quad2p (problem 3.2.1, positive)

Percentage Accurate: 51.6% → 85.6%

Time: 3.3s

Alternatives: 9

Speedup: 1.2×

Specification

Math

\[\begin{array}{l} \\ \frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (/ (+ (- b_2) (sqrt (- (* b_2 b_2) (* a c)))) a))

C

double code(double a, double b_2, double c) {
	return (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a;
}

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(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    code = (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a
end function

Java

public static double code(double a, double b_2, double c) {
	return (-b_2 + Math.sqrt(((b_2 * b_2) - (a * c)))) / a;
}

Python

def code(a, b_2, c):
	return (-b_2 + math.sqrt(((b_2 * b_2) - (a * c)))) / a

Julia

function code(a, b_2, c)
	return Float64(Float64(Float64(-b_2) + sqrt(Float64(Float64(b_2 * b_2) - Float64(a * c)))) / a)
end

MATLAB

function tmp = code(a, b_2, c)
	tmp = (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a;
end

Wolfram

code[a_, b$95$2_, c_] := N[(N[((-b$95$2) + N[Sqrt[N[(N[(b$95$2 * b$95$2), $MachinePrecision] - N[(a * c), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision]

Initial Program: 51.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (/ (+ (- b_2) (sqrt (- (* b_2 b_2) (* a c)))) a))

C

double code(double a, double b_2, double c) {
	return (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a;
}

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(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    code = (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a
end function

Java

public static double code(double a, double b_2, double c) {
	return (-b_2 + Math.sqrt(((b_2 * b_2) - (a * c)))) / a;
}

Python

def code(a, b_2, c):
	return (-b_2 + math.sqrt(((b_2 * b_2) - (a * c)))) / a

Julia

function code(a, b_2, c)
	return Float64(Float64(Float64(-b_2) + sqrt(Float64(Float64(b_2 * b_2) - Float64(a * c)))) / a)
end

MATLAB

function tmp = code(a, b_2, c)
	tmp = (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a;
end

Wolfram

code[a_, b$95$2_, c_] := N[(N[((-b$95$2) + N[Sqrt[N[(N[(b$95$2 * b$95$2), $MachinePrecision] - N[(a * c), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision]

Alternative 1: 85.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b\_2 \leq -5 \cdot 10^{+138}:\\ \;\;\;\;\frac{-2 \cdot b\_2}{a}\\ \mathbf{elif}\;b\_2 \leq 1.85 \cdot 10^{-69}:\\ \;\;\;\;\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{c}{b\_2} \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (if (<= b_2 -5e+138)
   (/ (* -2.0 b_2) a)
   (if (<= b_2 1.85e-69)
     (/ (+ (- b_2) (sqrt (- (* b_2 b_2) (* a c)))) a)
     (* (/ c b_2) -0.5))))

C

double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5e+138) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 1.85e-69) {
		tmp = (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a;
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b_2 <= (-5d+138)) then
        tmp = ((-2.0d0) * b_2) / a
    else if (b_2 <= 1.85d-69) then
        tmp = (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a
    else
        tmp = (c / b_2) * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5e+138) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 1.85e-69) {
		tmp = (-b_2 + Math.sqrt(((b_2 * b_2) - (a * c)))) / a;
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Python

def code(a, b_2, c):
	tmp = 0
	if b_2 <= -5e+138:
		tmp = (-2.0 * b_2) / a
	elif b_2 <= 1.85e-69:
		tmp = (-b_2 + math.sqrt(((b_2 * b_2) - (a * c)))) / a
	else:
		tmp = (c / b_2) * -0.5
	return tmp

Julia

function code(a, b_2, c)
	tmp = 0.0
	if (b_2 <= -5e+138)
		tmp = Float64(Float64(-2.0 * b_2) / a);
	elseif (b_2 <= 1.85e-69)
		tmp = Float64(Float64(Float64(-b_2) + sqrt(Float64(Float64(b_2 * b_2) - Float64(a * c)))) / a);
	else
		tmp = Float64(Float64(c / b_2) * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b_2, c)
	tmp = 0.0;
	if (b_2 <= -5e+138)
		tmp = (-2.0 * b_2) / a;
	elseif (b_2 <= 1.85e-69)
		tmp = (-b_2 + sqrt(((b_2 * b_2) - (a * c)))) / a;
	else
		tmp = (c / b_2) * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b$95$2_, c_] := If[LessEqual[b$95$2, -5e+138], N[(N[(-2.0 * b$95$2), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[b$95$2, 1.85e-69], N[(N[((-b$95$2) + N[Sqrt[N[(N[(b$95$2 * b$95$2), $MachinePrecision] - N[(a * c), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], N[(N[(c / b$95$2), $MachinePrecision] * -0.5), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b_2 < -5.00000000000000016e138

   1. Initial program 45.9%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in b_2 around -inf

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]
   3. Step-by-step derivation

      1. lower-*.f64 96.7

         \[\leadsto \frac{-2 \cdot \color{blue}{b\_2}}{a} \]

   4. Applied rewrites 96.7%

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]

   if -5.00000000000000016e138 < b_2 < 1.8500000000000001e-69

   1. Initial program 80.6%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   if 1.8500000000000001e-69 < b_2

   1. Initial program 18.0%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 86.4

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 86.4%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 2: 81.0% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b\_2 \leq -2.55 \cdot 10^{-73}:\\ \;\;\;\;\frac{-2 \cdot b\_2}{a}\\ \mathbf{elif}\;b\_2 \leq 1.05 \cdot 10^{-69}:\\ \;\;\;\;\frac{\left(-b\_2\right) + \sqrt{\left(-a\right) \cdot c}}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{c}{b\_2} \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (if (<= b_2 -2.55e-73)
   (/ (* -2.0 b_2) a)
   (if (<= b_2 1.05e-69)
     (/ (+ (- b_2) (sqrt (* (- a) c))) a)
     (* (/ c b_2) -0.5))))

