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

Percentage Accurate: 45.2% → 81.5%

Time: 3.1s

Alternatives: 8

Speedup: 4.6×

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: 45.2% 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: 81.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{max}\left(p, r\right)\right|\\ t_1 := \left|\mathsf{min}\left(p, r\right)\right|\\ \mathbf{if}\;\left|q\right| \leq 9 \cdot 10^{+110}:\\ \;\;\;\;\left(\mathsf{max}\left(p, r\right) - \left(\left(\mathsf{min}\left(p, r\right) - t\_0\right) - t\_1\right)\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t\_0 + t\_1, 0.5, \left|q\right|\right)\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (fabs (fmax p r))) (t_1 (fabs (fmin p r))))
   (if (<= (fabs q) 9e+110)
     (* (- (fmax p r) (- (- (fmin p r) t_0) t_1)) 0.5)
     (fma (+ t_0 t_1) 0.5 (fabs q)))))

C

double code(double p, double r, double q) {
	double t_0 = fabs(fmax(p, r));
	double t_1 = fabs(fmin(p, r));
	double tmp;
	if (fabs(q) <= 9e+110) {
		tmp = (fmax(p, r) - ((fmin(p, r) - t_0) - t_1)) * 0.5;
	} else {
		tmp = fma((t_0 + t_1), 0.5, fabs(q));
	}
	return tmp;
}

Julia

function code(p, r, q)
	t_0 = abs(fmax(p, r))
	t_1 = abs(fmin(p, r))
	tmp = 0.0
	if (abs(q) <= 9e+110)
		tmp = Float64(Float64(fmax(p, r) - Float64(Float64(fmin(p, r) - t_0) - t_1)) * 0.5);
	else
		tmp = fma(Float64(t_0 + t_1), 0.5, abs(q));
	end
	return 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]}, If[LessEqual[N[Abs[q], $MachinePrecision], 9e+110], N[(N[(N[Max[p, r], $MachinePrecision] - N[(N[(N[Min[p, r], $MachinePrecision] - t$95$0), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(t$95$0 + t$95$1), $MachinePrecision] * 0.5 + N[Abs[q], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if q < 9.0000000000000005e110

   1. Initial program 45.2%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 30.3%

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

   5. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. metadata-eval N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-+.f64 N/A

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

      10. lower-fabs.f64 N/A

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

      11. lower-*.f64 35.0%

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

   7. Applied rewrites 35.0%

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

      1. metadata-eval 35.0%

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

      2. metadata-eval 35.0%

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

      3. metadata-eval N/A

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

      4. metadata-eval N/A

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

   9. Applied rewrites 35.0%

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

   if 9.0000000000000005e110 < q

   1. Initial program 45.2%

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

   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 26.8%

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

   4. Applied rewrites 26.8%

      \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   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. sum-to-mult-rev N/A

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

      9. lower-+.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lift-+.f64 N/A

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

      13. +-commutative N/A

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

      14. lower-+.f64 N/A

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

      15. metadata-eval 29.3%

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

   6. Applied rewrites 29.3%

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

      1. metadata-eval N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-fabs.f64 N/A

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

      8. lift-fabs.f64 N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-fabs.f64 N/A

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

      11. lift-fabs.f64 N/A

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

      12. +-commutative N/A

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

      13. lift-+.f64 N/A

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

      14. metadata-eval 29.3%

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

   8. Applied rewrites 29.3%

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

Alternative 2: 81.5% accurate, 1.8× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{max}\left(p, r\right)\right|\\ t_1 := \left|\mathsf{min}\left(p, r\right)\right|\\ \mathbf{if}\;\left|q\right| \leq 9 \cdot 10^{+110}:\\ \;\;\;\;\left(\mathsf{max}\left(p, r\right) - \left(\left(\mathsf{min}\left(p, r\right) - t\_0\right) - t\_1\right)\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\frac{t\_0 + t\_1}{\left|q\right|}, 0.5 \cdot \left|q\right|, \left|q\right|\right)\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (fabs (fmax p r))) (t_1 (fabs (fmin p r))))
   (if (<= (fabs q) 9e+110)
     (* (- (fmax p r) (- (- (fmin p r) t_0) t_1)) 0.5)
     (fma (/ (+ t_0 t_1) (fabs q)) (* 0.5 (fabs q)) (fabs q)))))

