Falkner and Boettcher, Appendix B, 2

Average Error: 0.0 → 0.0

Time: 5.3s

Precision: binary64

Cost: 7360

Math

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

↓

\[\sqrt{\left(v \cdot \left(v \cdot -3\right) + 1\right) \cdot 0.125} \cdot \left(1 - v \cdot v\right) \]

FPCore

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

↓

(FPCore (v)
 :precision binary64
 (* (sqrt (* (+ (* v (* v -3.0)) 1.0) 0.125)) (- 1.0 (* v v))))

C

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

↓

double code(double v) {
	return sqrt((((v * (v * -3.0)) + 1.0) * 0.125)) * (1.0 - (v * v));
}

Fortran

real(8) function code(v)
    real(8), intent (in) :: v
    code = ((sqrt(2.0d0) / 4.0d0) * sqrt((1.0d0 - (3.0d0 * (v * v))))) * (1.0d0 - (v * v))
end function

↓

real(8) function code(v)
    real(8), intent (in) :: v
    code = sqrt((((v * (v * (-3.0d0))) + 1.0d0) * 0.125d0)) * (1.0d0 - (v * v))
end function

Java

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

↓

public static double code(double v) {
	return Math.sqrt((((v * (v * -3.0)) + 1.0) * 0.125)) * (1.0 - (v * v));
}

Python

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

↓

def code(v):
	return math.sqrt((((v * (v * -3.0)) + 1.0) * 0.125)) * (1.0 - (v * v))

Julia

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

↓

function code(v)
	return Float64(sqrt(Float64(Float64(Float64(v * Float64(v * -3.0)) + 1.0) * 0.125)) * Float64(1.0 - Float64(v * v)))
end

MATLAB

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

↓

function tmp = code(v)
	tmp = sqrt((((v * (v * -3.0)) + 1.0) * 0.125)) * (1.0 - (v * v));
end

Wolfram

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

↓

code[v_] := N[(N[Sqrt[N[(N[(N[(v * N[(v * -3.0), $MachinePrecision]), $MachinePrecision] + 1.0), $MachinePrecision] * 0.125), $MachinePrecision]], $MachinePrecision] * N[(1.0 - N[(v * v), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 0.0

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

2. Applied egg-rr 0.0

   \[\leadsto \color{blue}{\sqrt{\mathsf{fma}\left(v \cdot v, -3, 1\right) \cdot 0.125}} \cdot \left(1 - v \cdot v\right) \]

3. Applied egg-rr 0.0

   \[\leadsto \sqrt{\color{blue}{\left(v \cdot \left(v \cdot -3\right) + 1\right)} \cdot 0.125} \cdot \left(1 - v \cdot v\right) \]

4. Final simplification 0.0

   \[\leadsto \sqrt{\left(v \cdot \left(v \cdot -3\right) + 1\right) \cdot 0.125} \cdot \left(1 - v \cdot v\right) \]

Alternatives

Alternative 1
Error	0.3
Cost	7104

\[0.25 \cdot \left(\left(1 + \left(v \cdot v\right) \cdot -2.5\right) \cdot \sqrt{2}\right) \]

Alternative 2
Error	0.6
Cost	6976

\[\left(1 - v \cdot v\right) \cdot \left(0.25 \cdot \sqrt{2}\right) \]

Alternative 3
Error	0.7
Cost	6464

\[\sqrt{0.125} \]

Reproduce

herbie shell --seed 2022306 
(FPCore (v)
  :name "Falkner and Boettcher, Appendix B, 2"
  :precision binary64
  (* (* (/ (sqrt 2.0) 4.0) (sqrt (- 1.0 (* 3.0 (* v v))))) (- 1.0 (* v v))))