1/2(abs(p)+abs(r) + sqrt((p-r)^2 + 4q^2))

Percentage Accurate: 45.6% → 81.7%

Time: 3.6s

Alternatives: 7

Speedup: 35.6×

Specification

Math

\[\begin{array}{l} \\ \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (*
  (/ 1.0 2.0)
  (+ (+ (fabs p) (fabs r)) (sqrt (+ (pow (- p r) 2.0) (* 4.0 (pow q 2.0)))))))

C

double code(double p, double r, double q) {
	return (1.0 / 2.0) * ((fabs(p) + fabs(r)) + sqrt((pow((p - r), 2.0) + (4.0 * pow(q, 2.0)))));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    code = (1.0d0 / 2.0d0) * ((abs(p) + abs(r)) + sqrt((((p - r) ** 2.0d0) + (4.0d0 * (q ** 2.0d0)))))
end function

Java

public static double code(double p, double r, double q) {
	return (1.0 / 2.0) * ((Math.abs(p) + Math.abs(r)) + Math.sqrt((Math.pow((p - r), 2.0) + (4.0 * Math.pow(q, 2.0)))));
}

Python

def code(p, r, q):
	return (1.0 / 2.0) * ((math.fabs(p) + math.fabs(r)) + math.sqrt((math.pow((p - r), 2.0) + (4.0 * math.pow(q, 2.0)))))

Julia

function code(p, r, q)
	return Float64(Float64(1.0 / 2.0) * Float64(Float64(abs(p) + abs(r)) + sqrt(Float64((Float64(p - r) ^ 2.0) + Float64(4.0 * (q ^ 2.0))))))
end

MATLAB

function tmp = code(p, r, q)
	tmp = (1.0 / 2.0) * ((abs(p) + abs(r)) + sqrt((((p - r) ^ 2.0) + (4.0 * (q ^ 2.0)))));
end

Wolfram

code[p_, r_, q_] := N[(N[(1.0 / 2.0), $MachinePrecision] * N[(N[(N[Abs[p], $MachinePrecision] + N[Abs[r], $MachinePrecision]), $MachinePrecision] + N[Sqrt[N[(N[Power[N[(p - r), $MachinePrecision], 2.0], $MachinePrecision] + N[(4.0 * N[Power[q, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 45.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (*
  (/ 1.0 2.0)
  (+ (+ (fabs p) (fabs r)) (sqrt (+ (pow (- p r) 2.0) (* 4.0 (pow q 2.0)))))))

C

double code(double p, double r, double q) {
	return (1.0 / 2.0) * ((fabs(p) + fabs(r)) + sqrt((pow((p - r), 2.0) + (4.0 * pow(q, 2.0)))));
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    code = (1.0d0 / 2.0d0) * ((abs(p) + abs(r)) + sqrt((((p - r) ** 2.0d0) + (4.0d0 * (q ** 2.0d0)))))
end function

Java

public static double code(double p, double r, double q) {
	return (1.0 / 2.0) * ((Math.abs(p) + Math.abs(r)) + Math.sqrt((Math.pow((p - r), 2.0) + (4.0 * Math.pow(q, 2.0)))));
}

Python

def code(p, r, q):
	return (1.0 / 2.0) * ((math.fabs(p) + math.fabs(r)) + math.sqrt((math.pow((p - r), 2.0) + (4.0 * math.pow(q, 2.0)))))

Julia

function code(p, r, q)
	return Float64(Float64(1.0 / 2.0) * Float64(Float64(abs(p) + abs(r)) + sqrt(Float64((Float64(p - r) ^ 2.0) + Float64(4.0 * (q ^ 2.0))))))
end

MATLAB

function tmp = code(p, r, q)
	tmp = (1.0 / 2.0) * ((abs(p) + abs(r)) + sqrt((((p - r) ^ 2.0) + (4.0 * (q ^ 2.0)))));
end

Wolfram

code[p_, r_, q_] := N[(N[(1.0 / 2.0), $MachinePrecision] * N[(N[(N[Abs[p], $MachinePrecision] + N[Abs[r], $MachinePrecision]), $MachinePrecision] + N[Sqrt[N[(N[Power[N[(p - r), $MachinePrecision], 2.0], $MachinePrecision] + N[(4.0 * N[Power[q, 2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 81.7% accurate, 9.3× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ [p, r, q_m] = \mathsf{sort}([p, r, q_m])\\ \\ \begin{array}{l} t_0 := \left|r\right| + \left|p\right|\\ \mathbf{if}\;q\_m \leq 2.2 \cdot 10^{+58}:\\ \;\;\;\;\left(t\_0 + \left(\left(-p\right) + r\right)\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\left(q\_m \cdot 2 + t\_0\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
(FPCore (p r q_m)
 :precision binary64
 (let* ((t_0 (+ (fabs r) (fabs p))))
   (if (<= q_m 2.2e+58)
     (* (+ t_0 (+ (- p) r)) 0.5)
     (* (+ (* q_m 2.0) t_0) 0.5))))

C

q_m = fabs(q);
assert(p < r && r < q_m);
double code(double p, double r, double q_m) {
	double t_0 = fabs(r) + fabs(p);
	double tmp;
	if (q_m <= 2.2e+58) {
		tmp = (t_0 + (-p + r)) * 0.5;
	} else {
		tmp = ((q_m * 2.0) + t_0) * 0.5;
	}
	return tmp;
}