C

double code(double p, double r, double q) {
	double t_0 = fabs(fmax(p, r));
	double t_1 = fabs(fmin(p, r));
	double tmp;
	if (fabs(q) <= 9e+110) {
		tmp = (fmax(p, r) - ((fmin(p, r) - t_0) - t_1)) * 0.5;
	} else {
		tmp = fma(((t_0 + t_1) / fabs(q)), (0.5 * fabs(q)), fabs(q));
	}
	return tmp;
}

Julia

function code(p, r, q)
	t_0 = abs(fmax(p, r))
	t_1 = abs(fmin(p, r))
	tmp = 0.0
	if (abs(q) <= 9e+110)
		tmp = Float64(Float64(fmax(p, r) - Float64(Float64(fmin(p, r) - t_0) - t_1)) * 0.5);
	else
		tmp = fma(Float64(Float64(t_0 + t_1) / abs(q)), Float64(0.5 * abs(q)), abs(q));
	end
	return 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]}, If[LessEqual[N[Abs[q], $MachinePrecision], 9e+110], N[(N[(N[Max[p, r], $MachinePrecision] - N[(N[(N[Min[p, r], $MachinePrecision] - t$95$0), $MachinePrecision] - t$95$1), $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(N[(t$95$0 + t$95$1), $MachinePrecision] / N[Abs[q], $MachinePrecision]), $MachinePrecision] * N[(0.5 * N[Abs[q], $MachinePrecision]), $MachinePrecision] + N[Abs[q], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if q < 9.0000000000000005e110

   1. Initial program 45.2%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 30.3%

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

   5. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. metadata-eval N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-+.f64 N/A

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

      10. lower-fabs.f64 N/A

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

      11. lower-*.f64 35.0%

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

   7. Applied rewrites 35.0%

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

      1. metadata-eval 35.0%

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

      2. metadata-eval 35.0%

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

      3. metadata-eval N/A

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

      4. metadata-eval N/A

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

   9. Applied rewrites 35.0%

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

   if 9.0000000000000005e110 < q

   1. Initial program 45.2%

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

   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 26.8%

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

   4. Applied rewrites 26.8%

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

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

      4. +-commutative N/A

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

      5. distribute-lft-in N/A

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

      6. *-rgt-identity N/A

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

      7. *-commutative N/A

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

      8. lift-*.f64 N/A

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

      9. *-commutative N/A

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

      10. associate-*l* N/A

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

      11. lower-fma.f64 N/A

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

   6. Applied rewrites 26.8%

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

Alternative 3: 65.5% accurate, 1.6× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{min}\left(p, r\right)\right|\\ t_1 := \left|\mathsf{max}\left(p, r\right)\right|\\ t_2 := t\_0 + t\_1\\ \mathbf{if}\;\mathsf{min}\left(p, r\right) \leq -5.3 \cdot 10^{+82}:\\ \;\;\;\;\left(t\_2 - \mathsf{min}\left(p, r\right)\right) \cdot 0.5\\ \mathbf{elif}\;\mathsf{min}\left(p, r\right) \leq -2.8 \cdot 10^{-284}:\\ \;\;\;\;\mathsf{fma}\left(t\_1 + t\_0, 0.5, \left|q\right|\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\mathsf{max}\left(p, r\right) + t\_2\right) \cdot 0.5\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (fabs (fmin p r))) (t_1 (fabs (fmax p r))) (t_2 (+ t_0 t_1)))
   (if (<= (fmin p r) -5.3e+82)
     (* (- t_2 (fmin p r)) 0.5)
     (if (<= (fmin p r) -2.8e-284)
       (fma (+ t_1 t_0) 0.5 (fabs q))
       (* (+ (fmax p r) t_2) 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 t_2 = t_0 + t_1;
	double tmp;
	if (fmin(p, r) <= -5.3e+82) {
		tmp = (t_2 - fmin(p, r)) * 0.5;
	} else if (fmin(p, r) <= -2.8e-284) {
		tmp = fma((t_1 + t_0), 0.5, fabs(q));
	} else {
		tmp = (fmax(p, r) + t_2) * 0.5;
	}
	return tmp;
}

