Falkner and Boettcher, Equation (20:1,3)

Percentage Accurate: 99.3% → 99.6%

Time: 5.2s

Alternatives: 8

Speedup: 1.1×

Specification

Math

\[\begin{array}{l} \\ \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \end{array} \]

FPCore

(FPCore (v t)
 :precision binary64
 (/
  (- 1.0 (* 5.0 (* v v)))
  (* (* (* PI t) (sqrt (* 2.0 (- 1.0 (* 3.0 (* v v)))))) (- 1.0 (* v v)))))

C

double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / (((((double) M_PI) * t) * sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
}

Java

public static double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / (((Math.PI * t) * Math.sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
}

Python

def code(v, t):
	return (1.0 - (5.0 * (v * v))) / (((math.pi * t) * math.sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)))

Julia

function code(v, t)
	return Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(Float64(Float64(pi * t) * sqrt(Float64(2.0 * Float64(1.0 - Float64(3.0 * Float64(v * v)))))) * Float64(1.0 - Float64(v * v))))
end

MATLAB

function tmp = code(v, t)
	tmp = (1.0 - (5.0 * (v * v))) / (((pi * t) * sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
end

Wolfram

code[v_, t_] := N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(Pi * t), $MachinePrecision] * N[Sqrt[N[(2.0 * N[(1.0 - N[(3.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 99.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \end{array} \]

FPCore

(FPCore (v t)
 :precision binary64
 (/
  (- 1.0 (* 5.0 (* v v)))
  (* (* (* PI t) (sqrt (* 2.0 (- 1.0 (* 3.0 (* v v)))))) (- 1.0 (* v v)))))

C

double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / (((((double) M_PI) * t) * sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
}

Java

public static double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / (((Math.PI * t) * Math.sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
}

Python

def code(v, t):
	return (1.0 - (5.0 * (v * v))) / (((math.pi * t) * math.sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)))

Julia

function code(v, t)
	return Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(Float64(Float64(pi * t) * sqrt(Float64(2.0 * Float64(1.0 - Float64(3.0 * Float64(v * v)))))) * Float64(1.0 - Float64(v * v))))
end

MATLAB

function tmp = code(v, t)
	tmp = (1.0 - (5.0 * (v * v))) / (((pi * t) * sqrt((2.0 * (1.0 - (3.0 * (v * v)))))) * (1.0 - (v * v)));
end

Wolfram

code[v_, t_] := N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(Pi * t), $MachinePrecision] * N[Sqrt[N[(2.0 * N[(1.0 - N[(3.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.6% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \end{array} \]

FPCore

(FPCore (v t)
 :precision binary64
 (*
  (/ (/ (/ (+ (* (* v v) -5.0) 1.0) PI) t) (* (sqrt 2.0) (- 1.0 (* v v))))
  (sqrt (ratio-square-sum 1.0 (* (* -3.0 v) v)))))

Derivation

1. Initial program 99.1%

   \[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
2. Add Preprocessing

3. Taylor expanded in t around 0

   \[\leadsto \color{blue}{\frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \cdot \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}}} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}} \cdot \color{blue}{\frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)}} \]

   2. lower-*.f64 N/A

      \[\leadsto \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}} \cdot \color{blue}{\frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)}} \]

   3. lower-sqrt.f64 N/A

      \[\leadsto \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}} \cdot \frac{\color{blue}{1 - 5 \cdot {v}^{2}}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   4. metadata-eval N/A

      \[\leadsto \sqrt{\frac{1 \cdot 1}{1 - 3 \cdot {v}^{2}}} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   5. metadata-eval N/A

      \[\leadsto \sqrt{\frac{1 \cdot 1}{1 - \left(\mathsf{neg}\left(-3\right)\right) \cdot {v}^{2}}} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   6. fp-cancel-sign-sub-inv N/A

      \[\leadsto \sqrt{\frac{1 \cdot 1}{1 + -3 \cdot {v}^{2}}} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   7. lower-ratio-square-sum.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot {v}^{2}\right)\right)} \cdot \frac{\color{blue}{1} - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   8. lower-*.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot {v}^{2}\right)\right)} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   9. pow2 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   10. lift-*.f64 N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