Fortran

q_m =     private
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
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(p, r, q_m)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q_m
    real(8) :: t_0
    real(8) :: tmp
    t_0 = abs(r) + abs(p)
    if (q_m <= 2.2d+58) then
        tmp = (t_0 + (-p + r)) * 0.5d0
    else
        tmp = ((q_m * 2.0d0) + t_0) * 0.5d0
    end if
    code = tmp
end function

Java

q_m = Math.abs(q);
assert p < r && r < q_m;
public static double code(double p, double r, double q_m) {
	double t_0 = Math.abs(r) + Math.abs(p);
	double tmp;
	if (q_m <= 2.2e+58) {
		tmp = (t_0 + (-p + r)) * 0.5;
	} else {
		tmp = ((q_m * 2.0) + t_0) * 0.5;
	}
	return tmp;
}

Python

q_m = math.fabs(q)
[p, r, q_m] = sort([p, r, q_m])
def code(p, r, q_m):
	t_0 = math.fabs(r) + math.fabs(p)
	tmp = 0
	if q_m <= 2.2e+58:
		tmp = (t_0 + (-p + r)) * 0.5
	else:
		tmp = ((q_m * 2.0) + t_0) * 0.5
	return tmp

Julia

q_m = abs(q)
p, r, q_m = sort([p, r, q_m])
function code(p, r, q_m)
	t_0 = Float64(abs(r) + abs(p))
	tmp = 0.0
	if (q_m <= 2.2e+58)
		tmp = Float64(Float64(t_0 + Float64(Float64(-p) + r)) * 0.5);
	else
		tmp = Float64(Float64(Float64(q_m * 2.0) + t_0) * 0.5);
	end
	return tmp
end

MATLAB

q_m = abs(q);
p, r, q_m = num2cell(sort([p, r, q_m])){:}
function tmp_2 = code(p, r, q_m)
	t_0 = abs(r) + abs(p);
	tmp = 0.0;
	if (q_m <= 2.2e+58)
		tmp = (t_0 + (-p + r)) * 0.5;
	else
		tmp = ((q_m * 2.0) + t_0) * 0.5;
	end
	tmp_2 = tmp;
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
code[p_, r_, q$95$m_] := Block[{t$95$0 = N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[q$95$m, 2.2e+58], N[(N[(t$95$0 + N[((-p) + r), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(N[(q$95$m * 2.0), $MachinePrecision] + t$95$0), $MachinePrecision] * 0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if q < 2.2000000000000001e58

   1. Initial program 57.8%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in r around inf

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{r \cdot \left(1 + -1 \cdot \frac{p}{r}\right)}\right) \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(1 + -1 \cdot \frac{p}{r}\right) \cdot \color{blue}{r}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(1 + -1 \cdot \frac{p}{r}\right) \cdot \color{blue}{r}\right) \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-1 \cdot \frac{p}{r} + 1\right) \cdot r\right) \]

      4. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(\frac{p}{r} \cdot -1 + 1\right) \cdot r\right) \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(\frac{p}{r}, -1, 1\right) \cdot r\right) \]

      6. lower-/.f64 77.2

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(\frac{p}{r}, -1, 1\right) \cdot r\right) \]

   5. Applied rewrites 77.2%

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{\mathsf{fma}\left(\frac{p}{r}, -1, 1\right) \cdot r}\right) \]

   6. Taylor expanded in p around 0

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(r + \color{blue}{-1 \cdot p}\right)\right) \]
   7. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(r + \left(\mathsf{neg}\left(p\right)\right)\right)\right) \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(\left(\mathsf{neg}\left(p\right)\right) + r\right)\right) \]

      3. mul-1-neg N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-1 \cdot p + r\right)\right) \]

      4. lower-fma.f64 88.9

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \]

   8. Applied rewrites 88.9%

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, \color{blue}{p}, r\right)\right) \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right)} \]

      2. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2}} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2}} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2}} \]

      5. lift-+.f64 N/A

         \[\leadsto \left(\color{blue}{\left(\left|p\right| + \left|r\right|\right)} + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      6. lift-fabs.f64 N/A

         \[\leadsto \left(\left(\color{blue}{\left|p\right|} + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      7. lift-fabs.f64 N/A

         \[\leadsto \left(\left(\left|p\right| + \color{blue}{\left|r\right|}\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      8. +-commutative N/A

         \[\leadsto \left(\color{blue}{\left(\left|r\right| + \left|p\right|\right)} + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      9. lower-+.f64 N/A

         \[\leadsto \left(\color{blue}{\left(\left|r\right| + \left|p\right|\right)} + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      10. lift-fabs.f64 N/A

          \[\leadsto \left(\left(\color{blue}{\left|r\right|} + \left|p\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      11. lift-fabs.f64 N/A

          \[\leadsto \left(\left(\left|r\right| + \color{blue}{\left|p\right|}\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      12. metadata-eval 88.9

          \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \color{blue}{0.5} \]

   10. Applied rewrites 88.9%

       \[\leadsto \color{blue}{\left(\left(\left|r\right| + \left|p\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot 0.5} \]
   11. Step-by-step derivation

       1. lift-fma.f64 N/A

          \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + \left(-1 \cdot p + r\right)\right) \cdot \frac{1}{2} \]

       2. mul-1-neg N/A

          \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + \left(\left(\mathsf{neg}\left(p\right)\right) + r\right)\right) \cdot \frac{1}{2} \]

