Falkner and Boettcher, Appendix B, 1

Percentage Accurate: 99.1% → 99.1%

Time: 16.4s

Alternatives: 8

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: 99.1% 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: 99.1% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\sqrt{\pi}, \sqrt{\pi} \cdot 0.5, \sin^{-1} \left(\frac{\mathsf{fma}\left(5, v \cdot v, -1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)\right) \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (fma
  (sqrt PI)
  (* (sqrt PI) 0.5)
  (asin (/ (fma 5.0 (* v v) -1.0) (fma v v -1.0)))))

C

double code(double v) {
	return fma(sqrt(((double) M_PI)), (sqrt(((double) M_PI)) * 0.5), asin((fma(5.0, (v * v), -1.0) / fma(v, v, -1.0))));
}

Julia

function code(v)
	return fma(sqrt(pi), Float64(sqrt(pi) * 0.5), asin(Float64(fma(5.0, Float64(v * v), -1.0) / fma(v, v, -1.0))))
end

Wolfram

code[v_] := N[(N[Sqrt[Pi], $MachinePrecision] * N[(N[Sqrt[Pi], $MachinePrecision] * 0.5), $MachinePrecision] + N[ArcSin[N[(N[(5.0 * N[(v * v), $MachinePrecision] + -1.0), $MachinePrecision] / N[(v * v + -1.0), $MachinePrecision]), $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. Add Preprocessing
3. Step-by-step derivation

   1. acos-asin N/A

      \[\leadsto \color{blue}{\frac{\mathsf{PI}\left(\right)}{2} - \sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)} \]

   2. sub-neg N/A

      \[\leadsto \color{blue}{\frac{\mathsf{PI}\left(\right)}{2} + \left(\mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right)} \]

   3. div-inv N/A

      \[\leadsto \color{blue}{\mathsf{PI}\left(\right) \cdot \frac{1}{2}} + \left(\mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

   4. add-sqr-sqrt N/A

      \[\leadsto \color{blue}{\left(\sqrt{\mathsf{PI}\left(\right)} \cdot \sqrt{\mathsf{PI}\left(\right)}\right)} \cdot \frac{1}{2} + \left(\mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

   5. associate-*l* N/A

      \[\leadsto \color{blue}{\sqrt{\mathsf{PI}\left(\right)} \cdot \left(\sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}\right)} + \left(\mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

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

      \[\leadsto \color{blue}{\mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}, \mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right)} \]

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

      \[\leadsto \mathsf{fma}\left(\color{blue}{\sqrt{\mathsf{PI}\left(\right)}}, \sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}, \mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

   8. PI-lowering-PI.f64 N/A

      \[\leadsto \mathsf{fma}\left(\sqrt{\color{blue}{\mathsf{PI}\left(\right)}}, \sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}, \mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

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

      \[\leadsto \mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \color{blue}{\sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}}, \mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

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

       \[\leadsto \mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \color{blue}{\sqrt{\mathsf{PI}\left(\right)}} \cdot \frac{1}{2}, \mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

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

       \[\leadsto \mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \sqrt{\color{blue}{\mathsf{PI}\left(\right)}} \cdot \frac{1}{2}, \mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

   12. metadata-eval N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \sqrt{\mathsf{PI}\left(\right)} \cdot \color{blue}{\frac{1}{2}}, \mathsf{neg}\left(\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)\right) \]

   13. asin-neg N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}, \color{blue}{\sin^{-1} \left(\mathsf{neg}\left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)}\right) \]

   14. distribute-frac-neg2 N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}, \sin^{-1} \color{blue}{\left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{\mathsf{neg}\left(\left(v \cdot v - 1\right)\right)}\right)}\right) \]

   15. asin-lowering-asin.f64 N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}, \color{blue}{\sin^{-1} \left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{\mathsf{neg}\left(\left(v \cdot v - 1\right)\right)}\right)}\right) \]

