Falkner and Boettcher, Equation (22+)

Average Error: 1.0 → 0.0

Time: 3.8s

Precision: binary64

Cost: 13952

Math

\[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

↓

\[\frac{\frac{0.3333333333333333}{\pi}}{1 - v \cdot v} \cdot \frac{4}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

FPCore

(FPCore (v)
 :precision binary64
 (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))

↓

(FPCore (v)
 :precision binary64
 (*
  (/ (/ 0.3333333333333333 PI) (- 1.0 (* v v)))
  (/ 4.0 (sqrt (+ 2.0 (* (* v v) -6.0))))))

C

double code(double v) {
	return 4.0 / (((3.0 * ((double) M_PI)) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
}

↓

double code(double v) {
	return ((0.3333333333333333 / ((double) M_PI)) / (1.0 - (v * v))) * (4.0 / sqrt((2.0 + ((v * v) * -6.0))));
}

Java

public static double code(double v) {
	return 4.0 / (((3.0 * Math.PI) * (1.0 - (v * v))) * Math.sqrt((2.0 - (6.0 * (v * v)))));
}

↓

public static double code(double v) {
	return ((0.3333333333333333 / Math.PI) / (1.0 - (v * v))) * (4.0 / Math.sqrt((2.0 + ((v * v) * -6.0))));
}

Python

def code(v):
	return 4.0 / (((3.0 * math.pi) * (1.0 - (v * v))) * math.sqrt((2.0 - (6.0 * (v * v)))))

↓

def code(v):
	return ((0.3333333333333333 / math.pi) / (1.0 - (v * v))) * (4.0 / math.sqrt((2.0 + ((v * v) * -6.0))))

Julia

function code(v)
	return Float64(4.0 / Float64(Float64(Float64(3.0 * pi) * Float64(1.0 - Float64(v * v))) * sqrt(Float64(2.0 - Float64(6.0 * Float64(v * v))))))
end

↓

function code(v)
	return Float64(Float64(Float64(0.3333333333333333 / pi) / Float64(1.0 - Float64(v * v))) * Float64(4.0 / sqrt(Float64(2.0 + Float64(Float64(v * v) * -6.0)))))
end

MATLAB

function tmp = code(v)
	tmp = 4.0 / (((3.0 * pi) * (1.0 - (v * v))) * sqrt((2.0 - (6.0 * (v * v)))));
end

↓

function tmp = code(v)
	tmp = ((0.3333333333333333 / pi) / (1.0 - (v * v))) * (4.0 / sqrt((2.0 + ((v * v) * -6.0))));
end

Wolfram

code[v_] := N[(4.0 / N[(N[(N[(3.0 * Pi), $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[Sqrt[N[(2.0 - N[(6.0 * N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[v_] := N[(N[(N[(0.3333333333333333 / Pi), $MachinePrecision] / N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * N[(4.0 / N[Sqrt[N[(2.0 + N[(N[(v * v), $MachinePrecision] * -6.0), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 1.0

   \[\frac{4}{\left(\left(3 \cdot \pi\right) \cdot \left(1 - v \cdot v\right)\right) \cdot \sqrt{2 - 6 \cdot \left(v \cdot v\right)}} \]

2. Applied egg-rr 0.0

   \[\leadsto \color{blue}{\frac{\frac{0.3333333333333333}{\pi}}{1 - v \cdot v} \cdot \frac{4}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}}} \]

3. Final simplification 0.0

   \[\leadsto \frac{\frac{0.3333333333333333}{\pi}}{1 - v \cdot v} \cdot \frac{4}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

Alternatives

Alternative 1
Error	0.0
Cost	13824

\[\frac{\frac{1.3333333333333333}{\pi \cdot \left(1 - v \cdot v\right)}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

Alternative 2
Error	0.7
Cost	13568

\[\frac{1.3333333333333333 \cdot \frac{1}{\pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

Alternative 3
Error	0.7
Cost	13440

\[\frac{\frac{1.3333333333333333}{\pi}}{\sqrt{2 + \left(v \cdot v\right) \cdot -6}} \]

Alternative 4
Error	1.7
Cost	13056

\[1.3333333333333333 \cdot \frac{\sqrt{0.5}}{\pi} \]

Alternative 5
Error	0.8
Cost	13056

\[\frac{1.3333333333333333}{\frac{\pi}{\sqrt{0.5}}} \]

Reproduce

herbie shell --seed 2023027 
(FPCore (v)
  :name "Falkner and Boettcher, Equation (22+)"
  :precision binary64
  (/ 4.0 (* (* (* 3.0 PI) (- 1.0 (* v v))) (sqrt (- 2.0 (* 6.0 (* v v)))))))