       3. lift-neg.f64 N/A

          \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + \left(\left(-p\right) + r\right)\right) \cdot \frac{1}{2} \]

       4. lower-+.f64 88.9

          \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + \left(\left(-p\right) + r\right)\right) \cdot 0.5 \]

   12. Applied rewrites 88.9%

       \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + \left(\left(-p\right) + r\right)\right) \cdot 0.5 \]

   if 2.2000000000000001e58 < q

   1. Initial program 27.6%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in q around inf

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{2 \cdot q}\right) \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot \color{blue}{2}\right) \]

      2. lower-*.f64 71.0

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot \color{blue}{2}\right) \]

   5. Applied rewrites 71.0%

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{q \cdot 2}\right) \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right)} \]

      2. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2}} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right) \]

      3. metadata-eval N/A

         \[\leadsto \color{blue}{\frac{1}{2}} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right) \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right) \cdot \frac{1}{2}} \]

      5. lower-*.f64 71.0

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right) \cdot 0.5} \]

   7. Applied rewrites 71.0%

      \[\leadsto \color{blue}{\left(q \cdot 2 + \left(\left|r\right| + \left|p\right|\right)\right) \cdot 0.5} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 65.1% accurate, 7.6× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ [p, r, q_m] = \mathsf{sort}([p, r, q_m])\\ \\ \begin{array}{l} t_0 := \left|r\right| + \left|p\right|\\ \mathbf{if}\;r \leq -2.05 \cdot 10^{-212}:\\ \;\;\;\;\left(\left(-p\right) + t\_0\right) \cdot 0.5\\ \mathbf{elif}\;r \leq 1.15 \cdot 10^{+62}:\\ \;\;\;\;\left(q\_m \cdot 2 + t\_0\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(q\_m, \frac{q\_m}{r}, r\right)\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
(FPCore (p r q_m)
 :precision binary64
 (let* ((t_0 (+ (fabs r) (fabs p))))
   (if (<= r -2.05e-212)
     (* (+ (- p) t_0) 0.5)
     (if (<= r 1.15e+62) (* (+ (* q_m 2.0) t_0) 0.5) (fma q_m (/ q_m r) r)))))

C

q_m = fabs(q);
assert(p < r && r < q_m);
double code(double p, double r, double q_m) {
	double t_0 = fabs(r) + fabs(p);
	double tmp;
	if (r <= -2.05e-212) {
		tmp = (-p + t_0) * 0.5;
	} else if (r <= 1.15e+62) {
		tmp = ((q_m * 2.0) + t_0) * 0.5;
	} else {
		tmp = fma(q_m, (q_m / r), r);
	}
	return tmp;
}

Julia

q_m = abs(q)
p, r, q_m = sort([p, r, q_m])
function code(p, r, q_m)
	t_0 = Float64(abs(r) + abs(p))
	tmp = 0.0
	if (r <= -2.05e-212)
		tmp = Float64(Float64(Float64(-p) + t_0) * 0.5);
	elseif (r <= 1.15e+62)
		tmp = Float64(Float64(Float64(q_m * 2.0) + t_0) * 0.5);
	else
		tmp = fma(q_m, Float64(q_m / r), r);
	end
	return tmp
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
code[p_, r_, q$95$m_] := Block[{t$95$0 = N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[r, -2.05e-212], N[(N[((-p) + t$95$0), $MachinePrecision] * 0.5), $MachinePrecision], If[LessEqual[r, 1.15e+62], N[(N[(N[(q$95$m * 2.0), $MachinePrecision] + t$95$0), $MachinePrecision] * 0.5), $MachinePrecision], N[(q$95$m * N[(q$95$m / r), $MachinePrecision] + r), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if r < -2.05000000000000007e-212

   1. Initial program 42.7%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in p around -inf

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{-1 \cdot p}\right) \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(\mathsf{neg}\left(p\right)\right)\right) \]

      2. lower-neg.f64 72.4

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right) \]

   5. Applied rewrites 72.4%

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{\left(-p\right)}\right) \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right)} \]

      2. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2}} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right) \cdot \frac{1}{2}} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right) \cdot \frac{1}{2}} \]

   7. Applied rewrites 72.4%

      \[\leadsto \color{blue}{\left(\left(-p\right) + \left(\left|r\right| + \left|p\right|\right)\right) \cdot 0.5} \]

   if -2.05000000000000007e-212 < r < 1.14999999999999992e62

   1. Initial program 59.7%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in q around inf

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{2 \cdot q}\right) \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot \color{blue}{2}\right) \]

      2. lower-*.f64 56.2

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot \color{blue}{2}\right) \]

   5. Applied rewrites 56.2%

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{q \cdot 2}\right) \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right)} \]

      2. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2}} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right) \]

      3. metadata-eval N/A

         \[\leadsto \color{blue}{\frac{1}{2}} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right) \]

      4. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right) \cdot \frac{1}{2}} \]

      5. lower-*.f64 56.2

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + q \cdot 2\right) \cdot 0.5} \]

   7. Applied rewrites 56.2%

      \[\leadsto \color{blue}{\left(q \cdot 2 + \left(\left|r\right| + \left|p\right|\right)\right) \cdot 0.5} \]

   if 1.14999999999999992e62 < r

   1. Initial program 28.5%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in p around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(\left|p\right| + \left(\left|r\right| + \sqrt{4 \cdot {q}^{2} + {r}^{2}}\right)\right)} \]
   4. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \frac{1}{2} \cdot \left(\color{blue}{\left|p\right|} + \left(\left|r\right| + \sqrt{4 \cdot {q}^{2} + {r}^{2}}\right)\right) \]