Julia

function code(p, r, q)
	t_0 = abs(fmin(p, r))
	t_1 = abs(fmax(p, r))
	t_2 = Float64(t_0 + t_1)
	tmp = 0.0
	if (fmin(p, r) <= -5.3e+82)
		tmp = Float64(Float64(t_2 - fmin(p, r)) * 0.5);
	elseif (fmin(p, r) <= -2.8e-284)
		tmp = fma(Float64(t_1 + t_0), 0.5, abs(q));
	else
		tmp = Float64(Float64(fmax(p, r) + t_2) * 0.5);
	end
	return 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]}, Block[{t$95$2 = N[(t$95$0 + t$95$1), $MachinePrecision]}, If[LessEqual[N[Min[p, r], $MachinePrecision], -5.3e+82], N[(N[(t$95$2 - N[Min[p, r], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], If[LessEqual[N[Min[p, r], $MachinePrecision], -2.8e-284], N[(N[(t$95$1 + t$95$0), $MachinePrecision] * 0.5 + N[Abs[q], $MachinePrecision]), $MachinePrecision], N[(N[(N[Max[p, r], $MachinePrecision] + t$95$2), $MachinePrecision] * 0.5), $MachinePrecision]]]]]]

Derivation

1. Split input into 3 regimes

2. if p < -5.29999999999999977e82

   1. Initial program 45.2%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 30.3%

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

   5. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. metadata-eval N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-+.f64 N/A

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

      10. lower-fabs.f64 N/A

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

      11. lower-*.f64 35.0%

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

   7. Applied rewrites 35.0%

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

      1. metadata-eval 35.0%

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

      2. metadata-eval 35.0%

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

      3. metadata-eval N/A

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

      4. metadata-eval N/A

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

   9. Applied rewrites 35.0%

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

   10. Taylor expanded in r around 0

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

       1. lower--.f64 N/A

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

       2. lower-+.f64 N/A

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

       3. lower-fabs.f64 N/A

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

       4. lower-fabs.f64 24.1%

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

   12. Applied rewrites 24.1%

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

   if -5.29999999999999977e82 < p < -2.8000000000000003e-284

   1. Initial program 45.2%

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

   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 26.8%

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

   4. Applied rewrites 26.8%

      \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   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. sum-to-mult-rev N/A

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

      9. lower-+.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lift-+.f64 N/A

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

      13. +-commutative N/A

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

      14. lower-+.f64 N/A

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

      15. metadata-eval 29.3%

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

   6. Applied rewrites 29.3%

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

      1. metadata-eval N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-fabs.f64 N/A

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

      8. lift-fabs.f64 N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-fabs.f64 N/A

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

      11. lift-fabs.f64 N/A

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

      12. +-commutative N/A

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

      13. lift-+.f64 N/A

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

      14. metadata-eval 29.3%

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

   8. Applied rewrites 29.3%

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

   if -2.8000000000000003e-284 < p

   1. Initial program 45.2%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 30.3%

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

   5. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. metadata-eval N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-+.f64 N/A

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

      10. lower-fabs.f64 N/A

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

      11. lower-*.f64 35.0%

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

   7. Applied rewrites 35.0%

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

      1. metadata-eval 35.0%

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

      2. metadata-eval 35.0%

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

      3. metadata-eval N/A

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

      4. metadata-eval N/A

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

   9. Applied rewrites 35.0%

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

   10. Taylor expanded in p around 0

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

       1. lower-+.f64 N/A

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

       2. lower-+.f64 N/A

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

       3. lower-fabs.f64 N/A

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

       4. lower-fabs.f64 24.6%

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

   12. Applied rewrites 24.6%

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

Alternative 4: 60.6% accurate, 2.0× speedup

Math

\[\begin{array}{l} t_0 := \left|\mathsf{min}\left(p, r\right)\right|\\ t_1 := \left|\mathsf{max}\left(p, r\right)\right|\\ \mathbf{if}\;\mathsf{min}\left(p, r\right) \leq -5.3 \cdot 10^{+82}:\\ \;\;\;\;\left(\left(t\_0 + t\_1\right) - \mathsf{min}\left(p, r\right)\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(t\_1 + t\_0, 0.5, \left|q\right|\right)\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (let* ((t_0 (fabs (fmin p r))) (t_1 (fabs (fmax p r))))
   (if (<= (fmin p r) -5.3e+82)
     (* (- (+ t_0 t_1) (fmin p r)) 0.5)
     (fma (+ t_1 t_0) 0.5 (fabs q)))))

