ENA, Section 1.4, Exercise 4d

Percentage Accurate: 61.8% → 98.9%

Time: 10.1s

Alternatives: 8

Speedup: 1.0×

Specification

\[\left(0 \leq x \land x \leq 1000000000\right) \land \left(-1 \leq \varepsilon \land \varepsilon \leq 1\right)\]

Math

\[\begin{array}{l} \\ x - \sqrt{x \cdot x - \varepsilon} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (- x (sqrt (- (* x x) eps))))

C

double code(double x, double eps) {
	return x - sqrt(((x * x) - eps));
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = x - sqrt(((x * x) - eps))
end function

Java

public static double code(double x, double eps) {
	return x - Math.sqrt(((x * x) - eps));
}

Python

def code(x, eps):
	return x - math.sqrt(((x * x) - eps))

Julia

function code(x, eps)
	return Float64(x - sqrt(Float64(Float64(x * x) - eps)))
end

MATLAB

function tmp = code(x, eps)
	tmp = x - sqrt(((x * x) - eps));
end

Wolfram

code[x_, eps_] := N[(x - N[Sqrt[N[(N[(x * x), $MachinePrecision] - eps), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Initial Program: 61.8% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ x - \sqrt{x \cdot x - \varepsilon} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (- x (sqrt (- (* x x) eps))))

C

double code(double x, double eps) {
	return x - sqrt(((x * x) - eps));
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = x - sqrt(((x * x) - eps))
end function

Java

public static double code(double x, double eps) {
	return x - Math.sqrt(((x * x) - eps));
}

Python

def code(x, eps):
	return x - math.sqrt(((x * x) - eps))

Julia

function code(x, eps)
	return Float64(x - sqrt(Float64(Float64(x * x) - eps)))
end

MATLAB

function tmp = code(x, eps)
	tmp = x - sqrt(((x * x) - eps));
end

Wolfram

code[x_, eps_] := N[(x - N[Sqrt[N[(N[(x * x), $MachinePrecision] - eps), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

Alternative 1: 98.9% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x - \sqrt{x \cdot x - \varepsilon}\\ \mathbf{if}\;t\_0 \leq -1 \cdot 10^{-154}:\\ \;\;\;\;t\_0\\ \mathbf{else}:\\ \;\;\;\;\frac{\varepsilon}{x \cdot 2 + \varepsilon \cdot \frac{-0.5 + -0.125 \cdot \frac{\varepsilon}{x \cdot x}}{x}}\\ \end{array} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (let* ((t_0 (- x (sqrt (- (* x x) eps)))))
   (if (<= t_0 -1e-154)
     t_0
     (/ eps (+ (* x 2.0) (* eps (/ (+ -0.5 (* -0.125 (/ eps (* x x)))) x)))))))

C

double code(double x, double eps) {
	double t_0 = x - sqrt(((x * x) - eps));
	double tmp;
	if (t_0 <= -1e-154) {
		tmp = t_0;
	} else {
		tmp = eps / ((x * 2.0) + (eps * ((-0.5 + (-0.125 * (eps / (x * x)))) / x)));
	}
	return tmp;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x - sqrt(((x * x) - eps))
    if (t_0 <= (-1d-154)) then
        tmp = t_0
    else
        tmp = eps / ((x * 2.0d0) + (eps * (((-0.5d0) + ((-0.125d0) * (eps / (x * x)))) / x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double eps) {
	double t_0 = x - Math.sqrt(((x * x) - eps));
	double tmp;
	if (t_0 <= -1e-154) {
		tmp = t_0;
	} else {
		tmp = eps / ((x * 2.0) + (eps * ((-0.5 + (-0.125 * (eps / (x * x)))) / x)));
	}
	return tmp;
}

Python

def code(x, eps):
	t_0 = x - math.sqrt(((x * x) - eps))
	tmp = 0
	if t_0 <= -1e-154:
		tmp = t_0
	else:
		tmp = eps / ((x * 2.0) + (eps * ((-0.5 + (-0.125 * (eps / (x * x)))) / x)))
	return tmp

Julia

function code(x, eps)
	t_0 = Float64(x - sqrt(Float64(Float64(x * x) - eps)))
	tmp = 0.0
	if (t_0 <= -1e-154)
		tmp = t_0;
	else
		tmp = Float64(eps / Float64(Float64(x * 2.0) + Float64(eps * Float64(Float64(-0.5 + Float64(-0.125 * Float64(eps / Float64(x * x)))) / x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, eps)
	t_0 = x - sqrt(((x * x) - eps));
	tmp = 0.0;
	if (t_0 <= -1e-154)
		tmp = t_0;
	else
		tmp = eps / ((x * 2.0) + (eps * ((-0.5 + (-0.125 * (eps / (x * x)))) / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, eps_] := Block[{t$95$0 = N[(x - N[Sqrt[N[(N[(x * x), $MachinePrecision] - eps), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -1e-154], t$95$0, N[(eps / N[(N[(x * 2.0), $MachinePrecision] + N[(eps * N[(N[(-0.5 + N[(-0.125 * N[(eps / N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if (-.f64 x (sqrt.f64 (-.f64 (*.f64 x x) eps))) < -9.9999999999999997e-155