      2. *-commutative N/A

         \[\leadsto \left(\left|p\right| + \left(\left|r\right| + \sqrt{4 \cdot {q}^{2} + {r}^{2}}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\left|p\right| + \left(\left|r\right| + \sqrt{4 \cdot {q}^{2} + {r}^{2}}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

   5. Applied rewrites 26.9%

      \[\leadsto \color{blue}{\left(\left(\sqrt{\mathsf{fma}\left(q \cdot q, 4, r \cdot r\right)} + r\right) + p\right) \cdot 0.5} \]

   6. Taylor expanded in q around 0

      \[\leadsto \frac{1}{2} \cdot \left(p + 2 \cdot r\right) + \color{blue}{\frac{{q}^{2}}{r}} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \frac{1}{2} \cdot \left(p + 2 \cdot r\right) + \frac{{q}^{2}}{r} \]

      2. *-commutative N/A

         \[\leadsto \left(p + 2 \cdot r\right) \cdot \frac{1}{2} + \frac{{q}^{2}}{r} \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(p + 2 \cdot r, \frac{1}{\color{blue}{2}}, \frac{{q}^{2}}{r}\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(2 \cdot r + p, \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(r \cdot 2 + p, \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      6. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      7. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      9. pow2 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \frac{1}{2}, \frac{q \cdot q}{r}\right) \]

      10. lift-*.f64 64.6

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), 0.5, \frac{q \cdot q}{r}\right) \]

   8. Applied rewrites 64.6%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \color{blue}{0.5}, \frac{q \cdot q}{r}\right) \]

   9. Taylor expanded in p around 0

      \[\leadsto r + \frac{{q}^{2}}{\color{blue}{r}} \]
   10. Step-by-step derivation

       1. +-commutative N/A

          \[\leadsto \frac{{q}^{2}}{r} + r \]

       2. pow2 N/A

          \[\leadsto \frac{q \cdot q}{r} + r \]

       3. associate-/l* N/A

          \[\leadsto q \cdot \frac{q}{r} + r \]

       4. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(q, \frac{q}{r}, r\right) \]

       5. lower-/.f64 73.3

          \[\leadsto \mathsf{fma}\left(q, \frac{q}{r}, r\right) \]

   11. Applied rewrites 73.3%

       \[\leadsto \mathsf{fma}\left(q, \frac{q}{\color{blue}{r}}, r\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 64.1% accurate, 8.3× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ [p, r, q_m] = \mathsf{sort}([p, r, q_m])\\ \\ \begin{array}{l} \mathbf{if}\;p \leq -4.2 \cdot 10^{-51}:\\ \;\;\;\;\left(\left(-p\right) + \left(\left|r\right| + \left|p\right|\right)\right) \cdot 0.5\\ \mathbf{elif}\;p \leq 9.2 \cdot 10^{-281}:\\ \;\;\;\;\mathsf{fma}\left(r + p, 0.5, q\_m\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(q\_m, \frac{q\_m}{r}, r\right)\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
(FPCore (p r q_m)
 :precision binary64
 (if (<= p -4.2e-51)
   (* (+ (- p) (+ (fabs r) (fabs p))) 0.5)
   (if (<= p 9.2e-281) (fma (+ r p) 0.5 q_m) (fma q_m (/ q_m r) r))))

C

q_m = fabs(q);
assert(p < r && r < q_m);
double code(double p, double r, double q_m) {
	double tmp;
	if (p <= -4.2e-51) {
		tmp = (-p + (fabs(r) + fabs(p))) * 0.5;
	} else if (p <= 9.2e-281) {
		tmp = fma((r + p), 0.5, q_m);
	} else {
		tmp = fma(q_m, (q_m / r), r);
	}
	return tmp;
}

Julia

q_m = abs(q)
p, r, q_m = sort([p, r, q_m])
function code(p, r, q_m)
	tmp = 0.0
	if (p <= -4.2e-51)
		tmp = Float64(Float64(Float64(-p) + Float64(abs(r) + abs(p))) * 0.5);
	elseif (p <= 9.2e-281)
		tmp = fma(Float64(r + p), 0.5, q_m);
	else
		tmp = fma(q_m, Float64(q_m / r), r);
	end
	return tmp
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
code[p_, r_, q$95$m_] := If[LessEqual[p, -4.2e-51], N[(N[((-p) + N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], If[LessEqual[p, 9.2e-281], N[(N[(r + p), $MachinePrecision] * 0.5 + q$95$m), $MachinePrecision], N[(q$95$m * N[(q$95$m / r), $MachinePrecision] + r), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if p < -4.20000000000000003e-51

   1. Initial program 38.7%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in p around -inf

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{-1 \cdot p}\right) \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(\mathsf{neg}\left(p\right)\right)\right) \]

      2. lower-neg.f64 65.0

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right) \]

   5. Applied rewrites 65.0%

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{\left(-p\right)}\right) \]
   6. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right)} \]

      2. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2}} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right) \cdot \frac{1}{2}} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + \left(-p\right)\right) \cdot \frac{1}{2}} \]

