Eccentricity of an ellipse

Percentage Accurate: 77.2% → 100.0%

Time: 8.4s

Alternatives: 4

Speedup: 211.0×

Specification

\[\left(0 \leq b \land b \leq a\right) \land a \leq 1\]

Math

\[\begin{array}{l} \\ \sqrt{\left|\frac{a \cdot a - b \cdot b}{a \cdot a}\right|} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (sqrt (fabs (/ (- (* a a) (* b b)) (* a a)))))

C

double code(double a, double b) {
	return sqrt(fabs((((a * a) - (b * b)) / (a * a))));
}

Fortran

real(8) function code(a, b)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = sqrt(abs((((a * a) - (b * b)) / (a * a))))
end function

Java

public static double code(double a, double b) {
	return Math.sqrt(Math.abs((((a * a) - (b * b)) / (a * a))));
}

Python

def code(a, b):
	return math.sqrt(math.fabs((((a * a) - (b * b)) / (a * a))))

Julia

function code(a, b)
	return sqrt(abs(Float64(Float64(Float64(a * a) - Float64(b * b)) / Float64(a * a))))
end

MATLAB

function tmp = code(a, b)
	tmp = sqrt(abs((((a * a) - (b * b)) / (a * a))));
end

Wolfram

code[a_, b_] := N[Sqrt[N[Abs[N[(N[(N[(a * a), $MachinePrecision] - N[(b * b), $MachinePrecision]), $MachinePrecision] / N[(a * a), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]

Initial Program: 77.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{\left|\frac{a \cdot a - b \cdot b}{a \cdot a}\right|} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (sqrt (fabs (/ (- (* a a) (* b b)) (* a a)))))

C

double code(double a, double b) {
	return sqrt(fabs((((a * a) - (b * b)) / (a * a))));
}

Fortran

real(8) function code(a, b)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = sqrt(abs((((a * a) - (b * b)) / (a * a))))
end function

Java

public static double code(double a, double b) {
	return Math.sqrt(Math.abs((((a * a) - (b * b)) / (a * a))));
}

Python

def code(a, b):
	return math.sqrt(math.fabs((((a * a) - (b * b)) / (a * a))))

Julia

function code(a, b)
	return sqrt(abs(Float64(Float64(Float64(a * a) - Float64(b * b)) / Float64(a * a))))
end

MATLAB

function tmp = code(a, b)
	tmp = sqrt(abs((((a * a) - (b * b)) / (a * a))));
end

Wolfram

code[a_, b_] := N[Sqrt[N[Abs[N[(N[(N[(a * a), $MachinePrecision] - N[(b * b), $MachinePrecision]), $MachinePrecision] / N[(a * a), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision]

Alternative 1: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ e^{\mathsf{log1p}\left(\frac{b}{a} \cdot \frac{b}{-a}\right) \cdot 0.5} \end{array} \]

FPCore

(FPCore (a b)
 :precision binary64
 (exp (* (log1p (* (/ b a) (/ b (- a)))) 0.5)))

C

double code(double a, double b) {
	return exp((log1p(((b / a) * (b / -a))) * 0.5));
}

Java

public static double code(double a, double b) {
	return Math.exp((Math.log1p(((b / a) * (b / -a))) * 0.5));
}

Python

def code(a, b):
	return math.exp((math.log1p(((b / a) * (b / -a))) * 0.5))

Julia

function code(a, b)
	return exp(Float64(log1p(Float64(Float64(b / a) * Float64(b / Float64(-a)))) * 0.5))
end

Wolfram

code[a_, b_] := N[Exp[N[(N[Log[1 + N[(N[(b / a), $MachinePrecision] * N[(b / (-a)), $MachinePrecision]), $MachinePrecision]], $MachinePrecision] * 0.5), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 80.5%

   \[\sqrt{\left|\frac{a \cdot a - b \cdot b}{a \cdot a}\right|} \]
2. Step-by-step derivation

   1. sqr-neg 80.5%

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

   2. fabs-div 80.5%

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

   3. sqr-neg 80.5%

      \[\leadsto \sqrt{\frac{\left|a \cdot a - \color{blue}{b \cdot b}\right|}{\left|a \cdot a\right|}} \]