   16. distribute-frac-neg2 N/A

       \[\leadsto \mathsf{fma}\left(\sqrt{\mathsf{PI}\left(\right)}, \sqrt{\mathsf{PI}\left(\right)} \cdot \frac{1}{2}, \sin^{-1} \color{blue}{\left(\mathsf{neg}\left(\frac{1 - 5 \cdot \left(v \cdot v\right)}{v \cdot v - 1}\right)\right)}\right) \]

4. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{\mathsf{fma}\left(\sqrt{\pi}, \sqrt{\pi} \cdot 0.5, \sin^{-1} \left(\frac{\mathsf{fma}\left(5, v \cdot v, -1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)\right)} \]
5. Add Preprocessing

Alternative 2: 99.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

code[v_] := N[(Pi - N[ArcCos[N[(N[(5.0 * N[(v * v), $MachinePrecision] + -1.0), $MachinePrecision] / N[(v * v + -1.0), $MachinePrecision]), $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. Add Preprocessing
3. Step-by-step derivation

   1. frac-2neg N/A

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

   2. distribute-frac-neg N/A

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

   3. acos-neg N/A

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

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

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

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

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

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

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

   7. distribute-frac-neg2 N/A

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

   8. distribute-frac-neg N/A

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

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

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

   10. sub-neg N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\mathsf{neg}\left(\color{blue}{\left(1 + \left(\mathsf{neg}\left(5 \cdot \left(v \cdot v\right)\right)\right)\right)}\right)}{v \cdot v - 1}\right) \]

   11. +-commutative N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\mathsf{neg}\left(\color{blue}{\left(\left(\mathsf{neg}\left(5 \cdot \left(v \cdot v\right)\right)\right) + 1\right)}\right)}{v \cdot v - 1}\right) \]

   12. distribute-neg-in N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(5 \cdot \left(v \cdot v\right)\right)\right)\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}}{v \cdot v - 1}\right) \]

   13. remove-double-neg N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\color{blue}{5 \cdot \left(v \cdot v\right)} + \left(\mathsf{neg}\left(1\right)\right)}{v \cdot v - 1}\right) \]

   14. accelerator-lowering-fma.f64 N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\color{blue}{\mathsf{fma}\left(5, v \cdot v, \mathsf{neg}\left(1\right)\right)}}{v \cdot v - 1}\right) \]

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

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\mathsf{fma}\left(5, \color{blue}{v \cdot v}, \mathsf{neg}\left(1\right)\right)}{v \cdot v - 1}\right) \]

   16. metadata-eval N/A

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

   17. sub-neg N/A

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

   18. accelerator-lowering-fma.f64 N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\mathsf{fma}\left(5, v \cdot v, -1\right)}{\color{blue}{\mathsf{fma}\left(v, v, \mathsf{neg}\left(1\right)\right)}}\right) \]

   19. metadata-eval 99.1

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

4. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{\pi - \cos^{-1} \left(\frac{\mathsf{fma}\left(5, v \cdot v, -1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)} \]
5. Add Preprocessing

Alternative 3: 99.1% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

code[v_] := N[ArcCos[N[(N[(v * N[(v * -5.0), $MachinePrecision] + 1.0), $MachinePrecision] / N[(v * v + -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. Add Preprocessing
3. Step-by-step derivation

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

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

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

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

   3. sub-neg N/A

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

   4. +-commutative N/A

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

   5. associate-*r* N/A

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

   6. *-commutative N/A

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

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

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

   8. accelerator-lowering-fma.f64 N/A

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

   9. *-commutative N/A

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

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

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

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

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

   12. metadata-eval N/A

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

   13. sub-neg N/A

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

   14. accelerator-lowering-fma.f64 N/A

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

   15. metadata-eval 99.1

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

4. Applied egg-rr 99.1%

   \[\leadsto \color{blue}{\cos^{-1} \left(\frac{\mathsf{fma}\left(v, v \cdot -5, 1\right)}{\mathsf{fma}\left(v, v, -1\right)}\right)} \]
5. Add Preprocessing

