) * (lambda1 - lambda2))) * R
	else:
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2)
	return tmp

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi2 <= 5e-50)
		tmp = Float64(hypot(phi1, Float64(cos(Float64(0.5 * phi1)) * Float64(lambda1 - lambda2))) * R);
	elseif (phi2 <= 2.3e+54)
		tmp = Float64(hypot(phi1, Float64(cos(Float64(0.5 * phi2)) * Float64(lambda1 - lambda2))) * R);
	else
		tmp = Float64(R * Float64(Float64(Float64(Float64(-phi1) / phi2) + 1.0) * phi2));
	end
	return tmp
end

MATLAB

function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0;
	if (phi2 <= 5e-50)
		tmp = hypot(phi1, (cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	elseif (phi2 <= 2.3e+54)
		tmp = hypot(phi1, (cos((0.5 * phi2)) * (lambda1 - lambda2))) * R;
	else
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	end
	tmp_2 = tmp;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi2, 5e-50], N[(N[Sqrt[phi1 ^ 2 + N[(N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision] * R), $MachinePrecision], If[LessEqual[phi2, 2.3e+54], N[(N[Sqrt[phi1 ^ 2 + N[(N[Cos[N[(0.5 * phi2), $MachinePrecision]], $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision] * R), $MachinePrecision], N[(R * N[(N[(N[((-phi1) / phi2), $MachinePrecision] + 1.0), $MachinePrecision] * phi2), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if phi2 < 4.99999999999999968e-50

   1. Initial program 64.4%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      2. lift-sqrt.f64 N/A

         \[\leadsto R \cdot \color{blue}{\sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\color{blue}{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   4. Applied rewrites 96.8%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(\frac{\phi_2 + \phi_1}{2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R} \]

   5. Taylor expanded in phi1 around inf

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(\frac{1}{2} \cdot \phi_1\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
   6. Step-by-step derivation

      1. lift-*.f64 93.1

         \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \color{blue}{\phi_1}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   7. Applied rewrites 93.1%

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(0.5 \cdot \phi_1\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   8. Taylor expanded in phi1 around inf

      \[\leadsto \mathsf{hypot}\left(\color{blue}{\phi_1}, \cos \left(\frac{1}{2} \cdot \phi_1\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
   9. Step-by-step derivation

   10. Applied rewrites 76.9%

       \[\leadsto \mathsf{hypot}\left(\color{blue}{\phi_1}, \cos \left(0.5 \cdot \phi_1\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   if 4.99999999999999968e-50 < phi2 < 2.29999999999999994e54

   1. Initial program 83.4%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      2. lift-sqrt.f64 N/A

         \[\leadsto R \cdot \color{blue}{\sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\color{blue}{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   4. Applied rewrites 98.4%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(\frac{\phi_2 + \phi_1}{2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R} \]

   5. Taylor expanded in phi1 around 0

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(\frac{1}{2} \cdot \phi_2\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
   6. Step-by-step derivation

      1. lower-*.f64 98.4

         \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \color{blue}{\phi_2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   7. Applied rewrites 98.4%

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(0.5 \cdot \phi_2\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   8. Taylor expanded in phi1 around inf

      \[\leadsto \mathsf{hypot}\left(\color{blue}{\phi_1}, \cos \left(\frac{1}{2} \cdot \phi_2\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
   9. Step-by-step derivation

   10. Applied rewrites 83.5%

       \[\leadsto \mathsf{hypot}\left(\color{blue}{\phi_1}, \cos \left(0.5 \cdot \phi_2\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   if 2.29999999999999994e54 < phi2

   1. Initial program 58.0%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around inf

      \[\leadsto R \cdot \color{blue}{\left(\phi_2 \cdot \left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto R \cdot \left(\left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right) \cdot \color{blue}{\phi_2}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto R \cdot \left(\left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right) \cdot \color{blue}{\phi_2}\right) \]

      3. +-commutative N/A

         \[\leadsto R \cdot \left(\left(-1 \cdot \frac{\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      4. lower-+.f64 N/A

         \[\leadsto R \cdot \left(\left(-1 \cdot \frac{\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      5. associate-*r/ N/A

         \[\leadsto R \cdot \left(\left(\frac{-1 \cdot \phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      6. mul-1-neg N/A

         \[\leadsto R \cdot \left(\left(\frac{\mathsf{neg}\left(\phi_1\right)}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      7. lower-/.f64 N/A

         \[\leadsto R \cdot \left(\left(\frac{\mathsf{neg}\left(\phi_1\right)}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      8. lower-neg.f64 77.9

         \[\leadsto R \cdot \left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

   5. Applied rewrites 77.9%

      \[\leadsto R \cdot \color{blue}{\left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 76.1% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_2 \leq 2.3 \cdot 10^{+54}:\\ \;\;\;\;\mathsf{hypot}\left(\phi_1, \cos \left(0.5 \cdot \phi_1\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R\\ \mathbf{else}:\\ \;\;\;\;R \cdot \left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi2 2.3e+54)
   (* (hypot phi1 (* (cos (* 0.5 phi1)) (- lambda1 lambda2))) R)
   (* R (* (+ (/ (- phi1) phi2) 1.0) phi2))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 2.3e+54) {
		tmp = hypot(phi1, (cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	} else {
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	}
	return tmp;
}

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 2.3e+54) {
		tmp = Math.hypot(phi1, (Math.cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	} else {
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	}
	return tmp;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	tmp = 0
	if phi2 <= 2.3e+54:
		tmp = math.hypot(phi1, (math.cos((0.5 * phi1)) * (lambda1 - lambda2))) * R
	else:
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2)
	return tmp

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi2 <= 2.3e+54)
		tmp = Float64(hypot(phi1, Float64(cos(Float64(0.5 * phi1)) * Float64(lambda1 - lambda2))) * R);
	else
		tmp = Float64(R * Float64(Float64(Float64(Float64(-phi1) / phi2) + 1.0) * phi2));
	end
	return tmp
end

MATLAB

function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0;
	if (phi2 <= 2.3e+54)
		tmp = hypot(phi1, (cos((0.5 * phi1)) * (lambda1 - lambda2))) * R;
	else
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	end
	tmp_2 = tmp;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi2, 2.3e+54], N[(N[Sqrt[phi1 ^ 2 + N[(N[Cos[N[(0.5 * phi1), $MachinePrecision]], $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision] ^ 2], $MachinePrecision] * R), $MachinePrecision], N[(R * N[(N[(N[((-phi1) / phi2), $MachinePrecision] + 1.0), $MachinePrecision] * phi2), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if phi2 < 2.29999999999999994e54