C

double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -2.55e-73) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 1.05e-69) {
		tmp = (-b_2 + sqrt((-a * c))) / a;
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b_2 <= (-2.55d-73)) then
        tmp = ((-2.0d0) * b_2) / a
    else if (b_2 <= 1.05d-69) then
        tmp = (-b_2 + sqrt((-a * c))) / a
    else
        tmp = (c / b_2) * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -2.55e-73) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 1.05e-69) {
		tmp = (-b_2 + Math.sqrt((-a * c))) / a;
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Python

def code(a, b_2, c):
	tmp = 0
	if b_2 <= -2.55e-73:
		tmp = (-2.0 * b_2) / a
	elif b_2 <= 1.05e-69:
		tmp = (-b_2 + math.sqrt((-a * c))) / a
	else:
		tmp = (c / b_2) * -0.5
	return tmp

Julia

function code(a, b_2, c)
	tmp = 0.0
	if (b_2 <= -2.55e-73)
		tmp = Float64(Float64(-2.0 * b_2) / a);
	elseif (b_2 <= 1.05e-69)
		tmp = Float64(Float64(Float64(-b_2) + sqrt(Float64(Float64(-a) * c))) / a);
	else
		tmp = Float64(Float64(c / b_2) * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b_2, c)
	tmp = 0.0;
	if (b_2 <= -2.55e-73)
		tmp = (-2.0 * b_2) / a;
	elseif (b_2 <= 1.05e-69)
		tmp = (-b_2 + sqrt((-a * c))) / a;
	else
		tmp = (c / b_2) * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b$95$2_, c_] := If[LessEqual[b$95$2, -2.55e-73], N[(N[(-2.0 * b$95$2), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[b$95$2, 1.05e-69], N[(N[((-b$95$2) + N[Sqrt[N[((-a) * c), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] / a), $MachinePrecision], N[(N[(c / b$95$2), $MachinePrecision] * -0.5), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b_2 < -2.55e-73

   1. Initial program 68.4%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in b_2 around -inf

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]
   3. Step-by-step derivation

      1. lower-*.f64 85.8

         \[\leadsto \frac{-2 \cdot \color{blue}{b\_2}}{a} \]

   4. Applied rewrites 85.8%

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]

   if -2.55e-73 < b_2 < 1.05e-69

   1. Initial program 73.9%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around inf

      \[\leadsto \frac{\left(-b\_2\right) + \sqrt{\color{blue}{-1 \cdot \left(a \cdot c\right)}}}{a} \]
   3. Step-by-step derivation

      1. associate-*r* N/A

         \[\leadsto \frac{\left(-b\_2\right) + \sqrt{\left(-1 \cdot a\right) \cdot \color{blue}{c}}}{a} \]

      2. mul-1-neg N/A

         \[\leadsto \frac{\left(-b\_2\right) + \sqrt{\left(\mathsf{neg}\left(a\right)\right) \cdot c}}{a} \]

      3. lower-*.f64 N/A

         \[\leadsto \frac{\left(-b\_2\right) + \sqrt{\left(\mathsf{neg}\left(a\right)\right) \cdot \color{blue}{c}}}{a} \]

      4. lower-neg.f64 68.2

         \[\leadsto \frac{\left(-b\_2\right) + \sqrt{\left(-a\right) \cdot c}}{a} \]

   4. Applied rewrites 68.2%

      \[\leadsto \frac{\left(-b\_2\right) + \sqrt{\color{blue}{\left(-a\right) \cdot c}}}{a} \]

   if 1.05e-69 < b_2

   1. Initial program 18.0%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 86.4

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 86.4%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 3: 80.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b\_2 \leq -5.8 \cdot 10^{-75}:\\ \;\;\;\;\frac{-2 \cdot b\_2}{a}\\ \mathbf{elif}\;b\_2 \leq 1.85 \cdot 10^{-69}:\\ \;\;\;\;\frac{\sqrt{\left(-a\right) \cdot c}}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{c}{b\_2} \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (if (<= b_2 -5.8e-75)
   (/ (* -2.0 b_2) a)
   (if (<= b_2 1.85e-69) (/ (sqrt (* (- a) c)) a) (* (/ c b_2) -0.5))))

C

double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5.8e-75) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 1.85e-69) {
		tmp = sqrt((-a * c)) / a;
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b_2 <= (-5.8d-75)) then
        tmp = ((-2.0d0) * b_2) / a
    else if (b_2 <= 1.85d-69) then
        tmp = sqrt((-a * c)) / a
    else
        tmp = (c / b_2) * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5.8e-75) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 1.85e-69) {
		tmp = Math.sqrt((-a * c)) / a;
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Python

def code(a, b_2, c):
	tmp = 0
	if b_2 <= -5.8e-75:
		tmp = (-2.0 * b_2) / a
	elif b_2 <= 1.85e-69:
		tmp = math.sqrt((-a * c)) / a
	else:
		tmp = (c / b_2) * -0.5
	return tmp

Julia

function code(a, b_2, c)
	tmp = 0.0
	if (b_2 <= -5.8e-75)
		tmp = Float64(Float64(-2.0 * b_2) / a);
	elseif (b_2 <= 1.85e-69)
		tmp = Float64(sqrt(Float64(Float64(-a) * c)) / a);
	else
		tmp = Float64(Float64(c / b_2) * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b_2, c)
	tmp = 0.0;
	if (b_2 <= -5.8e-75)
		tmp = (-2.0 * b_2) / a;
	elseif (b_2 <= 1.85e-69)
		tmp = sqrt((-a * c)) / a;
	else
		tmp = (c / b_2) * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b$95$2_, c_] := If[LessEqual[b$95$2, -5.8e-75], N[(N[(-2.0 * b$95$2), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[b$95$2, 1.85e-69], N[(N[Sqrt[N[((-a) * c), $MachinePrecision]], $MachinePrecision] / a), $MachinePrecision], N[(N[(c / b$95$2), $MachinePrecision] * -0.5), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b_2 < -5.8000000000000003e-75

   1. Initial program 68.5%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in b_2 around -inf

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]
   3. Step-by-step derivation

      1. lower-*.f64 85.7

         \[\leadsto \frac{-2 \cdot \color{blue}{b\_2}}{a} \]

   4. Applied rewrites 85.7%

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]

   if -5.8000000000000003e-75 < b_2 < 1.8500000000000001e-69

   1. Initial program 73.7%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around inf

      \[\leadsto \frac{\color{blue}{\sqrt{a \cdot c} \cdot \sqrt{-1}}}{a} \]
   3. Step-by-step derivation

