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

Percentage Accurate: 44.8% → 82.1%

Time: 3.4s

Alternatives: 9

Speedup: 5.1×

Specification

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 44.8% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 82.1% accurate, 2.9× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ \begin{array}{l} \mathbf{if}\;q\_m \leq 7.6 \cdot 10^{+124}:\\ \;\;\;\;\left(\left(\left|r - p\right| + \left|r\right|\right) + \left|p\right|\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(\left|r\right| + \left|p\right|, 0.5, q\_m\right)\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m)
 :precision binary64
 (if (<= q_m 7.6e+124)
   (* (+ (+ (fabs (- r p)) (fabs r)) (fabs p)) 0.5)
   (fma (+ (fabs r) (fabs p)) 0.5 q_m)))

C

q_m = fabs(q);
double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 7.6e+124) {
		tmp = ((fabs((r - p)) + fabs(r)) + fabs(p)) * 0.5;
	} else {
		tmp = fma((fabs(r) + fabs(p)), 0.5, q_m);
	}
	return tmp;
}

Julia

q_m = abs(q)
function code(p, r, q_m)
	tmp = 0.0
	if (q_m <= 7.6e+124)
		tmp = Float64(Float64(Float64(abs(Float64(r - p)) + abs(r)) + abs(p)) * 0.5);
	else
		tmp = fma(Float64(abs(r) + abs(p)), 0.5, q_m);
	end
	return tmp
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := If[LessEqual[q$95$m, 7.6e+124], N[(N[(N[(N[Abs[N[(r - p), $MachinePrecision]], $MachinePrecision] + N[Abs[r], $MachinePrecision]), $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision] * 0.5 + q$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if q < 7.5999999999999997e124

   1. Initial program 44.8%

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

   2. Taylor expanded in q around 0

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

      1. metadata-eval N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 68.0%

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

   if 7.5999999999999997e124 < q

   1. Initial program 44.8%

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

   2. 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. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 42.8%

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

   5. Taylor expanded in q around 0

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

      1. metadata-eval N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-+.f64 N/A

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

      7. lift-fabs.f64 N/A

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

      8. lift-fabs.f64 N/A

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

      9. metadata-eval 45.3

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

   7. Applied rewrites 45.3%

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

Alternative 2: 52.3% accurate, 2.8× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ \begin{array}{l} t_0 := \left|r\right| + \left|p\right|\\ \mathbf{if}\;p \leq -1.8 \cdot 10^{+101}:\\ \;\;\;\;\left(t\_0 + \left|p\right|\right) \cdot 0.5\\ \mathbf{elif}\;p \leq -3.8 \cdot 10^{-253}:\\ \;\;\;\;\mathsf{fma}\left(t\_0, 0.5, q\_m\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left|r\right| + t\_0\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m)
 :precision binary64
 (let* ((t_0 (+ (fabs r) (fabs p))))
   (if (<= p -1.8e+101)
     (* (+ t_0 (fabs p)) 0.5)
     (if (<= p -3.8e-253) (fma t_0 0.5 q_m) (* (+ (fabs r) t_0) 0.5)))))

C

q_m = fabs(q);
double code(double p, double r, double q_m) {
	double t_0 = fabs(r) + fabs(p);
	double tmp;
	if (p <= -1.8e+101) {
		tmp = (t_0 + fabs(p)) * 0.5;
	} else if (p <= -3.8e-253) {
		tmp = fma(t_0, 0.5, q_m);
	} else {
		tmp = (fabs(r) + t_0) * 0.5;
	}
	return tmp;
}

