ENA, Section 1.4, Exercise 4b, n=5

Average Error: 7.2 → 1.0

Time: 3.3s

Precision: binary64

\[\left(-1000000000 \leq x \land x \leq 1000000000\right) \land \left(-1 \leq \varepsilon \land \varepsilon \leq 1\right)\]

Math

\[{\left(x + \varepsilon\right)}^{5} - {x}^{5} \]

↓

\[\begin{array}{l} t_0 := {\left(x + \varepsilon\right)}^{5} - {x}^{5}\\ \mathbf{if}\;t_0 \leq -9.395586129791518 \cdot 10^{-272}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;t_0 \leq 0:\\ \;\;\;\;\mathsf{expm1}\left(\mathsf{log1p}\left({x}^{3} \cdot \left(\varepsilon \cdot \mathsf{fma}\left(x, 5, \varepsilon \cdot 10\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

FPCore

(FPCore (x eps) :precision binary64 (- (pow (+ x eps) 5.0) (pow x 5.0)))

↓

(FPCore (x eps)
 :precision binary64
 (let* ((t_0 (- (pow (+ x eps) 5.0) (pow x 5.0))))
   (if (<= t_0 -9.395586129791518e-272)
     t_0
     (if (<= t_0 0.0)
       (expm1 (log1p (* (pow x 3.0) (* eps (fma x 5.0 (* eps 10.0))))))
       t_0))))

C

double code(double x, double eps) {
	return pow((x + eps), 5.0) - pow(x, 5.0);
}

↓

double code(double x, double eps) {
	double t_0 = pow((x + eps), 5.0) - pow(x, 5.0);
	double tmp;
	if (t_0 <= -9.395586129791518e-272) {
		tmp = t_0;
	} else if (t_0 <= 0.0) {
		tmp = expm1(log1p((pow(x, 3.0) * (eps * fma(x, 5.0, (eps * 10.0))))));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, eps)
	return Float64((Float64(x + eps) ^ 5.0) - (x ^ 5.0))
end

↓

function code(x, eps)
	t_0 = Float64((Float64(x + eps) ^ 5.0) - (x ^ 5.0))
	tmp = 0.0
	if (t_0 <= -9.395586129791518e-272)
		tmp = t_0;
	elseif (t_0 <= 0.0)
		tmp = expm1(log1p(Float64((x ^ 3.0) * Float64(eps * fma(x, 5.0, Float64(eps * 10.0))))));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, eps_] := N[(N[Power[N[(x + eps), $MachinePrecision], 5.0], $MachinePrecision] - N[Power[x, 5.0], $MachinePrecision]), $MachinePrecision]

↓

code[x_, eps_] := Block[{t$95$0 = N[(N[Power[N[(x + eps), $MachinePrecision], 5.0], $MachinePrecision] - N[Power[x, 5.0], $MachinePrecision]), $MachinePrecision]}, If[LessEqual[t$95$0, -9.395586129791518e-272], t$95$0, If[LessEqual[t$95$0, 0.0], N[(Exp[N[Log[1 + N[(N[Power[x, 3.0], $MachinePrecision] * N[(eps * N[(x * 5.0 + N[(eps * 10.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]], $MachinePrecision]] - 1), $MachinePrecision], t$95$0]]]

Error

Bits error versus x

Bits error versus eps

Derivation

1. Split input into 2 regimes

2. if (-.f64 (pow.f64 (+.f64 x eps) 5) (pow.f64 x 5)) < -9.39558612979151838e-272 or 0.0 < (-.f64 (pow.f64 (+.f64 x eps) 5) (pow.f64 x 5))

   1. Initial program 1.9

      \[{\left(x + \varepsilon\right)}^{5} - {x}^{5} \]

   if -9.39558612979151838e-272 < (-.f64 (pow.f64 (+.f64 x eps) 5) (pow.f64 x 5)) < 0.0

   1. Initial program 8.4

      \[{\left(x + \varepsilon\right)}^{5} - {x}^{5} \]

   2. Taylor expanded in x around inf 0.9

      \[\leadsto \color{blue}{5 \cdot \left(\varepsilon \cdot {x}^{4}\right) + 10 \cdot \left({\varepsilon}^{2} \cdot {x}^{3}\right)} \]

   3. Simplified 0.8

      \[\leadsto \color{blue}{\varepsilon \cdot \left({x}^{3} \cdot \left(5 \cdot x + \varepsilon \cdot 10\right)\right)} \]

   4. Applied egg-rr 0.9

      \[\leadsto \color{blue}{\mathsf{expm1}\left(\mathsf{log1p}\left({x}^{3} \cdot \left(\mathsf{fma}\left(x, 5, \varepsilon \cdot 10\right) \cdot \varepsilon\right)\right)\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 1.0

   \[\leadsto \begin{array}{l} \mathbf{if}\;{\left(x + \varepsilon\right)}^{5} - {x}^{5} \leq -9.395586129791518 \cdot 10^{-272}:\\ \;\;\;\;{\left(x + \varepsilon\right)}^{5} - {x}^{5}\\ \mathbf{elif}\;{\left(x + \varepsilon\right)}^{5} - {x}^{5} \leq 0:\\ \;\;\;\;\mathsf{expm1}\left(\mathsf{log1p}\left({x}^{3} \cdot \left(\varepsilon \cdot \mathsf{fma}\left(x, 5, \varepsilon \cdot 10\right)\right)\right)\right)\\ \mathbf{else}:\\ \;\;\;\;{\left(x + \varepsilon\right)}^{5} - {x}^{5}\\ \end{array} \]

Reproduce

herbie shell --seed 2022150 
(FPCore (x eps)
  :name "ENA, Section 1.4, Exercise 4b, n=5"
  :precision binary64
  :pre (and (and (<= -1000000000.0 x) (<= x 1000000000.0)) (and (<= -1.0 eps) (<= eps 1.0)))
  (- (pow (+ x eps) 5.0) (pow x 5.0)))