      1. sqrt-unprod N/A

         \[\leadsto \frac{\sqrt{\left(a \cdot c\right) \cdot -1}}{a} \]

      2. *-commutative N/A

         \[\leadsto \frac{\sqrt{-1 \cdot \left(a \cdot c\right)}}{a} \]

      3. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-1 \cdot \left(a \cdot c\right)}}{a} \]

      4. associate-*r* N/A

         \[\leadsto \frac{\sqrt{\left(-1 \cdot a\right) \cdot c}}{a} \]

      5. mul-1-neg N/A

         \[\leadsto \frac{\sqrt{\left(\mathsf{neg}\left(a\right)\right) \cdot c}}{a} \]

      6. lower-*.f64 N/A

         \[\leadsto \frac{\sqrt{\left(\mathsf{neg}\left(a\right)\right) \cdot c}}{a} \]

      7. lower-neg.f64 67.1

         \[\leadsto \frac{\sqrt{\left(-a\right) \cdot c}}{a} \]

   4. Applied rewrites 67.1%

      \[\leadsto \frac{\color{blue}{\sqrt{\left(-a\right) \cdot c}}}{a} \]

   if 1.8500000000000001e-69 < b_2

   1. Initial program 18.0%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 86.4

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 86.4%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 4: 73.9% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b\_2 \leq -7 \cdot 10^{-112}:\\ \;\;\;\;\frac{-2 \cdot b\_2}{a}\\ \mathbf{elif}\;b\_2 \leq 8.6 \cdot 10^{-108}:\\ \;\;\;\;\frac{\sqrt{-c}}{\sqrt{a}}\\ \mathbf{else}:\\ \;\;\;\;\frac{c}{b\_2} \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (if (<= b_2 -7e-112)
   (/ (* -2.0 b_2) a)
   (if (<= b_2 8.6e-108) (/ (sqrt (- c)) (sqrt a)) (* (/ c b_2) -0.5))))

C

double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -7e-112) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 8.6e-108) {
		tmp = sqrt(-c) / sqrt(a);
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b_2 <= (-7d-112)) then
        tmp = ((-2.0d0) * b_2) / a
    else if (b_2 <= 8.6d-108) then
        tmp = sqrt(-c) / sqrt(a)
    else
        tmp = (c / b_2) * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -7e-112) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 8.6e-108) {
		tmp = Math.sqrt(-c) / Math.sqrt(a);
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Python

def code(a, b_2, c):
	tmp = 0
	if b_2 <= -7e-112:
		tmp = (-2.0 * b_2) / a
	elif b_2 <= 8.6e-108:
		tmp = math.sqrt(-c) / math.sqrt(a)
	else:
		tmp = (c / b_2) * -0.5
	return tmp

Julia

function code(a, b_2, c)
	tmp = 0.0
	if (b_2 <= -7e-112)
		tmp = Float64(Float64(-2.0 * b_2) / a);
	elseif (b_2 <= 8.6e-108)
		tmp = Float64(sqrt(Float64(-c)) / sqrt(a));
	else
		tmp = Float64(Float64(c / b_2) * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b_2, c)
	tmp = 0.0;
	if (b_2 <= -7e-112)
		tmp = (-2.0 * b_2) / a;
	elseif (b_2 <= 8.6e-108)
		tmp = sqrt(-c) / sqrt(a);
	else
		tmp = (c / b_2) * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b$95$2_, c_] := If[LessEqual[b$95$2, -7e-112], N[(N[(-2.0 * b$95$2), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[b$95$2, 8.6e-108], N[(N[Sqrt[(-c)], $MachinePrecision] / N[Sqrt[a], $MachinePrecision]), $MachinePrecision], N[(N[(c / b$95$2), $MachinePrecision] * -0.5), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b_2 < -6.99999999999999988e-112

   1. Initial program 70.0%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in b_2 around -inf

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]
   3. Step-by-step derivation

      1. lower-*.f64 82.6

         \[\leadsto \frac{-2 \cdot \color{blue}{b\_2}}{a} \]

   4. Applied rewrites 82.6%

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]

   if -6.99999999999999988e-112 < b_2 < 8.6000000000000001e-108

   1. Initial program 74.0%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 9.2

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 9.2%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]

   5. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\sqrt{\frac{c}{a}} \cdot \sqrt{-1}} \]
   6. Step-by-step derivation

      1. sqrt-prod N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      3. *-commutative N/A

         \[\leadsto \sqrt{-1 \cdot \frac{c}{a}} \]

      4. associate-*r/ N/A

         \[\leadsto \sqrt{\frac{-1 \cdot c}{a}} \]

      5. mul-1-neg N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      6. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      7. lower-neg.f64 34.3

         \[\leadsto \sqrt{\frac{-c}{a}} \]

   7. Applied rewrites 34.3%

      \[\leadsto \color{blue}{\sqrt{\frac{-c}{a}}} \]
   8. Step-by-step derivation

      1. lift-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{-c}{a}} \]

      2. lift-/.f64 N/A

         \[\leadsto \sqrt{\frac{-c}{a}} \]

      3. sqrt-div N/A

         \[\leadsto \frac{\sqrt{-c}}{\color{blue}{\sqrt{a}}} \]

      4. lower-/.f64 N/A

         \[\leadsto \frac{\sqrt{-c}}{\color{blue}{\sqrt{a}}} \]

      5. lower-sqrt.f64 N/A

         \[\leadsto \frac{\sqrt{-c}}{\sqrt{\color{blue}{a}}} \]

      6. lower-sqrt.f64 44.0

         \[\leadsto \frac{\sqrt{-c}}{\sqrt{a}} \]

   9. Applied rewrites 44.0%

      \[\leadsto \frac{\sqrt{-c}}{\color{blue}{\sqrt{a}}} \]

   if 8.6000000000000001e-108 < b_2

   1. Initial program 20.5%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 83.2

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 83.2%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 5: 72.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b\_2 \leq -1.15 \cdot 10^{-126}:\\ \;\;\;\;\frac{-2 \cdot b\_2}{a}\\ \mathbf{elif}\;b\_2 \leq 3.3 \cdot 10^{-170}:\\ \;\;\;\;\sqrt{\frac{-c}{a}}\\ \mathbf{else}:\\ \;\;\;\;\frac{c}{b\_2} \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (if (<= b_2 -1.15e-126)
   (/ (* -2.0 b_2) a)
   (if (<= b_2 3.3e-170) (sqrt (/ (- c) a)) (* (/ c b_2) -0.5))))