5. Applied rewrites 99.1%

   \[\leadsto \color{blue}{\sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\pi \cdot t\right) \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)}} \]

7. Applied rewrites 99.2%

   \[\leadsto \color{blue}{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi \cdot t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)}} \]
8. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi \cdot t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(\color{blue}{1}, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   2. lift-+.f64 N/A

      \[\leadsto \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi \cdot t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi \cdot t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   4. lift-*.f64 N/A

      \[\leadsto \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi \cdot t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   5. lift-PI.f64 N/A

      \[\leadsto \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\mathsf{PI}\left(\right) \cdot t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   6. lift-*.f64 N/A

      \[\leadsto \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\mathsf{PI}\left(\right) \cdot t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   7. associate-/r* N/A

      \[\leadsto \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\mathsf{PI}\left(\right)}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(\color{blue}{1}, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   8. lower-/.f64 N/A

      \[\leadsto \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\mathsf{PI}\left(\right)}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(\color{blue}{1}, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   9. lower-/.f64 N/A

      \[\leadsto \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\mathsf{PI}\left(\right)}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   10. lift-*.f64 N/A

       \[\leadsto \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\mathsf{PI}\left(\right)}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   11. lift-*.f64 N/A

       \[\leadsto \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\mathsf{PI}\left(\right)}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   12. lift-+.f64 N/A

       \[\leadsto \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\mathsf{PI}\left(\right)}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

   13. lift-PI.f64 99.6

       \[\leadsto \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]

9. Applied rewrites 99.6%

   \[\leadsto \frac{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi}}{t}}{\sqrt{2} \cdot \left(1 - v \cdot v\right)} \cdot \sqrt{\mathsf{ratio\_square\_sum}\left(\color{blue}{1}, \left(\left(-3 \cdot v\right) \cdot v\right)\right)} \]
10. Add Preprocessing

Alternative 2: 99.6% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi}}{\left(\sqrt{\left(1 - \left(v \cdot v\right) \cdot 3\right) \cdot 2} \cdot t\right) \cdot \left(1 - v \cdot v\right)} \end{array} \]

FPCore

(FPCore (v t)
 :precision binary64
 (/
  (/ (+ (* (* v v) -5.0) 1.0) PI)
  (* (* (sqrt (* (- 1.0 (* (* v v) 3.0)) 2.0)) t) (- 1.0 (* v v)))))

C

double code(double v, double t) {
	return ((((v * v) * -5.0) + 1.0) / ((double) M_PI)) / ((sqrt(((1.0 - ((v * v) * 3.0)) * 2.0)) * t) * (1.0 - (v * v)));
}

Java

public static double code(double v, double t) {
	return ((((v * v) * -5.0) + 1.0) / Math.PI) / ((Math.sqrt(((1.0 - ((v * v) * 3.0)) * 2.0)) * t) * (1.0 - (v * v)));
}

Python

def code(v, t):
	return ((((v * v) * -5.0) + 1.0) / math.pi) / ((math.sqrt(((1.0 - ((v * v) * 3.0)) * 2.0)) * t) * (1.0 - (v * v)))

Julia

function code(v, t)
	return Float64(Float64(Float64(Float64(Float64(v * v) * -5.0) + 1.0) / pi) / Float64(Float64(sqrt(Float64(Float64(1.0 - Float64(Float64(v * v) * 3.0)) * 2.0)) * t) * Float64(1.0 - Float64(v * v))))
end

MATLAB

function tmp = code(v, t)
	tmp = ((((v * v) * -5.0) + 1.0) / pi) / ((sqrt(((1.0 - ((v * v) * 3.0)) * 2.0)) * t) * (1.0 - (v * v)));
end

Wolfram

code[v_, t_] := N[(N[(N[(N[(N[(v * v), $MachinePrecision] * -5.0), $MachinePrecision] + 1.0), $MachinePrecision] / Pi), $MachinePrecision] / N[(N[(N[Sqrt[N[(N[(1.0 - N[(N[(v * v), $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]], $MachinePrecision] * t), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)} \cdot \left(1 - v \cdot v\right)} \]