   1. Initial program 99.5%

      \[x - \sqrt{x \cdot x - \varepsilon} \]
   2. Add Preprocessing

   if -9.9999999999999997e-155 < (-.f64 x (sqrt.f64 (-.f64 (*.f64 x x) eps)))

   1. Initial program 8.5%

      \[x - \sqrt{x \cdot x - \varepsilon} \]
   2. Add Preprocessing

   3. Taylor expanded in eps around inf

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\color{blue}{\left(\varepsilon \cdot \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)}\right)\right) \]
   4. Step-by-step derivation

      1. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)\right)\right) \]

      2. sub-neg N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + \left(\mathsf{neg}\left(1\right)\right)\right)\right)\right)\right) \]

      3. metadata-eval N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + -1\right)\right)\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(-1 + \frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \left(\frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left({x}^{2}\right), \varepsilon\right)\right)\right)\right)\right) \]

      7. unpow2 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left(x \cdot x\right), \varepsilon\right)\right)\right)\right)\right) \]

      8. *-lowering-*.f64 8.6%

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right)\right)\right)\right)\right) \]

   5. Simplified 8.6%

      \[\leadsto x - \sqrt{\color{blue}{\varepsilon \cdot \left(-1 + \frac{x \cdot x}{\varepsilon}\right)}} \]

   7. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\left(\frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}} - \frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}\right) + \frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]
   8. Step-by-step derivation

      1. +-inverses N/A

         \[\leadsto 0 + \frac{\color{blue}{\varepsilon}}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}} \]

      2. +-lft-identity N/A

         \[\leadsto \frac{\varepsilon}{\color{blue}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}}} \]

      3. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \color{blue}{\left(x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right) \]

      4. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \color{blue}{\left({\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right)\right) \]

      5. pow-lowering-pow.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\left(x \cdot x - \varepsilon\right), \color{blue}{\frac{1}{2}}\right)\right)\right) \]

      6. --lowering--.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\left(x \cdot x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

      7. *-lowering-*.f64 100.0%

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

   9. Applied egg-rr 100.0%

      \[\leadsto \color{blue}{\frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]

   10. Taylor expanded in eps around 0

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \color{blue}{\left(2 \cdot x + \varepsilon \cdot \left(\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{3}} - \frac{1}{2} \cdot \frac{1}{x}\right)\right)}\right) \]
   11. Step-by-step derivation

       1. +-lowering-+.f64 N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\left(2 \cdot x\right), \color{blue}{\left(\varepsilon \cdot \left(\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{3}} - \frac{1}{2} \cdot \frac{1}{x}\right)\right)}\right)\right) \]

       2. *-commutative N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\left(x \cdot 2\right), \left(\color{blue}{\varepsilon} \cdot \left(\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{3}} - \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right)\right) \]

       3. *-lowering-*.f64 N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \left(\color{blue}{\varepsilon} \cdot \left(\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{3}} - \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right)\right) \]

       4. *-lowering-*.f64 N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \color{blue}{\left(\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{3}} - \frac{1}{2} \cdot \frac{1}{x}\right)}\right)\right)\right) \]

       5. unpow3 N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \left(\frac{-1}{8} \cdot \frac{\varepsilon}{\left(x \cdot x\right) \cdot x} - \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right)\right) \]

       6. unpow2 N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \left(\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{2} \cdot x} - \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right)\right) \]

       7. associate-/r* N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \left(\frac{-1}{8} \cdot \frac{\frac{\varepsilon}{{x}^{2}}}{x} - \frac{1}{2} \cdot \frac{1}{x}\right)\right)\right)\right) \]

       8. associate-/l* N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \left(\frac{\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{2}}}{x} - \color{blue}{\frac{1}{2}} \cdot \frac{1}{x}\right)\right)\right)\right) \]

       9. associate-*r/ N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \left(\frac{\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{2}}}{x} - \frac{\frac{1}{2} \cdot 1}{\color{blue}{x}}\right)\right)\right)\right) \]

       10. metadata-eval N/A

           \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \left(\frac{\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{2}}}{x} - \frac{\frac{1}{2}}{x}\right)\right)\right)\right) \]

       11. div-sub N/A

           \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \left(\frac{\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{2}} - \frac{1}{2}}{\color{blue}{x}}\right)\right)\right)\right) \]

       12. /-lowering-/.f64 N/A

           \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, 2\right), \mathsf{*.f64}\left(\varepsilon, \mathsf{/.f64}\left(\left(\frac{-1}{8} \cdot \frac{\varepsilon}{{x}^{2}} - \frac{1}{2}\right), \color{blue}{x}\right)\right)\right)\right) \]