C

double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -1.15e-126) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 3.3e-170) {
		tmp = sqrt((-c / a));
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b_2 <= (-1.15d-126)) then
        tmp = ((-2.0d0) * b_2) / a
    else if (b_2 <= 3.3d-170) then
        tmp = sqrt((-c / a))
    else
        tmp = (c / b_2) * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -1.15e-126) {
		tmp = (-2.0 * b_2) / a;
	} else if (b_2 <= 3.3e-170) {
		tmp = Math.sqrt((-c / a));
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Python

def code(a, b_2, c):
	tmp = 0
	if b_2 <= -1.15e-126:
		tmp = (-2.0 * b_2) / a
	elif b_2 <= 3.3e-170:
		tmp = math.sqrt((-c / a))
	else:
		tmp = (c / b_2) * -0.5
	return tmp

Julia

function code(a, b_2, c)
	tmp = 0.0
	if (b_2 <= -1.15e-126)
		tmp = Float64(Float64(-2.0 * b_2) / a);
	elseif (b_2 <= 3.3e-170)
		tmp = sqrt(Float64(Float64(-c) / a));
	else
		tmp = Float64(Float64(c / b_2) * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b_2, c)
	tmp = 0.0;
	if (b_2 <= -1.15e-126)
		tmp = (-2.0 * b_2) / a;
	elseif (b_2 <= 3.3e-170)
		tmp = sqrt((-c / a));
	else
		tmp = (c / b_2) * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b$95$2_, c_] := If[LessEqual[b$95$2, -1.15e-126], N[(N[(-2.0 * b$95$2), $MachinePrecision] / a), $MachinePrecision], If[LessEqual[b$95$2, 3.3e-170], N[Sqrt[N[((-c) / a), $MachinePrecision]], $MachinePrecision], N[(N[(c / b$95$2), $MachinePrecision] * -0.5), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b_2 < -1.15000000000000005e-126

   1. Initial program 70.3%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in b_2 around -inf

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]
   3. Step-by-step derivation

      1. lower-*.f64 81.7

         \[\leadsto \frac{-2 \cdot \color{blue}{b\_2}}{a} \]

   4. Applied rewrites 81.7%

      \[\leadsto \frac{\color{blue}{-2 \cdot b\_2}}{a} \]

   if -1.15000000000000005e-126 < b_2 < 3.30000000000000004e-170

   1. Initial program 76.5%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 4.4

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 4.4%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]

   5. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\sqrt{\frac{c}{a}} \cdot \sqrt{-1}} \]
   6. Step-by-step derivation

      1. sqrt-prod N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      3. *-commutative N/A

         \[\leadsto \sqrt{-1 \cdot \frac{c}{a}} \]

      4. associate-*r/ N/A

         \[\leadsto \sqrt{\frac{-1 \cdot c}{a}} \]

      5. mul-1-neg N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      6. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      7. lower-neg.f64 37.4

         \[\leadsto \sqrt{\frac{-c}{a}} \]

   7. Applied rewrites 37.4%

      \[\leadsto \color{blue}{\sqrt{\frac{-c}{a}}} \]

   if 3.30000000000000004e-170 < b_2

   1. Initial program 24.0%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 78.8

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 78.8%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 6: 54.2% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b\_2 \leq -5.5 \cdot 10^{+23}:\\ \;\;\;\;\frac{-b\_2}{a}\\ \mathbf{elif}\;b\_2 \leq 3.3 \cdot 10^{-170}:\\ \;\;\;\;\sqrt{\frac{-c}{a}}\\ \mathbf{else}:\\ \;\;\;\;\frac{c}{b\_2} \cdot -0.5\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (if (<= b_2 -5.5e+23)
   (/ (- b_2) a)
   (if (<= b_2 3.3e-170) (sqrt (/ (- c) a)) (* (/ c b_2) -0.5))))

C

double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5.5e+23) {
		tmp = -b_2 / a;
	} else if (b_2 <= 3.3e-170) {
		tmp = sqrt((-c / a));
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Fortran

module fmin_fmax_functions
    implicit none
    private
    public fmax
    public fmin

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

real(8) function code(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b_2 <= (-5.5d+23)) then
        tmp = -b_2 / a
    else if (b_2 <= 3.3d-170) then
        tmp = sqrt((-c / a))
    else
        tmp = (c / b_2) * (-0.5d0)
    end if
    code = tmp
end function

Java

public static double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5.5e+23) {
		tmp = -b_2 / a;
	} else if (b_2 <= 3.3e-170) {
		tmp = Math.sqrt((-c / a));
	} else {
		tmp = (c / b_2) * -0.5;
	}
	return tmp;
}

Python

def code(a, b_2, c):
	tmp = 0
	if b_2 <= -5.5e+23:
		tmp = -b_2 / a
	elif b_2 <= 3.3e-170:
		tmp = math.sqrt((-c / a))
	else:
		tmp = (c / b_2) * -0.5
	return tmp

Julia

function code(a, b_2, c)
	tmp = 0.0
	if (b_2 <= -5.5e+23)
		tmp = Float64(Float64(-b_2) / a);
	elseif (b_2 <= 3.3e-170)
		tmp = sqrt(Float64(Float64(-c) / a));
	else
		tmp = Float64(Float64(c / b_2) * -0.5);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b_2, c)
	tmp = 0.0;
	if (b_2 <= -5.5e+23)
		tmp = -b_2 / a;
	elseif (b_2 <= 3.3e-170)
		tmp = sqrt((-c / a));
	else
		tmp = (c / b_2) * -0.5;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b$95$2_, c_] := If[LessEqual[b$95$2, -5.5e+23], N[((-b$95$2) / a), $MachinePrecision], If[LessEqual[b$95$2, 3.3e-170], N[Sqrt[N[((-c) / a), $MachinePrecision]], $MachinePrecision], N[(N[(c / b$95$2), $MachinePrecision] * -0.5), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b_2 < -5.50000000000000004e23