   1. Initial program 66.2%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing
   3. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      2. lift-sqrt.f64 N/A

         \[\leadsto R \cdot \color{blue}{\sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

      3. lift-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\color{blue}{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)}} \]

   4. Applied rewrites 97.0%

      \[\leadsto \color{blue}{\mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(\frac{\phi_2 + \phi_1}{2}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R} \]

   5. Taylor expanded in phi1 around inf

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(\frac{1}{2} \cdot \phi_1\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
   6. Step-by-step derivation

      1. lift-*.f64 92.4

         \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \left(0.5 \cdot \color{blue}{\phi_1}\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   7. Applied rewrites 92.4%

      \[\leadsto \mathsf{hypot}\left(\phi_1 - \phi_2, \cos \color{blue}{\left(0.5 \cdot \phi_1\right)} \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   8. Taylor expanded in phi1 around inf

      \[\leadsto \mathsf{hypot}\left(\color{blue}{\phi_1}, \cos \left(\frac{1}{2} \cdot \phi_1\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]
   9. Step-by-step derivation

   10. Applied rewrites 76.3%

       \[\leadsto \mathsf{hypot}\left(\color{blue}{\phi_1}, \cos \left(0.5 \cdot \phi_1\right) \cdot \left(\lambda_1 - \lambda_2\right)\right) \cdot R \]

   if 2.29999999999999994e54 < phi2

   1. Initial program 58.0%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around inf

      \[\leadsto R \cdot \color{blue}{\left(\phi_2 \cdot \left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto R \cdot \left(\left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right) \cdot \color{blue}{\phi_2}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto R \cdot \left(\left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right) \cdot \color{blue}{\phi_2}\right) \]

      3. +-commutative N/A

         \[\leadsto R \cdot \left(\left(-1 \cdot \frac{\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      4. lower-+.f64 N/A

         \[\leadsto R \cdot \left(\left(-1 \cdot \frac{\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      5. associate-*r/ N/A

         \[\leadsto R \cdot \left(\left(\frac{-1 \cdot \phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      6. mul-1-neg N/A

         \[\leadsto R \cdot \left(\left(\frac{\mathsf{neg}\left(\phi_1\right)}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      7. lower-/.f64 N/A

         \[\leadsto R \cdot \left(\left(\frac{\mathsf{neg}\left(\phi_1\right)}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      8. lower-neg.f64 77.9

         \[\leadsto R \cdot \left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

   5. Applied rewrites 77.9%

      \[\leadsto R \cdot \color{blue}{\left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 54.6% accurate, 4.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_2 \leq 7.8 \cdot 10^{+48}:\\ \;\;\;\;R \cdot \sqrt{\mathsf{fma}\left(1, \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)}\\ \mathbf{else}:\\ \;\;\;\;R \cdot \left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi2 7.8e+48)
   (*
    R
    (sqrt (fma 1.0 (* (- lambda1 lambda2) (- lambda1 lambda2)) (* phi1 phi1))))
   (* R (* (+ (/ (- phi1) phi2) 1.0) phi2))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 7.8e+48) {
		tmp = R * sqrt(fma(1.0, ((lambda1 - lambda2) * (lambda1 - lambda2)), (phi1 * phi1)));
	} else {
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	}
	return tmp;
}

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi2 <= 7.8e+48)
		tmp = Float64(R * sqrt(fma(1.0, Float64(Float64(lambda1 - lambda2) * Float64(lambda1 - lambda2)), Float64(phi1 * phi1))));
	else
		tmp = Float64(R * Float64(Float64(Float64(Float64(-phi1) / phi2) + 1.0) * phi2));
	end
	return tmp
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi2, 7.8e+48], N[(R * N[Sqrt[N[(1.0 * N[(N[(lambda1 - lambda2), $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision] + N[(phi1 * phi1), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(R * N[(N[(N[((-phi1) / phi2), $MachinePrecision] + 1.0), $MachinePrecision] * phi2), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if phi2 < 7.8000000000000002e48

   1. Initial program 66.2%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around 0

      \[\leadsto R \cdot \sqrt{\color{blue}{{\cos \left(\frac{1}{2} \cdot \phi_1\right)}^{2} \cdot {\left(\lambda_1 - \lambda_2\right)}^{2} + {\phi_1}^{2}}} \]
   4. Step-by-step derivation

      1. lower-fma.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left({\cos \left(\frac{1}{2} \cdot \phi_1\right)}^{2}, \color{blue}{{\left(\lambda_1 - \lambda_2\right)}^{2}}, {\phi_1}^{2}\right)} \]

      2. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\cos \left(\frac{1}{2} \cdot \phi_1\right) \cdot \cos \left(\frac{1}{2} \cdot \phi_1\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      3. sqr-cos-a N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      4. lower-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \color{blue}{\lambda_2}\right)}^{2}, {\phi_1}^{2}\right)} \]

      6. lower-cos.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      9. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}, {\phi_1}^{2}\right)} \]

      10. lower-*.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}, {\phi_1}^{2}\right)} \]

      11. lift--.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\color{blue}{\lambda_1} - \lambda_2\right), {\phi_1}^{2}\right)} \]

      12. lift--.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \color{blue}{\lambda_2}\right), {\phi_1}^{2}\right)} \]

      13. unpow2 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

      14. lower-*.f64 54.0

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(0.5 + 0.5 \cdot \cos \left(2 \cdot \left(0.5 \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

   5. Applied rewrites 54.0%

      \[\leadsto R \cdot \sqrt{\color{blue}{\mathsf{fma}\left(0.5 + 0.5 \cdot \cos \left(2 \cdot \left(0.5 \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)}} \]

   6. Taylor expanded in phi1 around 0

      \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(1, \color{blue}{\left(\lambda_1 - \lambda_2\right)} \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]
   7. Step-by-step derivation