   12. Simplified 99.7%

       \[\leadsto \frac{\varepsilon}{\color{blue}{x \cdot 2 + \varepsilon \cdot \frac{-0.5 + -0.125 \cdot \frac{\varepsilon}{x \cdot x}}{x}}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 2: 99.6% accurate, 0.5× speedup

Math

\[\begin{array}{l} \\ \frac{\varepsilon}{x + {\left(\mathsf{fma}\left(x, x, 0 - \varepsilon\right)\right)}^{0.5}} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (/ eps (+ x (pow (fma x x (- 0.0 eps)) 0.5))))

C

double code(double x, double eps) {
	return eps / (x + pow(fma(x, x, (0.0 - eps)), 0.5));
}

Julia

function code(x, eps)
	return Float64(eps / Float64(x + (fma(x, x, Float64(0.0 - eps)) ^ 0.5)))
end

Wolfram

code[x_, eps_] := N[(eps / N[(x + N[Power[N[(x * x + N[(0.0 - eps), $MachinePrecision]), $MachinePrecision], 0.5], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 60.8%

   \[x - \sqrt{x \cdot x - \varepsilon} \]
2. Add Preprocessing

3. Taylor expanded in eps around inf

   \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\color{blue}{\left(\varepsilon \cdot \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)}\right)\right) \]
4. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)\right)\right) \]

   2. sub-neg N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + \left(\mathsf{neg}\left(1\right)\right)\right)\right)\right)\right) \]

   3. metadata-eval N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + -1\right)\right)\right)\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(-1 + \frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right) \]

   5. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \left(\frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right)\right) \]

   6. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left({x}^{2}\right), \varepsilon\right)\right)\right)\right)\right) \]

   7. unpow2 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left(x \cdot x\right), \varepsilon\right)\right)\right)\right)\right) \]

   8. *-lowering-*.f64 60.8%

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right)\right)\right)\right)\right) \]

5. Simplified 60.8%

   \[\leadsto x - \sqrt{\color{blue}{\varepsilon \cdot \left(-1 + \frac{x \cdot x}{\varepsilon}\right)}} \]

7. Applied egg-rr 99.5%

   \[\leadsto \color{blue}{\left(\frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}} - \frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}\right) + \frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]
8. Step-by-step derivation

   1. +-inverses N/A

      \[\leadsto 0 + \frac{\color{blue}{\varepsilon}}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}} \]

   2. +-lft-identity N/A

      \[\leadsto \frac{\varepsilon}{\color{blue}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}}} \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \color{blue}{\left(x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right) \]

   4. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \color{blue}{\left({\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right)\right) \]

   5. pow-lowering-pow.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\left(x \cdot x - \varepsilon\right), \color{blue}{\frac{1}{2}}\right)\right)\right) \]

   6. --lowering--.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\left(x \cdot x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

   7. *-lowering-*.f64 99.5%

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

9. Applied egg-rr 99.5%

   \[\leadsto \color{blue}{\frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]
10. Step-by-step derivation

    1. fmm-def N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\left(\mathsf{fma}\left(x, x, \mathsf{neg}\left(\varepsilon\right)\right)\right), \frac{1}{2}\right)\right)\right) \]

    2. fma-lowering-fma.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{fma.f64}\left(x, x, \left(\mathsf{neg}\left(\varepsilon\right)\right)\right), \frac{1}{2}\right)\right)\right) \]

    3. neg-sub0 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{fma.f64}\left(x, x, \left(0 - \varepsilon\right)\right), \frac{1}{2}\right)\right)\right) \]

    4. +-inverses N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{fma.f64}\left(x, x, \left(\left(x - x\right) - \varepsilon\right)\right), \frac{1}{2}\right)\right)\right) \]

    5. --lowering--.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{fma.f64}\left(x, x, \mathsf{\_.f64}\left(\left(x - x\right), \varepsilon\right)\right), \frac{1}{2}\right)\right)\right) \]

    6. +-inverses 99.6%

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{fma.f64}\left(x, x, \mathsf{\_.f64}\left(0, \varepsilon\right)\right), \frac{1}{2}\right)\right)\right) \]

11. Applied egg-rr 99.6%

    \[\leadsto \frac{\varepsilon}{x + {\color{blue}{\left(\mathsf{fma}\left(x, x, 0 - \varepsilon\right)\right)}}^{0.5}} \]
12. Add Preprocessing

Alternative 3: 88.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq 1.52 \cdot 10^{-111}:\\ \;\;\;\;x - \sqrt{0 - \varepsilon}\\ \mathbf{else}:\\ \;\;\;\;\frac{\varepsilon}{x + \left(x + \frac{\varepsilon \cdot -0.5}{x}\right)}\\ \end{array} \end{array} \]

FPCore

(FPCore (x eps)
 :precision binary64
 (if (<= x 1.52e-111)
   (- x (sqrt (- 0.0 eps)))
   (/ eps (+ x (+ x (/ (* eps -0.5) x))))))