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 (fmin(p, r) <= -5.3e+82) {
		tmp = ((t_0 + t_1) - fmin(p, r)) * 0.5;
	} else {
		tmp = fma((t_1 + t_0), 0.5, fabs(q));
	}
	return tmp;
}

Julia

function code(p, r, q)
	t_0 = abs(fmin(p, r))
	t_1 = abs(fmax(p, r))
	tmp = 0.0
	if (fmin(p, r) <= -5.3e+82)
		tmp = Float64(Float64(Float64(t_0 + t_1) - fmin(p, r)) * 0.5);
	else
		tmp = fma(Float64(t_1 + t_0), 0.5, abs(q));
	end
	return 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[Min[p, r], $MachinePrecision], -5.3e+82], N[(N[(N[(t$95$0 + t$95$1), $MachinePrecision] - N[Min[p, r], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(t$95$1 + t$95$0), $MachinePrecision] * 0.5 + N[Abs[q], $MachinePrecision]), $MachinePrecision]]]]

Derivation

1. Split input into 2 regimes

2. if p < -5.29999999999999977e82

   1. Initial program 45.2%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 30.3%

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

   5. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. metadata-eval N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-+.f64 N/A

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

      10. lower-fabs.f64 N/A

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

      11. lower-*.f64 35.0%

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

   7. Applied rewrites 35.0%

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

      1. metadata-eval 35.0%

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

      2. metadata-eval 35.0%

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

      3. metadata-eval N/A

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

      4. metadata-eval N/A

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

   9. Applied rewrites 35.0%

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

   10. Taylor expanded in r around 0

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

       1. lower--.f64 N/A

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

       2. lower-+.f64 N/A

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

       3. lower-fabs.f64 N/A

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

       4. lower-fabs.f64 24.1%

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

   12. Applied rewrites 24.1%

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

   if -5.29999999999999977e82 < p

   1. Initial program 45.2%

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

   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 26.8%

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

   4. Applied rewrites 26.8%

      \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
   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. sum-to-mult-rev N/A

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

      9. lower-+.f64 N/A

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

      10. *-commutative N/A

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

      11. lower-*.f64 N/A

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

      12. lift-+.f64 N/A

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

      13. +-commutative N/A

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

      14. lower-+.f64 N/A

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

      15. metadata-eval 29.3%

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

   6. Applied rewrites 29.3%

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

      1. metadata-eval N/A

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

      2. lift-+.f64 N/A

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

      3. +-commutative N/A

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

      4. lift-*.f64 N/A

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

      5. lift-+.f64 N/A

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

      6. +-commutative N/A

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

      7. lift-fabs.f64 N/A

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

      8. lift-fabs.f64 N/A

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

      9. lower-fma.f64 N/A

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

      10. lift-fabs.f64 N/A

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

      11. lift-fabs.f64 N/A

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

      12. +-commutative N/A

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

      13. lift-+.f64 N/A

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

      14. metadata-eval 29.3%

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

   8. Applied rewrites 29.3%

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

Alternative 5: 45.9% accurate, 4.6× speedup

Math

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

FPCore

(FPCore (p r q) :precision binary64 (fma (+ (fabs r) (fabs p)) 0.5 (fabs q)))

C

double code(double p, double r, double q) {
	return fma((fabs(r) + fabs(p)), 0.5, fabs(q));
}

Julia

function code(p, r, q)
	return fma(Float64(abs(r) + abs(p)), 0.5, abs(q))
end

Wolfram

code[p_, r_, q_] := N[(N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision] * 0.5 + N[Abs[q], $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 45.2%

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

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 26.8%

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

4. Applied rewrites 26.8%

   \[\leadsto \color{blue}{q \cdot \left(1 + 0.5 \cdot \frac{\left|p\right| + \left|r\right|}{q}\right)} \]
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. sum-to-mult-rev N/A