   8. Applied rewrites 52.9%

      \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(1, \color{blue}{\left(\lambda_1 - \lambda_2\right)} \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

   if 7.8000000000000002e48 < phi2

   1. Initial program 58.0%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around inf

      \[\leadsto R \cdot \color{blue}{\left(\phi_2 \cdot \left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto R \cdot \left(\left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right) \cdot \color{blue}{\phi_2}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto R \cdot \left(\left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right) \cdot \color{blue}{\phi_2}\right) \]

      3. +-commutative N/A

         \[\leadsto R \cdot \left(\left(-1 \cdot \frac{\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      4. lower-+.f64 N/A

         \[\leadsto R \cdot \left(\left(-1 \cdot \frac{\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      5. associate-*r/ N/A

         \[\leadsto R \cdot \left(\left(\frac{-1 \cdot \phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      6. mul-1-neg N/A

         \[\leadsto R \cdot \left(\left(\frac{\mathsf{neg}\left(\phi_1\right)}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      7. lower-/.f64 N/A

         \[\leadsto R \cdot \left(\left(\frac{\mathsf{neg}\left(\phi_1\right)}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      8. lower-neg.f64 77.9

         \[\leadsto R \cdot \left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

   5. Applied rewrites 77.9%

      \[\leadsto R \cdot \color{blue}{\left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right)} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 34.0% accurate, 4.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_2 \leq 7.8 \cdot 10^{-213}:\\ \;\;\;\;\left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right)\\ \mathbf{elif}\;\phi_2 \leq 1.7 \cdot 10^{+39}:\\ \;\;\;\;R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right)}\\ \mathbf{else}:\\ \;\;\;\;R \cdot \left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi2 7.8e-213)
   (* (- phi1) (* phi2 (/ R phi2)))
   (if (<= phi2 1.7e+39)
     (* R (sqrt (* (- lambda1 lambda2) (- lambda1 lambda2))))
     (* R (* (+ (/ (- phi1) phi2) 1.0) phi2)))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 7.8e-213) {
		tmp = -phi1 * (phi2 * (R / phi2));
	} else if (phi2 <= 1.7e+39) {
		tmp = R * sqrt(((lambda1 - lambda2) * (lambda1 - lambda2)));
	} else {
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	}
	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(r, lambda1, lambda2, phi1, phi2)
use fmin_fmax_functions
    real(8), intent (in) :: r
    real(8), intent (in) :: lambda1
    real(8), intent (in) :: lambda2
    real(8), intent (in) :: phi1
    real(8), intent (in) :: phi2
    real(8) :: tmp
    if (phi2 <= 7.8d-213) then
        tmp = -phi1 * (phi2 * (r / phi2))
    else if (phi2 <= 1.7d+39) then
        tmp = r * sqrt(((lambda1 - lambda2) * (lambda1 - lambda2)))
    else
        tmp = r * (((-phi1 / phi2) + 1.0d0) * phi2)
    end if
    code = tmp
end function

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 7.8e-213) {
		tmp = -phi1 * (phi2 * (R / phi2));
	} else if (phi2 <= 1.7e+39) {
		tmp = R * Math.sqrt(((lambda1 - lambda2) * (lambda1 - lambda2)));
	} else {
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	}
	return tmp;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	tmp = 0
	if phi2 <= 7.8e-213:
		tmp = -phi1 * (phi2 * (R / phi2))
	elif phi2 <= 1.7e+39:
		tmp = R * math.sqrt(((lambda1 - lambda2) * (lambda1 - lambda2)))
	else:
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2)
	return tmp

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi2 <= 7.8e-213)
		tmp = Float64(Float64(-phi1) * Float64(phi2 * Float64(R / phi2)));
	elseif (phi2 <= 1.7e+39)
		tmp = Float64(R * sqrt(Float64(Float64(lambda1 - lambda2) * Float64(lambda1 - lambda2))));
	else
		tmp = Float64(R * Float64(Float64(Float64(Float64(-phi1) / phi2) + 1.0) * phi2));
	end
	return tmp
end

MATLAB

function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0;
	if (phi2 <= 7.8e-213)
		tmp = -phi1 * (phi2 * (R / phi2));
	elseif (phi2 <= 1.7e+39)
		tmp = R * sqrt(((lambda1 - lambda2) * (lambda1 - lambda2)));
	else
		tmp = R * (((-phi1 / phi2) + 1.0) * phi2);
	end
	tmp_2 = tmp;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi2, 7.8e-213], N[((-phi1) * N[(phi2 * N[(R / phi2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[phi2, 1.7e+39], N[(R * N[Sqrt[N[(N[(lambda1 - lambda2), $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(R * N[(N[(N[((-phi1) / phi2), $MachinePrecision] + 1.0), $MachinePrecision] * phi2), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if phi2 < 7.79999999999999977e-213

   1. Initial program 65.1%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 17.0

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 17.0%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in phi2 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \color{blue}{\frac{R}{\phi_1}}\right)\right) \]

      2. lower--.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\color{blue}{\phi_1}}\right)\right) \]

      3. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

      4. lower-/.f64 18.9

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

   8. Applied rewrites 18.9%

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]

   9. Taylor expanded in phi1 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]
   10. Step-by-step derivation

       1. lift-/.f64 24.4

          \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   11. Applied rewrites 24.4%

       \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   if 7.79999999999999977e-213 < phi2 < 1.6999999999999999e39

   1. Initial program 68.4%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around 0

      \[\leadsto R \cdot \sqrt{\color{blue}{{\cos \left(\frac{1}{2} \cdot \phi_1\right)}^{2} \cdot {\left(\lambda_1 - \lambda_2\right)}^{2} + {\phi_1}^{2}}} \]
   4. Step-by-step derivation

      1. lower-fma.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left({\cos \left(\frac{1}{2} \cdot \phi_1\right)}^{2}, \color{blue}{{\left(\lambda_1 - \lambda_2\right)}^{2}}, {\phi_1}^{2}\right)} \]

      2. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\cos \left(\frac{1}{2} \cdot \phi_1\right) \cdot \cos \left(\frac{1}{2} \cdot \phi_1\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      3. sqr-cos-a N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      4. lower-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \color{blue}{\lambda_2}\right)}^{2}, {\phi_1}^{2}\right)} \]

      6. lower-cos.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      9. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}, {\phi_1}^{2}\right)} \]

      10. lower-*.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}, {\phi_1}^{2}\right)} \]

      11. lift--.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\color{blue}{\lambda_1} - \lambda_2\right), {\phi_1}^{2}\right)} \]

