Consider an integral to evaluate:
$$I=\int_a^b f(x)\,\mathrm dx.\tag1$$
In the Monte-Carlo quadrature, if $I_n$ is $n$th estimate of $I$, then for $(n+1)$th estimate we have:
$$I_{n+1}=\frac{nI_n+f(\operatorname{rand}())(b-a)}{n+1}.\tag2$$
This lets one track current estimate at each step, with constant rate of updating.
Monte-Carlo quadrature converges as $\delta I_n=O(n^{-1/2})$, which is quite slow. A faster approach could be the trapezoidal rule. With it, if we define (for $n$ being number of points)
$$S_{n+1,\,\alpha,\,\beta}=\sum_{k=1}^{n-1+\beta} f\left(a+\frac{b-a}n (k+\alpha)\right),$$
then
$$\begin{align} I_{n+1} &=\frac{b-a}{n }\left(\frac{f(a)+f(b)}2+S_{n+1,\,0,\,0}\right),\\ I_{2n+1}&=\frac{I_{n+1}}2+\frac{b-a}{2n}S_{n+1,\,0.5,\,1}. \end{align} \tag3$$
Its convergence in the number of points taken is better: $\delta I_n=O(n^2)$. But to update without needless re-evaluation of $f$ and without storing most of its values (except $f(a)+f(b)$), we have to do twofold increase of $n$ each time. This means that rate of updating drops exponentially each update, although the precision does increase quadratically with number of points.
I'm looking for a quadrature method, which would combine the good properties of the two methods described above:
- Rapid convergence (quadratic in number of points or better),
- Reuse of previous result without storing most of the values of integrand,
- Possibility of constant-rate updates.
Is there any such method?