C

double code(double x, double eps) {
	double tmp;
	if (x <= 1.52e-111) {
		tmp = x - sqrt((0.0 - eps));
	} else {
		tmp = eps / (x + (x + ((eps * -0.5) / x)));
	}
	return tmp;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    real(8) :: tmp
    if (x <= 1.52d-111) then
        tmp = x - sqrt((0.0d0 - eps))
    else
        tmp = eps / (x + (x + ((eps * (-0.5d0)) / x)))
    end if
    code = tmp
end function

Java

public static double code(double x, double eps) {
	double tmp;
	if (x <= 1.52e-111) {
		tmp = x - Math.sqrt((0.0 - eps));
	} else {
		tmp = eps / (x + (x + ((eps * -0.5) / x)));
	}
	return tmp;
}

Python

def code(x, eps):
	tmp = 0
	if x <= 1.52e-111:
		tmp = x - math.sqrt((0.0 - eps))
	else:
		tmp = eps / (x + (x + ((eps * -0.5) / x)))
	return tmp

Julia

function code(x, eps)
	tmp = 0.0
	if (x <= 1.52e-111)
		tmp = Float64(x - sqrt(Float64(0.0 - eps)));
	else
		tmp = Float64(eps / Float64(x + Float64(x + Float64(Float64(eps * -0.5) / x))));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, eps)
	tmp = 0.0;
	if (x <= 1.52e-111)
		tmp = x - sqrt((0.0 - eps));
	else
		tmp = eps / (x + (x + ((eps * -0.5) / x)));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, eps_] := If[LessEqual[x, 1.52e-111], N[(x - N[Sqrt[N[(0.0 - eps), $MachinePrecision]], $MachinePrecision]), $MachinePrecision], N[(eps / N[(x + N[(x + N[(N[(eps * -0.5), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < 1.51999999999999998e-111

   1. Initial program 97.3%

      \[x - \sqrt{x \cdot x - \varepsilon} \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\color{blue}{\left(-1 \cdot \varepsilon\right)}\right)\right) \]
   4. Step-by-step derivation

      1. mul-1-neg N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\left(\mathsf{neg}\left(\varepsilon\right)\right)\right)\right) \]

      2. neg-sub0 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\left(0 - \varepsilon\right)\right)\right) \]

      3. --lowering--.f64 95.1%

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{\_.f64}\left(0, \varepsilon\right)\right)\right) \]

   5. Simplified 95.1%

      \[\leadsto x - \sqrt{\color{blue}{0 - \varepsilon}} \]

   if 1.51999999999999998e-111 < x

   1. Initial program 25.4%

      \[x - \sqrt{x \cdot x - \varepsilon} \]
   2. Add Preprocessing

   3. Taylor expanded in eps around inf

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\color{blue}{\left(\varepsilon \cdot \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)}\right)\right) \]
   4. Step-by-step derivation

      1. *-lowering-*.f64 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)\right)\right) \]

      2. sub-neg N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + \left(\mathsf{neg}\left(1\right)\right)\right)\right)\right)\right) \]

      3. metadata-eval N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + -1\right)\right)\right)\right) \]

      4. +-commutative N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(-1 + \frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right) \]

      5. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \left(\frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right)\right) \]

      6. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left({x}^{2}\right), \varepsilon\right)\right)\right)\right)\right) \]

      7. unpow2 N/A

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left(x \cdot x\right), \varepsilon\right)\right)\right)\right)\right) \]

      8. *-lowering-*.f64 25.4%

         \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right)\right)\right)\right)\right) \]

   5. Simplified 25.4%

      \[\leadsto x - \sqrt{\color{blue}{\varepsilon \cdot \left(-1 + \frac{x \cdot x}{\varepsilon}\right)}} \]

   7. Applied egg-rr 99.9%

      \[\leadsto \color{blue}{\left(\frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}} - \frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}\right) + \frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]
   8. Step-by-step derivation

      1. +-inverses N/A

         \[\leadsto 0 + \frac{\color{blue}{\varepsilon}}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}} \]

      2. +-lft-identity N/A

         \[\leadsto \frac{\varepsilon}{\color{blue}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}}} \]

      3. /-lowering-/.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \color{blue}{\left(x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right) \]

      4. +-lowering-+.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \color{blue}{\left({\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right)\right) \]

      5. pow-lowering-pow.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\left(x \cdot x - \varepsilon\right), \color{blue}{\frac{1}{2}}\right)\right)\right) \]

      6. --lowering--.f64 N/A

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\left(x \cdot x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

      7. *-lowering-*.f64 99.9%

         \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

   9. Applied egg-rr 99.9%

      \[\leadsto \color{blue}{\frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]

   10. Taylor expanded in eps around 0

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \color{blue}{\left(x + \frac{-1}{2} \cdot \frac{\varepsilon}{x}\right)}\right)\right) \]
   11. Step-by-step derivation

       1. +-lowering-+.f64 N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{-1}{2} \cdot \frac{\varepsilon}{x}\right)}\right)\right)\right) \]