      12. lift--.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \color{blue}{\lambda_2}\right), {\phi_1}^{2}\right)} \]

      13. unpow2 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

      14. lower-*.f64 59.8

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(0.5 + 0.5 \cdot \cos \left(2 \cdot \left(0.5 \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

   5. Applied rewrites 59.8%

      \[\leadsto R \cdot \sqrt{\color{blue}{\mathsf{fma}\left(0.5 + 0.5 \cdot \cos \left(2 \cdot \left(0.5 \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)}} \]

   6. Taylor expanded in phi1 around 0

      \[\leadsto R \cdot \sqrt{{\left(\lambda_1 - \lambda_2\right)}^{\color{blue}{2}}} \]
   7. Step-by-step derivation

      1. pow2 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \color{blue}{\lambda_2}\right)} \]

      2. lift--.f64 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right)} \]

      3. lift--.f64 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right)} \]

      4. lift-*.f64 42.1

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \color{blue}{\lambda_2}\right)} \]

   8. Applied rewrites 42.1%

      \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}} \]

   if 1.6999999999999999e39 < phi2

   1. Initial program 59.5%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around inf

      \[\leadsto R \cdot \color{blue}{\left(\phi_2 \cdot \left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right)\right)} \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto R \cdot \left(\left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right) \cdot \color{blue}{\phi_2}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto R \cdot \left(\left(1 + -1 \cdot \frac{\phi_1}{\phi_2}\right) \cdot \color{blue}{\phi_2}\right) \]

      3. +-commutative N/A

         \[\leadsto R \cdot \left(\left(-1 \cdot \frac{\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      4. lower-+.f64 N/A

         \[\leadsto R \cdot \left(\left(-1 \cdot \frac{\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      5. associate-*r/ N/A

         \[\leadsto R \cdot \left(\left(\frac{-1 \cdot \phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      6. mul-1-neg N/A

         \[\leadsto R \cdot \left(\left(\frac{\mathsf{neg}\left(\phi_1\right)}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      7. lower-/.f64 N/A

         \[\leadsto R \cdot \left(\left(\frac{\mathsf{neg}\left(\phi_1\right)}{\phi_2} + 1\right) \cdot \phi_2\right) \]

      8. lower-neg.f64 76.9

         \[\leadsto R \cdot \left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right) \]

   5. Applied rewrites 76.9%

      \[\leadsto R \cdot \color{blue}{\left(\left(\frac{-\phi_1}{\phi_2} + 1\right) \cdot \phi_2\right)} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 33.5% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_2 \leq 7.8 \cdot 10^{-213}:\\ \;\;\;\;\left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right)\\ \mathbf{elif}\;\phi_2 \leq 1.7 \cdot 10^{+39}:\\ \;\;\;\;R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right)}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi2 7.8e-213)
   (* (- phi1) (* phi2 (/ R phi2)))
   (if (<= phi2 1.7e+39)
     (* R (sqrt (* (- lambda1 lambda2) (- lambda1 lambda2))))
     (fma -1.0 (* R phi1) (* R phi2)))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 7.8e-213) {
		tmp = -phi1 * (phi2 * (R / phi2));
	} else if (phi2 <= 1.7e+39) {
		tmp = R * sqrt(((lambda1 - lambda2) * (lambda1 - lambda2)));
	} else {
		tmp = fma(-1.0, (R * phi1), (R * phi2));
	}
	return tmp;
}

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi2 <= 7.8e-213)
		tmp = Float64(Float64(-phi1) * Float64(phi2 * Float64(R / phi2)));
	elseif (phi2 <= 1.7e+39)
		tmp = Float64(R * sqrt(Float64(Float64(lambda1 - lambda2) * Float64(lambda1 - lambda2))));
	else
		tmp = fma(-1.0, Float64(R * phi1), Float64(R * phi2));
	end
	return tmp
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi2, 7.8e-213], N[((-phi1) * N[(phi2 * N[(R / phi2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[phi2, 1.7e+39], N[(R * N[Sqrt[N[(N[(lambda1 - lambda2), $MachinePrecision] * N[(lambda1 - lambda2), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(-1.0 * N[(R * phi1), $MachinePrecision] + N[(R * phi2), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if phi2 < 7.79999999999999977e-213

   1. Initial program 65.1%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 17.0

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 17.0%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in phi2 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \color{blue}{\frac{R}{\phi_1}}\right)\right) \]

      2. lower--.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\color{blue}{\phi_1}}\right)\right) \]

      3. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

      4. lower-/.f64 18.9

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

   8. Applied rewrites 18.9%

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]

   9. Taylor expanded in phi1 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]
   10. Step-by-step derivation

       1. lift-/.f64 24.4

          \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   11. Applied rewrites 24.4%

       \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   if 7.79999999999999977e-213 < phi2 < 1.6999999999999999e39

   1. Initial program 68.4%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around 0

      \[\leadsto R \cdot \sqrt{\color{blue}{{\cos \left(\frac{1}{2} \cdot \phi_1\right)}^{2} \cdot {\left(\lambda_1 - \lambda_2\right)}^{2} + {\phi_1}^{2}}} \]
   4. Step-by-step derivation

      1. lower-fma.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left({\cos \left(\frac{1}{2} \cdot \phi_1\right)}^{2}, \color{blue}{{\left(\lambda_1 - \lambda_2\right)}^{2}}, {\phi_1}^{2}\right)} \]

      2. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\cos \left(\frac{1}{2} \cdot \phi_1\right) \cdot \cos \left(\frac{1}{2} \cdot \phi_1\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      3. sqr-cos-a N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      4. lower-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \color{blue}{\lambda_2}\right)}^{2}, {\phi_1}^{2}\right)} \]

      6. lower-cos.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      9. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}, {\phi_1}^{2}\right)} \]

      10. lower-*.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}, {\phi_1}^{2}\right)} \]

      11. lift--.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\color{blue}{\lambda_1} - \lambda_2\right), {\phi_1}^{2}\right)} \]

      12. lift--.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \color{blue}{\lambda_2}\right), {\phi_1}^{2}\right)} \]

      13. unpow2 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

      14. lower-*.f64 59.8

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(0.5 + 0.5 \cdot \cos \left(2 \cdot \left(0.5 \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

