Falkner and Boettcher, Appendix B, 1

Percentage Accurate: 99.2% → 99.2%

Time: 18.1s

Alternatives: 5

Speedup: 1.0×

Specification

Math

\[\begin{array}{l} \\ \cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right) \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (acos (/ (- 1.0 (* 5.0 (* v v))) (- (* v v) 1.0))))

C

double code(double v) {
	return acos(((1.0 - (5.0 * (v * v))) / ((v * v) - 1.0)));
}

Fortran

real(8) function code(v)
    real(8), intent (in) :: v
    code = acos(((1.0d0 - (5.0d0 * (v * v))) / ((v * v) - 1.0d0)))
end function

Java

public static double code(double v) {
	return Math.acos(((1.0 - (5.0 * (v * v))) / ((v * v) - 1.0)));
}

Python

def code(v):
	return math.acos(((1.0 - (5.0 * (v * v))) / ((v * v) - 1.0)))

Julia

function code(v)
	return acos(Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(Float64(v * v) - 1.0)))
end

MATLAB

function tmp = code(v)
	tmp = acos(((1.0 - (5.0 * (v * v))) / ((v * v) - 1.0)));
end

Wolfram

code[v_] := N[ArcCos[N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(v * v), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Initial Program: 99.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right) \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (acos (/ (- 1.0 (* 5.0 (* v v))) (- (* v v) 1.0))))

C

double code(double v) {
	return acos(((1.0 - (5.0 * (v * v))) / ((v * v) - 1.0)));
}

Fortran

real(8) function code(v)
    real(8), intent (in) :: v
    code = acos(((1.0d0 - (5.0d0 * (v * v))) / ((v * v) - 1.0d0)))
end function

Java

public static double code(double v) {
	return Math.acos(((1.0 - (5.0 * (v * v))) / ((v * v) - 1.0)));
}

Python

def code(v):
	return math.acos(((1.0 - (5.0 * (v * v))) / ((v * v) - 1.0)))

Julia

function code(v)
	return acos(Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(Float64(v * v) - 1.0)))
end

MATLAB

function tmp = code(v)
	tmp = acos(((1.0 - (5.0 * (v * v))) / ((v * v) - 1.0)));
end

Wolfram

code[v_] := N[ArcCos[N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(v * v), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Alternative 1: 99.2% accurate, 0.1× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(v \cdot v, -5, 1\right)\\ 2 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{t_0}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(t_0 \cdot \frac{1}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \end{array} \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (let* ((t_0 (fma (* v v) -5.0 1.0)))
   (+
    (* 2.0 (log (cbrt (exp (acos (/ t_0 (fma v v -1.0)))))))
    (log (cbrt (exp (acos (* t_0 (/ 1.0 (fma v v -1.0))))))))))

C

double code(double v) {
	double t_0 = fma((v * v), -5.0, 1.0);
	return (2.0 * log(cbrt(exp(acos((t_0 / fma(v, v, -1.0))))))) + log(cbrt(exp(acos((t_0 * (1.0 / fma(v, v, -1.0)))))));
}

Julia

function code(v)
	t_0 = fma(Float64(v * v), -5.0, 1.0)
	return Float64(Float64(2.0 * log(cbrt(exp(acos(Float64(t_0 / fma(v, v, -1.0))))))) + log(cbrt(exp(acos(Float64(t_0 * Float64(1.0 / fma(v, v, -1.0))))))))
end

Wolfram

code[v_] := Block[{t$95$0 = N[(N[(v * v), $MachinePrecision] * -5.0 + 1.0), $MachinePrecision]}, N[(N[(2.0 * N[Log[N[Power[N[Exp[N[ArcCos[N[(t$95$0 / N[(v * v + -1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision], 1/3], $MachinePrecision]], $MachinePrecision]), $MachinePrecision] + N[Log[N[Power[N[Exp[N[ArcCos[N[(t$95$0 * N[(1.0 / N[(v * v + -1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision], 1/3], $MachinePrecision]], $MachinePrecision]), $MachinePrecision]]

Derivation

1. Initial program 99.1%

   \[\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right) \]
2. Step-by-step derivation

   1. add-log-exp 99.1%

      \[\leadsto \color{blue}{\log \left(e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}\right)} \]

   2. add-cube-cbrt 99.1%

      \[\leadsto \log \color{blue}{\left(\left(\sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}} \cdot \sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}}\right) \cdot \sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}}\right)} \]