       2. associate-*r/ N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \left(\frac{\frac{-1}{2} \cdot \varepsilon}{\color{blue}{x}}\right)\right)\right)\right) \]

       3. /-lowering-/.f64 N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \mathsf{/.f64}\left(\left(\frac{-1}{2} \cdot \varepsilon\right), \color{blue}{x}\right)\right)\right)\right) \]

       4. *-commutative N/A

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \mathsf{/.f64}\left(\left(\varepsilon \cdot \frac{-1}{2}\right), x\right)\right)\right)\right) \]

       5. *-lowering-*.f64 82.2%

          \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{-1}{2}\right), x\right)\right)\right)\right) \]

   12. Simplified 82.2%

       \[\leadsto \frac{\varepsilon}{x + \color{blue}{\left(x + \frac{\varepsilon \cdot -0.5}{x}\right)}} \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\varepsilon}{x + \sqrt{x \cdot x - \varepsilon}} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (/ eps (+ x (sqrt (- (* x x) eps)))))

C

double code(double x, double eps) {
	return eps / (x + sqrt(((x * x) - eps)));
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = eps / (x + sqrt(((x * x) - eps)))
end function

Java

public static double code(double x, double eps) {
	return eps / (x + Math.sqrt(((x * x) - eps)));
}

Python

def code(x, eps):
	return eps / (x + math.sqrt(((x * x) - eps)))

Julia

function code(x, eps)
	return Float64(eps / Float64(x + sqrt(Float64(Float64(x * x) - eps))))
end

MATLAB

function tmp = code(x, eps)
	tmp = eps / (x + sqrt(((x * x) - eps)));
end

Wolfram

code[x_, eps_] := N[(eps / N[(x + N[Sqrt[N[(N[(x * x), $MachinePrecision] - eps), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 60.8%

   \[x - \sqrt{x \cdot x - \varepsilon} \]
2. Add Preprocessing

3. Taylor expanded in eps around inf

   \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\color{blue}{\left(\varepsilon \cdot \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)}\right)\right) \]
4. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)\right)\right) \]

   2. sub-neg N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + \left(\mathsf{neg}\left(1\right)\right)\right)\right)\right)\right) \]

   3. metadata-eval N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + -1\right)\right)\right)\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(-1 + \frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right) \]

   5. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \left(\frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right)\right) \]

   6. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left({x}^{2}\right), \varepsilon\right)\right)\right)\right)\right) \]

   7. unpow2 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left(x \cdot x\right), \varepsilon\right)\right)\right)\right)\right) \]

   8. *-lowering-*.f64 60.8%

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right)\right)\right)\right)\right) \]

5. Simplified 60.8%

   \[\leadsto x - \sqrt{\color{blue}{\varepsilon \cdot \left(-1 + \frac{x \cdot x}{\varepsilon}\right)}} \]

7. Applied egg-rr 99.5%

   \[\leadsto \color{blue}{\left(\frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}} - \frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}\right) + \frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]
8. Step-by-step derivation

   1. +-inverses N/A

      \[\leadsto 0 + \frac{\color{blue}{\varepsilon}}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}} \]

   2. +-lft-identity N/A

      \[\leadsto \frac{\varepsilon}{\color{blue}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}}} \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \color{blue}{\left(x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right) \]

   4. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \color{blue}{\left({\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right)\right) \]

   5. pow-lowering-pow.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\left(x \cdot x - \varepsilon\right), \color{blue}{\frac{1}{2}}\right)\right)\right) \]

   6. --lowering--.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\left(x \cdot x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

   7. *-lowering-*.f64 99.5%

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

9. Applied egg-rr 99.5%

   \[\leadsto \color{blue}{\frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]
10. Step-by-step derivation

    1. +-commutative N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \left({\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}} + \color{blue}{x}\right)\right) \]

    2. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\left({\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right), \color{blue}{x}\right)\right) \]

    3. unpow1/2 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\left(\sqrt{x \cdot x - \varepsilon}\right), x\right)\right) \]

    4. sqrt-lowering-sqrt.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{sqrt.f64}\left(\left(x \cdot x - \varepsilon\right)\right), x\right)\right) \]

    5. --lowering--.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{sqrt.f64}\left(\mathsf{\_.f64}\left(\left(x \cdot x\right), \varepsilon\right)\right), x\right)\right) \]

    6. *-lowering-*.f64 99.5%

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{sqrt.f64}\left(\mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right)\right), x\right)\right) \]

11. Applied egg-rr 99.5%

    \[\leadsto \frac{\varepsilon}{\color{blue}{\sqrt{x \cdot x - \varepsilon} + x}} \]

12. Final simplification 99.5%

    \[\leadsto \frac{\varepsilon}{x + \sqrt{x \cdot x - \varepsilon}} \]
13. Add Preprocessing