   5. Applied rewrites 59.8%

      \[\leadsto R \cdot \sqrt{\color{blue}{\mathsf{fma}\left(0.5 + 0.5 \cdot \cos \left(2 \cdot \left(0.5 \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)}} \]

   6. Taylor expanded in phi1 around 0

      \[\leadsto R \cdot \sqrt{{\left(\lambda_1 - \lambda_2\right)}^{\color{blue}{2}}} \]
   7. Step-by-step derivation

      1. pow2 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \color{blue}{\lambda_2}\right)} \]

      2. lift--.f64 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right)} \]

      3. lift--.f64 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right)} \]

      4. lift-*.f64 42.1

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \color{blue}{\lambda_2}\right)} \]

   8. Applied rewrites 42.1%

      \[\leadsto R \cdot \sqrt{\left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}} \]

   if 1.6999999999999999e39 < phi2

   1. Initial program 59.5%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 65.8

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 65.8%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in phi1 around 0

      \[\leadsto -1 \cdot \left(R \cdot \phi_1\right) + \color{blue}{R \cdot \phi_2} \]
   7. Step-by-step derivation

      1. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \color{blue}{\phi_1}, R \cdot \phi_2\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right) \]

      3. lower-*.f64 73.5

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right) \]

   8. Applied rewrites 73.5%

      \[\leadsto \mathsf{fma}\left(-1, \color{blue}{R \cdot \phi_1}, R \cdot \phi_2\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 9: 30.9% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_2 \leq 3.8 \cdot 10^{-200}:\\ \;\;\;\;\left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right)\\ \mathbf{elif}\;\phi_2 \leq 8.6 \cdot 10^{+48}:\\ \;\;\;\;\left(-\phi_1\right) \cdot \left(R \cdot \left(1 - \frac{\phi_2}{\phi_1}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi2 3.8e-200)
   (* (- phi1) (* phi2 (/ R phi2)))
   (if (<= phi2 8.6e+48)
     (* (- phi1) (* R (- 1.0 (/ phi2 phi1))))
     (fma -1.0 (* R phi1) (* R phi2)))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 3.8e-200) {
		tmp = -phi1 * (phi2 * (R / phi2));
	} else if (phi2 <= 8.6e+48) {
		tmp = -phi1 * (R * (1.0 - (phi2 / phi1)));
	} else {
		tmp = fma(-1.0, (R * phi1), (R * phi2));
	}
	return tmp;
}

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi2 <= 3.8e-200)
		tmp = Float64(Float64(-phi1) * Float64(phi2 * Float64(R / phi2)));
	elseif (phi2 <= 8.6e+48)
		tmp = Float64(Float64(-phi1) * Float64(R * Float64(1.0 - Float64(phi2 / phi1))));
	else
		tmp = fma(-1.0, Float64(R * phi1), Float64(R * phi2));
	end
	return tmp
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi2, 3.8e-200], N[((-phi1) * N[(phi2 * N[(R / phi2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[phi2, 8.6e+48], N[((-phi1) * N[(R * N[(1.0 - N[(phi2 / phi1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(-1.0 * N[(R * phi1), $MachinePrecision] + N[(R * phi2), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if phi2 < 3.8e-200

   1. Initial program 64.3%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 16.9

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 16.9%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in phi2 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \color{blue}{\frac{R}{\phi_1}}\right)\right) \]

      2. lower--.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\color{blue}{\phi_1}}\right)\right) \]

      3. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

      4. lower-/.f64 18.7

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

   8. Applied rewrites 18.7%

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]

   9. Taylor expanded in phi1 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]
   10. Step-by-step derivation

       1. lift-/.f64 24.1

          \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   11. Applied rewrites 24.1%

       \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   if 3.8e-200 < phi2 < 8.59999999999999957e48

   1. Initial program 72.3%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 27.3

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 27.3%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in R around 0

      \[\leadsto \left(-\phi_1\right) \cdot \left(R \cdot \color{blue}{\left(1 - \frac{\phi_2}{\phi_1}\right)}\right) \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(R \cdot \left(1 - \color{blue}{\frac{\phi_2}{\phi_1}}\right)\right) \]

      2. lower--.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(R \cdot \left(1 - \frac{\phi_2}{\color{blue}{\phi_1}}\right)\right) \]

      3. lower-/.f64 27.3

         \[\leadsto \left(-\phi_1\right) \cdot \left(R \cdot \left(1 - \frac{\phi_2}{\phi_1}\right)\right) \]

   8. Applied rewrites 27.3%

      \[\leadsto \left(-\phi_1\right) \cdot \left(R \cdot \color{blue}{\left(1 - \frac{\phi_2}{\phi_1}\right)}\right) \]

   if 8.59999999999999957e48 < phi2

   1. Initial program 58.0%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 66.3

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 66.3%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in phi1 around 0

      \[\leadsto -1 \cdot \left(R \cdot \phi_1\right) + \color{blue}{R \cdot \phi_2} \]
   7. Step-by-step derivation

      1. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \color{blue}{\phi_1}, R \cdot \phi_2\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right) \]

      3. lower-*.f64 74.3

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right) \]

   8. Applied rewrites 74.3%

      \[\leadsto \mathsf{fma}\left(-1, \color{blue}{R \cdot \phi_1}, R \cdot \phi_2\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 10: 30.2% accurate, 6.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_2 \leq 2 \cdot 10^{-49}:\\ \;\;\;\;\left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi2 2e-49)
   (* (- phi1) (* phi2 (/ R phi2)))
   (fma -1.0 (* R phi1) (* R phi2))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 2e-49) {
		tmp = -phi1 * (phi2 * (R / phi2));
	} else {
		tmp = fma(-1.0, (R * phi1), (R * phi2));
	}
	return tmp;
}

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi2 <= 2e-49)
		tmp = Float64(Float64(-phi1) * Float64(phi2 * Float64(R / phi2)));
	else
		tmp = fma(-1.0, Float64(R * phi1), Float64(R * phi2));
	end
	return tmp
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi2, 2e-49], N[((-phi1) * N[(phi2 * N[(R / phi2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(-1.0 * N[(R * phi1), $MachinePrecision] + N[(R * phi2), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if phi2 < 1.99999999999999987e-49

   1. Initial program 64.4%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 17.8

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 17.8%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in phi2 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \color{blue}{\frac{R}{\phi_1}}\right)\right) \]