Alternative 4: 98.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \cos^{-1} \left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot \mathsf{fma}\left(v, v, 1\right), 4, -1\right)\right) \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (acos (fma (* (* v v) (fma v v 1.0)) 4.0 -1.0)))

C

double code(double v) {
	return acos(fma(((v * v) * fma(v, v, 1.0)), 4.0, -1.0));
}

Julia

function code(v)
	return acos(fma(Float64(Float64(v * v) * fma(v, v, 1.0)), 4.0, -1.0))
end

Wolfram

code[v_] := N[ArcCos[N[(N[(N[(v * v), $MachinePrecision] * N[(v * v + 1.0), $MachinePrecision]), $MachinePrecision] * 4.0 + -1.0), $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. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \cos^{-1} \color{blue}{\left({v}^{2} \cdot \left(4 + 4 \cdot {v}^{2}\right) - 1\right)} \]
4. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto \cos^{-1} \color{blue}{\left({v}^{2} \cdot \left(4 + 4 \cdot {v}^{2}\right) + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

   2. unpow2 N/A

      \[\leadsto \cos^{-1} \left(\color{blue}{\left(v \cdot v\right)} \cdot \left(4 + 4 \cdot {v}^{2}\right) + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   3. associate-*l* N/A

      \[\leadsto \cos^{-1} \left(\color{blue}{v \cdot \left(v \cdot \left(4 + 4 \cdot {v}^{2}\right)\right)} + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   4. *-commutative N/A

      \[\leadsto \cos^{-1} \left(v \cdot \color{blue}{\left(\left(4 + 4 \cdot {v}^{2}\right) \cdot v\right)} + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   5. metadata-eval N/A

      \[\leadsto \cos^{-1} \left(v \cdot \left(\left(4 + 4 \cdot {v}^{2}\right) \cdot v\right) + \color{blue}{-1}\right) \]

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

      \[\leadsto \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v, \left(4 + 4 \cdot {v}^{2}\right) \cdot v, -1\right)\right)} \]

   7. *-commutative N/A

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, \color{blue}{v \cdot \left(4 + 4 \cdot {v}^{2}\right)}, -1\right)\right) \]

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

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, \color{blue}{v \cdot \left(4 + 4 \cdot {v}^{2}\right)}, -1\right)\right) \]

   9. +-commutative N/A

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \color{blue}{\left(4 \cdot {v}^{2} + 4\right)}, -1\right)\right) \]

   10. *-commutative N/A

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \left(\color{blue}{{v}^{2} \cdot 4} + 4\right), -1\right)\right) \]

   11. accelerator-lowering-fma.f64 N/A

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \color{blue}{\mathsf{fma}\left({v}^{2}, 4, 4\right)}, -1\right)\right) \]

   12. unpow2 N/A

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \mathsf{fma}\left(\color{blue}{v \cdot v}, 4, 4\right), -1\right)\right) \]

   13. *-lowering-*.f64 98.9

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \mathsf{fma}\left(\color{blue}{v \cdot v}, 4, 4\right), -1\right)\right) \]

5. Simplified 98.9%

   \[\leadsto \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v, v \cdot \mathsf{fma}\left(v \cdot v, 4, 4\right), -1\right)\right)} \]
6. Step-by-step derivation

   1. associate-*r* N/A

      \[\leadsto \cos^{-1} \left(\color{blue}{\left(v \cdot v\right) \cdot \left(\left(v \cdot v\right) \cdot 4 + 4\right)} + -1\right) \]

   2. distribute-lft1-in N/A

      \[\leadsto \cos^{-1} \left(\left(v \cdot v\right) \cdot \color{blue}{\left(\left(v \cdot v + 1\right) \cdot 4\right)} + -1\right) \]

   3. metadata-eval N/A

      \[\leadsto \cos^{-1} \left(\left(v \cdot v\right) \cdot \left(\left(v \cdot v + \color{blue}{\left(\mathsf{neg}\left(-1\right)\right)}\right) \cdot 4\right) + -1\right) \]