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

   9. lower-+.f64 N/A

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

   10. *-commutative N/A

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

   11. lower-*.f64 N/A

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

   12. lift-+.f64 N/A

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

   13. +-commutative N/A

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

   14. lower-+.f64 N/A

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

   15. metadata-eval 29.3%

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

6. Applied rewrites 29.3%

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

   1. metadata-eval N/A

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

   2. lift-+.f64 N/A

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

   3. +-commutative N/A

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

   4. lift-*.f64 N/A

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

   5. lift-+.f64 N/A

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

   6. +-commutative N/A

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

   7. lift-fabs.f64 N/A

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

   8. lift-fabs.f64 N/A

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

   9. lower-fma.f64 N/A

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

   10. lift-fabs.f64 N/A

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

   11. lift-fabs.f64 N/A

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

   12. +-commutative N/A

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

   13. lift-+.f64 N/A

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

   14. metadata-eval 29.3%

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

8. Applied rewrites 29.3%

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

Alternative 6: 39.8% accurate, 4.0× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\left|q\right| \leq 1.15 \cdot 10^{-87}:\\ \;\;\;\;0.5 \cdot \left(\left|p\right| + \left|r\right|\right)\\ \mathbf{else}:\\ \;\;\;\;\left|q\right|\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (if (<= (fabs q) 1.15e-87) (* 0.5 (+ (fabs p) (fabs r))) (fabs q)))

C

double code(double p, double r, double q) {
	double tmp;
	if (fabs(q) <= 1.15e-87) {
		tmp = 0.5 * (fabs(p) + fabs(r));
	} else {
		tmp = fabs(q);
	}
	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) <= 1.15d-87) then
        tmp = 0.5d0 * (abs(p) + abs(r))
    else
        tmp = abs(q)
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double tmp;
	if (Math.abs(q) <= 1.15e-87) {
		tmp = 0.5 * (Math.abs(p) + Math.abs(r));
	} else {
		tmp = Math.abs(q);
	}
	return tmp;
}

Python

def code(p, r, q):
	tmp = 0
	if math.fabs(q) <= 1.15e-87:
		tmp = 0.5 * (math.fabs(p) + math.fabs(r))
	else:
		tmp = math.fabs(q)
	return tmp

Julia

function code(p, r, q)
	tmp = 0.0
	if (abs(q) <= 1.15e-87)
		tmp = Float64(0.5 * Float64(abs(p) + abs(r)));
	else
		tmp = abs(q);
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	tmp = 0.0;
	if (abs(q) <= 1.15e-87)
		tmp = 0.5 * (abs(p) + abs(r));
	else
		tmp = abs(q);
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := If[LessEqual[N[Abs[q], $MachinePrecision], 1.15e-87], N[(0.5 * N[(N[Abs[p], $MachinePrecision] + N[Abs[r], $MachinePrecision]), $MachinePrecision]), $MachinePrecision], N[Abs[q], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if q < 1.1500000000000001e-87

   1. Initial program 45.2%

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

   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 26.8%

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

   4. Applied rewrites 26.8%

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

   5. Taylor expanded in q around 0

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. metadata-eval N/A

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

      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 14.3%

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

   7. Applied rewrites 14.3%

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

   if 1.1500000000000001e-87 < q

   1. Initial program 45.2%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 30.3%

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

   5. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. metadata-eval N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-+.f64 N/A

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

      10. lower-fabs.f64 N/A

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

      11. lower-*.f64 35.0%

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

   7. Applied rewrites 35.0%

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

      1. metadata-eval 35.0%

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

      2. metadata-eval 35.0%

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

      3. metadata-eval N/A

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

      4. metadata-eval N/A

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

   9. Applied rewrites 35.0%

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

   10. Taylor expanded in q around inf

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

   12. Applied rewrites 18.6%

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

Alternative 7: 37.1% accurate, 4.1× speedup

Math

\[\begin{array}{l} \mathbf{if}\;\mathsf{min}\left(p, r\right) \leq -5.5 \cdot 10^{+176}:\\ \;\;\;\;-0.5 \cdot \mathsf{min}\left(p, r\right)\\ \mathbf{else}:\\ \;\;\;\;\left|q\right|\\ \end{array} \]

FPCore

(FPCore (p r q)
 :precision binary64
 (if (<= (fmin p r) -5.5e+176) (* -0.5 (fmin p r)) (fabs q)))

C

double code(double p, double r, double q) {
	double tmp;
	if (fmin(p, r) <= -5.5e+176) {
		tmp = -0.5 * fmin(p, r);
	} else {
		tmp = fabs(q);
	}
	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) <= (-5.5d+176)) then
        tmp = (-0.5d0) * fmin(p, r)
    else
        tmp = abs(q)
    end if
    code = tmp
end function