      2. lower--.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\color{blue}{\phi_1}}\right)\right) \]

      3. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

      4. lower-/.f64 20.9

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

   8. Applied rewrites 20.9%

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]

   9. Taylor expanded in phi1 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]
   10. Step-by-step derivation

       1. lift-/.f64 23.8

          \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   11. Applied rewrites 23.8%

       \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   if 1.99999999999999987e-49 < phi2

   1. Initial program 64.4%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 58.3

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 58.3%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in phi1 around 0

      \[\leadsto -1 \cdot \left(R \cdot \phi_1\right) + \color{blue}{R \cdot \phi_2} \]
   7. Step-by-step derivation

      1. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \color{blue}{\phi_1}, R \cdot \phi_2\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right) \]

      3. lower-*.f64 61.7

         \[\leadsto \mathsf{fma}\left(-1, R \cdot \phi_1, R \cdot \phi_2\right) \]

   8. Applied rewrites 61.7%

      \[\leadsto \mathsf{fma}\left(-1, \color{blue}{R \cdot \phi_1}, R \cdot \phi_2\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 29.5% accurate, 6.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_1 \leq -4.2 \cdot 10^{-24}:\\ \;\;\;\;R \cdot \left(-\phi_1\right)\\ \mathbf{elif}\;\phi_1 \leq -4.4 \cdot 10^{-113}:\\ \;\;\;\;R \cdot \sqrt{\lambda_1 \cdot \lambda_1}\\ \mathbf{else}:\\ \;\;\;\;R \cdot \phi_2\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi1 -4.2e-24)
   (* R (- phi1))
   (if (<= phi1 -4.4e-113) (* R (sqrt (* lambda1 lambda1))) (* R phi2))))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi1 <= -4.2e-24) {
		tmp = R * -phi1;
	} else if (phi1 <= -4.4e-113) {
		tmp = R * sqrt((lambda1 * lambda1));
	} else {
		tmp = R * phi2;
	}
	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(r, lambda1, lambda2, phi1, phi2)
use fmin_fmax_functions
    real(8), intent (in) :: r
    real(8), intent (in) :: lambda1
    real(8), intent (in) :: lambda2
    real(8), intent (in) :: phi1
    real(8), intent (in) :: phi2
    real(8) :: tmp
    if (phi1 <= (-4.2d-24)) then
        tmp = r * -phi1
    else if (phi1 <= (-4.4d-113)) then
        tmp = r * sqrt((lambda1 * lambda1))
    else
        tmp = r * phi2
    end if
    code = tmp
end function

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi1 <= -4.2e-24) {
		tmp = R * -phi1;
	} else if (phi1 <= -4.4e-113) {
		tmp = R * Math.sqrt((lambda1 * lambda1));
	} else {
		tmp = R * phi2;
	}
	return tmp;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	tmp = 0
	if phi1 <= -4.2e-24:
		tmp = R * -phi1
	elif phi1 <= -4.4e-113:
		tmp = R * math.sqrt((lambda1 * lambda1))
	else:
		tmp = R * phi2
	return tmp

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi1 <= -4.2e-24)
		tmp = Float64(R * Float64(-phi1));
	elseif (phi1 <= -4.4e-113)
		tmp = Float64(R * sqrt(Float64(lambda1 * lambda1)));
	else
		tmp = Float64(R * phi2);
	end
	return tmp
end

MATLAB

function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0;
	if (phi1 <= -4.2e-24)
		tmp = R * -phi1;
	elseif (phi1 <= -4.4e-113)
		tmp = R * sqrt((lambda1 * lambda1));
	else
		tmp = R * phi2;
	end
	tmp_2 = tmp;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi1, -4.2e-24], N[(R * (-phi1)), $MachinePrecision], If[LessEqual[phi1, -4.4e-113], N[(R * N[Sqrt[N[(lambda1 * lambda1), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(R * phi2), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if phi1 < -4.1999999999999999e-24

   1. Initial program 55.7%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto R \cdot \color{blue}{\left(-1 \cdot \phi_1\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto R \cdot \left(\mathsf{neg}\left(\phi_1\right)\right) \]

      2. lower-neg.f64 58.8

         \[\leadsto R \cdot \left(-\phi_1\right) \]

   5. Applied rewrites 58.8%

      \[\leadsto R \cdot \color{blue}{\left(-\phi_1\right)} \]

   if -4.1999999999999999e-24 < phi1 < -4.40000000000000008e-113

   1. Initial program 82.6%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around 0

      \[\leadsto R \cdot \sqrt{\color{blue}{{\cos \left(\frac{1}{2} \cdot \phi_1\right)}^{2} \cdot {\left(\lambda_1 - \lambda_2\right)}^{2} + {\phi_1}^{2}}} \]
   4. Step-by-step derivation

      1. lower-fma.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left({\cos \left(\frac{1}{2} \cdot \phi_1\right)}^{2}, \color{blue}{{\left(\lambda_1 - \lambda_2\right)}^{2}}, {\phi_1}^{2}\right)} \]

      2. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\cos \left(\frac{1}{2} \cdot \phi_1\right) \cdot \cos \left(\frac{1}{2} \cdot \phi_1\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      3. sqr-cos-a N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      4. lower-+.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\color{blue}{\left(\lambda_1 - \lambda_2\right)}}^{2}, {\phi_1}^{2}\right)} \]

      5. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \color{blue}{\lambda_2}\right)}^{2}, {\phi_1}^{2}\right)} \]

      6. lower-cos.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      8. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), {\left(\lambda_1 - \lambda_2\right)}^{2}, {\phi_1}^{2}\right)} \]

      9. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}, {\phi_1}^{2}\right)} \]

      10. lower-*.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \color{blue}{\left(\lambda_1 - \lambda_2\right)}, {\phi_1}^{2}\right)} \]

      11. lift--.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\color{blue}{\lambda_1} - \lambda_2\right), {\phi_1}^{2}\right)} \]

      12. lift--.f64 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \color{blue}{\lambda_2}\right), {\phi_1}^{2}\right)} \]

      13. unpow2 N/A

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \left(2 \cdot \left(\frac{1}{2} \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

      14. lower-*.f64 69.9

          \[\leadsto R \cdot \sqrt{\mathsf{fma}\left(0.5 + 0.5 \cdot \cos \left(2 \cdot \left(0.5 \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)} \]