Alternative 5: 45.3% accurate, 9.7× speedup

Math

\[\begin{array}{l} \\ \frac{\varepsilon}{x + \left(x + \frac{\varepsilon \cdot -0.5}{x}\right)} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (/ eps (+ x (+ x (/ (* eps -0.5) x)))))

C

double code(double x, double eps) {
	return eps / (x + (x + ((eps * -0.5) / x)));
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = eps / (x + (x + ((eps * (-0.5d0)) / x)))
end function

Java

public static double code(double x, double eps) {
	return eps / (x + (x + ((eps * -0.5) / x)));
}

Python

def code(x, eps):
	return eps / (x + (x + ((eps * -0.5) / x)))

Julia

function code(x, eps)
	return Float64(eps / Float64(x + Float64(x + Float64(Float64(eps * -0.5) / x))))
end

MATLAB

function tmp = code(x, eps)
	tmp = eps / (x + (x + ((eps * -0.5) / x)));
end

Wolfram

code[x_, eps_] := N[(eps / N[(x + N[(x + N[(N[(eps * -0.5), $MachinePrecision] / x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 60.8%

   \[x - \sqrt{x \cdot x - \varepsilon} \]
2. Add Preprocessing

3. Taylor expanded in eps around inf

   \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\color{blue}{\left(\varepsilon \cdot \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)}\right)\right) \]
4. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)\right)\right) \]

   2. sub-neg N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + \left(\mathsf{neg}\left(1\right)\right)\right)\right)\right)\right) \]

   3. metadata-eval N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + -1\right)\right)\right)\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(-1 + \frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right) \]

   5. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \left(\frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right)\right) \]

   6. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left({x}^{2}\right), \varepsilon\right)\right)\right)\right)\right) \]

   7. unpow2 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left(x \cdot x\right), \varepsilon\right)\right)\right)\right)\right) \]

   8. *-lowering-*.f64 60.8%

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right)\right)\right)\right)\right) \]

5. Simplified 60.8%

   \[\leadsto x - \sqrt{\color{blue}{\varepsilon \cdot \left(-1 + \frac{x \cdot x}{\varepsilon}\right)}} \]

7. Applied egg-rr 99.5%

   \[\leadsto \color{blue}{\left(\frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}} - \frac{x \cdot x}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}\right) + \frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]
8. Step-by-step derivation

   1. +-inverses N/A

      \[\leadsto 0 + \frac{\color{blue}{\varepsilon}}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}} \]

   2. +-lft-identity N/A

      \[\leadsto \frac{\varepsilon}{\color{blue}{x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}}} \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \color{blue}{\left(x + {\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right) \]

   4. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \color{blue}{\left({\left(x \cdot x - \varepsilon\right)}^{\frac{1}{2}}\right)}\right)\right) \]

   5. pow-lowering-pow.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\left(x \cdot x - \varepsilon\right), \color{blue}{\frac{1}{2}}\right)\right)\right) \]

   6. --lowering--.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\left(x \cdot x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

   7. *-lowering-*.f64 99.5%

      \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{pow.f64}\left(\mathsf{\_.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right), \frac{1}{2}\right)\right)\right) \]

9. Applied egg-rr 99.5%

   \[\leadsto \color{blue}{\frac{\varepsilon}{x + {\left(x \cdot x - \varepsilon\right)}^{0.5}}} \]

10. Taylor expanded in eps around 0

    \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \color{blue}{\left(x + \frac{-1}{2} \cdot \frac{\varepsilon}{x}\right)}\right)\right) \]
11. Step-by-step derivation

    1. +-lowering-+.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \color{blue}{\left(\frac{-1}{2} \cdot \frac{\varepsilon}{x}\right)}\right)\right)\right) \]

    2. associate-*r/ N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \left(\frac{\frac{-1}{2} \cdot \varepsilon}{\color{blue}{x}}\right)\right)\right)\right) \]

    3. /-lowering-/.f64 N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \mathsf{/.f64}\left(\left(\frac{-1}{2} \cdot \varepsilon\right), \color{blue}{x}\right)\right)\right)\right) \]

    4. *-commutative N/A

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \mathsf{/.f64}\left(\left(\varepsilon \cdot \frac{-1}{2}\right), x\right)\right)\right)\right) \]

    5. *-lowering-*.f64 46.3%

       \[\leadsto \mathsf{/.f64}\left(\varepsilon, \mathsf{+.f64}\left(x, \mathsf{+.f64}\left(x, \mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{-1}{2}\right), x\right)\right)\right)\right) \]

12. Simplified 46.3%

    \[\leadsto \frac{\varepsilon}{x + \color{blue}{\left(x + \frac{\varepsilon \cdot -0.5}{x}\right)}} \]
13. Add Preprocessing

Alternative 6: 44.5% accurate, 21.4× speedup

Math

\[\begin{array}{l} \\ \frac{\varepsilon \cdot 0.5}{x} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (/ (* eps 0.5) x))

C

double code(double x, double eps) {
	return (eps * 0.5) / x;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = (eps * 0.5d0) / x
end function