   2. lift-PI.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\color{blue}{\mathsf{PI}\left(\right)} \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\color{blue}{\left(\mathsf{PI}\left(\right) \cdot t\right)} \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   4. lift-sqrt.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \color{blue}{\sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}}\right) \cdot \left(1 - v \cdot v\right)} \]

   5. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{\color{blue}{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}}\right) \cdot \left(1 - v \cdot v\right)} \]

   6. lift--.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{2 \cdot \color{blue}{\left(1 - 3 \cdot \left(v \cdot v\right)\right)}}\right) \cdot \left(1 - v \cdot v\right)} \]

   7. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - \color{blue}{3 \cdot \left(v \cdot v\right)}\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   8. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \color{blue}{\left(v \cdot v\right)}\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   9. associate-*l* N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\mathsf{PI}\left(\right) \cdot \left(t \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)\right)} \cdot \left(1 - v \cdot v\right)} \]

   10. lower-*.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\mathsf{PI}\left(\right) \cdot \left(t \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)\right)} \cdot \left(1 - v \cdot v\right)} \]

   11. lift-PI.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\color{blue}{\pi} \cdot \left(t \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)\right) \cdot \left(1 - v \cdot v\right)} \]

   12. lower-*.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \color{blue}{\left(t \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   13. lower-sqrt.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \left(t \cdot \color{blue}{\sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}}\right)\right) \cdot \left(1 - v \cdot v\right)} \]

   14. *-commutative N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \left(t \cdot \sqrt{\color{blue}{\left(1 - 3 \cdot \left(v \cdot v\right)\right) \cdot 2}}\right)\right) \cdot \left(1 - v \cdot v\right)} \]

   15. lower-*.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \left(t \cdot \sqrt{\color{blue}{\left(1 - 3 \cdot \left(v \cdot v\right)\right) \cdot 2}}\right)\right) \cdot \left(1 - v \cdot v\right)} \]

4. Applied rewrites 99.1%

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\pi \cdot \left(t \cdot \sqrt{\left(-3 \cdot \left(v \cdot v\right) + 1\right) \cdot 2}\right)\right)} \cdot \left(1 - v \cdot v\right)} \]

6. Applied rewrites 99.2%

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\pi \cdot \left(\left(\sqrt{\left(1 - \left(v \cdot v\right) \cdot 3\right) \cdot 2} \cdot t\right) \cdot \left(1 - v \cdot v\right)\right)}} \]

8. Applied rewrites 99.6%

   \[\leadsto \color{blue}{\frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{\pi}}{\left(\sqrt{\left(1 - \left(v \cdot v\right) \cdot 3\right) \cdot 2} \cdot t\right) \cdot \left(1 - v \cdot v\right)}} \]
9. Add Preprocessing

Alternative 3: 99.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \frac{1 - 5 \cdot \left(v \cdot v\right)}{\pi \cdot \left(\left(\sqrt{\left(1 - \left(v \cdot v\right) \cdot 3\right) \cdot 2} \cdot t\right) \cdot \left(1 - v \cdot v\right)\right)} \end{array} \]

FPCore

(FPCore (v t)
 :precision binary64
 (/
  (- 1.0 (* 5.0 (* v v)))
  (* PI (* (* (sqrt (* (- 1.0 (* (* v v) 3.0)) 2.0)) t) (- 1.0 (* v v))))))

C

double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / (((double) M_PI) * ((sqrt(((1.0 - ((v * v) * 3.0)) * 2.0)) * t) * (1.0 - (v * v))));
}

Java

public static double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / (Math.PI * ((Math.sqrt(((1.0 - ((v * v) * 3.0)) * 2.0)) * t) * (1.0 - (v * v))));
}

Python

def code(v, t):
	return (1.0 - (5.0 * (v * v))) / (math.pi * ((math.sqrt(((1.0 - ((v * v) * 3.0)) * 2.0)) * t) * (1.0 - (v * v))))

Julia

function code(v, t)
	return Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(pi * Float64(Float64(sqrt(Float64(Float64(1.0 - Float64(Float64(v * v) * 3.0)) * 2.0)) * t) * Float64(1.0 - Float64(v * v)))))
end

MATLAB

function tmp = code(v, t)
	tmp = (1.0 - (5.0 * (v * v))) / (pi * ((sqrt(((1.0 - ((v * v) * 3.0)) * 2.0)) * t) * (1.0 - (v * v))));
end