   5. Applied rewrites 69.9%

      \[\leadsto R \cdot \sqrt{\color{blue}{\mathsf{fma}\left(0.5 + 0.5 \cdot \cos \left(2 \cdot \left(0.5 \cdot \phi_1\right)\right), \left(\lambda_1 - \lambda_2\right) \cdot \left(\lambda_1 - \lambda_2\right), \phi_1 \cdot \phi_1\right)}} \]

   6. Taylor expanded in lambda1 around inf

      \[\leadsto R \cdot \sqrt{{\lambda_1}^{2} \cdot \color{blue}{\left(\frac{1}{2} + \frac{1}{2} \cdot \cos \phi_1\right)}} \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{{\lambda_1}^{2} \cdot \left(\frac{1}{2} + \color{blue}{\frac{1}{2} \cdot \cos \phi_1}\right)} \]

      2. unpow2 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 \cdot \lambda_1\right) \cdot \left(\frac{1}{2} + \color{blue}{\frac{1}{2}} \cdot \cos \phi_1\right)} \]

      3. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 \cdot \lambda_1\right) \cdot \left(\frac{1}{2} + \color{blue}{\frac{1}{2}} \cdot \cos \phi_1\right)} \]

      4. fp-cancel-sign-sub-inv N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 \cdot \lambda_1\right) \cdot \left(\frac{1}{2} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right) \cdot \color{blue}{\cos \phi_1}\right)} \]

      5. lower--.f64 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 \cdot \lambda_1\right) \cdot \left(\frac{1}{2} - \left(\mathsf{neg}\left(\frac{1}{2}\right)\right) \cdot \color{blue}{\cos \phi_1}\right)} \]

      6. metadata-eval N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 \cdot \lambda_1\right) \cdot \left(\frac{1}{2} - \frac{-1}{2} \cdot \cos \phi_1\right)} \]

      7. lower-*.f64 N/A

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 \cdot \lambda_1\right) \cdot \left(\frac{1}{2} - \frac{-1}{2} \cdot \cos \phi_1\right)} \]

      8. lower-cos.f64 33.6

         \[\leadsto R \cdot \sqrt{\left(\lambda_1 \cdot \lambda_1\right) \cdot \left(0.5 - -0.5 \cdot \cos \phi_1\right)} \]

   8. Applied rewrites 33.6%

      \[\leadsto R \cdot \sqrt{\left(\lambda_1 \cdot \lambda_1\right) \cdot \color{blue}{\left(0.5 - -0.5 \cdot \cos \phi_1\right)}} \]

   9. Taylor expanded in phi1 around 0

      \[\leadsto R \cdot \sqrt{{\lambda_1}^{2}} \]
   10. Step-by-step derivation

       1. pow2 N/A

          \[\leadsto R \cdot \sqrt{\lambda_1 \cdot \lambda_1} \]

       2. lift-*.f64 33.6

          \[\leadsto R \cdot \sqrt{\lambda_1 \cdot \lambda_1} \]

   11. Applied rewrites 33.6%

       \[\leadsto R \cdot \sqrt{\lambda_1 \cdot \lambda_1} \]

   if -4.40000000000000008e-113 < phi1

   1. Initial program 65.9%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around inf

      \[\leadsto R \cdot \color{blue}{\phi_2} \]
   4. Step-by-step derivation

   5. Applied rewrites 19.0%

      \[\leadsto R \cdot \color{blue}{\phi_2} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 12: 28.5% accurate, 7.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_2 \leq 5.6 \cdot 10^{-7}:\\ \;\;\;\;\left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right)\\ \mathbf{else}:\\ \;\;\;\;R \cdot \phi_2\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi2 5.6e-7) (* (- phi1) (* phi2 (/ R phi2))) (* R phi2)))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 5.6e-7) {
		tmp = -phi1 * (phi2 * (R / phi2));
	} else {
		tmp = R * phi2;
	}
	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(r, lambda1, lambda2, phi1, phi2)
use fmin_fmax_functions
    real(8), intent (in) :: r
    real(8), intent (in) :: lambda1
    real(8), intent (in) :: lambda2
    real(8), intent (in) :: phi1
    real(8), intent (in) :: phi2
    real(8) :: tmp
    if (phi2 <= 5.6d-7) then
        tmp = -phi1 * (phi2 * (r / phi2))
    else
        tmp = r * phi2
    end if
    code = tmp
end function

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi2 <= 5.6e-7) {
		tmp = -phi1 * (phi2 * (R / phi2));
	} else {
		tmp = R * phi2;
	}
	return tmp;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	tmp = 0
	if phi2 <= 5.6e-7:
		tmp = -phi1 * (phi2 * (R / phi2))
	else:
		tmp = R * phi2
	return tmp

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi2 <= 5.6e-7)
		tmp = Float64(Float64(-phi1) * Float64(phi2 * Float64(R / phi2)));
	else
		tmp = Float64(R * phi2);
	end
	return tmp
end

MATLAB

function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0;
	if (phi2 <= 5.6e-7)
		tmp = -phi1 * (phi2 * (R / phi2));
	else
		tmp = R * phi2;
	end
	tmp_2 = tmp;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi2, 5.6e-7], N[((-phi1) * N[(phi2 * N[(R / phi2), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(R * phi2), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if phi2 < 5.60000000000000038e-7

   1. Initial program 64.7%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(\phi_1 \cdot \left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)\right)} \]
   4. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \left(-1 \cdot \phi_1\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      2. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(\phi_1\right)\right) \cdot \color{blue}{\left(R + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right)} \]

      4. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\color{blue}{R} + -1 \cdot \frac{R \cdot \phi_2}{\phi_1}\right) \]

      5. +-commutative N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      6. lower-+.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(-1 \cdot \frac{R \cdot \phi_2}{\phi_1} + \color{blue}{R}\right) \]

      7. mul-1-neg N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(\mathsf{neg}\left(\frac{R \cdot \phi_2}{\phi_1}\right)\right) + R\right) \]

      8. lower-neg.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      9. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{R \cdot \phi_2}{\phi_1}\right) + R\right) \]

      10. *-commutative N/A

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

      11. lower-*.f64 18.3

          \[\leadsto \left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right) \]