   4. fabs-sub 80.5%

      \[\leadsto \sqrt{\frac{\color{blue}{\left|b \cdot b - a \cdot a\right|}}{\left|a \cdot a\right|}} \]

   5. sqr-neg 80.5%

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

   6. distribute-rgt-neg-out 80.5%

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

   7. fabs-neg 80.5%

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

   8. fabs-div 80.5%

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

   9. cancel-sign-sub-inv 80.5%

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

   10. +-commutative 80.5%

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

   11. sqr-neg 80.5%

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

   12. cancel-sign-sub-inv 80.5%

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

3. Simplified 81.1%

   \[\leadsto \color{blue}{\sqrt{\left|1 - b \cdot \frac{b}{a \cdot a}\right|}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. pow1/2 81.1%

      \[\leadsto \color{blue}{{\left(\left|1 - b \cdot \frac{b}{a \cdot a}\right|\right)}^{0.5}} \]

   2. pow-to-exp 81.1%

      \[\leadsto \color{blue}{e^{\log \left(\left|1 - b \cdot \frac{b}{a \cdot a}\right|\right) \cdot 0.5}} \]

   3. add-sqr-sqrt 80.5%

      \[\leadsto e^{\log \left(\left|\color{blue}{\sqrt{1 - b \cdot \frac{b}{a \cdot a}} \cdot \sqrt{1 - b \cdot \frac{b}{a \cdot a}}}\right|\right) \cdot 0.5} \]

   4. fabs-sqr 80.5%

      \[\leadsto e^{\log \color{blue}{\left(\sqrt{1 - b \cdot \frac{b}{a \cdot a}} \cdot \sqrt{1 - b \cdot \frac{b}{a \cdot a}}\right)} \cdot 0.5} \]

   5. add-sqr-sqrt 80.5%

      \[\leadsto e^{\log \color{blue}{\left(1 - b \cdot \frac{b}{a \cdot a}\right)} \cdot 0.5} \]

   6. sub-neg 80.5%

      \[\leadsto e^{\log \color{blue}{\left(1 + \left(-b \cdot \frac{b}{a \cdot a}\right)\right)} \cdot 0.5} \]

   7. log1p-define 80.5%

      \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(-b \cdot \frac{b}{a \cdot a}\right)} \cdot 0.5} \]

   8. associate-*r/ 80.5%

      \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{\frac{b \cdot b}{a \cdot a}}\right) \cdot 0.5} \]

   9. frac-times 100.0%

      \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{\frac{b}{a} \cdot \frac{b}{a}}\right) \cdot 0.5} \]

   10. pow2 100.0%

       \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{{\left(\frac{b}{a}\right)}^{2}}\right) \cdot 0.5} \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(-{\left(\frac{b}{a}\right)}^{2}\right) \cdot 0.5}} \]
7. Step-by-step derivation

   1. unpow2 100.0%

      \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{\frac{b}{a} \cdot \frac{b}{a}}\right) \cdot 0.5} \]

   2. distribute-rgt-neg-in 100.0%

      \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\frac{b}{a} \cdot \left(-\frac{b}{a}\right)}\right) \cdot 0.5} \]

8. Applied egg-rr 100.0%

   \[\leadsto e^{\mathsf{log1p}\left(\color{blue}{\frac{b}{a} \cdot \left(-\frac{b}{a}\right)}\right) \cdot 0.5} \]

9. Final simplification 100.0%

   \[\leadsto e^{\mathsf{log1p}\left(\frac{b}{a} \cdot \frac{b}{-a}\right) \cdot 0.5} \]
10. Add Preprocessing

Alternative 2: 100.0% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \sqrt{1 - {\left(\frac{b}{a}\right)}^{2}} \end{array} \]

FPCore

(FPCore (a b) :precision binary64 (sqrt (- 1.0 (pow (/ b a) 2.0))))

C

double code(double a, double b) {
	return sqrt((1.0 - pow((b / a), 2.0)));
}

Fortran

real(8) function code(a, b)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = sqrt((1.0d0 - ((b / a) ** 2.0d0)))
end function