Julia

q_m = abs(q)
function code(p, r, q_m)
	t_0 = Float64(abs(r) + abs(p))
	tmp = 0.0
	if (p <= -1.8e+101)
		tmp = Float64(Float64(t_0 + abs(p)) * 0.5);
	elseif (p <= -3.8e-253)
		tmp = fma(t_0, 0.5, q_m);
	else
		tmp = Float64(Float64(abs(r) + t_0) * 0.5);
	end
	return tmp
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := Block[{t$95$0 = N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[p, -1.8e+101], N[(N[(t$95$0 + N[Abs[p], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], If[LessEqual[p, -3.8e-253], N[(t$95$0 * 0.5 + q$95$m), $MachinePrecision], N[(N[(N[Abs[r], $MachinePrecision] + t$95$0), $MachinePrecision] * 0.5), $MachinePrecision]]]]

Derivation

1. Split input into 3 regimes

2. if p < -1.80000000000000015e101

   1. Initial program 44.8%

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

   2. Taylor expanded in r around 0

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

      1. metadata-eval N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 35.2%

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

   5. Taylor expanded in q around 0

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

      1. pow2 N/A

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

      2. rem-sqrt-square-rev N/A

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

      3. lift-fabs.f64 40.5

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

   7. Applied rewrites 40.5%

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

   if -1.80000000000000015e101 < p < -3.80000000000000012e-253

   1. Initial program 44.8%

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

   2. 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. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 42.8%

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

   5. Taylor expanded in q around 0

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

      1. metadata-eval N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-+.f64 N/A

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

      7. lift-fabs.f64 N/A

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

      8. lift-fabs.f64 N/A

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

      9. metadata-eval 45.3

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

   7. Applied rewrites 45.3%

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

   if -3.80000000000000012e-253 < p

   1. Initial program 44.8%

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

   2. Taylor expanded in q around 0

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

      1. metadata-eval N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 68.0%

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

   5. Taylor expanded in p around 0

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

   7. Applied rewrites 40.8%

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

      1. lift-fabs.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. lift-fabs.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. associate-+l+ N/A

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

      6. +-commutative N/A

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

      7. lower-+.f64 N/A

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

      8. +-commutative N/A

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

      9. lift-+.f64 N/A

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

      10. lift-fabs.f64 N/A

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

      11. lift-fabs.f64 40.8

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

   9. Applied rewrites 40.8%

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

Alternative 3: 50.3% accurate, 3.4× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ \begin{array}{l} t_0 := \left|r\right| + \left|p\right|\\ \mathbf{if}\;r \leq 32:\\ \;\;\;\;\mathsf{fma}\left(t\_0, 0.5, q\_m\right)\\ \mathbf{else}:\\ \;\;\;\;\left(\left|r\right| + t\_0\right) \cdot 0.5\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m)
 :precision binary64
 (let* ((t_0 (+ (fabs r) (fabs p))))
   (if (<= r 32.0) (fma t_0 0.5 q_m) (* (+ (fabs r) t_0) 0.5))))

C

q_m = fabs(q);
double code(double p, double r, double q_m) {
	double t_0 = fabs(r) + fabs(p);
	double tmp;
	if (r <= 32.0) {
		tmp = fma(t_0, 0.5, q_m);
	} else {
		tmp = (fabs(r) + t_0) * 0.5;
	}
	return tmp;
}

Julia

q_m = abs(q)
function code(p, r, q_m)
	t_0 = Float64(abs(r) + abs(p))
	tmp = 0.0
	if (r <= 32.0)
		tmp = fma(t_0, 0.5, q_m);
	else
		tmp = Float64(Float64(abs(r) + t_0) * 0.5);
	end
	return tmp
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := Block[{t$95$0 = N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[r, 32.0], N[(t$95$0 * 0.5 + q$95$m), $MachinePrecision], N[(N[(N[Abs[r], $MachinePrecision] + t$95$0), $MachinePrecision] * 0.5), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if r < 32

   1. Initial program 44.8%

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

   2. 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. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 42.8%