   4. sub-neg N/A

      \[\leadsto \cos^{-1} \left(\left(v \cdot v\right) \cdot \left(\color{blue}{\left(v \cdot v - -1\right)} \cdot 4\right) + -1\right) \]

   5. associate-*r* N/A

      \[\leadsto \cos^{-1} \left(\color{blue}{\left(\left(v \cdot v\right) \cdot \left(v \cdot v - -1\right)\right) \cdot 4} + -1\right) \]

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

      \[\leadsto \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot \left(v \cdot v - -1\right), 4, -1\right)\right)} \]

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

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(\color{blue}{\left(v \cdot v\right) \cdot \left(v \cdot v - -1\right)}, 4, -1\right)\right) \]

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

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(\color{blue}{\left(v \cdot v\right)} \cdot \left(v \cdot v - -1\right), 4, -1\right)\right) \]

   9. sub-neg N/A

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot \color{blue}{\left(v \cdot v + \left(\mathsf{neg}\left(-1\right)\right)\right)}, 4, -1\right)\right) \]

   10. metadata-eval N/A

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot \left(v \cdot v + \color{blue}{1}\right), 4, -1\right)\right) \]

   11. accelerator-lowering-fma.f64 98.9

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot \color{blue}{\mathsf{fma}\left(v, v, 1\right)}, 4, -1\right)\right) \]

7. Applied egg-rr 98.9%

   \[\leadsto \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(\left(v \cdot v\right) \cdot \mathsf{fma}\left(v, v, 1\right), 4, -1\right)\right)} \]
8. Add Preprocessing

Alternative 5: 98.8% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \mathsf{fma}\left(v \cdot v, 4, 4\right), -1\right)\right) \end{array} \]

FPCore

(FPCore (v)
 :precision binary64
 (acos (fma v (* v (fma (* v v) 4.0 4.0)) -1.0)))

C

double code(double v) {
	return acos(fma(v, (v * fma((v * v), 4.0, 4.0)), -1.0));
}

Julia

function code(v)
	return acos(fma(v, Float64(v * fma(Float64(v * v), 4.0, 4.0)), -1.0))
end

Wolfram

code[v_] := N[ArcCos[N[(v * N[(v * N[(N[(v * v), $MachinePrecision] * 4.0 + 4.0), $MachinePrecision]), $MachinePrecision] + -1.0), $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. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \cos^{-1} \color{blue}{\left({v}^{2} \cdot \left(4 + 4 \cdot {v}^{2}\right) - 1\right)} \]
4. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto \cos^{-1} \color{blue}{\left({v}^{2} \cdot \left(4 + 4 \cdot {v}^{2}\right) + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

   2. unpow2 N/A

      \[\leadsto \cos^{-1} \left(\color{blue}{\left(v \cdot v\right)} \cdot \left(4 + 4 \cdot {v}^{2}\right) + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   3. associate-*l* N/A

      \[\leadsto \cos^{-1} \left(\color{blue}{v \cdot \left(v \cdot \left(4 + 4 \cdot {v}^{2}\right)\right)} + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   4. *-commutative N/A

      \[\leadsto \cos^{-1} \left(v \cdot \color{blue}{\left(\left(4 + 4 \cdot {v}^{2}\right) \cdot v\right)} + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   5. metadata-eval N/A

      \[\leadsto \cos^{-1} \left(v \cdot \left(\left(4 + 4 \cdot {v}^{2}\right) \cdot v\right) + \color{blue}{-1}\right) \]

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

      \[\leadsto \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v, \left(4 + 4 \cdot {v}^{2}\right) \cdot v, -1\right)\right)} \]

   7. *-commutative N/A

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, \color{blue}{v \cdot \left(4 + 4 \cdot {v}^{2}\right)}, -1\right)\right) \]