   1. Initial program 62.4%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in c around inf

      \[\leadsto \frac{\color{blue}{c \cdot \left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right)}}{a} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right) \cdot \color{blue}{c}}{a} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right) \cdot \color{blue}{c}}{a} \]

      3. +-commutative N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c}} \cdot \sqrt{-1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      4. lower-+.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c}} \cdot \sqrt{-1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      5. sqrt-unprod N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      9. associate-*r/ N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-1 \cdot b\_2}{c}\right) \cdot c}{a} \]

      10. mul-1-neg N/A

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{\mathsf{neg}\left(b\_2\right)}{c}\right) \cdot c}{a} \]

      11. lift-neg.f64 N/A

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}{a} \]

      12. lower-/.f64 27.2

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}{a} \]

   4. Applied rewrites 27.2%

      \[\leadsto \frac{\color{blue}{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}}{a} \]

   5. Taylor expanded in b_2 around inf

      \[\leadsto \frac{-1 \cdot \color{blue}{b\_2}}{a} \]
   6. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{\mathsf{neg}\left(b\_2\right)}{a} \]

      2. lift-neg.f64 42.0

         \[\leadsto \frac{-b\_2}{a} \]

   7. Applied rewrites 42.0%

      \[\leadsto \frac{-b\_2}{a} \]

   if -5.50000000000000004e23 < b_2 < 3.30000000000000004e-170

   1. Initial program 81.7%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 3.8

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 3.8%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]

   5. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\sqrt{\frac{c}{a}} \cdot \sqrt{-1}} \]
   6. Step-by-step derivation

      1. sqrt-prod N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      3. *-commutative N/A

         \[\leadsto \sqrt{-1 \cdot \frac{c}{a}} \]

      4. associate-*r/ N/A

         \[\leadsto \sqrt{\frac{-1 \cdot c}{a}} \]

      5. mul-1-neg N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      6. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      7. lower-neg.f64 30.1

         \[\leadsto \sqrt{\frac{-c}{a}} \]

   7. Applied rewrites 30.1%

      \[\leadsto \color{blue}{\sqrt{\frac{-c}{a}}} \]

   if 3.30000000000000004e-170 < b_2

   1. Initial program 24.0%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 78.8

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 78.8%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 7: 54.1% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b\_2 \leq -5.5 \cdot 10^{+23}:\\ \;\;\;\;\frac{-b\_2}{a}\\ \mathbf{elif}\;b\_2 \leq 3.3 \cdot 10^{-170}:\\ \;\;\;\;\sqrt{\frac{-c}{a}}\\ \mathbf{else}:\\ \;\;\;\;\frac{-0.5}{b\_2} \cdot c\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (if (<= b_2 -5.5e+23)
   (/ (- b_2) a)
   (if (<= b_2 3.3e-170) (sqrt (/ (- c) a)) (* (/ -0.5 b_2) c))))

C

double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5.5e+23) {
		tmp = -b_2 / a;
	} else if (b_2 <= 3.3e-170) {
		tmp = sqrt((-c / a));
	} else {
		tmp = (-0.5 / b_2) * c;
	}
	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(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b_2 <= (-5.5d+23)) then
        tmp = -b_2 / a
    else if (b_2 <= 3.3d-170) then
        tmp = sqrt((-c / a))
    else
        tmp = ((-0.5d0) / b_2) * c
    end if
    code = tmp
end function

Java

public static double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5.5e+23) {
		tmp = -b_2 / a;
	} else if (b_2 <= 3.3e-170) {
		tmp = Math.sqrt((-c / a));
	} else {
		tmp = (-0.5 / b_2) * c;
	}
	return tmp;
}

Python

def code(a, b_2, c):
	tmp = 0
	if b_2 <= -5.5e+23:
		tmp = -b_2 / a
	elif b_2 <= 3.3e-170:
		tmp = math.sqrt((-c / a))
	else:
		tmp = (-0.5 / b_2) * c
	return tmp

Julia

function code(a, b_2, c)
	tmp = 0.0
	if (b_2 <= -5.5e+23)
		tmp = Float64(Float64(-b_2) / a);
	elseif (b_2 <= 3.3e-170)
		tmp = sqrt(Float64(Float64(-c) / a));
	else
		tmp = Float64(Float64(-0.5 / b_2) * c);
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b_2, c)
	tmp = 0.0;
	if (b_2 <= -5.5e+23)
		tmp = -b_2 / a;
	elseif (b_2 <= 3.3e-170)
		tmp = sqrt((-c / a));
	else
		tmp = (-0.5 / b_2) * c;
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b$95$2_, c_] := If[LessEqual[b$95$2, -5.5e+23], N[((-b$95$2) / a), $MachinePrecision], If[LessEqual[b$95$2, 3.3e-170], N[Sqrt[N[((-c) / a), $MachinePrecision]], $MachinePrecision], N[(N[(-0.5 / b$95$2), $MachinePrecision] * c), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if b_2 < -5.50000000000000004e23

   1. Initial program 62.4%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in c around inf

      \[\leadsto \frac{\color{blue}{c \cdot \left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right)}}{a} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right) \cdot \color{blue}{c}}{a} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right) \cdot \color{blue}{c}}{a} \]

      3. +-commutative N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c}} \cdot \sqrt{-1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      4. lower-+.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c}} \cdot \sqrt{-1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      5. sqrt-unprod N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      9. associate-*r/ N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-1 \cdot b\_2}{c}\right) \cdot c}{a} \]

      10. mul-1-neg N/A

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{\mathsf{neg}\left(b\_2\right)}{c}\right) \cdot c}{a} \]

      11. lift-neg.f64 N/A

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}{a} \]

      12. lower-/.f64 27.2

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}{a} \]

   4. Applied rewrites 27.2%

      \[\leadsto \frac{\color{blue}{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}}{a} \]

   5. Taylor expanded in b_2 around inf

      \[\leadsto \frac{-1 \cdot \color{blue}{b\_2}}{a} \]
   6. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{\mathsf{neg}\left(b\_2\right)}{a} \]

      2. lift-neg.f64 42.0

         \[\leadsto \frac{-b\_2}{a} \]

