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

Percentage Accurate: 45.6% → 81.8%

Time: 3.1s

Alternatives: 12

Speedup: 2.2×

Specification

Math

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

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

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

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: 81.8% accurate, 1.7× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{min}\left(p, r\right)\right| + \left|\mathsf{max}\left(p, r\right)\right|\\ \mathbf{if}\;\left|q\right| \leq 7.8 \cdot 10^{+55}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, \mathsf{min}\left(p, r\right), 0.5 \cdot \left(\mathsf{max}\left(p, r\right) + t\_0\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t\_0}{\left|q\right|}, 0.5, 1\right) \cdot \left|q\right|\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (+ (fabs (fmin p r)) (fabs (fmax p r)))))
   (if (<= (fabs q) 7.8e+55)
     (fma -0.5 (fmin p r) (* 0.5 (+ (fmax p r) t_0)))
     (* (fma (/ t_0 (fabs q)) 0.5 1.0) (fabs q)))))

C

double code(double p, double r, double q) {
	double t_0 = fabs(fmin(p, r)) + fabs(fmax(p, r));
	double tmp;
	if (fabs(q) <= 7.8e+55) {
		tmp = fma(-0.5, fmin(p, r), (0.5 * (fmax(p, r) + t_0)));
	} else {
		tmp = fma((t_0 / fabs(q)), 0.5, 1.0) * fabs(q);
	}
	return tmp;
}

Julia

function code(p, r, q)
	t_0 = Float64(abs(fmin(p, r)) + abs(fmax(p, r)))
	tmp = 0.0
	if (abs(q) <= 7.8e+55)
		tmp = fma(-0.5, fmin(p, r), Float64(0.5 * Float64(fmax(p, r) + t_0)));
	else
		tmp = Float64(fma(Float64(t_0 / abs(q)), 0.5, 1.0) * abs(q));
	end
	return tmp
end

Wolfram

code[p_, r_, q_] := Block[{t$95$0 = N[(N[Abs[N[Min[p, r], $MachinePrecision]], $MachinePrecision] + N[Abs[N[Max[p, r], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Abs[q], $MachinePrecision], 7.8e+55], N[(-0.5 * N[Min[p, r], $MachinePrecision] + N[(0.5 * N[(N[Max[p, r], $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[(N[(t$95$0 / N[Abs[q], $MachinePrecision]), $MachinePrecision] * 0.5 + 1.0), $MachinePrecision] * N[Abs[q], $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if q < 7.8000000000000005e55

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 N/A

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

      7. lower-fabs.f64 N/A

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

      8. lower-fabs.f64 34.5%

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

   7. Applied rewrites 34.5%

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

   if 7.8000000000000005e55 < q

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

      \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   5. 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. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. 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. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-*.f64 N/A

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

      8. *-commutative N/A

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

      9. lower-fma.f64 N/A

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

      10. metadata-eval 26.6%

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

   6. Applied rewrites 26.6%

      \[\leadsto \mathsf{fma}\left(\frac{\left|p\right| + \left|r\right|}{q}, 0.5, 1\right) \cdot \color{blue}{q} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 81.8% accurate, 1.7× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{min}\left(p, r\right)\right| + \left|\mathsf{max}\left(p, r\right)\right|\\ \mathbf{if}\;\left|q\right| \leq 7.8 \cdot 10^{+55}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, \mathsf{min}\left(p, r\right), 0.5 \cdot \left(\mathsf{max}\left(p, r\right) + t\_0\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left|q\right| + t\_0 \cdot 0.5\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (+ (fabs (fmin p r)) (fabs (fmax p r)))))
   (if (<= (fabs q) 7.8e+55)
     (fma -0.5 (fmin p r) (* 0.5 (+ (fmax p r) t_0)))
     (+ (fabs q) (* t_0 0.5)))))

C

double code(double p, double r, double q) {
	double t_0 = fabs(fmin(p, r)) + fabs(fmax(p, r));
	double tmp;
	if (fabs(q) <= 7.8e+55) {
		tmp = fma(-0.5, fmin(p, r), (0.5 * (fmax(p, r) + t_0)));
	} else {
		tmp = fabs(q) + (t_0 * 0.5);
	}
	return tmp;
}

Julia

function code(p, r, q)
	t_0 = Float64(abs(fmin(p, r)) + abs(fmax(p, r)))
	tmp = 0.0
	if (abs(q) <= 7.8e+55)
		tmp = fma(-0.5, fmin(p, r), Float64(0.5 * Float64(fmax(p, r) + t_0)));
	else
		tmp = Float64(abs(q) + Float64(t_0 * 0.5));
	end
	return tmp
end

Wolfram

code[p_, r_, q_] := Block[{t$95$0 = N[(N[Abs[N[Min[p, r], $MachinePrecision]], $MachinePrecision] + N[Abs[N[Max[p, r], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Abs[q], $MachinePrecision], 7.8e+55], N[(-0.5 * N[Min[p, r], $MachinePrecision] + N[(0.5 * N[(N[Max[p, r], $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Abs[q], $MachinePrecision] + N[(t$95$0 * 0.5), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if q < 7.8000000000000005e55

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 N/A

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

      7. lower-fabs.f64 N/A

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

      8. lower-fabs.f64 34.5%

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

   7. Applied rewrites 34.5%

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

   if 7.8000000000000005e55 < q

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

      \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   5. 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. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. associate-*r/ N/A