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

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, \color{blue}{v \cdot \left(4 + 4 \cdot {v}^{2}\right)}, -1\right)\right) \]

   9. +-commutative N/A

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \color{blue}{\left(4 \cdot {v}^{2} + 4\right)}, -1\right)\right) \]

   10. *-commutative N/A

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \left(\color{blue}{{v}^{2} \cdot 4} + 4\right), -1\right)\right) \]

   11. accelerator-lowering-fma.f64 N/A

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \color{blue}{\mathsf{fma}\left({v}^{2}, 4, 4\right)}, -1\right)\right) \]

   12. unpow2 N/A

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \mathsf{fma}\left(\color{blue}{v \cdot v}, 4, 4\right), -1\right)\right) \]

   13. *-lowering-*.f64 98.9

       \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot \mathsf{fma}\left(\color{blue}{v \cdot v}, 4, 4\right), -1\right)\right) \]

5. Simplified 98.9%

   \[\leadsto \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v, v \cdot \mathsf{fma}\left(v \cdot v, 4, 4\right), -1\right)\right)} \]
6. Add Preprocessing

Alternative 6: 98.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \pi - \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot -4, 1\right)\right) \end{array} \]

FPCore

(FPCore (v) :precision binary64 (- PI (acos (fma v (* v -4.0) 1.0))))

C

double code(double v) {
	return ((double) M_PI) - acos(fma(v, (v * -4.0), 1.0));
}

Julia

function code(v)
	return Float64(pi - acos(fma(v, Float64(v * -4.0), 1.0)))
end

Wolfram

code[v_] := N[(Pi - N[ArcCos[N[(v * N[(v * -4.0), $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. Add Preprocessing
3. Step-by-step derivation

   1. frac-2neg N/A

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

   2. distribute-frac-neg N/A

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

   3. acos-neg N/A

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

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

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

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

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

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

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

   7. distribute-frac-neg2 N/A

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

   8. distribute-frac-neg N/A

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

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

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

   10. sub-neg N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\mathsf{neg}\left(\color{blue}{\left(1 + \left(\mathsf{neg}\left(5 \cdot \left(v \cdot v\right)\right)\right)\right)}\right)}{v \cdot v - 1}\right) \]

   11. +-commutative N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\mathsf{neg}\left(\color{blue}{\left(\left(\mathsf{neg}\left(5 \cdot \left(v \cdot v\right)\right)\right) + 1\right)}\right)}{v \cdot v - 1}\right) \]

   12. distribute-neg-in N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\color{blue}{\left(\mathsf{neg}\left(\left(\mathsf{neg}\left(5 \cdot \left(v \cdot v\right)\right)\right)\right)\right) + \left(\mathsf{neg}\left(1\right)\right)}}{v \cdot v - 1}\right) \]

   13. remove-double-neg N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\color{blue}{5 \cdot \left(v \cdot v\right)} + \left(\mathsf{neg}\left(1\right)\right)}{v \cdot v - 1}\right) \]

   14. accelerator-lowering-fma.f64 N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\color{blue}{\mathsf{fma}\left(5, v \cdot v, \mathsf{neg}\left(1\right)\right)}}{v \cdot v - 1}\right) \]

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

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\mathsf{fma}\left(5, \color{blue}{v \cdot v}, \mathsf{neg}\left(1\right)\right)}{v \cdot v - 1}\right) \]

   16. metadata-eval N/A

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

   17. sub-neg N/A

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

   18. accelerator-lowering-fma.f64 N/A

       \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\frac{\mathsf{fma}\left(5, v \cdot v, -1\right)}{\color{blue}{\mathsf{fma}\left(v, v, \mathsf{neg}\left(1\right)\right)}}\right) \]

   19. metadata-eval 99.1

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

4. Applied egg-rr 99.1%

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

5. Taylor expanded in v around 0

   \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \color{blue}{\left(1 + -4 \cdot {v}^{2}\right)} \]
6. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \color{blue}{\left(-4 \cdot {v}^{2} + 1\right)} \]

