Average Error: 0.4 → 0.1
Time: 5.3s
Precision: binary64
\[\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\left(\pi \cdot t\right) \cdot \sqrt{2 \cdot \left(1 - 3 \cdot \left(v \cdot v\right)\right)}\right) \cdot \left(1 - v \cdot v\right)} \]
↓
\[\frac{\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \sqrt{2}\right) \cdot \left(1 - v \cdot v\right)}}{t} \cdot \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}} \]
(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)))))
↓
(FPCore (v t)
 :precision binary64
 (*
  (/ (/ (- 1.0 (* 5.0 (* v v))) (* (* PI (sqrt 2.0)) (- 1.0 (* v v)))) t)
  (sqrt (/ 1.0 (- 1.0 (* 3.0 (pow v 2.0)))))))
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)));
}
↓
double code(double v, double t) {
	return (((1.0 - (5.0 * (v * v))) / ((((double) M_PI) * sqrt(2.0)) * (1.0 - (v * v)))) / t) * sqrt((1.0 / (1.0 - (3.0 * pow(v, 2.0)))));
}

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

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

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

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

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

\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)}
↓
\frac{\frac{1 - 5 \cdot \left(v \cdot v\right)}{\left(\pi \cdot \sqrt{2}\right) \cdot \left(1 - v \cdot v\right)}}{t} \cdot \sqrt{\frac{1}{1 - 3 \cdot {v}^{2}}}

Error

Bits error versus v

Bits error versus t

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.4

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

  2. Taylor expanded in t around 0 0.3

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

  3. Applied egg-rr0.3

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

  4. Applied egg-rr0.1

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

  5. Final simplification0.1

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

Reproduce

herbie shell --seed 2022133 
(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)))))