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

      8. lift-*.f64 N/A

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

      9. sum-to-mult-rev N/A

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

      10. lower-+.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lower-*.f64 N/A

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

      14. metadata-eval 29.1%

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

   6. Applied rewrites 29.1%

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

Alternative 3: 81.8% accurate, 1.8× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{min}\left(p, r\right)\right|\\ t_1 := \left|\mathsf{max}\left(p, r\right)\right|\\ \mathbf{if}\;\left|q\right| \leq 7.8 \cdot 10^{+55}:\\ \;\;\;\;\left(\left(\left(\mathsf{min}\left(p, r\right) - t\_1\right) - \mathsf{max}\left(p, r\right)\right) - t\_0\right) \cdot -0.5\\ \mathbf{else}:\\ \;\;\;\;\left|q\right| + \left(t\_0 + t\_1\right) \cdot 0.5\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (fabs (fmin p r))) (t_1 (fabs (fmax p r))))
   (if (<= (fabs q) 7.8e+55)
     (* (- (- (- (fmin p r) t_1) (fmax p r)) t_0) -0.5)
     (+ (fabs q) (* (+ t_0 t_1) 0.5)))))

C

double code(double p, double r, double q) {
	double t_0 = fabs(fmin(p, r));
	double t_1 = fabs(fmax(p, r));
	double tmp;
	if (fabs(q) <= 7.8e+55) {
		tmp = (((fmin(p, r) - t_1) - fmax(p, r)) - t_0) * -0.5;
	} else {
		tmp = fabs(q) + ((t_0 + t_1) * 0.5);
	}
	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(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = abs(fmin(p, r))
    t_1 = abs(fmax(p, r))
    if (abs(q) <= 7.8d+55) then
        tmp = (((fmin(p, r) - t_1) - fmax(p, r)) - t_0) * (-0.5d0)
    else
        tmp = abs(q) + ((t_0 + t_1) * 0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double t_0 = Math.abs(fmin(p, r));
	double t_1 = Math.abs(fmax(p, r));
	double tmp;
	if (Math.abs(q) <= 7.8e+55) {
		tmp = (((fmin(p, r) - t_1) - fmax(p, r)) - t_0) * -0.5;
	} else {
		tmp = Math.abs(q) + ((t_0 + t_1) * 0.5);
	}
	return tmp;
}

Python

def code(p, r, q):
	t_0 = math.fabs(fmin(p, r))
	t_1 = math.fabs(fmax(p, r))
	tmp = 0
	if math.fabs(q) <= 7.8e+55:
		tmp = (((fmin(p, r) - t_1) - fmax(p, r)) - t_0) * -0.5
	else:
		tmp = math.fabs(q) + ((t_0 + t_1) * 0.5)
	return tmp

Julia

function code(p, r, q)
	t_0 = abs(fmin(p, r))
	t_1 = abs(fmax(p, r))
	tmp = 0.0
	if (abs(q) <= 7.8e+55)
		tmp = Float64(Float64(Float64(Float64(fmin(p, r) - t_1) - fmax(p, r)) - t_0) * -0.5);
	else
		tmp = Float64(abs(q) + Float64(Float64(t_0 + t_1) * 0.5));
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	t_0 = abs(min(p, r));
	t_1 = abs(max(p, r));
	tmp = 0.0;
	if (abs(q) <= 7.8e+55)
		tmp = (((min(p, r) - t_1) - max(p, r)) - t_0) * -0.5;
	else
		tmp = abs(q) + ((t_0 + t_1) * 0.5);
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := Block[{t$95$0 = N[Abs[N[Min[p, r], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Abs[N[Max[p, r], $MachinePrecision]], $MachinePrecision]}, If[LessEqual[N[Abs[q], $MachinePrecision], 7.8e+55], N[(N[(N[(N[(N[Min[p, r], $MachinePrecision] - t$95$1), $MachinePrecision] - N[Max[p, r], $MachinePrecision]), $MachinePrecision] - t$95$0), $MachinePrecision] * -0.5), $MachinePrecision], N[(N[Abs[q], $MachinePrecision] + N[(N[(t$95$0 + t$95$1), $MachinePrecision] * 0.5), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if q < 7.8000000000000005e55

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 N/A

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

      7. lower-fabs.f64 N/A

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

      8. lower-fabs.f64 34.5%

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

   7. Applied rewrites 34.5%

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

      1. metadata-eval 34.5%

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

      2. metadata-eval N/A

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

   9. Applied rewrites 34.6%

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

       1. lift-*.f64 N/A

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

       2. *-commutative N/A

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

       3. lower-*.f64 34.6%

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

       4. lift--.f64 N/A

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

       5. lift-+.f64 N/A

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

       6. associate--r+ N/A

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

       7. lower--.f64 N/A

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

       8. lift-+.f64 N/A

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

       9. +-commutative N/A

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

       10. associate--r+ N/A

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

       11. lower--.f64 N/A

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

       12. lower--.f64 34.8%

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

   11. Applied rewrites 34.8%

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

   if 7.8000000000000005e55 < q

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

      \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   5. 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. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. associate-*r/ N/A