   7. Applied rewrites 65.0%

      \[\leadsto \color{blue}{\left(\left(-p\right) + \left(\left|r\right| + \left|p\right|\right)\right) \cdot 0.5} \]

   if -4.20000000000000003e-51 < p < 9.19999999999999956e-281

   1. Initial program 59.4%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   4. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto q \cdot \left(1 + \frac{1}{2} \cdot \frac{\color{blue}{\left|p\right| + \left|r\right|}}{q}\right) \]

      2. *-commutative N/A

         \[\leadsto \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \cdot \color{blue}{q} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \cdot \color{blue}{q} \]

   5. Applied rewrites 56.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{r + p}{q}, 0.5, 1\right) \cdot q} \]

   6. Taylor expanded in q around 0

      \[\leadsto q + \color{blue}{\frac{1}{2} \cdot \left(p + r\right)} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto q + \frac{1}{2} \cdot \left(p + r\right) \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(p + r\right) + q \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(r + p\right) + q \]

      4. *-commutative N/A

         \[\leadsto \left(r + p\right) \cdot \frac{1}{2} + q \]

      5. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(r + p, \frac{1}{\color{blue}{2}}, q\right) \]

      6. lift-+.f64 N/A

         \[\leadsto \mathsf{fma}\left(r + p, \frac{1}{2}, q\right) \]

      7. metadata-eval 57.8

         \[\leadsto \mathsf{fma}\left(r + p, 0.5, q\right) \]

   8. Applied rewrites 57.8%

      \[\leadsto \mathsf{fma}\left(r + p, \color{blue}{0.5}, q\right) \]

   if 9.19999999999999956e-281 < p

   1. Initial program 44.9%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in p around 0

      \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(\left|p\right| + \left(\left|r\right| + \sqrt{4 \cdot {q}^{2} + {r}^{2}}\right)\right)} \]
   4. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \frac{1}{2} \cdot \left(\color{blue}{\left|p\right|} + \left(\left|r\right| + \sqrt{4 \cdot {q}^{2} + {r}^{2}}\right)\right) \]

      2. *-commutative N/A

         \[\leadsto \left(\left|p\right| + \left(\left|r\right| + \sqrt{4 \cdot {q}^{2} + {r}^{2}}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(\left|p\right| + \left(\left|r\right| + \sqrt{4 \cdot {q}^{2} + {r}^{2}}\right)\right) \cdot \color{blue}{\frac{1}{2}} \]

   5. Applied rewrites 44.2%

      \[\leadsto \color{blue}{\left(\left(\sqrt{\mathsf{fma}\left(q \cdot q, 4, r \cdot r\right)} + r\right) + p\right) \cdot 0.5} \]

   6. Taylor expanded in q around 0

      \[\leadsto \frac{1}{2} \cdot \left(p + 2 \cdot r\right) + \color{blue}{\frac{{q}^{2}}{r}} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \frac{1}{2} \cdot \left(p + 2 \cdot r\right) + \frac{{q}^{2}}{r} \]

      2. *-commutative N/A

         \[\leadsto \left(p + 2 \cdot r\right) \cdot \frac{1}{2} + \frac{{q}^{2}}{r} \]

      3. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(p + 2 \cdot r, \frac{1}{\color{blue}{2}}, \frac{{q}^{2}}{r}\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{fma}\left(2 \cdot r + p, \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      5. *-commutative N/A

         \[\leadsto \mathsf{fma}\left(r \cdot 2 + p, \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      6. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      7. metadata-eval N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      8. lower-/.f64 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \frac{1}{2}, \frac{{q}^{2}}{r}\right) \]

      9. pow2 N/A

         \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \frac{1}{2}, \frac{q \cdot q}{r}\right) \]

      10. lift-*.f64 62.2

          \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), 0.5, \frac{q \cdot q}{r}\right) \]

   8. Applied rewrites 62.2%

      \[\leadsto \mathsf{fma}\left(\mathsf{fma}\left(r, 2, p\right), \color{blue}{0.5}, \frac{q \cdot q}{r}\right) \]

   9. Taylor expanded in p around 0

      \[\leadsto r + \frac{{q}^{2}}{\color{blue}{r}} \]
   10. Step-by-step derivation

       1. +-commutative N/A

          \[\leadsto \frac{{q}^{2}}{r} + r \]

       2. pow2 N/A

          \[\leadsto \frac{q \cdot q}{r} + r \]

       3. associate-/l* N/A

          \[\leadsto q \cdot \frac{q}{r} + r \]

       4. lower-fma.f64 N/A

          \[\leadsto \mathsf{fma}\left(q, \frac{q}{r}, r\right) \]

       5. lower-/.f64 69.7

          \[\leadsto \mathsf{fma}\left(q, \frac{q}{r}, r\right) \]

   11. Applied rewrites 69.7%

       \[\leadsto \mathsf{fma}\left(q, \frac{q}{\color{blue}{r}}, r\right) \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 58.0% accurate, 11.4× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ [p, r, q_m] = \mathsf{sort}([p, r, q_m])\\ \\ \begin{array}{l} \mathbf{if}\;q\_m \leq 2.15 \cdot 10^{+55}:\\ \;\;\;\;\left(\left(\left|r\right| + \left|p\right|\right) + r\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(r + p, 0.5, q\_m\right)\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
(FPCore (p r q_m)
 :precision binary64
 (if (<= q_m 2.15e+55)
   (* (+ (+ (fabs r) (fabs p)) r) 0.5)
   (fma (+ r p) 0.5 q_m)))