Java

public static double code(double a, double b) {
	return Math.sqrt((1.0 - Math.pow((b / a), 2.0)));
}

Python

def code(a, b):
	return math.sqrt((1.0 - math.pow((b / a), 2.0)))

Julia

function code(a, b)
	return sqrt(Float64(1.0 - (Float64(b / a) ^ 2.0)))
end

MATLAB

function tmp = code(a, b)
	tmp = sqrt((1.0 - ((b / a) ^ 2.0)));
end

Wolfram

code[a_, b_] := N[Sqrt[N[(1.0 - N[Power[N[(b / a), $MachinePrecision], 2.0], $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 80.5%

   \[\sqrt{\left|\frac{a \cdot a - b \cdot b}{a \cdot a}\right|} \]
2. Step-by-step derivation

   1. sqr-neg 80.5%

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

   2. fabs-div 80.5%

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

   3. sqr-neg 80.5%

      \[\leadsto \sqrt{\frac{\left|a \cdot a - \color{blue}{b \cdot b}\right|}{\left|a \cdot a\right|}} \]

   4. fabs-sub 80.5%

      \[\leadsto \sqrt{\frac{\color{blue}{\left|b \cdot b - a \cdot a\right|}}{\left|a \cdot a\right|}} \]

   5. sqr-neg 80.5%

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

   6. distribute-rgt-neg-out 80.5%

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

   7. fabs-neg 80.5%

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

   8. fabs-div 80.5%

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

   9. cancel-sign-sub-inv 80.5%

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

   10. +-commutative 80.5%

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

   11. sqr-neg 80.5%

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

   12. cancel-sign-sub-inv 80.5%

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

3. Simplified 81.1%

   \[\leadsto \color{blue}{\sqrt{\left|1 - b \cdot \frac{b}{a \cdot a}\right|}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. fabs-sub 81.1%

      \[\leadsto \sqrt{\color{blue}{\left|b \cdot \frac{b}{a \cdot a} - 1\right|}} \]

   2. sub-neg 81.1%

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

   3. metadata-eval 81.1%

      \[\leadsto \sqrt{\left|b \cdot \frac{b}{a \cdot a} + \color{blue}{-1}\right|} \]

   4. associate-*r/ 80.5%

      \[\leadsto \sqrt{\left|\color{blue}{\frac{b \cdot b}{a \cdot a}} + -1\right|} \]

   5. frac-times 100.0%

      \[\leadsto \sqrt{\left|\color{blue}{\frac{b}{a} \cdot \frac{b}{a}} + -1\right|} \]

   6. fma-undefine 100.0%

      \[\leadsto \sqrt{\left|\color{blue}{\mathsf{fma}\left(\frac{b}{a}, \frac{b}{a}, -1\right)}\right|} \]

   7. add-exp-log 100.0%

      \[\leadsto \color{blue}{e^{\log \left(\sqrt{\left|\mathsf{fma}\left(\frac{b}{a}, \frac{b}{a}, -1\right)\right|}\right)}} \]

   8. add-sqr-sqrt 100.0%

      \[\leadsto e^{\log \color{blue}{\left(\sqrt{\sqrt{\left|\mathsf{fma}\left(\frac{b}{a}, \frac{b}{a}, -1\right)\right|}} \cdot \sqrt{\sqrt{\left|\mathsf{fma}\left(\frac{b}{a}, \frac{b}{a}, -1\right)\right|}}\right)}} \]

   9. log-prod 100.0%

      \[\leadsto e^{\color{blue}{\log \left(\sqrt{\sqrt{\left|\mathsf{fma}\left(\frac{b}{a}, \frac{b}{a}, -1\right)\right|}}\right) + \log \left(\sqrt{\sqrt{\left|\mathsf{fma}\left(\frac{b}{a}, \frac{b}{a}, -1\right)\right|}}\right)}} \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{e^{0.5 \cdot \mathsf{log1p}\left(-{\left(\frac{b}{a}\right)}^{2}\right)}} \]
7. Step-by-step derivation