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

      8. lift-*.f64 N/A

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

      9. sum-to-mult-rev N/A

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

      10. lower-+.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lower-*.f64 N/A

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

      14. metadata-eval 29.1%

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

   6. Applied rewrites 29.1%

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

Alternative 4: 65.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{min}\left(p, r\right)\right| + \left|\mathsf{max}\left(p, r\right)\right|\\ \mathbf{if}\;\mathsf{min}\left(p, r\right) \leq -5 \cdot 10^{+79}:\\ \;\;\;\;\mathsf{fma}\left(-0.5, \mathsf{min}\left(p, r\right), 0.5 \cdot t\_0\right)\\ \mathbf{elif}\;\mathsf{min}\left(p, r\right) \leq 9 \cdot 10^{-179}:\\ \;\;\;\;\left|q\right| + t\_0 \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \left(\mathsf{max}\left(p, r\right) + t\_0\right)\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (+ (fabs (fmin p r)) (fabs (fmax p r)))))
   (if (<= (fmin p r) -5e+79)
     (fma -0.5 (fmin p r) (* 0.5 t_0))
     (if (<= (fmin p r) 9e-179)
       (+ (fabs q) (* t_0 0.5))
       (* 0.5 (+ (fmax p r) t_0))))))

C

double code(double p, double r, double q) {
	double t_0 = fabs(fmin(p, r)) + fabs(fmax(p, r));
	double tmp;
	if (fmin(p, r) <= -5e+79) {
		tmp = fma(-0.5, fmin(p, r), (0.5 * t_0));
	} else if (fmin(p, r) <= 9e-179) {
		tmp = fabs(q) + (t_0 * 0.5);
	} else {
		tmp = 0.5 * (fmax(p, r) + t_0);
	}
	return tmp;
}

Julia

function code(p, r, q)
	t_0 = Float64(abs(fmin(p, r)) + abs(fmax(p, r)))
	tmp = 0.0
	if (fmin(p, r) <= -5e+79)
		tmp = fma(-0.5, fmin(p, r), Float64(0.5 * t_0));
	elseif (fmin(p, r) <= 9e-179)
		tmp = Float64(abs(q) + Float64(t_0 * 0.5));
	else
		tmp = Float64(0.5 * Float64(fmax(p, r) + t_0));
	end
	return tmp
end

Wolfram

code[p_, r_, q_] := Block[{t$95$0 = N[(N[Abs[N[Min[p, r], $MachinePrecision]], $MachinePrecision] + N[Abs[N[Max[p, r], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Min[p, r], $MachinePrecision], -5e+79], N[(-0.5 * N[Min[p, r], $MachinePrecision] + N[(0.5 * t$95$0), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[Min[p, r], $MachinePrecision], 9e-179], N[(N[Abs[q], $MachinePrecision] + N[(t$95$0 * 0.5), $MachinePrecision]), $MachinePrecision], N[(0.5 * N[(N[Max[p, r], $MachinePrecision] + t$95$0), $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if p < -5e79

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 N/A

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

      7. lower-fabs.f64 N/A

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

      8. lower-fabs.f64 34.5%

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

   7. Applied rewrites 34.5%

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

   8. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-+.f64 N/A

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

      6. lower-fabs.f64 N/A

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

      7. lower-fabs.f64 24.0%

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

   10. Applied rewrites 24.0%

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

   if -5e79 < p < 8.9999999999999998e-179

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

      \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   5. 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. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. associate-*r/ N/A

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

      8. lift-*.f64 N/A

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

      9. sum-to-mult-rev N/A

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

      10. lower-+.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lower-*.f64 N/A

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

      14. metadata-eval 29.1%

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

   6. Applied rewrites 29.1%

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

   if 8.9999999999999998e-179 < p

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. metadata-eval N/A

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

      4. lower-+.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-fabs.f64 N/A

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

      7. lower-fabs.f64 24.4%

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

   7. Applied rewrites 24.4%

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

Alternative 5: 65.3% accurate, 1.6× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{max}\left(p, r\right)\right|\\ t_1 := \left|\mathsf{min}\left(p, r\right)\right|\\ t_2 := t\_1 + t\_0\\ \mathbf{if}\;\mathsf{min}\left(p, r\right) \leq -5 \cdot 10^{+79}:\\ \;\;\;\;-0.5 \cdot \left(\mathsf{min}\left(p, r\right) - \left(t\_0 + t\_1\right)\right)\\ \mathbf{elif}\;\mathsf{min}\left(p, r\right) \leq 9 \cdot 10^{-179}:\\ \;\;\;\;\left|q\right| + t\_2 \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \left(\mathsf{max}\left(p, r\right) + t\_2\right)\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (fabs (fmax p r))) (t_1 (fabs (fmin p r))) (t_2 (+ t_1 t_0)))
   (if (<= (fmin p r) -5e+79)
     (* -0.5 (- (fmin p r) (+ t_0 t_1)))
     (if (<= (fmin p r) 9e-179)
       (+ (fabs q) (* t_2 0.5))
       (* 0.5 (+ (fmax p r) t_2))))))

