Henrywood and Agarwal, Equation (9a)

Average Error: 14.0 → 7.7

Time: 7.9s

Precision: binary64

\[ \begin{array}{c}[M, D] = \mathsf{sort}([M, D])\\ \end{array} \]

Math

\[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

↓

\[\begin{array}{l} t_0 := \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}}\\ \mathbf{if}\;t_0 \leq 5 \cdot 10^{+134}:\\ \;\;\;\;w0 \cdot {\left(1 - \frac{{\left(\frac{D \cdot \left(M \cdot 0.5\right)}{d}\right)}^{2}}{\frac{\ell}{h}}\right)}^{0.5}\\ \mathbf{elif}\;t_0 \leq \infty:\\ \;\;\;\;w0 \cdot \left(M \cdot \left(-\sqrt{\left(\frac{h}{\ell} \cdot \left(\frac{D}{d} \cdot \frac{D}{d}\right)\right) \cdot -0.25}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;w0\\ \end{array} \]

FPCore

(FPCore (w0 M D h l d)
 :precision binary64
 (* w0 (sqrt (- 1.0 (* (pow (/ (* M D) (* 2.0 d)) 2.0) (/ h l))))))

↓

(FPCore (w0 M D h l d)
 :precision binary64
 (let* ((t_0 (sqrt (- 1.0 (* (pow (/ (* M D) (* 2.0 d)) 2.0) (/ h l))))))
   (if (<= t_0 5e+134)
     (* w0 (pow (- 1.0 (/ (pow (/ (* D (* M 0.5)) d) 2.0) (/ l h))) 0.5))
     (if (<= t_0 INFINITY)
       (* w0 (* M (- (sqrt (* (* (/ h l) (* (/ D d) (/ D d))) -0.25)))))
       w0))))

C

double code(double w0, double M, double D, double h, double l, double d) {
	return w0 * sqrt((1.0 - (pow(((M * D) / (2.0 * d)), 2.0) * (h / l))));
}

↓

double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = sqrt((1.0 - (pow(((M * D) / (2.0 * d)), 2.0) * (h / l))));
	double tmp;
	if (t_0 <= 5e+134) {
		tmp = w0 * pow((1.0 - (pow(((D * (M * 0.5)) / d), 2.0) / (l / h))), 0.5);
	} else if (t_0 <= ((double) INFINITY)) {
		tmp = w0 * (M * -sqrt((((h / l) * ((D / d) * (D / d))) * -0.25)));
	} else {
		tmp = w0;
	}
	return tmp;
}

Java

public static double code(double w0, double M, double D, double h, double l, double d) {
	return w0 * Math.sqrt((1.0 - (Math.pow(((M * D) / (2.0 * d)), 2.0) * (h / l))));
}

↓

public static double code(double w0, double M, double D, double h, double l, double d) {
	double t_0 = Math.sqrt((1.0 - (Math.pow(((M * D) / (2.0 * d)), 2.0) * (h / l))));
	double tmp;
	if (t_0 <= 5e+134) {
		tmp = w0 * Math.pow((1.0 - (Math.pow(((D * (M * 0.5)) / d), 2.0) / (l / h))), 0.5);
	} else if (t_0 <= Double.POSITIVE_INFINITY) {
		tmp = w0 * (M * -Math.sqrt((((h / l) * ((D / d) * (D / d))) * -0.25)));
	} else {
		tmp = w0;
	}
	return tmp;
}

Python

def code(w0, M, D, h, l, d):
	return w0 * math.sqrt((1.0 - (math.pow(((M * D) / (2.0 * d)), 2.0) * (h / l))))

↓

def code(w0, M, D, h, l, d):
	t_0 = math.sqrt((1.0 - (math.pow(((M * D) / (2.0 * d)), 2.0) * (h / l))))
	tmp = 0
	if t_0 <= 5e+134:
		tmp = w0 * math.pow((1.0 - (math.pow(((D * (M * 0.5)) / d), 2.0) / (l / h))), 0.5)
	elif t_0 <= math.inf:
		tmp = w0 * (M * -math.sqrt((((h / l) * ((D / d) * (D / d))) * -0.25)))
	else:
		tmp = w0
	return tmp

Julia

function code(w0, M, D, h, l, d)
	return Float64(w0 * sqrt(Float64(1.0 - Float64((Float64(Float64(M * D) / Float64(2.0 * d)) ^ 2.0) * Float64(h / l)))))
end

↓

function code(w0, M, D, h, l, d)
	t_0 = sqrt(Float64(1.0 - Float64((Float64(Float64(M * D) / Float64(2.0 * d)) ^ 2.0) * Float64(h / l))))
	tmp = 0.0
	if (t_0 <= 5e+134)
		tmp = Float64(w0 * (Float64(1.0 - Float64((Float64(Float64(D * Float64(M * 0.5)) / d) ^ 2.0) / Float64(l / h))) ^ 0.5));
	elseif (t_0 <= Inf)
		tmp = Float64(w0 * Float64(M * Float64(-sqrt(Float64(Float64(Float64(h / l) * Float64(Float64(D / d) * Float64(D / d))) * -0.25)))));
	else
		tmp = w0;
	end
	return tmp
end

MATLAB

function tmp = code(w0, M, D, h, l, d)
	tmp = w0 * sqrt((1.0 - ((((M * D) / (2.0 * d)) ^ 2.0) * (h / l))));
end