   1. *-commutative 100.0%

      \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(-{\left(\frac{b}{a}\right)}^{2}\right) \cdot 0.5}} \]

   2. log1p-undefine 100.0%

      \[\leadsto e^{\color{blue}{\log \left(1 + \left(-{\left(\frac{b}{a}\right)}^{2}\right)\right)} \cdot 0.5} \]

   3. +-commutative 100.0%

      \[\leadsto e^{\log \color{blue}{\left(\left(-{\left(\frac{b}{a}\right)}^{2}\right) + 1\right)} \cdot 0.5} \]

   4. metadata-eval 100.0%

      \[\leadsto e^{\log \left(\left(-{\left(\frac{b}{a}\right)}^{2}\right) + \color{blue}{\left(--1\right)}\right) \cdot 0.5} \]

   5. distribute-neg-in 100.0%

      \[\leadsto e^{\log \color{blue}{\left(-\left({\left(\frac{b}{a}\right)}^{2} + -1\right)\right)} \cdot 0.5} \]

   6. exp-to-pow 100.0%

      \[\leadsto \color{blue}{{\left(-\left({\left(\frac{b}{a}\right)}^{2} + -1\right)\right)}^{0.5}} \]

   7. unpow1/2 100.0%

      \[\leadsto \color{blue}{\sqrt{-\left({\left(\frac{b}{a}\right)}^{2} + -1\right)}} \]

   8. distribute-neg-in 100.0%

      \[\leadsto \sqrt{\color{blue}{\left(-{\left(\frac{b}{a}\right)}^{2}\right) + \left(--1\right)}} \]

   9. metadata-eval 100.0%

      \[\leadsto \sqrt{\left(-{\left(\frac{b}{a}\right)}^{2}\right) + \color{blue}{1}} \]

   10. +-commutative 100.0%

       \[\leadsto \sqrt{\color{blue}{1 + \left(-{\left(\frac{b}{a}\right)}^{2}\right)}} \]

   11. sub-neg 100.0%

       \[\leadsto \sqrt{\color{blue}{1 - {\left(\frac{b}{a}\right)}^{2}}} \]

8. Simplified 100.0%

   \[\leadsto \color{blue}{\sqrt{1 - {\left(\frac{b}{a}\right)}^{2}}} \]
9. Add Preprocessing

Alternative 3: 99.0% accurate, 19.2× speedup

Math

\[\begin{array}{l} \\ 1 + \left(\frac{b}{a} \cdot \frac{b}{a}\right) \cdot -0.5 \end{array} \]

FPCore

(FPCore (a b) :precision binary64 (+ 1.0 (* (* (/ b a) (/ b a)) -0.5)))

C

double code(double a, double b) {
	return 1.0 + (((b / a) * (b / a)) * -0.5);
}

Fortran

real(8) function code(a, b)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = 1.0d0 + (((b / a) * (b / a)) * (-0.5d0))
end function

Java

public static double code(double a, double b) {
	return 1.0 + (((b / a) * (b / a)) * -0.5);
}

Python

def code(a, b):
	return 1.0 + (((b / a) * (b / a)) * -0.5)

Julia

function code(a, b)
	return Float64(1.0 + Float64(Float64(Float64(b / a) * Float64(b / a)) * -0.5))
end

MATLAB

function tmp = code(a, b)
	tmp = 1.0 + (((b / a) * (b / a)) * -0.5);
end

Wolfram

code[a_, b_] := N[(1.0 + N[(N[(N[(b / a), $MachinePrecision] * N[(b / a), $MachinePrecision]), $MachinePrecision] * -0.5), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 80.5%

   \[\sqrt{\left|\frac{a \cdot a - b \cdot b}{a \cdot a}\right|} \]
2. Step-by-step derivation

   1. sqr-neg 80.5%

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

   2. fabs-div 80.5%

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

   3. sqr-neg 80.5%

      \[\leadsto \sqrt{\frac{\left|a \cdot a - \color{blue}{b \cdot b}\right|}{\left|a \cdot a\right|}} \]

   4. fabs-sub 80.5%

      \[\leadsto \sqrt{\frac{\color{blue}{\left|b \cdot b - a \cdot a\right|}}{\left|a \cdot a\right|}} \]