Wolfram

code[v_, t_] := N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(Pi * N[(N[(N[Sqrt[N[(N[(1.0 - N[(N[(v * v), $MachinePrecision] * 3.0), $MachinePrecision]), $MachinePrecision] * 2.0), $MachinePrecision]], $MachinePrecision] * t), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)} \cdot \left(1 - v \cdot v\right)} \]

   2. lift-PI.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\color{blue}{\mathsf{PI}\left(\right)} \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\color{blue}{\left(\mathsf{PI}\left(\right) \cdot t\right)} \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   4. lift-sqrt.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \color{blue}{\sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}}\right) \cdot \left(1 - v \cdot v\right)} \]

   5. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{\color{blue}{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}}\right) \cdot \left(1 - v \cdot v\right)} \]

   6. lift--.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{2 \cdot \color{blue}{\left(1 - 3 \cdot \left(v \cdot v\right)\right)}}\right) \cdot \left(1 - v \cdot v\right)} \]

   7. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - \color{blue}{3 \cdot \left(v \cdot v\right)}\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   8. lift-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \color{blue}{\left(v \cdot v\right)}\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   9. associate-*l* N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\mathsf{PI}\left(\right) \cdot \left(t \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)\right)} \cdot \left(1 - v \cdot v\right)} \]

   10. lower-*.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\mathsf{PI}\left(\right) \cdot \left(t \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)\right)} \cdot \left(1 - v \cdot v\right)} \]

   11. lift-PI.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\color{blue}{\pi} \cdot \left(t \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)\right) \cdot \left(1 - v \cdot v\right)} \]

   12. lower-*.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \color{blue}{\left(t \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right)}\right) \cdot \left(1 - v \cdot v\right)} \]

   13. lower-sqrt.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \left(t \cdot \color{blue}{\sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}}\right)\right) \cdot \left(1 - v \cdot v\right)} \]

   14. *-commutative N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \left(t \cdot \sqrt{\color{blue}{\left(1 - 3 \cdot \left(v \cdot v\right)\right) \cdot 2}}\right)\right) \cdot \left(1 - v \cdot v\right)} \]

   15. lower-*.f64 N/A

       \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \left(t \cdot \sqrt{\color{blue}{\left(1 - 3 \cdot \left(v \cdot v\right)\right) \cdot 2}}\right)\right) \cdot \left(1 - v \cdot v\right)} \]

4. Applied rewrites 99.1%

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\pi \cdot \left(t \cdot \sqrt{\left(-3 \cdot \left(v \cdot v\right) + 1\right) \cdot 2}\right)\right)} \cdot \left(1 - v \cdot v\right)} \]

6. Applied rewrites 99.2%

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\pi \cdot \left(\left(\sqrt{\left(1 - \left(v \cdot v\right) \cdot 3\right) \cdot 2} \cdot t\right) \cdot \left(1 - v \cdot v\right)\right)}} \]
7. Add Preprocessing

Alternative 4: 99.3% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{-6 \cdot \left(v \cdot v\right) + 2}\right) \cdot \left(1 - v \cdot v\right)} \end{array} \]

FPCore

(FPCore (v t)
 :precision binary64
 (/
  (- 1.0 (* 5.0 (* v v)))
  (* (* (* PI t) (sqrt (+ (* -6.0 (* v v)) 2.0))) (- 1.0 (* v v)))))

C

double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / (((((double) M_PI) * t) * sqrt(((-6.0 * (v * v)) + 2.0))) * (1.0 - (v * v)));
}

Java

public static double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / (((Math.PI * t) * Math.sqrt(((-6.0 * (v * v)) + 2.0))) * (1.0 - (v * v)));
}

Python

def code(v, t):
	return (1.0 - (5.0 * (v * v))) / (((math.pi * t) * math.sqrt(((-6.0 * (v * v)) + 2.0))) * (1.0 - (v * v)))

Julia

function code(v, t)
	return Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(Float64(Float64(pi * t) * sqrt(Float64(Float64(-6.0 * Float64(v * v)) + 2.0))) * Float64(1.0 - Float64(v * v))))
end