C

double code(double p, double r, double q) {
	double t_0 = fabs(fmax(p, r));
	double t_1 = fabs(fmin(p, r));
	double t_2 = t_1 + t_0;
	double tmp;
	if (fmin(p, r) <= -5e+79) {
		tmp = -0.5 * (fmin(p, r) - (t_0 + t_1));
	} else if (fmin(p, r) <= 9e-179) {
		tmp = fabs(q) + (t_2 * 0.5);
	} else {
		tmp = 0.5 * (fmax(p, r) + t_2);
	}
	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(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = abs(fmax(p, r))
    t_1 = abs(fmin(p, r))
    t_2 = t_1 + t_0
    if (fmin(p, r) <= (-5d+79)) then
        tmp = (-0.5d0) * (fmin(p, r) - (t_0 + t_1))
    else if (fmin(p, r) <= 9d-179) then
        tmp = abs(q) + (t_2 * 0.5d0)
    else
        tmp = 0.5d0 * (fmax(p, r) + t_2)
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double t_0 = Math.abs(fmax(p, r));
	double t_1 = Math.abs(fmin(p, r));
	double t_2 = t_1 + t_0;
	double tmp;
	if (fmin(p, r) <= -5e+79) {
		tmp = -0.5 * (fmin(p, r) - (t_0 + t_1));
	} else if (fmin(p, r) <= 9e-179) {
		tmp = Math.abs(q) + (t_2 * 0.5);
	} else {
		tmp = 0.5 * (fmax(p, r) + t_2);
	}
	return tmp;
}

Python

def code(p, r, q):
	t_0 = math.fabs(fmax(p, r))
	t_1 = math.fabs(fmin(p, r))
	t_2 = t_1 + t_0
	tmp = 0
	if fmin(p, r) <= -5e+79:
		tmp = -0.5 * (fmin(p, r) - (t_0 + t_1))
	elif fmin(p, r) <= 9e-179:
		tmp = math.fabs(q) + (t_2 * 0.5)
	else:
		tmp = 0.5 * (fmax(p, r) + t_2)
	return tmp

Julia

function code(p, r, q)
	t_0 = abs(fmax(p, r))
	t_1 = abs(fmin(p, r))
	t_2 = Float64(t_1 + t_0)
	tmp = 0.0
	if (fmin(p, r) <= -5e+79)
		tmp = Float64(-0.5 * Float64(fmin(p, r) - Float64(t_0 + t_1)));
	elseif (fmin(p, r) <= 9e-179)
		tmp = Float64(abs(q) + Float64(t_2 * 0.5));
	else
		tmp = Float64(0.5 * Float64(fmax(p, r) + t_2));
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	t_0 = abs(max(p, r));
	t_1 = abs(min(p, r));
	t_2 = t_1 + t_0;
	tmp = 0.0;
	if (min(p, r) <= -5e+79)
		tmp = -0.5 * (min(p, r) - (t_0 + t_1));
	elseif (min(p, r) <= 9e-179)
		tmp = abs(q) + (t_2 * 0.5);
	else
		tmp = 0.5 * (max(p, r) + t_2);
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := Block[{t$95$0 = N[Abs[N[Max[p, r], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Abs[N[Min[p, r], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(t$95$1 + t$95$0), $MachinePrecision]}, If[LessEqual[N[Min[p, r], $MachinePrecision], -5e+79], N[(-0.5 * N[(N[Min[p, r], $MachinePrecision] - N[(t$95$0 + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], If[LessEqual[N[Min[p, r], $MachinePrecision], 9e-179], N[(N[Abs[q], $MachinePrecision] + N[(t$95$2 * 0.5), $MachinePrecision]), $MachinePrecision], N[(0.5 * N[(N[Max[p, r], $MachinePrecision] + t$95$2), $MachinePrecision]), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if p < -5e79

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 N/A

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

      7. lower-fabs.f64 N/A

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

      8. lower-fabs.f64 34.5%

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

   7. Applied rewrites 34.5%

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

      1. metadata-eval 34.5%

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

      2. metadata-eval N/A

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

   9. Applied rewrites 34.6%

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

   10. Taylor expanded in r around 0

       \[\leadsto -0.5 \cdot \left(p - \left(\left|r\right| + \left|p\right|\right)\right) \]
   11. Step-by-step derivation

       1. lower-fabs.f64 24.0%

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

   12. Applied rewrites 24.0%

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

   if -5e79 < p < 8.9999999999999998e-179

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

      \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   5. 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. lift-*.f64 N/A

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

      3. *-commutative N/A

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

      4. lift-+.f64 N/A

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

      5. lift-*.f64 N/A

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

      6. lift-/.f64 N/A

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

      7. associate-*r/ N/A

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

      8. lift-*.f64 N/A

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

      9. sum-to-mult-rev N/A

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

      10. lower-+.f64 N/A

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

      11. lift-*.f64 N/A

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

      12. *-commutative N/A

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

      13. lower-*.f64 N/A

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

      14. metadata-eval 29.1%

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

   6. Applied rewrites 29.1%

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

   if 8.9999999999999998e-179 < p

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. metadata-eval N/A

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

      4. lower-+.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-fabs.f64 N/A

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

      7. lower-fabs.f64 24.4%

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

   7. Applied rewrites 24.4%

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

Alternative 6: 61.4% accurate, 1.4× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{max}\left(p, r\right)\right|\\ t_1 := \left|\mathsf{min}\left(p, r\right)\right|\\ t_2 := -0.5 \cdot \left(\mathsf{min}\left(p, r\right) - \left(t\_0 + t\_1\right)\right)\\ \mathbf{if}\;\mathsf{max}\left(p, r\right) \leq 10^{-251}:\\ \;\;\;\;t\_2\\ \mathbf{elif}\;\mathsf{max}\left(p, r\right) \leq 1.06 \cdot 10^{-122}:\\ \;\;\;\;\left|q\right| \cdot 1\\ \mathbf{elif}\;\mathsf{max}\left(p, r\right) \leq 3.2 \cdot 10^{-42}:\\ \;\;\;\;t\_2\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \left(\mathsf{max}\left(p, r\right) + \left(t\_1 + t\_0\right)\right)\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (fabs (fmax p r)))
        (t_1 (fabs (fmin p r)))
        (t_2 (* -0.5 (- (fmin p r) (+ t_0 t_1)))))
   (if (<= (fmax p r) 1e-251)
     t_2
     (if (<= (fmax p r) 1.06e-122)
       (* (fabs q) 1.0)
       (if (<= (fmax p r) 3.2e-42) t_2 (* 0.5 (+ (fmax p r) (+ t_1 t_0))))))))