   3. log-prod 99.1%

      \[\leadsto \color{blue}{\log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}} \cdot \sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}}\right)} \]

3. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{\log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}} \cdot \sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)} \]
4. Step-by-step derivation

   1. log-prod 99.1%

      \[\leadsto \color{blue}{\left(\log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)\right)} + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \]

   2. count-2 99.1%

      \[\leadsto \color{blue}{2 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)} + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \]

5. Simplified 99.1%

   \[\leadsto \color{blue}{2 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)} \]
6. Step-by-step derivation

   1. div-inv 99.1%

      \[\leadsto 2 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v \cdot v, -5, 1\right) \cdot \frac{1}{\mathsf{fma}\left(v, v, -1\right)}\right)}}}\right) \]

7. Applied egg-rr 99.1%

   \[\leadsto 2 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v \cdot v, -5, 1\right) \cdot \frac{1}{\mathsf{fma}\left(v, v, -1\right)}\right)}}}\right) \]

8. Final simplification 99.1%

   \[\leadsto 2 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\mathsf{fma}\left(v \cdot v, -5, 1\right) \cdot \frac{1}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \]

Alternative 2: 99.2% accurate, 0.2× speedup

Math

\[\begin{array}{l} \\ \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \cdot 3 \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (* (log (cbrt (exp (acos (/ (fma (* v v) -5.0 1.0) (fma v v -1.0)))))) 3.0))

C

double code(double v) {
	return log(cbrt(exp(acos((fma((v * v), -5.0, 1.0) / fma(v, v, -1.0)))))) * 3.0;
}

Julia

function code(v)
	return Float64(log(cbrt(exp(acos(Float64(fma(Float64(v * v), -5.0, 1.0) / fma(v, v, -1.0)))))) * 3.0)
end

Wolfram

code[v_] := N[(N[Log[N[Power[N[Exp[N[ArcCos[N[(N[(N[(v * v), $MachinePrecision] * -5.0 + 1.0), $MachinePrecision] / N[(v * v + -1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]], $MachinePrecision], 1/3], $MachinePrecision]], $MachinePrecision] * 3.0), $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right) \]
2. Step-by-step derivation

   1. add-log-exp 99.1%

      \[\leadsto \color{blue}{\log \left(e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}\right)} \]

   2. add-cube-cbrt 99.1%

      \[\leadsto \log \color{blue}{\left(\left(\sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}} \cdot \sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}}\right) \cdot \sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}}\right)} \]

   3. log-prod 99.1%

      \[\leadsto \color{blue}{\log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}} \cdot \sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)}}\right)} \]

3. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{\log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}} \cdot \sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)} \]
4. Step-by-step derivation

   1. log-prod 99.1%

      \[\leadsto \color{blue}{\left(\log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)\right)} + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \]

   2. count-2 99.1%

      \[\leadsto \color{blue}{2 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)} + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \]

5. Simplified 99.1%

   \[\leadsto \color{blue}{2 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) + \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)} \]
6. Step-by-step derivation

   1. distribute-lft1-in 99.1%

      \[\leadsto \color{blue}{\left(2 + 1\right) \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)} \]

   2. metadata-eval 99.1%

      \[\leadsto \color{blue}{3} \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \]

7. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{3 \cdot \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right)} \]

8. Final simplification 99.1%

   \[\leadsto \log \left(\sqrt[3]{e^{\cos^{-1} \left(\frac{\mathsf{fma}\left(v \cdot v, -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)}}\right) \cdot 3 \]

Alternative 3: 99.2% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \cos^{-1} \left(\frac{1 - {\left(\sqrt[3]{\left(v \cdot v\right) \cdot 5}\right)}^{3}}{v \cdot v + -1}\right) \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (acos (/ (- 1.0 (pow (cbrt (* (* v v) 5.0)) 3.0)) (+ (* v v) -1.0))))

C

double code(double v) {
	return acos(((1.0 - pow(cbrt(((v * v) * 5.0)), 3.0)) / ((v * v) + -1.0)));
}

Java

public static double code(double v) {
	return Math.acos(((1.0 - Math.pow(Math.cbrt(((v * v) * 5.0)), 3.0)) / ((v * v) + -1.0)));
}

Julia

function code(v)
	return acos(Float64(Float64(1.0 - (cbrt(Float64(Float64(v * v) * 5.0)) ^ 3.0)) / Float64(Float64(v * v) + -1.0)))
end