MATLAB

function tmp = code(v, t)
	tmp = (1.0 - (5.0 * (v * v))) / (((pi * t) * sqrt(((-6.0 * (v * v)) + 2.0))) * (1.0 - (v * v)));
end

Wolfram

code[v_, t_] := N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[(Pi * t), $MachinePrecision] * N[Sqrt[N[(N[(-6.0 * N[(v * v), $MachinePrecision]), $MachinePrecision] + 2.0), $MachinePrecision]], $MachinePrecision]), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{\color{blue}{2 + -6 \cdot {v}^{2}}}\right) \cdot \left(1 - v \cdot v\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{-6 \cdot {v}^{2} + \color{blue}{2}}\right) \cdot \left(1 - v \cdot v\right)} \]

   2. lower-+.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{-6 \cdot {v}^{2} + \color{blue}{2}}\right) \cdot \left(1 - v \cdot v\right)} \]

   3. lower-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{-6 \cdot {v}^{2} + 2}\right) \cdot \left(1 - v \cdot v\right)} \]

   4. pow2 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{-6 \cdot \left(v \cdot v\right) + 2}\right) \cdot \left(1 - v \cdot v\right)} \]

   5. lift-*.f64 99.1

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{-6 \cdot \left(v \cdot v\right) + 2}\right) \cdot \left(1 - v \cdot v\right)} \]

5. Applied rewrites 99.1%

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{\color{blue}{-6 \cdot \left(v \cdot v\right) + 2}}\right) \cdot \left(1 - v \cdot v\right)} \]
6. Add Preprocessing

Alternative 5: 98.5% accurate, 1.1× speedup

Math

\[\begin{array}{l} \\ 1 \cdot \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{t}}{\left(\sqrt{2} \cdot \pi\right) \cdot \left(1 - v \cdot v\right)} \end{array} \]

FPCore

(FPCore (v t)
 :precision binary64
 (*
  1.0
  (/ (/ (+ (* (* v v) -5.0) 1.0) t) (* (* (sqrt 2.0) PI) (- 1.0 (* v v))))))

C

double code(double v, double t) {
	return 1.0 * (((((v * v) * -5.0) + 1.0) / t) / ((sqrt(2.0) * ((double) M_PI)) * (1.0 - (v * v))));
}

Java

public static double code(double v, double t) {
	return 1.0 * (((((v * v) * -5.0) + 1.0) / t) / ((Math.sqrt(2.0) * Math.PI) * (1.0 - (v * v))));
}

Python

def code(v, t):
	return 1.0 * (((((v * v) * -5.0) + 1.0) / t) / ((math.sqrt(2.0) * math.pi) * (1.0 - (v * v))))

Julia

function code(v, t)
	return Float64(1.0 * Float64(Float64(Float64(Float64(Float64(v * v) * -5.0) + 1.0) / t) / Float64(Float64(sqrt(2.0) * pi) * Float64(1.0 - Float64(v * v)))))
end

MATLAB

function tmp = code(v, t)
	tmp = 1.0 * (((((v * v) * -5.0) + 1.0) / t) / ((sqrt(2.0) * pi) * (1.0 - (v * v))));
end

Wolfram

code[v_, t_] := N[(1.0 * N[(N[(N[(N[(N[(v * v), $MachinePrecision] * -5.0), $MachinePrecision] + 1.0), $MachinePrecision] / t), $MachinePrecision] / N[(N[(N[Sqrt[2.0], $MachinePrecision] * Pi), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
2. Add Preprocessing

3. Taylor expanded in t around 0

   \[\leadsto \color{blue}{\frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \cdot \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}}} \]
4. Step-by-step derivation

   1. *-commutative N/A

      \[\leadsto \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}} \cdot \color{blue}{\frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)}} \]

   2. lower-*.f64 N/A

      \[\leadsto \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}} \cdot \color{blue}{\frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)}} \]