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

   5. Taylor expanded in q around 0

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

      1. metadata-eval N/A

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

      2. +-commutative N/A

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

      3. *-commutative N/A

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

      4. lower-fma.f64 N/A

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

      5. +-commutative N/A

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

      6. lift-+.f64 N/A

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

      7. lift-fabs.f64 N/A

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

      8. lift-fabs.f64 N/A

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

      9. metadata-eval 45.3

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

   7. Applied rewrites 45.3%

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

   if 32 < r

   1. Initial program 44.8%

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

   2. Taylor expanded in q around 0

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

      1. metadata-eval N/A

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

      2. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 68.0%

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

   5. Taylor expanded in p around 0

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

   7. Applied rewrites 40.8%

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

      1. lift-fabs.f64 N/A

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

      2. lift-+.f64 N/A

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

      3. lift-fabs.f64 N/A

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

      4. lift-+.f64 N/A

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

      5. associate-+l+ N/A

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

      6. +-commutative N/A

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

      7. lower-+.f64 N/A

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

      8. +-commutative N/A

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

      9. lift-+.f64 N/A

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

      10. lift-fabs.f64 N/A

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

      11. lift-fabs.f64 40.8

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

   9. Applied rewrites 40.8%

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

Alternative 4: 45.3% accurate, 5.1× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ \mathsf{fma}\left(\left|r\right| + \left|p\right|, 0.5, q\_m\right) \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m) :precision binary64 (fma (+ (fabs r) (fabs p)) 0.5 q_m))

C

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

Julia

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

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := N[(N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision] * 0.5 + q$95$m), $MachinePrecision]

Derivation

1. Initial program 44.8%

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

2. 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. *-commutative N/A

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

   3. lower-*.f64 N/A

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

4. Applied rewrites 42.8%

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

5. Taylor expanded in q around 0

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

   1. metadata-eval N/A

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

   2. +-commutative N/A

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

   3. *-commutative N/A

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

   4. lower-fma.f64 N/A

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

   5. +-commutative N/A

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

   6. lift-+.f64 N/A

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

   7. lift-fabs.f64 N/A

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

   8. lift-fabs.f64 N/A

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

   9. metadata-eval 45.3

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

7. Applied rewrites 45.3%

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

Alternative 5: 38.9% accurate, 4.4× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ \begin{array}{l} \mathbf{if}\;q\_m \leq 1.15 \cdot 10^{-30}:\\ \;\;\;\;\left(\left|r\right| + \left|p\right|\right) \cdot 0.5\\ \mathbf{else}:\\ \;\;\;\;1 \cdot q\_m\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m)
 :precision binary64
 (if (<= q_m 1.15e-30) (* (+ (fabs r) (fabs p)) 0.5) (* 1.0 q_m)))

C

q_m = fabs(q);
double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 1.15e-30) {
		tmp = (fabs(r) + fabs(p)) * 0.5;
	} else {
		tmp = 1.0 * q_m;
	}
	return tmp;
}

Fortran

q_m =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q_m)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q_m
    real(8) :: tmp
    if (q_m <= 1.15d-30) then
        tmp = (abs(r) + abs(p)) * 0.5d0
    else
        tmp = 1.0d0 * q_m
    end if
    code = tmp
end function

Java

q_m = Math.abs(q);
public static double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 1.15e-30) {
		tmp = (Math.abs(r) + Math.abs(p)) * 0.5;
	} else {
		tmp = 1.0 * q_m;
	}
	return tmp;
}

Python

q_m = math.fabs(q)
def code(p, r, q_m):
	tmp = 0
	if q_m <= 1.15e-30:
		tmp = (math.fabs(r) + math.fabs(p)) * 0.5
	else:
		tmp = 1.0 * q_m
	return tmp

Julia

q_m = abs(q)
function code(p, r, q_m)
	tmp = 0.0
	if (q_m <= 1.15e-30)
		tmp = Float64(Float64(abs(r) + abs(p)) * 0.5);
	else
		tmp = Float64(1.0 * q_m);
	end
	return tmp
end