   5. Applied rewrites 18.3%

      \[\leadsto \color{blue}{\left(-\phi_1\right) \cdot \left(\left(-\frac{\phi_2 \cdot R}{\phi_1}\right) + R\right)} \]

   6. Taylor expanded in phi2 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]
   7. Step-by-step derivation

      1. lower-*.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \color{blue}{\frac{R}{\phi_1}}\right)\right) \]

      2. lower--.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\color{blue}{\phi_1}}\right)\right) \]

      3. lower-/.f64 N/A

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

      4. lower-/.f64 20.7

         \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)\right) \]

   8. Applied rewrites 20.7%

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \color{blue}{\left(\frac{R}{\phi_2} - \frac{R}{\phi_1}\right)}\right) \]

   9. Taylor expanded in phi1 around inf

      \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]
   10. Step-by-step derivation

       1. lift-/.f64 23.0

          \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   11. Applied rewrites 23.0%

       \[\leadsto \left(-\phi_1\right) \cdot \left(\phi_2 \cdot \frac{R}{\phi_2}\right) \]

   if 5.60000000000000038e-7 < phi2

   1. Initial program 63.6%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around inf

      \[\leadsto R \cdot \color{blue}{\phi_2} \]
   4. Step-by-step derivation

   5. Applied rewrites 56.5%

      \[\leadsto R \cdot \color{blue}{\phi_2} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 13: 27.7% accurate, 12.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;\phi_1 \leq -2.3 \cdot 10^{-112}:\\ \;\;\;\;R \cdot \left(-\phi_1\right)\\ \mathbf{else}:\\ \;\;\;\;R \cdot \phi_2\\ \end{array} \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2)
 :precision binary64
 (if (<= phi1 -2.3e-112) (* R (- phi1)) (* R phi2)))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi1 <= -2.3e-112) {
		tmp = R * -phi1;
	} else {
		tmp = R * phi2;
	}
	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(r, lambda1, lambda2, phi1, phi2)
use fmin_fmax_functions
    real(8), intent (in) :: r
    real(8), intent (in) :: lambda1
    real(8), intent (in) :: lambda2
    real(8), intent (in) :: phi1
    real(8), intent (in) :: phi2
    real(8) :: tmp
    if (phi1 <= (-2.3d-112)) then
        tmp = r * -phi1
    else
        tmp = r * phi2
    end if
    code = tmp
end function

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	double tmp;
	if (phi1 <= -2.3e-112) {
		tmp = R * -phi1;
	} else {
		tmp = R * phi2;
	}
	return tmp;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	tmp = 0
	if phi1 <= -2.3e-112:
		tmp = R * -phi1
	else:
		tmp = R * phi2
	return tmp

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0
	if (phi1 <= -2.3e-112)
		tmp = Float64(R * Float64(-phi1));
	else
		tmp = Float64(R * phi2);
	end
	return tmp
end

MATLAB

function tmp_2 = code(R, lambda1, lambda2, phi1, phi2)
	tmp = 0.0;
	if (phi1 <= -2.3e-112)
		tmp = R * -phi1;
	else
		tmp = R * phi2;
	end
	tmp_2 = tmp;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := If[LessEqual[phi1, -2.3e-112], N[(R * (-phi1)), $MachinePrecision], N[(R * phi2), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if phi1 < -2.29999999999999991e-112

   1. Initial program 61.9%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi1 around -inf

      \[\leadsto R \cdot \color{blue}{\left(-1 \cdot \phi_1\right)} \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto R \cdot \left(\mathsf{neg}\left(\phi_1\right)\right) \]

      2. lower-neg.f64 50.3

         \[\leadsto R \cdot \left(-\phi_1\right) \]

   5. Applied rewrites 50.3%

      \[\leadsto R \cdot \color{blue}{\left(-\phi_1\right)} \]

   if -2.29999999999999991e-112 < phi1

   1. Initial program 65.9%

      \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
   2. Add Preprocessing

   3. Taylor expanded in phi2 around inf

      \[\leadsto R \cdot \color{blue}{\phi_2} \]
   4. Step-by-step derivation

   5. Applied rewrites 19.0%

      \[\leadsto R \cdot \color{blue}{\phi_2} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 14: 17.5% accurate, 27.0× speedup

Math

\[\begin{array}{l} \\ R \cdot \phi_2 \end{array} \]

FPCore

(FPCore (R lambda1 lambda2 phi1 phi2) :precision binary64 (* R phi2))

C

double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	return R * phi2;
}

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(r, lambda1, lambda2, phi1, phi2)
use fmin_fmax_functions
    real(8), intent (in) :: r
    real(8), intent (in) :: lambda1
    real(8), intent (in) :: lambda2
    real(8), intent (in) :: phi1
    real(8), intent (in) :: phi2
    code = r * phi2
end function

Java

public static double code(double R, double lambda1, double lambda2, double phi1, double phi2) {
	return R * phi2;
}

Python

def code(R, lambda1, lambda2, phi1, phi2):
	return R * phi2

Julia

function code(R, lambda1, lambda2, phi1, phi2)
	return Float64(R * phi2)
end

MATLAB

function tmp = code(R, lambda1, lambda2, phi1, phi2)
	tmp = R * phi2;
end

Wolfram

code[R_, lambda1_, lambda2_, phi1_, phi2_] := N[(R * phi2), $MachinePrecision]

Derivation

1. Initial program 64.4%

   \[R \cdot \sqrt{\left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) \cdot \left(\left(\lambda_1 - \lambda_2\right) \cdot \cos \left(\frac{\phi_1 + \phi_2}{2}\right)\right) + \left(\phi_1 - \phi_2\right) \cdot \left(\phi_1 - \phi_2\right)} \]
2. Add Preprocessing

3. Taylor expanded in phi2 around inf

   \[\leadsto R \cdot \color{blue}{\phi_2} \]
4. Step-by-step derivation

5. Applied rewrites 17.5%

   \[\leadsto R \cdot \color{blue}{\phi_2} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2025064 
(FPCore (R lambda1 lambda2 phi1 phi2)
  :name "Equirectangular approximation to distance on a great circle"
  :precision binary64
  (* R (sqrt (+ (* (* (- lambda1 lambda2) (cos (/ (+ phi1 phi2) 2.0))) (* (- lambda1 lambda2) (cos (/ (+ phi1 phi2) 2.0)))) (* (- phi1 phi2) (- phi1 phi2))))))