   3. lower-sqrt.f64 N/A

      \[\leadsto \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}} \cdot \frac{\color{blue}{1 - 5 \cdot {v}^{2}}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   4. metadata-eval N/A

      \[\leadsto \sqrt{\frac{1 \cdot 1}{1 - 3 \cdot {v}^{2}}} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   5. metadata-eval N/A

      \[\leadsto \sqrt{\frac{1 \cdot 1}{1 - \left(\mathsf{neg}\left(-3\right)\right) \cdot {v}^{2}}} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   6. fp-cancel-sign-sub-inv N/A

      \[\leadsto \sqrt{\frac{1 \cdot 1}{1 + -3 \cdot {v}^{2}}} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   7. lower-ratio-square-sum.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot {v}^{2}\right)\right)} \cdot \frac{\color{blue}{1} - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   8. lower-*.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot {v}^{2}\right)\right)} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   9. pow2 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

   10. lift-*.f64 N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 - 5 \cdot {v}^{2}}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)} \]

5. Applied rewrites 99.1%

   \[\leadsto \color{blue}{\sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\pi \cdot t\right) \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)}} \]
6. Step-by-step derivation

   1. lift-/.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\color{blue}{\left(\pi \cdot t\right) \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)}} \]

   2. lift-+.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\color{blue}{\left(\pi \cdot t\right)} \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)} \]

   3. lift-*.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\pi \cdot t\right) \cdot \color{blue}{\left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)}} \]

   4. lift-PI.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \left(\sqrt{\color{blue}{2}} \cdot \left(1 - v \cdot v\right)\right)} \]

   5. lift-*.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \left(\color{blue}{\sqrt{2}} \cdot \left(1 - v \cdot v\right)\right)} \]

   6. lift-*.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \left(\sqrt{2} \cdot \color{blue}{\left(1 - v \cdot v\right)}\right)} \]

   7. lift-sqrt.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \left(\sqrt{2} \cdot \left(\color{blue}{1} - v \cdot v\right)\right)} \]

   8. lift--.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \left(\sqrt{2} \cdot \left(1 - \color{blue}{v \cdot v}\right)\right)} \]

   9. lift-*.f64 N/A

      \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{-5 \cdot \left(v \cdot v\right) + 1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot \color{blue}{v}\right)\right)} \]

   10. +-commutative N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 + -5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\mathsf{PI}\left(\right) \cdot t\right)} \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)} \]

   11. lift-*.f64 N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 + -5 \cdot \left(v \cdot v\right)}{\left(\mathsf{PI}\left(\right) \cdot \color{blue}{t}\right) \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)} \]

   12. lift-*.f64 N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 + -5 \cdot \left(v \cdot v\right)}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)} \]

   13. pow2 N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 + -5 \cdot {v}^{2}}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \left(\sqrt{2} \cdot \left(1 - v \cdot v\right)\right)} \]

   14. *-commutative N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 + -5 \cdot {v}^{2}}{\left(t \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\color{blue}{\sqrt{2}} \cdot \left(1 - v \cdot v\right)\right)} \]

   15. pow2 N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 + -5 \cdot {v}^{2}}{\left(t \cdot \mathsf{PI}\left(\right)\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{\color{blue}{2}}\right)\right)} \]

   16. associate-*r* N/A

       \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{1 + -5 \cdot {v}^{2}}{t \cdot \color{blue}{\left(\mathsf{PI}\left(\right) \cdot \left(\sqrt{2} \cdot \left(1 - {v}^{2}\right)\right)\right)}} \]

7. Applied rewrites 99.5%

   \[\leadsto \sqrt{\mathsf{ratio\_square\_sum}\left(1, \left(-3 \cdot \left(v \cdot v\right)\right)\right)} \cdot \frac{\frac{\left(v \cdot v\right) \cdot -5 + 1}{t}}{\color{blue}{\left(\sqrt{2} \cdot \pi\right) \cdot \left(1 - v \cdot v\right)}} \]

8. Taylor expanded in v around 0

   \[\leadsto 1 \cdot \frac{\color{blue}{\frac{\left(v \cdot v\right) \cdot -5 + 1}{t}}}{\left(\sqrt{2} \cdot \pi\right) \cdot \left(1 - v \cdot v\right)} \]
9. Step-by-step derivation

10. Applied rewrites 98.6%

    \[\leadsto 1 \cdot \frac{\color{blue}{\frac{\left(v \cdot v\right) \cdot -5 + 1}{t}}}{\left(\sqrt{2} \cdot \pi\right) \cdot \left(1 - v \cdot v\right)} \]
11. Add Preprocessing