MATLAB

q_m = abs(q);
function tmp_2 = code(p, r, q_m)
	tmp = 0.0;
	if (q_m <= 1.15e-30)
		tmp = (abs(r) + abs(p)) * 0.5;
	else
		tmp = 1.0 * q_m;
	end
	tmp_2 = tmp;
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := If[LessEqual[q$95$m, 1.15e-30], N[(N[(N[Abs[r], $MachinePrecision] + N[Abs[p], $MachinePrecision]), $MachinePrecision] * 0.5), $MachinePrecision], N[(1.0 * q$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if q < 1.14999999999999992e-30

   1. Initial program 44.8%

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

   2. 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. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 42.8%

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

   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. *-commutative N/A

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

      3. lower-*.f64 N/A

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

      4. +-commutative N/A

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

      5. lift-+.f64 N/A

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

      6. lift-fabs.f64 N/A

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

      7. lift-fabs.f64 N/A

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

      8. metadata-eval 14.4

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

   7. Applied rewrites 14.4%

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

   if 1.14999999999999992e-30 < q

   1. Initial program 44.8%

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

   2. 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. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 42.8%

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

   5. Taylor expanded in q around inf

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

   7. Applied rewrites 35.3%

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

Alternative 6: 35.6% accurate, 7.3× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ \begin{array}{l} \mathbf{if}\;q\_m \leq 1.1 \cdot 10^{-119}:\\ \;\;\;\;0.5 \cdot r\\ \mathbf{else}:\\ \;\;\;\;1 \cdot q\_m\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m)
 :precision binary64
 (if (<= q_m 1.1e-119) (* 0.5 r) (* 1.0 q_m)))

C

q_m = fabs(q);
double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 1.1e-119) {
		tmp = 0.5 * r;
	} else {
		tmp = 1.0 * q_m;
	}
	return tmp;
}

Fortran

q_m =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q_m)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q_m
    real(8) :: tmp
    if (q_m <= 1.1d-119) then
        tmp = 0.5d0 * r
    else
        tmp = 1.0d0 * q_m
    end if
    code = tmp
end function

Java

q_m = Math.abs(q);
public static double code(double p, double r, double q_m) {
	double tmp;
	if (q_m <= 1.1e-119) {
		tmp = 0.5 * r;
	} else {
		tmp = 1.0 * q_m;
	}
	return tmp;
}

Python

q_m = math.fabs(q)
def code(p, r, q_m):
	tmp = 0
	if q_m <= 1.1e-119:
		tmp = 0.5 * r
	else:
		tmp = 1.0 * q_m
	return tmp

Julia

q_m = abs(q)
function code(p, r, q_m)
	tmp = 0.0
	if (q_m <= 1.1e-119)
		tmp = Float64(0.5 * r);
	else
		tmp = Float64(1.0 * q_m);
	end
	return tmp
end

MATLAB

q_m = abs(q);
function tmp_2 = code(p, r, q_m)
	tmp = 0.0;
	if (q_m <= 1.1e-119)
		tmp = 0.5 * r;
	else
		tmp = 1.0 * q_m;
	end
	tmp_2 = tmp;
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := If[LessEqual[q$95$m, 1.1e-119], N[(0.5 * r), $MachinePrecision], N[(1.0 * q$95$m), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if q < 1.1e-119

   1. Initial program 44.8%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. metadata-eval 5.3

         \[\leadsto 0.5 \cdot r \]

   4. Applied rewrites 5.3%

      \[\leadsto \color{blue}{0.5 \cdot r} \]

   if 1.1e-119 < q

   1. Initial program 44.8%

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

   2. 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. *-commutative N/A

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

      3. lower-*.f64 N/A

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

   4. Applied rewrites 42.8%

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

   5. Taylor expanded in q around inf

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

   7. Applied rewrites 35.3%

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

Alternative 7: 7.6% accurate, 7.3× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ \begin{array}{l} \mathbf{if}\;r \leq 0.0009:\\ \;\;\;\;-0.5 \cdot p\\ \mathbf{else}:\\ \;\;\;\;0.5 \cdot r\\ \end{array} \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m) :precision binary64 (if (<= r 0.0009) (* -0.5 p) (* 0.5 r)))