   5. sqr-neg 80.5%

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

   6. distribute-rgt-neg-out 80.5%

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

   7. fabs-neg 80.5%

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

   8. fabs-div 80.5%

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

   9. cancel-sign-sub-inv 80.5%

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

   10. +-commutative 80.5%

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

   11. sqr-neg 80.5%

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

   12. cancel-sign-sub-inv 80.5%

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

3. Simplified 81.1%

   \[\leadsto \color{blue}{\sqrt{\left|1 - b \cdot \frac{b}{a \cdot a}\right|}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. pow1/2 81.1%

      \[\leadsto \color{blue}{{\left(\left|1 - b \cdot \frac{b}{a \cdot a}\right|\right)}^{0.5}} \]

   2. pow-to-exp 81.1%

      \[\leadsto \color{blue}{e^{\log \left(\left|1 - b \cdot \frac{b}{a \cdot a}\right|\right) \cdot 0.5}} \]

   3. add-sqr-sqrt 80.5%

      \[\leadsto e^{\log \left(\left|\color{blue}{\sqrt{1 - b \cdot \frac{b}{a \cdot a}} \cdot \sqrt{1 - b \cdot \frac{b}{a \cdot a}}}\right|\right) \cdot 0.5} \]

   4. fabs-sqr 80.5%

      \[\leadsto e^{\log \color{blue}{\left(\sqrt{1 - b \cdot \frac{b}{a \cdot a}} \cdot \sqrt{1 - b \cdot \frac{b}{a \cdot a}}\right)} \cdot 0.5} \]

   5. add-sqr-sqrt 80.5%

      \[\leadsto e^{\log \color{blue}{\left(1 - b \cdot \frac{b}{a \cdot a}\right)} \cdot 0.5} \]

   6. sub-neg 80.5%

      \[\leadsto e^{\log \color{blue}{\left(1 + \left(-b \cdot \frac{b}{a \cdot a}\right)\right)} \cdot 0.5} \]

   7. log1p-define 80.5%

      \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(-b \cdot \frac{b}{a \cdot a}\right)} \cdot 0.5} \]

   8. associate-*r/ 80.5%

      \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{\frac{b \cdot b}{a \cdot a}}\right) \cdot 0.5} \]

   9. frac-times 100.0%

      \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{\frac{b}{a} \cdot \frac{b}{a}}\right) \cdot 0.5} \]

   10. pow2 100.0%

       \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{{\left(\frac{b}{a}\right)}^{2}}\right) \cdot 0.5} \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(-{\left(\frac{b}{a}\right)}^{2}\right) \cdot 0.5}} \]

7. Taylor expanded in b around 0 79.5%

   \[\leadsto \color{blue}{1 + -0.5 \cdot \frac{{b}^{2}}{{a}^{2}}} \]
8. Step-by-step derivation

   1. +-commutative 79.5%

      \[\leadsto \color{blue}{-0.5 \cdot \frac{{b}^{2}}{{a}^{2}} + 1} \]

   2. fma-define 79.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5, \frac{{b}^{2}}{{a}^{2}}, 1\right)} \]

   3. unpow2 79.5%

      \[\leadsto \mathsf{fma}\left(-0.5, \frac{\color{blue}{b \cdot b}}{{a}^{2}}, 1\right) \]

   4. unpow2 79.5%

      \[\leadsto \mathsf{fma}\left(-0.5, \frac{b \cdot b}{\color{blue}{a \cdot a}}, 1\right) \]

   5. times-frac 98.5%

      \[\leadsto \mathsf{fma}\left(-0.5, \color{blue}{\frac{b}{a} \cdot \frac{b}{a}}, 1\right) \]

   6. unpow2 98.5%

      \[\leadsto \mathsf{fma}\left(-0.5, \color{blue}{{\left(\frac{b}{a}\right)}^{2}}, 1\right) \]

9. Simplified 98.5%

   \[\leadsto \color{blue}{\mathsf{fma}\left(-0.5, {\left(\frac{b}{a}\right)}^{2}, 1\right)} \]
10. Step-by-step derivation