C

double code(double p, double r, double q) {
	double t_0 = fabs(fmax(p, r));
	double t_1 = fabs(fmin(p, r));
	double t_2 = -0.5 * (fmin(p, r) - (t_0 + t_1));
	double tmp;
	if (fmax(p, r) <= 1e-251) {
		tmp = t_2;
	} else if (fmax(p, r) <= 1.06e-122) {
		tmp = fabs(q) * 1.0;
	} else if (fmax(p, r) <= 3.2e-42) {
		tmp = t_2;
	} else {
		tmp = 0.5 * (fmax(p, r) + (t_1 + t_0));
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    real(8) :: t_0
    real(8) :: t_1
    real(8) :: t_2
    real(8) :: tmp
    t_0 = abs(fmax(p, r))
    t_1 = abs(fmin(p, r))
    t_2 = (-0.5d0) * (fmin(p, r) - (t_0 + t_1))
    if (fmax(p, r) <= 1d-251) then
        tmp = t_2
    else if (fmax(p, r) <= 1.06d-122) then
        tmp = abs(q) * 1.0d0
    else if (fmax(p, r) <= 3.2d-42) then
        tmp = t_2
    else
        tmp = 0.5d0 * (fmax(p, r) + (t_1 + t_0))
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double t_0 = Math.abs(fmax(p, r));
	double t_1 = Math.abs(fmin(p, r));
	double t_2 = -0.5 * (fmin(p, r) - (t_0 + t_1));
	double tmp;
	if (fmax(p, r) <= 1e-251) {
		tmp = t_2;
	} else if (fmax(p, r) <= 1.06e-122) {
		tmp = Math.abs(q) * 1.0;
	} else if (fmax(p, r) <= 3.2e-42) {
		tmp = t_2;
	} else {
		tmp = 0.5 * (fmax(p, r) + (t_1 + t_0));
	}
	return tmp;
}

Python

def code(p, r, q):
	t_0 = math.fabs(fmax(p, r))
	t_1 = math.fabs(fmin(p, r))
	t_2 = -0.5 * (fmin(p, r) - (t_0 + t_1))
	tmp = 0
	if fmax(p, r) <= 1e-251:
		tmp = t_2
	elif fmax(p, r) <= 1.06e-122:
		tmp = math.fabs(q) * 1.0
	elif fmax(p, r) <= 3.2e-42:
		tmp = t_2
	else:
		tmp = 0.5 * (fmax(p, r) + (t_1 + t_0))
	return tmp

Julia

function code(p, r, q)
	t_0 = abs(fmax(p, r))
	t_1 = abs(fmin(p, r))
	t_2 = Float64(-0.5 * Float64(fmin(p, r) - Float64(t_0 + t_1)))
	tmp = 0.0
	if (fmax(p, r) <= 1e-251)
		tmp = t_2;
	elseif (fmax(p, r) <= 1.06e-122)
		tmp = Float64(abs(q) * 1.0);
	elseif (fmax(p, r) <= 3.2e-42)
		tmp = t_2;
	else
		tmp = Float64(0.5 * Float64(fmax(p, r) + Float64(t_1 + t_0)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	t_0 = abs(max(p, r));
	t_1 = abs(min(p, r));
	t_2 = -0.5 * (min(p, r) - (t_0 + t_1));
	tmp = 0.0;
	if (max(p, r) <= 1e-251)
		tmp = t_2;
	elseif (max(p, r) <= 1.06e-122)
		tmp = abs(q) * 1.0;
	elseif (max(p, r) <= 3.2e-42)
		tmp = t_2;
	else
		tmp = 0.5 * (max(p, r) + (t_1 + t_0));
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := Block[{t$95$0 = N[Abs[N[Max[p, r], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$1 = N[Abs[N[Min[p, r], $MachinePrecision]], $MachinePrecision]}, Block[{t$95$2 = N[(-0.5 * N[(N[Min[p, r], $MachinePrecision] - N[(t$95$0 + t$95$1), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[N[Max[p, r], $MachinePrecision], 1e-251], t$95$2, If[LessEqual[N[Max[p, r], $MachinePrecision], 1.06e-122], N[(N[Abs[q], $MachinePrecision] * 1.0), $MachinePrecision], If[LessEqual[N[Max[p, r], $MachinePrecision], 3.2e-42], t$95$2, N[(0.5 * N[(N[Max[p, r], $MachinePrecision] + N[(t$95$1 + t$95$0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]]]]]

Derivation

1. Split input into 3 regimes

2. if r < 1e-251 or 1.0599999999999999e-122 < r < 3.2000000000000002e-42

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 N/A

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

      7. lower-fabs.f64 N/A

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

      8. lower-fabs.f64 34.5%

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

   7. Applied rewrites 34.5%

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

      1. metadata-eval 34.5%

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

      2. metadata-eval N/A

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

   9. Applied rewrites 34.6%

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

   10. Taylor expanded in r around 0

       \[\leadsto -0.5 \cdot \left(p - \left(\left|r\right| + \left|p\right|\right)\right) \]
   11. Step-by-step derivation