   2. unpow2 N/A

      \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(-4 \cdot \color{blue}{\left(v \cdot v\right)} + 1\right) \]

   3. associate-*r* N/A

      \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\color{blue}{\left(-4 \cdot v\right) \cdot v} + 1\right) \]

   4. *-commutative N/A

      \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\color{blue}{v \cdot \left(-4 \cdot v\right)} + 1\right) \]

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

      \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v, -4 \cdot v, 1\right)\right)} \]

   6. *-commutative N/A

      \[\leadsto \mathsf{PI}\left(\right) - \cos^{-1} \left(\mathsf{fma}\left(v, \color{blue}{v \cdot -4}, 1\right)\right) \]

   7. *-lowering-*.f64 98.8

      \[\leadsto \pi - \cos^{-1} \left(\mathsf{fma}\left(v, \color{blue}{v \cdot -4}, 1\right)\right) \]

7. Simplified 98.8%

   \[\leadsto \pi - \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v, v \cdot -4, 1\right)\right)} \]
8. Add Preprocessing

Alternative 7: 98.6% accurate, 1.2× speedup

Math

\[\begin{array}{l} \\ \cos^{-1} \left(\mathsf{fma}\left(v, v \cdot 4, -1\right)\right) \end{array} \]

FPCore

(FPCore (v) :precision binary64 (acos (fma v (* v 4.0) -1.0)))

C

double code(double v) {
	return acos(fma(v, (v * 4.0), -1.0));
}

Julia

function code(v)
	return acos(fma(v, Float64(v * 4.0), -1.0))
end

Wolfram

code[v_] := N[ArcCos[N[(v * N[(v * 4.0), $MachinePrecision] + -1.0), $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. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \cos^{-1} \color{blue}{\left(4 \cdot {v}^{2} - 1\right)} \]
4. Step-by-step derivation

   1. sub-neg N/A

      \[\leadsto \cos^{-1} \color{blue}{\left(4 \cdot {v}^{2} + \left(\mathsf{neg}\left(1\right)\right)\right)} \]

   2. unpow2 N/A

      \[\leadsto \cos^{-1} \left(4 \cdot \color{blue}{\left(v \cdot v\right)} + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   3. associate-*r* N/A

      \[\leadsto \cos^{-1} \left(\color{blue}{\left(4 \cdot v\right) \cdot v} + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   4. *-commutative N/A

      \[\leadsto \cos^{-1} \left(\color{blue}{v \cdot \left(4 \cdot v\right)} + \left(\mathsf{neg}\left(1\right)\right)\right) \]

   5. metadata-eval N/A

      \[\leadsto \cos^{-1} \left(v \cdot \left(4 \cdot v\right) + \color{blue}{-1}\right) \]

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

      \[\leadsto \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v, 4 \cdot v, -1\right)\right)} \]

   7. *-commutative N/A

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, \color{blue}{v \cdot 4}, -1\right)\right) \]

   8. *-lowering-*.f64 98.8

      \[\leadsto \cos^{-1} \left(\mathsf{fma}\left(v, \color{blue}{v \cdot 4}, -1\right)\right) \]

5. Simplified 98.8%

   \[\leadsto \cos^{-1} \color{blue}{\left(\mathsf{fma}\left(v, v \cdot 4, -1\right)\right)} \]
6. Add Preprocessing

Alternative 8: 97.9% accurate, 1.3× 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. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \cos^{-1} \color{blue}{-1} \]
4. Step-by-step derivation

5. Simplified 98.3%

   \[\leadsto \cos^{-1} \color{blue}{-1} \]
6. Add Preprocessing

Reproduce

herbie shell --seed 2024198 
(FPCore (v)
  :name "Falkner and Boettcher, Appendix B, 1"
  :precision binary64
  (acos (/ (- 1.0 (* 5.0 (* v v))) (- (* v v) 1.0))))