C

q_m = fabs(q);
assert(p < r && r < q_m);
double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 2.15e+55) {
		tmp = ((fabs(r) + fabs(p)) + r) * 0.5;
	} else {
		tmp = fma((r + p), 0.5, q_m);
	}
	return tmp;
}

Julia

q_m = abs(q)
p, r, q_m = sort([p, r, q_m])
function code(p, r, q_m)
	tmp = 0.0
	if (q_m <= 2.15e+55)
		tmp = Float64(Float64(Float64(abs(r) + abs(p)) + r) * 0.5);
	else
		tmp = fma(Float64(r + p), 0.5, q_m);
	end
	return tmp
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
code[p_, r_, q$95$m_] := If[LessEqual[q$95$m, 2.15e+55], N[(N[(N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision] + r), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(r + p), $MachinePrecision] * 0.5 + q$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if q < 2.1499999999999999e55

   1. Initial program 57.7%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in r around inf

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{r \cdot \left(1 + -1 \cdot \frac{p}{r}\right)}\right) \]
   4. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(1 + -1 \cdot \frac{p}{r}\right) \cdot \color{blue}{r}\right) \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(1 + -1 \cdot \frac{p}{r}\right) \cdot \color{blue}{r}\right) \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-1 \cdot \frac{p}{r} + 1\right) \cdot r\right) \]

      4. *-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(\frac{p}{r} \cdot -1 + 1\right) \cdot r\right) \]

      5. lower-fma.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(\frac{p}{r}, -1, 1\right) \cdot r\right) \]

      6. lower-/.f64 77.4

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(\frac{p}{r}, -1, 1\right) \cdot r\right) \]

   5. Applied rewrites 77.4%

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \color{blue}{\mathsf{fma}\left(\frac{p}{r}, -1, 1\right) \cdot r}\right) \]

   6. Taylor expanded in p around 0

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(r + \color{blue}{-1 \cdot p}\right)\right) \]
   7. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(r + \left(\mathsf{neg}\left(p\right)\right)\right)\right) \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(\left(\mathsf{neg}\left(p\right)\right) + r\right)\right) \]

      3. mul-1-neg N/A

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \left(-1 \cdot p + r\right)\right) \]

      4. lower-fma.f64 89.1

         \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \]

   8. Applied rewrites 89.1%

      \[\leadsto \frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, \color{blue}{p}, r\right)\right) \]
   9. Step-by-step derivation

      1. lift-*.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right)} \]

      2. lift-/.f64 N/A

         \[\leadsto \color{blue}{\frac{1}{2}} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \]

      3. *-commutative N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2}} \]

      4. lower-*.f64 N/A

         \[\leadsto \color{blue}{\left(\left(\left|p\right| + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2}} \]

      5. lift-+.f64 N/A

         \[\leadsto \left(\color{blue}{\left(\left|p\right| + \left|r\right|\right)} + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      6. lift-fabs.f64 N/A

         \[\leadsto \left(\left(\color{blue}{\left|p\right|} + \left|r\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      7. lift-fabs.f64 N/A

         \[\leadsto \left(\left(\left|p\right| + \color{blue}{\left|r\right|}\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      8. +-commutative N/A

         \[\leadsto \left(\color{blue}{\left(\left|r\right| + \left|p\right|\right)} + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      9. lower-+.f64 N/A

         \[\leadsto \left(\color{blue}{\left(\left|r\right| + \left|p\right|\right)} + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      10. lift-fabs.f64 N/A

          \[\leadsto \left(\left(\color{blue}{\left|r\right|} + \left|p\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      11. lift-fabs.f64 N/A

          \[\leadsto \left(\left(\left|r\right| + \color{blue}{\left|p\right|}\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \frac{1}{2} \]

      12. metadata-eval 89.1

          \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot \color{blue}{0.5} \]

   10. Applied rewrites 89.1%

       \[\leadsto \color{blue}{\left(\left(\left|r\right| + \left|p\right|\right) + \mathsf{fma}\left(-1, p, r\right)\right) \cdot 0.5} \]

   11. Taylor expanded in p around 0

       \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + r\right) \cdot \frac{1}{2} \]
   12. Step-by-step derivation

   13. Applied rewrites 51.8%

       \[\leadsto \left(\left(\left|r\right| + \left|p\right|\right) + r\right) \cdot 0.5 \]

   if 2.1499999999999999e55 < q

   1. Initial program 28.1%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   4. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto q \cdot \left(1 + \frac{1}{2} \cdot \frac{\color{blue}{\left|p\right| + \left|r\right|}}{q}\right) \]

      2. *-commutative N/A

         \[\leadsto \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \cdot \color{blue}{q} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \cdot \color{blue}{q} \]

   5. Applied rewrites 66.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{r + p}{q}, 0.5, 1\right) \cdot q} \]

   6. Taylor expanded in q around 0

      \[\leadsto q + \color{blue}{\frac{1}{2} \cdot \left(p + r\right)} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto q + \frac{1}{2} \cdot \left(p + r\right) \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(p + r\right) + q \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(r + p\right) + q \]

      4. *-commutative N/A

         \[\leadsto \left(r + p\right) \cdot \frac{1}{2} + q \]

      5. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(r + p, \frac{1}{\color{blue}{2}}, q\right) \]

      6. lift-+.f64 N/A

         \[\leadsto \mathsf{fma}\left(r + p, \frac{1}{2}, q\right) \]

      7. metadata-eval 66.9

         \[\leadsto \mathsf{fma}\left(r + p, 0.5, q\right) \]