       1. lower-fabs.f64 24.0%

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

   12. Applied rewrites 24.0%

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

   if 1e-251 < r < 1.0599999999999999e-122

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

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

   5. Taylor expanded in q around inf

      \[\leadsto q \cdot 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 18.3%

      \[\leadsto q \cdot 1 \]

   if 3.2000000000000002e-42 < r

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. metadata-eval N/A

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

      4. lower-+.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-fabs.f64 N/A

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

      7. lower-fabs.f64 24.4%

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

   7. Applied rewrites 24.4%

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

Alternative 7: 57.5% accurate, 2.2× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\left|q\right| \leq 7.8 \cdot 10^{+55}:\\ \;\;\;\;-0.5 \cdot \left(\mathsf{min}\left(p, r\right) - \left(\left|\mathsf{max}\left(p, r\right)\right| + \left|\mathsf{min}\left(p, r\right)\right|\right)\right)\\ \mathbf{else}:\\ \;\;\;\;\left|q\right| \cdot 1\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (if (<= (fabs q) 7.8e+55)
   (* -0.5 (- (fmin p r) (+ (fabs (fmax p r)) (fabs (fmin p r)))))
   (* (fabs q) 1.0)))

C

double code(double p, double r, double q) {
	double tmp;
	if (fabs(q) <= 7.8e+55) {
		tmp = -0.5 * (fmin(p, r) - (fabs(fmax(p, r)) + fabs(fmin(p, r))));
	} else {
		tmp = fabs(q) * 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    real(8) :: tmp
    if (abs(q) <= 7.8d+55) then
        tmp = (-0.5d0) * (fmin(p, r) - (abs(fmax(p, r)) + abs(fmin(p, r))))
    else
        tmp = abs(q) * 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double tmp;
	if (Math.abs(q) <= 7.8e+55) {
		tmp = -0.5 * (fmin(p, r) - (Math.abs(fmax(p, r)) + Math.abs(fmin(p, r))));
	} else {
		tmp = Math.abs(q) * 1.0;
	}
	return tmp;
}

Python

def code(p, r, q):
	tmp = 0
	if math.fabs(q) <= 7.8e+55:
		tmp = -0.5 * (fmin(p, r) - (math.fabs(fmax(p, r)) + math.fabs(fmin(p, r))))
	else:
		tmp = math.fabs(q) * 1.0
	return tmp

Julia

function code(p, r, q)
	tmp = 0.0
	if (abs(q) <= 7.8e+55)
		tmp = Float64(-0.5 * Float64(fmin(p, r) - Float64(abs(fmax(p, r)) + abs(fmin(p, r)))));
	else
		tmp = Float64(abs(q) * 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	tmp = 0.0;
	if (abs(q) <= 7.8e+55)
		tmp = -0.5 * (min(p, r) - (abs(max(p, r)) + abs(min(p, r))));
	else
		tmp = abs(q) * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := If[LessEqual[N[Abs[q], $MachinePrecision], 7.8e+55], N[(-0.5 * N[(N[Min[p, r], $MachinePrecision] - N[(N[Abs[N[Max[p, r], $MachinePrecision]], $MachinePrecision] + N[Abs[N[Min[p, r], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Abs[q], $MachinePrecision] * 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if q < 7.8000000000000005e55

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{-1 \cdot \left(p \cdot \left(\frac{1}{2} + \frac{-1}{2} \cdot \frac{r + \left(\left|p\right| + \left|r\right|\right)}{p}\right)\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 30.1%

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

   5. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-fma.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-+.f64 N/A

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

      6. lower-+.f64 N/A

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

      7. lower-fabs.f64 N/A

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

      8. lower-fabs.f64 34.5%

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

   7. Applied rewrites 34.5%

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

      1. metadata-eval 34.5%

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

      2. metadata-eval N/A

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

   9. Applied rewrites 34.6%

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

   10. Taylor expanded in r around 0

       \[\leadsto -0.5 \cdot \left(p - \left(\left|r\right| + \left|p\right|\right)\right) \]
   11. Step-by-step derivation

       1. lower-fabs.f64 24.0%

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

   12. Applied rewrites 24.0%

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

   if 7.8000000000000005e55 < q

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

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

   5. Taylor expanded in q around inf

      \[\leadsto q \cdot 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 18.3%

      \[\leadsto q \cdot 1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 8: 39.3% accurate, 4.0× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\left|q\right| \leq 3.1 \cdot 10^{-61}:\\ \;\;\;\;0.5 \cdot \left(\left|p\right| + \left|r\right|\right)\\ \mathbf{else}:\\ \;\;\;\;\left|q\right| \cdot 1\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (if (<= (fabs q) 3.1e-61) (* 0.5 (+ (fabs p) (fabs r))) (* (fabs q) 1.0)))

C

double code(double p, double r, double q) {
	double tmp;
	if (fabs(q) <= 3.1e-61) {
		tmp = 0.5 * (fabs(p) + fabs(r));
	} else {
		tmp = fabs(q) * 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    real(8) :: tmp
    if (abs(q) <= 3.1d-61) then
        tmp = 0.5d0 * (abs(p) + abs(r))
    else
        tmp = abs(q) * 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double tmp;
	if (Math.abs(q) <= 3.1e-61) {
		tmp = 0.5 * (Math.abs(p) + Math.abs(r));
	} else {
		tmp = Math.abs(q) * 1.0;
	}
	return tmp;
}