Alternative 6: 98.4% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \end{array} \]

FPCore

(FPCore (v t)
 :precision binary64
 (/ (- 1.0 (* 5.0 (* v v))) (* (* (sqrt 2.0) PI) t)))

C

double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / ((sqrt(2.0) * ((double) M_PI)) * t);
}

Java

public static double code(double v, double t) {
	return (1.0 - (5.0 * (v * v))) / ((Math.sqrt(2.0) * Math.PI) * t);
}

Python

def code(v, t):
	return (1.0 - (5.0 * (v * v))) / ((math.sqrt(2.0) * math.pi) * t)

Julia

function code(v, t)
	return Float64(Float64(1.0 - Float64(5.0 * Float64(v * v))) / Float64(Float64(sqrt(2.0) * pi) * t))
end

MATLAB

function tmp = code(v, t)
	tmp = (1.0 - (5.0 * (v * v))) / ((sqrt(2.0) * pi) * t);
end

Wolfram

code[v_, t_] := N[(N[(1.0 - N[(5.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] / N[(N[(N[Sqrt[2.0], $MachinePrecision] * Pi), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \sqrt{2}\right)}} \]
4. Step-by-step derivation

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   4. lower-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   5. lower-sqrt.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   6. lift-PI.f64 98.3

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

5. Applied rewrites 98.3%

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\sqrt{2} \cdot \pi\right) \cdot t}} \]
6. Add Preprocessing

Alternative 7: 98.4% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \end{array} \]

FPCore

(FPCore (v t) :precision binary64 (/ 1.0 (* (* (sqrt 2.0) PI) t)))

C

double code(double v, double t) {
	return 1.0 / ((sqrt(2.0) * ((double) M_PI)) * t);
}

Java

public static double code(double v, double t) {
	return 1.0 / ((Math.sqrt(2.0) * Math.PI) * t);
}

Python

def code(v, t):
	return 1.0 / ((math.sqrt(2.0) * math.pi) * t)

Julia

function code(v, t)
	return Float64(1.0 / Float64(Float64(sqrt(2.0) * pi) * t))
end

MATLAB

function tmp = code(v, t)
	tmp = 1.0 / ((sqrt(2.0) * pi) * t);
end

Wolfram

code[v_, t_] := N[(1.0 / N[(N[(N[Sqrt[2.0], $MachinePrecision] * Pi), $MachinePrecision] * t), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \sqrt{2}\right)}} \]
4. Step-by-step derivation

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   4. lower-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   5. lower-sqrt.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   6. lift-PI.f64 98.3

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

5. Applied rewrites 98.3%

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\sqrt{2} \cdot \pi\right) \cdot t}} \]

6. Taylor expanded in v around 0

   \[\leadsto \frac{\color{blue}{1}}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]
7. Step-by-step derivation

   1. pow2 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

   2. metadata-eval 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

   3. fp-cancel-sign-sub-inv 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

   4. +-commutative 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

   5. pow2 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

8. Applied rewrites 98.3%

   \[\leadsto \frac{\color{blue}{1}}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]
9. Add Preprocessing

Alternative 8: 98.3% accurate, 2.4× speedup

Math

\[\begin{array}{l} \\ \frac{1}{\left(t \cdot \pi\right) \cdot \sqrt{2}} \end{array} \]

FPCore

(FPCore (v t) :precision binary64 (/ 1.0 (* (* t PI) (sqrt 2.0))))

C

double code(double v, double t) {
	return 1.0 / ((t * ((double) M_PI)) * sqrt(2.0));
}

Java

public static double code(double v, double t) {
	return 1.0 / ((t * Math.PI) * Math.sqrt(2.0));
}

Python

def code(v, t):
	return 1.0 / ((t * math.pi) * math.sqrt(2.0))

Julia

function code(v, t)
	return Float64(1.0 / Float64(Float64(t * pi) * sqrt(2.0)))
end