   8. Applied rewrites 66.9%

      \[\leadsto \mathsf{fma}\left(r + p, \color{blue}{0.5}, q\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 53.9% accurate, 15.6× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ [p, r, q_m] = \mathsf{sort}([p, r, q_m])\\ \\ \begin{array}{l} \mathbf{if}\;q\_m \leq 2.1 \cdot 10^{+55}:\\ \;\;\;\;r\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(r + p, 0.5, q\_m\right)\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
(FPCore (p r q_m)
 :precision binary64
 (if (<= q_m 2.1e+55) r (fma (+ r p) 0.5 q_m)))

C

q_m = fabs(q);
assert(p < r && r < q_m);
double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 2.1e+55) {
		tmp = r;
	} else {
		tmp = fma((r + p), 0.5, q_m);
	}
	return tmp;
}

Julia

q_m = abs(q)
p, r, q_m = sort([p, r, q_m])
function code(p, r, q_m)
	tmp = 0.0
	if (q_m <= 2.1e+55)
		tmp = r;
	else
		tmp = fma(Float64(r + p), 0.5, q_m);
	end
	return tmp
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
code[p_, r_, q$95$m_] := If[LessEqual[q$95$m, 2.1e+55], r, N[(N[(r + p), $MachinePrecision] * 0.5 + q$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if q < 2.1000000000000001e55

   1. Initial program 57.7%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   4. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto -1 \cdot \left(p \cdot \left(\frac{1}{2} + \color{blue}{\frac{-1}{2}} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right) \]

      2. associate-*r* N/A

         \[\leadsto \left(-1 \cdot p\right) \cdot \color{blue}{\left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)} \]

      3. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(p\right)\right) \cdot \left(\color{blue}{\frac{1}{2}} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(p\right)\right) \cdot \color{blue}{\left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)} \]

      5. lower-neg.f64 N/A

         \[\leadsto \left(-p\right) \cdot \left(\color{blue}{\frac{1}{2}} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right) \]

      6. +-commutative N/A

         \[\leadsto \left(-p\right) \cdot \left(\frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p} + \color{blue}{\frac{1}{2}}\right) \]

      7. *-commutative N/A

         \[\leadsto \left(-p\right) \cdot \left(\frac{r + \left(\left|p\right| + \left|r\right|\right)}{p} \cdot \frac{-1}{2} + \frac{\color{blue}{1}}{2}\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(-p\right) \cdot \mathsf{fma}\left(\frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}, \color{blue}{\frac{-1}{2}}, \frac{1}{2}\right) \]

   5. Applied rewrites 32.9%

      \[\leadsto \color{blue}{\left(-p\right) \cdot \mathsf{fma}\left(\frac{\left(r + p\right) + r}{p}, -0.5, 0.5\right)} \]

   6. Taylor expanded in p around 0

      \[\leadsto r \]
   7. Step-by-step derivation

   8. Applied rewrites 45.0%

      \[\leadsto r \]

   if 2.1000000000000001e55 < q

   1. Initial program 28.1%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   4. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto q \cdot \left(1 + \frac{1}{2} \cdot \frac{\color{blue}{\left|p\right| + \left|r\right|}}{q}\right) \]

      2. *-commutative N/A

         \[\leadsto \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \cdot \color{blue}{q} \]

      3. lower-*.f64 N/A

         \[\leadsto \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \cdot \color{blue}{q} \]

   5. Applied rewrites 66.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(\frac{r + p}{q}, 0.5, 1\right) \cdot q} \]

   6. Taylor expanded in q around 0

      \[\leadsto q + \color{blue}{\frac{1}{2} \cdot \left(p + r\right)} \]
   7. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto q + \frac{1}{2} \cdot \left(p + r\right) \]

      2. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(p + r\right) + q \]

      3. +-commutative N/A

         \[\leadsto \frac{1}{2} \cdot \left(r + p\right) + q \]

      4. *-commutative N/A

         \[\leadsto \left(r + p\right) \cdot \frac{1}{2} + q \]

      5. lower-fma.f64 N/A

         \[\leadsto \mathsf{fma}\left(r + p, \frac{1}{\color{blue}{2}}, q\right) \]

      6. lift-+.f64 N/A

         \[\leadsto \mathsf{fma}\left(r + p, \frac{1}{2}, q\right) \]

      7. metadata-eval 66.9

         \[\leadsto \mathsf{fma}\left(r + p, 0.5, q\right) \]

   8. Applied rewrites 66.9%

      \[\leadsto \mathsf{fma}\left(r + p, \color{blue}{0.5}, q\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 53.4% accurate, 35.6× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ [p, r, q_m] = \mathsf{sort}([p, r, q_m])\\ \\ \begin{array}{l} \mathbf{if}\;q\_m \leq 2.1 \cdot 10^{+55}:\\ \;\;\;\;r\\ \mathbf{else}:\\ \;\;\;\;q\_m\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
(FPCore (p r q_m) :precision binary64 (if (<= q_m 2.1e+55) r q_m))

C

q_m = fabs(q);
assert(p < r && r < q_m);
double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 2.1e+55) {
		tmp = r;
	} else {
		tmp = q_m;
	}
	return tmp;
}

Fortran

q_m =     private
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
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(p, r, q_m)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q_m
    real(8) :: tmp
    if (q_m <= 2.1d+55) then
        tmp = r
    else
        tmp = q_m
    end if
    code = tmp
end function