Java

public static double code(double x, double eps) {
	return (eps * 0.5) / x;
}

Python

def code(x, eps):
	return (eps * 0.5) / x

Julia

function code(x, eps)
	return Float64(Float64(eps * 0.5) / x)
end

MATLAB

function tmp = code(x, eps)
	tmp = (eps * 0.5) / x;
end

Wolfram

code[x_, eps_] := N[(N[(eps * 0.5), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 60.8%

   \[x - \sqrt{x \cdot x - \varepsilon} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{1}{2} \cdot \frac{\varepsilon}{x}} \]
4. Step-by-step derivation

   1. associate-*r/ N/A

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

   2. metadata-eval N/A

      \[\leadsto \frac{\left(\mathsf{neg}\left(\frac{-1}{2}\right)\right) \cdot \varepsilon}{x} \]

   3. distribute-lft-neg-in N/A

      \[\leadsto \frac{\mathsf{neg}\left(\frac{-1}{2} \cdot \varepsilon\right)}{x} \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\mathsf{neg}\left(\frac{-1}{2} \cdot \varepsilon\right)\right), \color{blue}{x}\right) \]

   5. *-commutative N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\mathsf{neg}\left(\varepsilon \cdot \frac{-1}{2}\right)\right), x\right) \]

   6. distribute-rgt-neg-in N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\varepsilon \cdot \left(\mathsf{neg}\left(\frac{-1}{2}\right)\right)\right), x\right) \]

   7. metadata-eval N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\varepsilon \cdot \frac{1}{2}\right), x\right) \]

   8. *-lowering-*.f64 45.6%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{2}\right), x\right) \]

5. Simplified 45.6%

   \[\leadsto \color{blue}{\frac{\varepsilon \cdot 0.5}{x}} \]
6. Add Preprocessing

Alternative 7: 44.4% accurate, 21.4× speedup

Math

\[\begin{array}{l} \\ \varepsilon \cdot \frac{0.5}{x} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (* eps (/ 0.5 x)))

C

double code(double x, double eps) {
	return eps * (0.5 / x);
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = eps * (0.5d0 / x)
end function

Java

public static double code(double x, double eps) {
	return eps * (0.5 / x);
}

Python

def code(x, eps):
	return eps * (0.5 / x)

Julia

function code(x, eps)
	return Float64(eps * Float64(0.5 / x))
end

MATLAB

function tmp = code(x, eps)
	tmp = eps * (0.5 / x);
end

Wolfram

code[x_, eps_] := N[(eps * N[(0.5 / x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 60.8%

   \[x - \sqrt{x \cdot x - \varepsilon} \]
2. Add Preprocessing

3. Taylor expanded in eps around inf

   \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\color{blue}{\left(\varepsilon \cdot \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)}\right)\right) \]
4. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} - 1\right)\right)\right)\right) \]

   2. sub-neg N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + \left(\mathsf{neg}\left(1\right)\right)\right)\right)\right)\right) \]

   3. metadata-eval N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(\frac{{x}^{2}}{\varepsilon} + -1\right)\right)\right)\right) \]

   4. +-commutative N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \left(-1 + \frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right) \]

   5. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \left(\frac{{x}^{2}}{\varepsilon}\right)\right)\right)\right)\right) \]

   6. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left({x}^{2}\right), \varepsilon\right)\right)\right)\right)\right) \]

   7. unpow2 N/A

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\left(x \cdot x\right), \varepsilon\right)\right)\right)\right)\right) \]

   8. *-lowering-*.f64 60.8%

      \[\leadsto \mathsf{\_.f64}\left(x, \mathsf{sqrt.f64}\left(\mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(-1, \mathsf{/.f64}\left(\mathsf{*.f64}\left(x, x\right), \varepsilon\right)\right)\right)\right)\right) \]

5. Simplified 60.8%

   \[\leadsto x - \sqrt{\color{blue}{\varepsilon \cdot \left(-1 + \frac{x \cdot x}{\varepsilon}\right)}} \]

6. Taylor expanded in eps around 0

   \[\leadsto \color{blue}{\varepsilon \cdot \left(\frac{1}{8} \cdot \frac{\varepsilon}{{x}^{3}} + \frac{1}{2} \cdot \frac{1}{x}\right)} \]
7. Step-by-step derivation

   1. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\left(\frac{1}{8} \cdot \frac{\varepsilon}{{x}^{3}} + \frac{1}{2} \cdot \frac{1}{x}\right)}\right) \]

   2. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\left(\frac{1}{8} \cdot \frac{\varepsilon}{{x}^{3}}\right), \color{blue}{\left(\frac{1}{2} \cdot \frac{1}{x}\right)}\right)\right) \]

   3. associate-*r/ N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\left(\frac{\frac{1}{8} \cdot \varepsilon}{{x}^{3}}\right), \left(\color{blue}{\frac{1}{2}} \cdot \frac{1}{x}\right)\right)\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(\frac{1}{8} \cdot \varepsilon\right), \left({x}^{3}\right)\right), \left(\color{blue}{\frac{1}{2}} \cdot \frac{1}{x}\right)\right)\right) \]