↓

function tmp_2 = code(w0, M, D, h, l, d)
	t_0 = sqrt((1.0 - ((((M * D) / (2.0 * d)) ^ 2.0) * (h / l))));
	tmp = 0.0;
	if (t_0 <= 5e+134)
		tmp = w0 * ((1.0 - ((((D * (M * 0.5)) / d) ^ 2.0) / (l / h))) ^ 0.5);
	elseif (t_0 <= Inf)
		tmp = w0 * (M * -sqrt((((h / l) * ((D / d) * (D / d))) * -0.25)));
	else
		tmp = w0;
	end
	tmp_2 = tmp;
end

Wolfram

code[w0_, M_, D_, h_, l_, d_] := N[(w0 * N[Sqrt[N[(1.0 - N[(N[Power[N[(N[(M * D), $MachinePrecision] / N[(2.0 * d), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]), $MachinePrecision]

↓

code[w0_, M_, D_, h_, l_, d_] := Block[{t$95$0 = N[Sqrt[N[(1.0 - N[(N[Power[N[(N[(M * D), $MachinePrecision] / N[(2.0 * d), $MachinePrecision]), $MachinePrecision], 2.0], $MachinePrecision] * N[(h / l), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]}, If[LessEqual[t$95$0, 5e+134], N[(w0 * N[Power[N[(1.0 - N[(N[Power[N[(N[(D * N[(M * 0.5), $MachinePrecision]), $MachinePrecision] / d), $MachinePrecision], 2.0], $MachinePrecision] / N[(l / h), $MachinePrecision]), $MachinePrecision]), $MachinePrecision], 0.5], $MachinePrecision]), $MachinePrecision], If[LessEqual[t$95$0, Infinity], N[(w0 * N[(M * (-N[Sqrt[N[(N[(N[(h / l), $MachinePrecision] * N[(N[(D / d), $MachinePrecision] * N[(D / d), $MachinePrecision]), $MachinePrecision]), $MachinePrecision] * -0.25), $MachinePrecision]], $MachinePrecision])), $MachinePrecision]), $MachinePrecision], w0]]]

Derivation

1. Split input into 3 regimes

2. if (sqrt.f64 (-.f64 1 (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 2 d)) 2) (/.f64 h l)))) < 4.99999999999999981e134

   1. Initial program 0.1

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Simplified 0.8

      \[\leadsto \color{blue}{w0 \cdot \sqrt{1 - {\left(M \cdot \frac{D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}}} \]

   3. Applied egg-rr 1.4

      \[\leadsto w0 \cdot \sqrt{1 - \color{blue}{\frac{1}{\frac{\ell}{{\left(\frac{M}{2} \cdot \frac{D}{d}\right)}^{2} \cdot h}}}} \]

   4. Applied egg-rr 0.1

      \[\leadsto w0 \cdot \color{blue}{{\left(1 - \frac{{\left(\frac{\left(M \cdot 0.5\right) \cdot D}{d}\right)}^{2}}{\frac{\ell}{h}}\right)}^{0.5}} \]

   if 4.99999999999999981e134 < (sqrt.f64 (-.f64 1 (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 2 d)) 2) (/.f64 h l)))) < +inf.0

   1. Initial program 61.6

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Simplified 59.0

      \[\leadsto \color{blue}{w0 \cdot \sqrt{1 - {\left(M \cdot \frac{D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}}} \]

   3. Taylor expanded in M around -inf 55.8

      \[\leadsto w0 \cdot \color{blue}{\left(-1 \cdot \left(\sqrt{-0.25 \cdot \frac{{D}^{2} \cdot h}{\ell \cdot {d}^{2}}} \cdot M\right)\right)} \]

   4. Simplified 47.3

      \[\leadsto w0 \cdot \color{blue}{\left(M \cdot \left(-\sqrt{\left(\left(\frac{D}{d} \cdot \frac{D}{d}\right) \cdot \frac{h}{\ell}\right) \cdot -0.25}\right)\right)} \]

   if +inf.0 < (sqrt.f64 (-.f64 1 (*.f64 (pow.f64 (/.f64 (*.f64 M D) (*.f64 2 d)) 2) (/.f64 h l))))

   1. Initial program 64.0

      \[w0 \cdot \sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \]

   2. Simplified 62.0

      \[\leadsto \color{blue}{w0 \cdot \sqrt{1 - {\left(M \cdot \frac{D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}}} \]

   3. Taylor expanded in M around 0 15.0

      \[\leadsto \color{blue}{w0} \]
3. Recombined 3 regimes into one program.

4. Final simplification 7.7

   \[\leadsto \begin{array}{l} \mathbf{if}\;\sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \leq 5 \cdot 10^{+134}:\\ \;\;\;\;w0 \cdot {\left(1 - \frac{{\left(\frac{D \cdot \left(M \cdot 0.5\right)}{d}\right)}^{2}}{\frac{\ell}{h}}\right)}^{0.5}\\ \mathbf{elif}\;\sqrt{1 - {\left(\frac{M \cdot D}{2 \cdot d}\right)}^{2} \cdot \frac{h}{\ell}} \leq \infty:\\ \;\;\;\;w0 \cdot \left(M \cdot \left(-\sqrt{\left(\frac{h}{\ell} \cdot \left(\frac{D}{d} \cdot \frac{D}{d}\right)\right) \cdot -0.25}\right)\right)\\ \mathbf{else}:\\ \;\;\;\;w0\\ \end{array} \]

Reproduce

herbie shell --seed 2022202 
(FPCore (w0 M D h l d)
  :name "Henrywood and Agarwal, Equation (9a)"
  :precision binary64
  (* w0 (sqrt (- 1.0 (* (pow (/ (* M D) (* 2.0 d)) 2.0) (/ h l))))))