Java

public static double code(double p, double r, double q) {
	double tmp;
	if (fmin(p, r) <= -5.5e+176) {
		tmp = -0.5 * fmin(p, r);
	} else {
		tmp = Math.abs(q);
	}
	return tmp;
}

Python

def code(p, r, q):
	tmp = 0
	if fmin(p, r) <= -5.5e+176:
		tmp = -0.5 * fmin(p, r)
	else:
		tmp = math.fabs(q)
	return tmp

Julia

function code(p, r, q)
	tmp = 0.0
	if (fmin(p, r) <= -5.5e+176)
		tmp = Float64(-0.5 * fmin(p, r));
	else
		tmp = abs(q);
	end
	return tmp
end

MATLAB

function tmp_2 = code(p, r, q)
	tmp = 0.0;
	if (min(p, r) <= -5.5e+176)
		tmp = -0.5 * min(p, r);
	else
		tmp = abs(q);
	end
	tmp_2 = tmp;
end

Wolfram

code[p_, r_, q_] := If[LessEqual[N[Min[p, r], $MachinePrecision], -5.5e+176], N[(-0.5 * N[Min[p, r], $MachinePrecision]), $MachinePrecision], N[Abs[q], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if p < -5.4999999999999995e176

   1. Initial program 45.2%

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

   2. Taylor expanded in p around -inf

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

      1. lower-*.f64 5.2%

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

   4. Applied rewrites 5.2%

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

   if -5.4999999999999995e176 < p

   1. Initial program 45.2%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-*.f64 N/A

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

      4. lower-+.f64 N/A

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

      5. metadata-eval N/A

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

      6. lower-*.f64 N/A

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

      7. metadata-eval N/A

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

      8. lower-/.f64 N/A

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

   4. Applied rewrites 30.3%

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

   5. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. metadata-eval N/A

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

      3. lower-fma.f64 N/A

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

      4. metadata-eval N/A

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

      5. lower-*.f64 N/A

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

      6. metadata-eval N/A

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

      7. lower-+.f64 N/A

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

      8. lower-fabs.f64 N/A

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

      9. lower-+.f64 N/A

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

      10. lower-fabs.f64 N/A

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

      11. lower-*.f64 35.0%

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

   7. Applied rewrites 35.0%

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

      1. metadata-eval 35.0%

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

      2. metadata-eval 35.0%

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

      3. metadata-eval N/A

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

      4. metadata-eval N/A

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

   9. Applied rewrites 35.0%

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

   10. Taylor expanded in q around inf

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

   12. Applied rewrites 18.6%

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

Alternative 8: 36.0% accurate, 26.0× speedup

Math

\[\left|q\right| \]

FPCore

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

C

double code(double p, double r, double q) {
	return fabs(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 = abs(q)
end function

Java

public static double code(double p, double r, double q) {
	return Math.abs(q);
}

Python

def code(p, r, q):
	return math.fabs(q)

Julia

function code(p, r, q)
	return abs(q)
end

MATLAB

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

Wolfram

code[p_, r_, q_] := N[Abs[q], $MachinePrecision]

Derivation

1. Initial program 45.2%

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

2. Taylor expanded in r around inf

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

   1. metadata-eval N/A

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

   2. metadata-eval N/A

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

   3. lower-*.f64 N/A

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

   4. lower-+.f64 N/A

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

   5. metadata-eval N/A

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

   6. lower-*.f64 N/A

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

   7. metadata-eval N/A

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

   8. lower-/.f64 N/A

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

4. Applied rewrites 30.3%

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

5. Taylor expanded in r around 0

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

   1. metadata-eval N/A

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

   2. metadata-eval N/A

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

   3. lower-fma.f64 N/A

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

   4. metadata-eval N/A

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

   5. lower-*.f64 N/A

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

   6. metadata-eval N/A

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

   7. lower-+.f64 N/A

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

   8. lower-fabs.f64 N/A

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

   9. lower-+.f64 N/A

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

   10. lower-fabs.f64 N/A

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

   11. lower-*.f64 35.0%

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

7. Applied rewrites 35.0%

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

   1. metadata-eval 35.0%

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

   2. metadata-eval 35.0%

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

   3. metadata-eval N/A

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

   4. metadata-eval N/A

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

9. Applied rewrites 35.0%

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

10. Taylor expanded in q around inf

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

12. Applied rewrites 18.6%

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

Reproduce

herbie shell --seed 2025181 
(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)))))))