Java

q_m = Math.abs(q);
assert p < r && r < q_m;
public static double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 2.1e+55) {
		tmp = r;
	} else {
		tmp = q_m;
	}
	return tmp;
}

Python

q_m = math.fabs(q)
[p, r, q_m] = sort([p, r, q_m])
def code(p, r, q_m):
	tmp = 0
	if q_m <= 2.1e+55:
		tmp = r
	else:
		tmp = q_m
	return tmp

Julia

q_m = abs(q)
p, r, q_m = sort([p, r, q_m])
function code(p, r, q_m)
	tmp = 0.0
	if (q_m <= 2.1e+55)
		tmp = r;
	else
		tmp = q_m;
	end
	return tmp
end

MATLAB

q_m = abs(q);
p, r, q_m = num2cell(sort([p, r, q_m])){:}
function tmp_2 = code(p, r, q_m)
	tmp = 0.0;
	if (q_m <= 2.1e+55)
		tmp = r;
	else
		tmp = q_m;
	end
	tmp_2 = tmp;
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
code[p_, r_, q$95$m_] := If[LessEqual[q$95$m, 2.1e+55], r, q$95$m]

Derivation

1. Split input into 2 regimes

2. if q < 2.1000000000000001e55

   1. Initial program 57.7%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   4. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto -1 \cdot \left(p \cdot \left(\frac{1}{2} + \color{blue}{\frac{-1}{2}} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right) \]

      2. associate-*r* N/A

         \[\leadsto \left(-1 \cdot p\right) \cdot \color{blue}{\left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)} \]

      3. mul-1-neg N/A

         \[\leadsto \left(\mathsf{neg}\left(p\right)\right) \cdot \left(\color{blue}{\frac{1}{2}} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right) \]

      4. lower-*.f64 N/A

         \[\leadsto \left(\mathsf{neg}\left(p\right)\right) \cdot \color{blue}{\left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)} \]

      5. lower-neg.f64 N/A

         \[\leadsto \left(-p\right) \cdot \left(\color{blue}{\frac{1}{2}} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right) \]

      6. +-commutative N/A

         \[\leadsto \left(-p\right) \cdot \left(\frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p} + \color{blue}{\frac{1}{2}}\right) \]

      7. *-commutative N/A

         \[\leadsto \left(-p\right) \cdot \left(\frac{r + \left(\left|p\right| + \left|r\right|\right)}{p} \cdot \frac{-1}{2} + \frac{\color{blue}{1}}{2}\right) \]

      8. lower-fma.f64 N/A

         \[\leadsto \left(-p\right) \cdot \mathsf{fma}\left(\frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}, \color{blue}{\frac{-1}{2}}, \frac{1}{2}\right) \]

   5. Applied rewrites 32.9%

      \[\leadsto \color{blue}{\left(-p\right) \cdot \mathsf{fma}\left(\frac{\left(r + p\right) + r}{p}, -0.5, 0.5\right)} \]

   6. Taylor expanded in p around 0

      \[\leadsto r \]
   7. Step-by-step derivation

   8. Applied rewrites 45.0%

      \[\leadsto r \]

   if 2.1000000000000001e55 < q

   1. Initial program 28.1%

      \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
   2. Add Preprocessing

   3. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q} \]
   4. Step-by-step derivation

   5. Applied rewrites 65.7%

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

Alternative 7: 35.2% accurate, 250.0× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ [p, r, q_m] = \mathsf{sort}([p, r, q_m])\\ \\ q\_m \end{array} \]

FPCore

q_m = (fabs.f64 q)
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
(FPCore (p r q_m) :precision binary64 q_m)

C

q_m = fabs(q);
assert(p < r && r < q_m);
double code(double p, double r, double q_m) {
	return q_m;
}

Fortran

q_m =     private
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
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(p, r, q_m)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q_m
    code = q_m
end function

Java

q_m = Math.abs(q);
assert p < r && r < q_m;
public static double code(double p, double r, double q_m) {
	return q_m;
}

Python

q_m = math.fabs(q)
[p, r, q_m] = sort([p, r, q_m])
def code(p, r, q_m):
	return q_m

Julia

q_m = abs(q)
p, r, q_m = sort([p, r, q_m])
function code(p, r, q_m)
	return q_m
end

MATLAB

q_m = abs(q);
p, r, q_m = num2cell(sort([p, r, q_m])){:}
function tmp = code(p, r, q_m)
	tmp = q_m;
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
NOTE: p, r, and q_m should be sorted in increasing order before calling this function.
code[p_, r_, q$95$m_] := q$95$m

Derivation

1. Initial program 45.6%

   \[\frac{1}{2} \cdot \left(\left(\left|p\right| + \left|r\right|\right) + \sqrt{{\left(p - r\right)}^{2} + 4 \cdot {q}^{2}}\right) \]
2. Add Preprocessing

3. Taylor expanded in q around inf

   \[\leadsto \color{blue}{q} \]
4. Step-by-step derivation

5. Applied rewrites 35.2%

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

Reproduce

herbie shell --seed 2025089 
(FPCore (p r q)
  :name "1/2(abs(p)+abs(r) + sqrt((p-r)^2 + 4q^2))"
  :precision binary64
  (* (/ 1.0 2.0) (+ (+ (fabs p) (fabs r)) (sqrt (+ (pow (- p r) 2.0) (* 4.0 (pow q 2.0)))))))