Python

def code(p, r, q):
	tmp = 0
	if math.fabs(q) <= 3.1e-61:
		tmp = 0.5 * (math.fabs(p) + math.fabs(r))
	else:
		tmp = math.fabs(q) * 1.0
	return tmp

Julia

function code(p, r, q)
	tmp = 0.0
	if (abs(q) <= 3.1e-61)
		tmp = Float64(0.5 * Float64(abs(p) + abs(r)));
	else
		tmp = Float64(abs(q) * 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	tmp = 0.0;
	if (abs(q) <= 3.1e-61)
		tmp = 0.5 * (abs(p) + abs(r));
	else
		tmp = abs(q) * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := If[LessEqual[N[Abs[q], $MachinePrecision], 3.1e-61], N[(0.5 * N[(N[Abs[p], $MachinePrecision] + N[Abs[r], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[(N[Abs[q], $MachinePrecision] * 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if q < 3.0999999999999999e-61

   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. Taylor expanded in r around -inf

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. lower-/.f64 N/A

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

   4. Applied rewrites 31.5%

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

   5. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. metadata-eval N/A

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

      4. lower-+.f64 N/A

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

      5. lower-+.f64 N/A

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

      6. lower-fabs.f64 N/A

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

      7. lower-fabs.f64 25.1%

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

   7. Applied rewrites 25.1%

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

   8. Taylor expanded in p around 0

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. metadata-eval N/A

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

      4. lower-+.f64 N/A

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

      5. lower-fabs.f64 N/A

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

      6. lower-fabs.f64 14.4%

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

   10. Applied rewrites 14.4%

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

   if 3.0999999999999999e-61 < q

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

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

   5. Taylor expanded in q around inf

      \[\leadsto q \cdot 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 18.3%

      \[\leadsto q \cdot 1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 36.7% accurate, 1.8× speedup

Math

\[\begin{array}{l} \mathbf{if}\;4 \cdot {\left(\left|q\right|\right)}^{2} \leq 2 \cdot 10^{-273}:\\ \;\;\;\;-0.5 \cdot \mathsf{min}\left(p, r\right)\\ \mathbf{else}:\\ \;\;\;\;\left|q\right| \cdot 1\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (if (<= (* 4.0 (pow (fabs q) 2.0)) 2e-273)
   (* -0.5 (fmin p r))
   (* (fabs q) 1.0)))

C

double code(double p, double r, double q) {
	double tmp;
	if ((4.0 * pow(fabs(q), 2.0)) <= 2e-273) {
		tmp = -0.5 * fmin(p, r);
	} else {
		tmp = fabs(q) * 1.0;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    real(8) :: tmp
    if ((4.0d0 * (abs(q) ** 2.0d0)) <= 2d-273) then
        tmp = (-0.5d0) * fmin(p, r)
    else
        tmp = abs(q) * 1.0d0
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double tmp;
	if ((4.0 * Math.pow(Math.abs(q), 2.0)) <= 2e-273) {
		tmp = -0.5 * fmin(p, r);
	} else {
		tmp = Math.abs(q) * 1.0;
	}
	return tmp;
}

Python

def code(p, r, q):
	tmp = 0
	if (4.0 * math.pow(math.fabs(q), 2.0)) <= 2e-273:
		tmp = -0.5 * fmin(p, r)
	else:
		tmp = math.fabs(q) * 1.0
	return tmp

Julia

function code(p, r, q)
	tmp = 0.0
	if (Float64(4.0 * (abs(q) ^ 2.0)) <= 2e-273)
		tmp = Float64(-0.5 * fmin(p, r));
	else
		tmp = Float64(abs(q) * 1.0);
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	tmp = 0.0;
	if ((4.0 * (abs(q) ^ 2.0)) <= 2e-273)
		tmp = -0.5 * min(p, r);
	else
		tmp = abs(q) * 1.0;
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := If[LessEqual[N[(4.0 * N[Power[N[Abs[q], $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision], 2e-273], N[(-0.5 * N[Min[p, r], $MachinePrecision]), $MachinePrecision], N[(N[Abs[q], $MachinePrecision] * 1.0), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if (*.f64 #s(literal 4 binary64) (pow.f64 q #s(literal 2 binary64))) < 2.0000000000000002e-273

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot p} \]
   3. Step-by-step derivation

      1. lower-*.f64 5.1%

         \[\leadsto -0.5 \cdot \color{blue}{p} \]

   4. Applied rewrites 5.1%

      \[\leadsto \color{blue}{-0.5 \cdot p} \]

   if 2.0000000000000002e-273 < (*.f64 #s(literal 4 binary64) (pow.f64 q #s(literal 2 binary64)))

   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. Taylor expanded in q around inf

      \[\leadsto \color{blue}{q \cdot \left(1 + \frac{1}{2} \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   3. 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. lower-*.f64 N/A

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

      3. lower-+.f64 N/A

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

      4. lower-*.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-/.f64 N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-fabs.f64 26.6%

         \[\leadsto q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right) \]