    1. fma-undefine 98.5%

       \[\leadsto \color{blue}{-0.5 \cdot {\left(\frac{b}{a}\right)}^{2} + 1} \]

    2. *-commutative 98.5%

       \[\leadsto \color{blue}{{\left(\frac{b}{a}\right)}^{2} \cdot -0.5} + 1 \]

    3. clear-num 98.5%

       \[\leadsto {\color{blue}{\left(\frac{1}{\frac{a}{b}}\right)}}^{2} \cdot -0.5 + 1 \]

    4. inv-pow 98.5%

       \[\leadsto {\color{blue}{\left({\left(\frac{a}{b}\right)}^{-1}\right)}}^{2} \cdot -0.5 + 1 \]

    5. pow-pow 98.5%

       \[\leadsto \color{blue}{{\left(\frac{a}{b}\right)}^{\left(-1 \cdot 2\right)}} \cdot -0.5 + 1 \]

    6. metadata-eval 98.5%

       \[\leadsto {\left(\frac{a}{b}\right)}^{\color{blue}{-2}} \cdot -0.5 + 1 \]

11. Applied egg-rr 98.5%

    \[\leadsto \color{blue}{{\left(\frac{a}{b}\right)}^{-2} \cdot -0.5 + 1} \]
12. Step-by-step derivation

    1. sqr-pow 98.5%

       \[\leadsto \color{blue}{\left({\left(\frac{a}{b}\right)}^{\left(\frac{-2}{2}\right)} \cdot {\left(\frac{a}{b}\right)}^{\left(\frac{-2}{2}\right)}\right)} \cdot -0.5 + 1 \]

    2. metadata-eval 98.5%

       \[\leadsto \left({\left(\frac{a}{b}\right)}^{\color{blue}{-1}} \cdot {\left(\frac{a}{b}\right)}^{\left(\frac{-2}{2}\right)}\right) \cdot -0.5 + 1 \]

    3. unpow-1 98.5%

       \[\leadsto \left(\color{blue}{\frac{1}{\frac{a}{b}}} \cdot {\left(\frac{a}{b}\right)}^{\left(\frac{-2}{2}\right)}\right) \cdot -0.5 + 1 \]

    4. clear-num 98.5%

       \[\leadsto \left(\color{blue}{\frac{b}{a}} \cdot {\left(\frac{a}{b}\right)}^{\left(\frac{-2}{2}\right)}\right) \cdot -0.5 + 1 \]

    5. metadata-eval 98.5%

       \[\leadsto \left(\frac{b}{a} \cdot {\left(\frac{a}{b}\right)}^{\color{blue}{-1}}\right) \cdot -0.5 + 1 \]

    6. unpow-1 98.5%

       \[\leadsto \left(\frac{b}{a} \cdot \color{blue}{\frac{1}{\frac{a}{b}}}\right) \cdot -0.5 + 1 \]

    7. clear-num 98.5%

       \[\leadsto \left(\frac{b}{a} \cdot \color{blue}{\frac{b}{a}}\right) \cdot -0.5 + 1 \]

13. Applied egg-rr 98.5%

    \[\leadsto \color{blue}{\left(\frac{b}{a} \cdot \frac{b}{a}\right)} \cdot -0.5 + 1 \]

14. Final simplification 98.5%

    \[\leadsto 1 + \left(\frac{b}{a} \cdot \frac{b}{a}\right) \cdot -0.5 \]
15. Add Preprocessing

Alternative 4: 97.8% accurate, 211.0× speedup

Math

\[\begin{array}{l} \\ 1 \end{array} \]

FPCore

(FPCore (a b) :precision binary64 1.0)

C

double code(double a, double b) {
	return 1.0;
}

Fortran

real(8) function code(a, b)
    real(8), intent (in) :: a
    real(8), intent (in) :: b
    code = 1.0d0
end function