Wolfram

code[v_] := N[ArcCos[N[(N[(1.0 - N[Power[N[Power[N[(N[(v * v), $MachinePrecision] * 5.0), $MachinePrecision], 1/3], $MachinePrecision], 3.0], $MachinePrecision]), $MachinePrecision] / N[(N[(v * v), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right) \]
2. Step-by-step derivation

   1. add-cube-cbrt 99.1%

      \[\leadsto \cos^{-1} \left(\frac{1 - \color{blue}{\left(\sqrt[3]{5 \cdot \left(v \cdot v\right)} \cdot \sqrt[3]{5 \cdot \left(v \cdot v\right)}\right) \cdot \sqrt[3]{5 \cdot \left(v \cdot v\right)}}}{v \cdot v - 1}\right) \]

   2. pow3 99.1%

      \[\leadsto \cos^{-1} \left(\frac{1 - \color{blue}{{\left(\sqrt[3]{5 \cdot \left(v \cdot v\right)}\right)}^{3}}}{v \cdot v - 1}\right) \]

3. Applied egg-rr 99.1%

   \[\leadsto \cos^{-1} \left(\frac{1 - \color{blue}{{\left(\sqrt[3]{5 \cdot \left(v \cdot v\right)}\right)}^{3}}}{v \cdot v - 1}\right) \]

4. Final simplification 99.1%

   \[\leadsto \cos^{-1} \left(\frac{1 - {\left(\sqrt[3]{\left(v \cdot v\right) \cdot 5}\right)}^{3}}{v \cdot v + -1}\right) \]

Alternative 4: 99.2% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \cos^{-1} \left(\frac{1 - \left(v \cdot v\right) \cdot 5}{v \cdot v + -1}\right) \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (acos (/ (- 1.0 (* (* v v) 5.0)) (+ (* v v) -1.0))))

C

double code(double v) {
	return acos(((1.0 - ((v * v) * 5.0)) / ((v * v) + -1.0)));
}

Fortran

real(8) function code(v)
    real(8), intent (in) :: v
    code = acos(((1.0d0 - ((v * v) * 5.0d0)) / ((v * v) + (-1.0d0))))
end function

Java

public static double code(double v) {
	return Math.acos(((1.0 - ((v * v) * 5.0)) / ((v * v) + -1.0)));
}

Python

def code(v):
	return math.acos(((1.0 - ((v * v) * 5.0)) / ((v * v) + -1.0)))

Julia

function code(v)
	return acos(Float64(Float64(1.0 - Float64(Float64(v * v) * 5.0)) / Float64(Float64(v * v) + -1.0)))
end

MATLAB

function tmp = code(v)
	tmp = acos(((1.0 - ((v * v) * 5.0)) / ((v * v) + -1.0)));
end

Wolfram

code[v_] := N[ArcCos[N[(N[(1.0 - N[(N[(v * v), $MachinePrecision] * 5.0), $MachinePrecision]), $MachinePrecision] / N[(N[(v * v), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right) \]

2. Final simplification 99.1%

   \[\leadsto \cos^{-1} \left(\frac{1 - \left(v \cdot v\right) \cdot 5}{v \cdot v + -1}\right) \]

Alternative 5: 98.1% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \cos^{-1} -1 \end{array} \]

FPCore

(FPCore (v) :precision binary64 (acos -1.0))

C

double code(double v) {
	return acos(-1.0);
}

Fortran

real(8) function code(v)
    real(8), intent (in) :: v
    code = acos((-1.0d0))
end function

Java

public static double code(double v) {
	return Math.acos(-1.0);
}

Python

def code(v):
	return math.acos(-1.0)

Julia

function code(v)
	return acos(-1.0)
end

MATLAB

function tmp = code(v)
	tmp = acos(-1.0);
end

Wolfram

code[v_] := N[ArcCos[-1.0], $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\cos^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right) \]

2. Taylor expanded in v around 0 97.8%

   \[\leadsto \cos^{-1} \color{blue}{-1} \]

3. Final simplification 97.8%

   \[\leadsto \cos^{-1} -1 \]

Reproduce

herbie shell --seed 2023207 
(FPCore (v)
  :name "Falkner and Boettcher, Appendix B, 1"
  :precision binary64
  (acos (/ (- 1.0 (* 5.0 (* v v))) (- (* v v) 1.0))))