   4. Applied rewrites 26.6%

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

   5. Taylor expanded in q around inf

      \[\leadsto q \cdot 1 \]
   6. Step-by-step derivation

   7. Applied rewrites 18.3%

      \[\leadsto q \cdot 1 \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 10: 18.1% accurate, 4.1× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\mathsf{min}\left(p, r\right) \leq -3.4 \cdot 10^{-82}:\\ \;\;\;\;-0.5 \cdot \mathsf{min}\left(p, r\right)\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot \mathsf{max}\left(p, r\right)\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (if (<= (fmin p r) -3.4e-82) (* -0.5 (fmin p r)) (* 0.5 (fmax p r))))

C

double code(double p, double r, double q) {
	double tmp;
	if (fmin(p, r) <= -3.4e-82) {
		tmp = -0.5 * fmin(p, r);
	} else {
		tmp = 0.5 * fmax(p, r);
	}
	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(p, r, q)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q
    real(8) :: tmp
    if (fmin(p, r) <= (-3.4d-82)) then
        tmp = (-0.5d0) * fmin(p, r)
    else
        tmp = 0.5d0 * fmax(p, r)
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double tmp;
	if (fmin(p, r) <= -3.4e-82) {
		tmp = -0.5 * fmin(p, r);
	} else {
		tmp = 0.5 * fmax(p, r);
	}
	return tmp;
}

Python

def code(p, r, q):
	tmp = 0
	if fmin(p, r) <= -3.4e-82:
		tmp = -0.5 * fmin(p, r)
	else:
		tmp = 0.5 * fmax(p, r)
	return tmp

Julia

function code(p, r, q)
	tmp = 0.0
	if (fmin(p, r) <= -3.4e-82)
		tmp = Float64(-0.5 * fmin(p, r));
	else
		tmp = Float64(0.5 * fmax(p, r));
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	tmp = 0.0;
	if (min(p, r) <= -3.4e-82)
		tmp = -0.5 * min(p, r);
	else
		tmp = 0.5 * max(p, r);
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := If[LessEqual[N[Min[p, r], $MachinePrecision], -3.4e-82], N[(-0.5 * N[Min[p, r], $MachinePrecision]), $MachinePrecision], N[(0.5 * N[Max[p, r], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if p < -3.3999999999999998e-82

   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. Taylor expanded in p around -inf

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot p} \]
   3. Step-by-step derivation

      1. lower-*.f64 5.1%

         \[\leadsto -0.5 \cdot \color{blue}{p} \]

   4. Applied rewrites 5.1%

      \[\leadsto \color{blue}{-0.5 \cdot p} \]

   if -3.3999999999999998e-82 < p

   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. Taylor expanded in r around inf

      \[\leadsto \color{blue}{\frac{1}{2} \cdot r} \]
   3. Step-by-step derivation

      1. metadata-eval N/A

         \[\leadsto \frac{1}{2} \cdot r \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{1}{2} \cdot \color{blue}{r} \]

      3. metadata-eval 5.2%

         \[\leadsto 0.5 \cdot r \]

   4. Applied rewrites 5.2%

      \[\leadsto \color{blue}{0.5 \cdot r} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 11: 13.0% accurate, 8.0× speedup

Math

\[-0.5 \cdot \mathsf{min}\left(p, r\right) \]

FPCore

(FPCore (p r q) :precision binary64 (* -0.5 (fmin p r)))

C

double code(double p, double r, double q) {
	return -0.5 * fmin(p, r);
}

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 = (-0.5d0) * fmin(p, r)
end function

Java

public static double code(double p, double r, double q) {
	return -0.5 * fmin(p, r);
}

Python

def code(p, r, q):
	return -0.5 * fmin(p, r)

Julia

function code(p, r, q)
	return Float64(-0.5 * fmin(p, r))
end

MATLAB

function tmp = code(p, r, q)
	tmp = -0.5 * min(p, r);
end

Wolfram

code[p_, r_, q_] := N[(-0.5 * N[Min[p, r], $MachinePrecision]), $MachinePrecision]

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. Taylor expanded in p around -inf

   \[\leadsto \color{blue}{\frac{-1}{2} \cdot p} \]
3. Step-by-step derivation

   1. lower-*.f64 5.1%

      \[\leadsto -0.5 \cdot \color{blue}{p} \]

4. Applied rewrites 5.1%

   \[\leadsto \color{blue}{-0.5 \cdot p} \]
5. Add Preprocessing

Alternative 12: 8.6% accurate, 28.9× speedup

Math

\[-q \]

FPCore

(FPCore (p r q) :precision binary64 (- q))

C

double code(double p, double r, double q) {
	return -q;
}

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 = -q
end function

Java

public static double code(double p, double r, double q) {
	return -q;
}

Python

def code(p, r, q):
	return -q

Julia

function code(p, r, q)
	return Float64(-q)
end

MATLAB

function tmp = code(p, r, q)
	tmp = -q;
end

Wolfram

code[p_, r_, q_] := (-q)

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. Taylor expanded in q around -inf

   \[\leadsto \color{blue}{-1 \cdot q} \]
3. Step-by-step derivation

   1. lower-*.f64 18.1%

      \[\leadsto -1 \cdot \color{blue}{q} \]

4. Applied rewrites 18.1%

   \[\leadsto \color{blue}{-1 \cdot q} \]
5. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto -1 \cdot \color{blue}{q} \]

   2. mul-1-neg N/A

      \[\leadsto \mathsf{neg}\left(q\right) \]

   3. lower-neg.f64 18.1%

      \[\leadsto -q \]

6. Applied rewrites 18.1%

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

Reproduce

herbie shell --seed 2025191 
(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)))))))