MATLAB

function tmp = code(v, t)
	tmp = 1.0 / ((t * pi) * sqrt(2.0));
end

Wolfram

code[v_, t_] := N[(1.0 / N[(N[(t * Pi), $MachinePrecision] * N[Sqrt[2.0], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.1%

   \[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
2. Add Preprocessing

3. Taylor expanded in v around 0

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{t \cdot \left(\mathsf{PI}\left(\right) \cdot \sqrt{2}\right)}} \]
4. Step-by-step derivation

   1. *-commutative N/A

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

   2. lower-*.f64 N/A

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

   3. *-commutative N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   4. lower-*.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   5. lower-sqrt.f64 N/A

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   6. lift-PI.f64 98.3

      \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

5. Applied rewrites 98.3%

   \[\leadsto \frac{1 - 5 \cdot \left(v \cdot v\right)}{\color{blue}{\left(\sqrt{2} \cdot \pi\right) \cdot t}} \]

6. Taylor expanded in v around 0

   \[\leadsto \frac{\color{blue}{1}}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]
7. Step-by-step derivation

   1. pow2 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

   2. metadata-eval 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

   3. fp-cancel-sign-sub-inv 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

   4. +-commutative 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

   5. pow2 98.3

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]

8. Applied rewrites 98.3%

   \[\leadsto \frac{\color{blue}{1}}{\left(\sqrt{2} \cdot \pi\right) \cdot t} \]
9. Step-by-step derivation

   1. lift-*.f64 N/A

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \pi\right) \cdot \color{blue}{t}} \]

   2. lift-PI.f64 N/A

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   3. lift-*.f64 N/A

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   4. lift-sqrt.f64 N/A

      \[\leadsto \frac{1}{\left(\sqrt{2} \cdot \mathsf{PI}\left(\right)\right) \cdot t} \]

   5. *-commutative N/A

      \[\leadsto \frac{1}{\left(\mathsf{PI}\left(\right) \cdot \sqrt{2}\right) \cdot t} \]

   6. *-commutative N/A

      \[\leadsto \frac{1}{t \cdot \color{blue}{\left(\mathsf{PI}\left(\right) \cdot \sqrt{2}\right)}} \]

   7. associate-*r* N/A

      \[\leadsto \frac{1}{\left(t \cdot \mathsf{PI}\left(\right)\right) \cdot \color{blue}{\sqrt{2}}} \]

   8. *-commutative N/A

      \[\leadsto \frac{1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{\color{blue}{2}}} \]

   9. lift-*.f64 N/A

      \[\leadsto \frac{1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{\color{blue}{2}}} \]

   10. lift-PI.f64 N/A

       \[\leadsto \frac{1}{\left(\pi \cdot t\right) \cdot \sqrt{2}} \]

   11. lower-*.f64 N/A

       \[\leadsto \frac{1}{\left(\pi \cdot t\right) \cdot \color{blue}{\sqrt{2}}} \]

   12. lift-PI.f64 N/A

       \[\leadsto \frac{1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{2}} \]

   13. lift-*.f64 N/A

       \[\leadsto \frac{1}{\left(\mathsf{PI}\left(\right) \cdot t\right) \cdot \sqrt{\color{blue}{2}}} \]

   14. *-commutative N/A

       \[\leadsto \frac{1}{\left(t \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{2}}} \]

   15. lower-*.f64 N/A

       \[\leadsto \frac{1}{\left(t \cdot \mathsf{PI}\left(\right)\right) \cdot \sqrt{\color{blue}{2}}} \]

   16. lift-PI.f64 N/A

       \[\leadsto \frac{1}{\left(t \cdot \pi\right) \cdot \sqrt{2}} \]

   17. lift-sqrt.f64 98.2

       \[\leadsto \frac{1}{\left(t \cdot \pi\right) \cdot \sqrt{2}} \]

10. Applied rewrites 98.2%

    \[\leadsto \frac{1}{\left(t \cdot \pi\right) \cdot \color{blue}{\sqrt{2}}} \]
11. Add Preprocessing

Reproduce

herbie shell --seed 2025058 
(FPCore (v t)
  :name "Falkner and Boettcher, Equation (20:1,3)"
  :precision binary64
  (/ (- 1.0 (* 5.0 (* v v))) (* (* (* PI t) (sqrt (* 2.0 (- 1.0 (* 3.0 (* v v)))))) (- 1.0 (* v v)))))