   7. Applied rewrites 42.0%

      \[\leadsto \frac{-b\_2}{a} \]

   if -5.50000000000000004e23 < b_2 < 3.30000000000000004e-170

   1. Initial program 81.7%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 3.8

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 3.8%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]

   5. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\sqrt{\frac{c}{a}} \cdot \sqrt{-1}} \]
   6. Step-by-step derivation

      1. sqrt-prod N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      3. *-commutative N/A

         \[\leadsto \sqrt{-1 \cdot \frac{c}{a}} \]

      4. associate-*r/ N/A

         \[\leadsto \sqrt{\frac{-1 \cdot c}{a}} \]

      5. mul-1-neg N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      6. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      7. lower-neg.f64 30.1

         \[\leadsto \sqrt{\frac{-c}{a}} \]

   7. Applied rewrites 30.1%

      \[\leadsto \color{blue}{\sqrt{\frac{-c}{a}}} \]

   if 3.30000000000000004e-170 < b_2

   1. Initial program 24.0%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in c around 0

      \[\leadsto \color{blue}{c \cdot \left(\frac{-1}{8} \cdot \frac{a \cdot c}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right)} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \left(\frac{-1}{8} \cdot \frac{a \cdot c}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot \color{blue}{c} \]

      2. lower-*.f64 N/A

         \[\leadsto \left(\frac{-1}{8} \cdot \frac{a \cdot c}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot \color{blue}{c} \]

      3. lower--.f64 N/A

         \[\leadsto \left(\frac{-1}{8} \cdot \frac{a \cdot c}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      4. associate-*r/ N/A

         \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(a \cdot c\right)}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      5. lower-/.f64 N/A

         \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(a \cdot c\right)}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      6. lower-*.f64 N/A

         \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(a \cdot c\right)}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      7. *-commutative N/A

         \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      8. lower-*.f64 N/A

         \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{{b\_2}^{3}} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      9. unpow3 N/A

         \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{\left(b\_2 \cdot b\_2\right) \cdot b\_2} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      10. pow2 N/A

          \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{{b\_2}^{2} \cdot b\_2} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      11. lower-*.f64 N/A

          \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{{b\_2}^{2} \cdot b\_2} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      12. pow2 N/A

          \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{\left(b\_2 \cdot b\_2\right) \cdot b\_2} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      13. lift-*.f64 N/A

          \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{\left(b\_2 \cdot b\_2\right) \cdot b\_2} - \frac{1}{2} \cdot \frac{1}{b\_2}\right) \cdot c \]

      14. associate-*r/ N/A

          \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{\left(b\_2 \cdot b\_2\right) \cdot b\_2} - \frac{\frac{1}{2} \cdot 1}{b\_2}\right) \cdot c \]

      15. metadata-eval N/A

          \[\leadsto \left(\frac{\frac{-1}{8} \cdot \left(c \cdot a\right)}{\left(b\_2 \cdot b\_2\right) \cdot b\_2} - \frac{\frac{1}{2}}{b\_2}\right) \cdot c \]

      16. lower-/.f64 71.9

          \[\leadsto \left(\frac{-0.125 \cdot \left(c \cdot a\right)}{\left(b\_2 \cdot b\_2\right) \cdot b\_2} - \frac{0.5}{b\_2}\right) \cdot c \]

   4. Applied rewrites 71.9%

      \[\leadsto \color{blue}{\left(\frac{-0.125 \cdot \left(c \cdot a\right)}{\left(b\_2 \cdot b\_2\right) \cdot b\_2} - \frac{0.5}{b\_2}\right) \cdot c} \]

   5. Taylor expanded in a around 0

      \[\leadsto \frac{\frac{-1}{2}}{b\_2} \cdot c \]
   6. Step-by-step derivation

      1. lower-/.f64 78.5

         \[\leadsto \frac{-0.5}{b\_2} \cdot c \]

   7. Applied rewrites 78.5%

      \[\leadsto \frac{-0.5}{b\_2} \cdot c \]
3. Recombined 3 regimes into one program.
4. Add Preprocessing

Alternative 8: 27.3% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;b\_2 \leq -5.5 \cdot 10^{+23}:\\ \;\;\;\;\frac{-b\_2}{a}\\ \mathbf{else}:\\ \;\;\;\;\sqrt{\frac{-c}{a}}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (if (<= b_2 -5.5e+23) (/ (- b_2) a) (sqrt (/ (- c) a))))

C

double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5.5e+23) {
		tmp = -b_2 / a;
	} else {
		tmp = sqrt((-c / a));
	}
	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(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    real(8) :: tmp
    if (b_2 <= (-5.5d+23)) then
        tmp = -b_2 / a
    else
        tmp = sqrt((-c / a))
    end if
    code = tmp
end function

Java

public static double code(double a, double b_2, double c) {
	double tmp;
	if (b_2 <= -5.5e+23) {
		tmp = -b_2 / a;
	} else {
		tmp = Math.sqrt((-c / a));
	}
	return tmp;
}

Python

def code(a, b_2, c):
	tmp = 0
	if b_2 <= -5.5e+23:
		tmp = -b_2 / a
	else:
		tmp = math.sqrt((-c / a))
	return tmp

Julia

function code(a, b_2, c)
	tmp = 0.0
	if (b_2 <= -5.5e+23)
		tmp = Float64(Float64(-b_2) / a);
	else
		tmp = sqrt(Float64(Float64(-c) / a));
	end
	return tmp
end

MATLAB

function tmp_2 = code(a, b_2, c)
	tmp = 0.0;
	if (b_2 <= -5.5e+23)
		tmp = -b_2 / a;
	else
		tmp = sqrt((-c / a));
	end
	tmp_2 = tmp;
end

Wolfram

code[a_, b$95$2_, c_] := If[LessEqual[b$95$2, -5.5e+23], N[((-b$95$2) / a), $MachinePrecision], N[Sqrt[N[((-c) / a), $MachinePrecision]], $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if b_2 < -5.50000000000000004e23

   1. Initial program 62.4%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in c around inf

      \[\leadsto \frac{\color{blue}{c \cdot \left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right)}}{a} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{\left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right) \cdot \color{blue}{c}}{a} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{\left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right) \cdot \color{blue}{c}}{a} \]