C

q_m = fabs(q);
double code(double p, double r, double q_m) {
	double tmp;
	if (r <= 0.0009) {
		tmp = -0.5 * p;
	} else {
		tmp = 0.5 * r;
	}
	return tmp;
}

Fortran

q_m =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q_m)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q_m
    real(8) :: tmp
    if (r <= 0.0009d0) then
        tmp = (-0.5d0) * p
    else
        tmp = 0.5d0 * r
    end if
    code = tmp
end function

Java

q_m = Math.abs(q);
public static double code(double p, double r, double q_m) {
	double tmp;
	if (r <= 0.0009) {
		tmp = -0.5 * p;
	} else {
		tmp = 0.5 * r;
	}
	return tmp;
}

Python

q_m = math.fabs(q)
def code(p, r, q_m):
	tmp = 0
	if r <= 0.0009:
		tmp = -0.5 * p
	else:
		tmp = 0.5 * r
	return tmp

Julia

q_m = abs(q)
function code(p, r, q_m)
	tmp = 0.0
	if (r <= 0.0009)
		tmp = Float64(-0.5 * p);
	else
		tmp = Float64(0.5 * r);
	end
	return tmp
end

MATLAB

q_m = abs(q);
function tmp_2 = code(p, r, q_m)
	tmp = 0.0;
	if (r <= 0.0009)
		tmp = -0.5 * p;
	else
		tmp = 0.5 * r;
	end
	tmp_2 = tmp;
end

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := If[LessEqual[r, 0.0009], N[(-0.5 * p), $MachinePrecision], N[(0.5 * r), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if r < 8.9999999999999998e-4

   1. Initial program 44.8%

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

   2. Taylor expanded in p around -inf

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

      1. lower-*.f64 5.3

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

   4. Applied rewrites 5.3%

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

   if 8.9999999999999998e-4 < r

   1. Initial program 44.8%

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

   2. Taylor expanded in r around inf

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

      1. metadata-eval N/A

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

      2. lower-*.f64 N/A

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

      3. metadata-eval 5.3

         \[\leadsto 0.5 \cdot r \]

   4. Applied rewrites 5.3%

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

Alternative 8: 5.3% accurate, 14.4× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ -0.5 \cdot p \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m) :precision binary64 (* -0.5 p))

C

q_m = fabs(q);
double code(double p, double r, double q_m) {
	return -0.5 * p;
}

Fortran

q_m =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q_m)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q_m
    code = (-0.5d0) * p
end function

Java

q_m = Math.abs(q);
public static double code(double p, double r, double q_m) {
	return -0.5 * p;
}

Python

q_m = math.fabs(q)
def code(p, r, q_m):
	return -0.5 * p

Julia

q_m = abs(q)
function code(p, r, q_m)
	return Float64(-0.5 * p)
end

MATLAB

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

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := N[(-0.5 * p), $MachinePrecision]

Derivation

1. Initial program 44.8%

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

2. Taylor expanded in p around -inf

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

   1. lower-*.f64 5.3

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

4. Applied rewrites 5.3%

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

Alternative 9: 1.2% accurate, 28.9× speedup

Math

\[\begin{array}{l} q_m = \left|q\right| \\ -q\_m \end{array} \]

FPCore

q_m = (fabs.f64 q)
(FPCore (p r q_m) :precision binary64 (- q_m))

C

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

Fortran

q_m =     private
module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(p, r, q_m)
use fmin_fmax_functions
    real(8), intent (in) :: p
    real(8), intent (in) :: r
    real(8), intent (in) :: q_m
    code = -q_m
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

q_m = N[Abs[q], $MachinePrecision]
code[p_, r_, q$95$m_] := (-q$95$m)

Derivation

1. Initial program 44.8%

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

2. Taylor expanded in q around -inf

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

   1. mul-1-neg N/A

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

   2. lower-neg.f64 1.2

      \[\leadsto -q \]

4. Applied rewrites 1.2%

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

Reproduce

herbie shell --seed 2025135 
(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)))))))