Java

public static double code(double a, double b) {
	return 1.0;
}

Python

def code(a, b):
	return 1.0

Julia

function code(a, b)
	return 1.0
end

MATLAB

function tmp = code(a, b)
	tmp = 1.0;
end

Wolfram

code[a_, b_] := 1.0

Derivation

1. Initial program 80.5%

   \[\sqrt{\left|\frac{a \cdot a - b \cdot b}{a \cdot a}\right|} \]
2. Step-by-step derivation

   1. sqr-neg 80.5%

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

   2. fabs-div 80.5%

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

   3. sqr-neg 80.5%

      \[\leadsto \sqrt{\frac{\left|a \cdot a - \color{blue}{b \cdot b}\right|}{\left|a \cdot a\right|}} \]

   4. fabs-sub 80.5%

      \[\leadsto \sqrt{\frac{\color{blue}{\left|b \cdot b - a \cdot a\right|}}{\left|a \cdot a\right|}} \]

   5. sqr-neg 80.5%

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

   6. distribute-rgt-neg-out 80.5%

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

   7. fabs-neg 80.5%

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

   8. fabs-div 80.5%

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

   9. cancel-sign-sub-inv 80.5%

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

   10. +-commutative 80.5%

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

   11. sqr-neg 80.5%

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

   12. cancel-sign-sub-inv 80.5%

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

3. Simplified 81.1%

   \[\leadsto \color{blue}{\sqrt{\left|1 - b \cdot \frac{b}{a \cdot a}\right|}} \]
4. Add Preprocessing
5. Step-by-step derivation

   1. pow1/2 81.1%

      \[\leadsto \color{blue}{{\left(\left|1 - b \cdot \frac{b}{a \cdot a}\right|\right)}^{0.5}} \]

   2. pow-to-exp 81.1%

      \[\leadsto \color{blue}{e^{\log \left(\left|1 - b \cdot \frac{b}{a \cdot a}\right|\right) \cdot 0.5}} \]

   3. add-sqr-sqrt 80.5%

      \[\leadsto e^{\log \left(\left|\color{blue}{\sqrt{1 - b \cdot \frac{b}{a \cdot a}} \cdot \sqrt{1 - b \cdot \frac{b}{a \cdot a}}}\right|\right) \cdot 0.5} \]

   4. fabs-sqr 80.5%

      \[\leadsto e^{\log \color{blue}{\left(\sqrt{1 - b \cdot \frac{b}{a \cdot a}} \cdot \sqrt{1 - b \cdot \frac{b}{a \cdot a}}\right)} \cdot 0.5} \]

   5. add-sqr-sqrt 80.5%

      \[\leadsto e^{\log \color{blue}{\left(1 - b \cdot \frac{b}{a \cdot a}\right)} \cdot 0.5} \]

   6. sub-neg 80.5%

      \[\leadsto e^{\log \color{blue}{\left(1 + \left(-b \cdot \frac{b}{a \cdot a}\right)\right)} \cdot 0.5} \]

   7. log1p-define 80.5%

      \[\leadsto e^{\color{blue}{\mathsf{log1p}\left(-b \cdot \frac{b}{a \cdot a}\right)} \cdot 0.5} \]

   8. associate-*r/ 80.5%

      \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{\frac{b \cdot b}{a \cdot a}}\right) \cdot 0.5} \]

   9. frac-times 100.0%

      \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{\frac{b}{a} \cdot \frac{b}{a}}\right) \cdot 0.5} \]

   10. pow2 100.0%

       \[\leadsto e^{\mathsf{log1p}\left(-\color{blue}{{\left(\frac{b}{a}\right)}^{2}}\right) \cdot 0.5} \]

6. Applied egg-rr 100.0%

   \[\leadsto \color{blue}{e^{\mathsf{log1p}\left(-{\left(\frac{b}{a}\right)}^{2}\right) \cdot 0.5}} \]

7. Taylor expanded in b around 0 97.1%

   \[\leadsto \color{blue}{1} \]
8. Add Preprocessing

Reproduce

herbie shell --seed 2024110 
(FPCore (a b)
  :name "Eccentricity of an ellipse"
  :precision binary64
  :pre (and (and (<= 0.0 b) (<= b a)) (<= a 1.0))
  (sqrt (fabs (/ (- (* a a) (* b b)) (* a a)))))