      3. +-commutative N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c}} \cdot \sqrt{-1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      4. lower-+.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c}} \cdot \sqrt{-1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      5. sqrt-unprod N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      6. lower-sqrt.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      7. lower-*.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      8. lower-/.f64 N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

      9. associate-*r/ N/A

         \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-1 \cdot b\_2}{c}\right) \cdot c}{a} \]

      10. mul-1-neg N/A

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{\mathsf{neg}\left(b\_2\right)}{c}\right) \cdot c}{a} \]

      11. lift-neg.f64 N/A

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}{a} \]

      12. lower-/.f64 27.2

          \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}{a} \]

   4. Applied rewrites 27.2%

      \[\leadsto \frac{\color{blue}{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}}{a} \]

   5. Taylor expanded in b_2 around inf

      \[\leadsto \frac{-1 \cdot \color{blue}{b\_2}}{a} \]
   6. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \frac{\mathsf{neg}\left(b\_2\right)}{a} \]

      2. lift-neg.f64 42.0

         \[\leadsto \frac{-b\_2}{a} \]

   7. Applied rewrites 42.0%

      \[\leadsto \frac{-b\_2}{a} \]

   if -5.50000000000000004e23 < b_2

   1. Initial program 47.5%

      \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

   2. Taylor expanded in a around 0

      \[\leadsto \color{blue}{\frac{-1}{2} \cdot \frac{c}{b\_2}} \]
   3. Step-by-step derivation

      1. *-commutative N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      2. lower-*.f64 N/A

         \[\leadsto \frac{c}{b\_2} \cdot \color{blue}{\frac{-1}{2}} \]

      3. lower-/.f64 48.2

         \[\leadsto \frac{c}{b\_2} \cdot -0.5 \]

   4. Applied rewrites 48.2%

      \[\leadsto \color{blue}{\frac{c}{b\_2} \cdot -0.5} \]

   5. Taylor expanded in a around inf

      \[\leadsto \color{blue}{\sqrt{\frac{c}{a}} \cdot \sqrt{-1}} \]
   6. Step-by-step derivation

      1. sqrt-prod N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      2. lower-sqrt.f64 N/A

         \[\leadsto \sqrt{\frac{c}{a} \cdot -1} \]

      3. *-commutative N/A

         \[\leadsto \sqrt{-1 \cdot \frac{c}{a}} \]

      4. associate-*r/ N/A

         \[\leadsto \sqrt{\frac{-1 \cdot c}{a}} \]

      5. mul-1-neg N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      6. lower-/.f64 N/A

         \[\leadsto \sqrt{\frac{\mathsf{neg}\left(c\right)}{a}} \]

      7. lower-neg.f64 21.6

         \[\leadsto \sqrt{\frac{-c}{a}} \]

   7. Applied rewrites 21.6%

      \[\leadsto \color{blue}{\sqrt{\frac{-c}{a}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 9: 15.2% accurate, 3.4× speedup

Math

\[\begin{array}{l} \\ \frac{-b\_2}{a} \end{array} \]

FPCore

(FPCore (a b_2 c) :precision binary64 (/ (- b_2) a))

C

double code(double a, double b_2, double c) {
	return -b_2 / a;
}

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(a, b_2, c)
use fmin_fmax_functions
    real(8), intent (in) :: a
    real(8), intent (in) :: b_2
    real(8), intent (in) :: c
    code = -b_2 / a
end function

Java

public static double code(double a, double b_2, double c) {
	return -b_2 / a;
}

Python

def code(a, b_2, c):
	return -b_2 / a

Julia

function code(a, b_2, c)
	return Float64(Float64(-b_2) / a)
end

MATLAB

function tmp = code(a, b_2, c)
	tmp = -b_2 / a;
end

Wolfram

code[a_, b$95$2_, c_] := N[((-b$95$2) / a), $MachinePrecision]

Derivation

1. Initial program 51.6%

   \[\frac{\left(-b\_2\right) + \sqrt{b\_2 \cdot b\_2 - a \cdot c}}{a} \]

2. Taylor expanded in c around inf

   \[\leadsto \frac{\color{blue}{c \cdot \left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right)}}{a} \]
3. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \frac{\left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right) \cdot \color{blue}{c}}{a} \]

   2. lower-*.f64 N/A

      \[\leadsto \frac{\left(-1 \cdot \frac{b\_2}{c} + \sqrt{\frac{a}{c}} \cdot \sqrt{-1}\right) \cdot \color{blue}{c}}{a} \]

   3. +-commutative N/A

      \[\leadsto \frac{\left(\sqrt{\frac{a}{c}} \cdot \sqrt{-1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

   4. lower-+.f64 N/A

      \[\leadsto \frac{\left(\sqrt{\frac{a}{c}} \cdot \sqrt{-1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

   5. sqrt-unprod N/A

      \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

   6. lower-sqrt.f64 N/A

      \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

   7. lower-*.f64 N/A

      \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

   8. lower-/.f64 N/A

      \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + -1 \cdot \frac{b\_2}{c}\right) \cdot c}{a} \]

   9. associate-*r/ N/A

      \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-1 \cdot b\_2}{c}\right) \cdot c}{a} \]

   10. mul-1-neg N/A

       \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{\mathsf{neg}\left(b\_2\right)}{c}\right) \cdot c}{a} \]

   11. lift-neg.f64 N/A

       \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}{a} \]

   12. lower-/.f64 20.4

       \[\leadsto \frac{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}{a} \]

4. Applied rewrites 20.4%

   \[\leadsto \frac{\color{blue}{\left(\sqrt{\frac{a}{c} \cdot -1} + \frac{-b\_2}{c}\right) \cdot c}}{a} \]

5. Taylor expanded in b_2 around inf

   \[\leadsto \frac{-1 \cdot \color{blue}{b\_2}}{a} \]
6. Step-by-step derivation

   1. mul-1-neg N/A

      \[\leadsto \frac{\mathsf{neg}\left(b\_2\right)}{a} \]

   2. lift-neg.f64 15.2

      \[\leadsto \frac{-b\_2}{a} \]