   5. *-commutative N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(\varepsilon \cdot \frac{1}{8}\right), \left({x}^{3}\right)\right), \left(\frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) \]

   6. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \left({x}^{3}\right)\right), \left(\frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) \]

   7. cube-mult N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \left(x \cdot \left(x \cdot x\right)\right)\right), \left(\frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) \]

   8. unpow2 N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \left(x \cdot {x}^{2}\right)\right), \left(\frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) \]

   9. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \mathsf{*.f64}\left(x, \left({x}^{2}\right)\right)\right), \left(\frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) \]

   10. unpow2 N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \mathsf{*.f64}\left(x, \left(x \cdot x\right)\right)\right), \left(\frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) \]

   11. *-lowering-*.f64 N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right)\right), \left(\frac{1}{2} \cdot \frac{1}{x}\right)\right)\right) \]

   12. associate-*r/ N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right)\right), \left(\frac{\frac{1}{2} \cdot 1}{\color{blue}{x}}\right)\right)\right) \]

   13. metadata-eval N/A

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right)\right), \left(\frac{\frac{1}{2}}{x}\right)\right)\right) \]

   14. /-lowering-/.f64 40.0%

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{*.f64}\left(\varepsilon, \frac{1}{8}\right), \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, x\right)\right)\right), \mathsf{/.f64}\left(\frac{1}{2}, \color{blue}{x}\right)\right)\right) \]

8. Simplified 40.0%

   \[\leadsto \color{blue}{\varepsilon \cdot \left(\frac{\varepsilon \cdot 0.125}{x \cdot \left(x \cdot x\right)} + \frac{0.5}{x}\right)} \]

9. Taylor expanded in eps around 0

   \[\leadsto \mathsf{*.f64}\left(\varepsilon, \color{blue}{\left(\frac{\frac{1}{2}}{x}\right)}\right) \]
10. Step-by-step derivation

    1. /-lowering-/.f64 45.4%

       \[\leadsto \mathsf{*.f64}\left(\varepsilon, \mathsf{/.f64}\left(\frac{1}{2}, \color{blue}{x}\right)\right) \]

11. Simplified 45.4%

    \[\leadsto \varepsilon \cdot \color{blue}{\frac{0.5}{x}} \]
12. Add Preprocessing

Alternative 8: 4.3% accurate, 107.0× speedup

Math

\[\begin{array}{l} \\ 0 \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 0.0)

C

double code(double x, double eps) {
	return 0.0;
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = 0.0d0
end function

Java

public static double code(double x, double eps) {
	return 0.0;
}

Python

def code(x, eps):
	return 0.0

Julia

function code(x, eps)
	return 0.0
end

MATLAB

function tmp = code(x, eps)
	tmp = 0.0;
end

Wolfram

code[x_, eps_] := 0.0

Derivation

1. Initial program 60.8%

   \[x - \sqrt{x \cdot x - \varepsilon} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \mathsf{\_.f64}\left(x, \color{blue}{x}\right) \]
4. Step-by-step derivation

5. Simplified 4.3%

   \[\leadsto x - \color{blue}{x} \]
6. Step-by-step derivation

   1. +-inverses 4.3%

      \[\leadsto 0 \]

7. Applied egg-rr 4.3%

   \[\leadsto \color{blue}{0} \]
8. Add Preprocessing

Developer Target 1: 99.6% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{\varepsilon}{x + \sqrt{x \cdot x - \varepsilon}} \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (/ eps (+ x (sqrt (- (* x x) eps)))))

C

double code(double x, double eps) {
	return eps / (x + sqrt(((x * x) - eps)));
}

Fortran

real(8) function code(x, eps)
    real(8), intent (in) :: x
    real(8), intent (in) :: eps
    code = eps / (x + sqrt(((x * x) - eps)))
end function

Java

public static double code(double x, double eps) {
	return eps / (x + Math.sqrt(((x * x) - eps)));
}

Python

def code(x, eps):
	return eps / (x + math.sqrt(((x * x) - eps)))

Julia

function code(x, eps)
	return Float64(eps / Float64(x + sqrt(Float64(Float64(x * x) - eps))))
end

MATLAB

function tmp = code(x, eps)
	tmp = eps / (x + sqrt(((x * x) - eps)));
end

Wolfram

code[x_, eps_] := N[(eps / N[(x + N[Sqrt[N[(N[(x * x), $MachinePrecision] - eps), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024139 
(FPCore (x eps)
  :name "ENA, Section 1.4, Exercise 4d"
  :precision binary64
  :pre (and (and (<= 0.0 x) (<= x 1000000000.0)) (and (<= -1.0 eps) (<= eps 1.0)))

  :alt
  (! :herbie-platform default (/ eps (+ x (sqrt (- (* x x) eps)))))

  (- x (sqrt (- (* x x) eps))))