7. Applied rewrites 15.2%

   \[\leadsto \frac{-b\_2}{a} \]
8. Add Preprocessing

Developer Target 1: 99.6% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \sqrt{\left|a\right|} \cdot \sqrt{\left|c\right|}\\ t_1 := \begin{array}{l} \mathbf{if}\;\mathsf{copysign}\left(a, c\right) = a:\\ \;\;\;\;\sqrt{\left|b\_2\right| - t\_0} \cdot \sqrt{\left|b\_2\right| + t\_0}\\ \mathbf{else}:\\ \;\;\;\;\mathsf{hypot}\left(b\_2, t\_0\right)\\ \end{array}\\ \mathbf{if}\;b\_2 < 0:\\ \;\;\;\;\frac{t\_1 - b\_2}{a}\\ \mathbf{else}:\\ \;\;\;\;\frac{-c}{b\_2 + t\_1}\\ \end{array} \end{array} \]

FPCore

(FPCore (a b_2 c)
 :precision binary64
 (let* ((t_0 (* (sqrt (fabs a)) (sqrt (fabs c))))
        (t_1
         (if (== (copysign a c) a)
           (* (sqrt (- (fabs b_2) t_0)) (sqrt (+ (fabs b_2) t_0)))
           (hypot b_2 t_0))))
   (if (< b_2 0.0) (/ (- t_1 b_2) a) (/ (- c) (+ b_2 t_1)))))

C

double code(double a, double b_2, double c) {
	double t_0 = sqrt(fabs(a)) * sqrt(fabs(c));
	double tmp;
	if (copysign(a, c) == a) {
		tmp = sqrt((fabs(b_2) - t_0)) * sqrt((fabs(b_2) + t_0));
	} else {
		tmp = hypot(b_2, t_0);
	}
	double t_1 = tmp;
	double tmp_1;
	if (b_2 < 0.0) {
		tmp_1 = (t_1 - b_2) / a;
	} else {
		tmp_1 = -c / (b_2 + t_1);
	}
	return tmp_1;
}

Java

public static double code(double a, double b_2, double c) {
	double t_0 = Math.sqrt(Math.abs(a)) * Math.sqrt(Math.abs(c));
	double tmp;
	if (Math.copySign(a, c) == a) {
		tmp = Math.sqrt((Math.abs(b_2) - t_0)) * Math.sqrt((Math.abs(b_2) + t_0));
	} else {
		tmp = Math.hypot(b_2, t_0);
	}
	double t_1 = tmp;
	double tmp_1;
	if (b_2 < 0.0) {
		tmp_1 = (t_1 - b_2) / a;
	} else {
		tmp_1 = -c / (b_2 + t_1);
	}
	return tmp_1;
}

Python

def code(a, b_2, c):
	t_0 = math.sqrt(math.fabs(a)) * math.sqrt(math.fabs(c))
	tmp = 0
	if math.copysign(a, c) == a:
		tmp = math.sqrt((math.fabs(b_2) - t_0)) * math.sqrt((math.fabs(b_2) + t_0))
	else:
		tmp = math.hypot(b_2, t_0)
	t_1 = tmp
	tmp_1 = 0
	if b_2 < 0.0:
		tmp_1 = (t_1 - b_2) / a
	else:
		tmp_1 = -c / (b_2 + t_1)
	return tmp_1

Julia

function code(a, b_2, c)
	t_0 = Float64(sqrt(abs(a)) * sqrt(abs(c)))
	tmp = 0.0
	if (copysign(a, c) == a)
		tmp = Float64(sqrt(Float64(abs(b_2) - t_0)) * sqrt(Float64(abs(b_2) + t_0)));
	else
		tmp = hypot(b_2, t_0);
	end
	t_1 = tmp
	tmp_1 = 0.0
	if (b_2 < 0.0)
		tmp_1 = Float64(Float64(t_1 - b_2) / a);
	else
		tmp_1 = Float64(Float64(-c) / Float64(b_2 + t_1));
	end
	return tmp_1
end

MATLAB

function tmp_3 = code(a, b_2, c)
	t_0 = sqrt(abs(a)) * sqrt(abs(c));
	tmp = 0.0;
	if ((sign(c) * abs(a)) == a)
		tmp = sqrt((abs(b_2) - t_0)) * sqrt((abs(b_2) + t_0));
	else
		tmp = hypot(b_2, t_0);
	end
	t_1 = tmp;
	tmp_2 = 0.0;
	if (b_2 < 0.0)
		tmp_2 = (t_1 - b_2) / a;
	else
		tmp_2 = -c / (b_2 + t_1);
	end
	tmp_3 = tmp_2;
end

Wolfram

code[a_, b$95$2_, c_] := Block[{t$95$0 = N[(N[Sqrt[N[Abs[a], $MachinePrecision]], $MachinePrecision] * N[Sqrt[N[Abs[c], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = If[Equal[N[With[{TMP1 = Abs[a], TMP2 = Sign[c]}, TMP1 * If[TMP2 == 0, 1, TMP2]], $MachinePrecision], a], N[(N[Sqrt[N[(N[Abs[b$95$2], $MachinePrecision] - t$95$0), $MachinePrecision]], $MachinePrecision] * N[Sqrt[N[(N[Abs[b$95$2], $MachinePrecision] + t$95$0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[Sqrt[b$95$2 ^ 2 + t$95$0 ^ 2], $MachinePrecision]]}, If[Less[b$95$2, 0.0], N[(N[(t$95$1 - b$95$2), $MachinePrecision] / a), $MachinePrecision], N[((-c) / N[(b$95$2 + t$95$1), $MachinePrecision]), $MachinePrecision]]]]

Reproduce

herbie shell --seed 2025114 
(FPCore (a b_2 c)
  :name "quad2p (problem 3.2.1, positive)"
  :precision binary64
  :herbie-expected 10

  :alt
  (! :herbie-platform c (let ((sqtD (let ((x (* (sqrt (fabs a)) (sqrt (fabs c))))) (if (== (copysign a c) a) (* (sqrt (- (fabs b_2) x)) (sqrt (+ (fabs b_2) x))) (hypot b_2 x))))) (if (< b_2 0) (/ (- sqtD b_2) a) (/ (- c) (+ b_2 sqtD)))))

  (/ (+ (- b_2) (sqrt (- (* b_2 b_2) (* a c)))) a))