Sidi's generalized secant method

Sidi's generalized secant method is a root-finding algorithm, that is, a numerical method for solving equations of the form $f(x)=0$ . The method was published by Avram Sidi.

The method is a generalization of the secant method. Like the secant method, it is an iterative method which requires one evaluation of $f$ in each iteration and no derivatives of $f$. The method can converge much faster though, with an order which approaches 2 provided that $f$ satisfies the regularity conditions described below.

Algorithm

We call $\alpha$ the root of $f$, that is, $f(\alpha )=0$. Sidi's method is an iterative method which generates a sequence $\{x_{i}\}$ of approximations of $\alpha$. Starting with k 1 initial approximations $x_{1},\dots ,x_{k 1}$, the approximation $x_{k 2}$ is calculated in the first iteration, the approximation $x_{k 3}$ is calculated in the second iteration, etc. Each iteration takes as input the last k 1 approximations and the value of $f$ at those approximations. Hence the nth iteration takes as input the approximations $x_{n},\dots ,x_{n k}$ and the values $f(x_{n}),\dots ,f(x_{n k})$.

The number k must be 1 or larger: k = 1, 2, 3, .... It remains fixed during the execution of the algorithm. In order to obtain the starting approximations $x_{1},\dots ,x_{k 1}$ one could carry out a few initializing iterations with a lower value of k.

The approximation $x_{n k 1}$ is calculated as follows in the nth iteration. A polynomial of interpolation $p_{n,k}(x)$ of degree k is fitted to the k 1 points $(x_{n},f(x_{n})),\dots (x_{n k},f(x_{n k}))$. With this polynomial, the next approximation $x_{n k 1}$ of $\alpha$ is calculated as

$x_{n k 1}=x_{n k}-{\frac {f(x_{n k})}{p_{n,k}'(x_{n k})}}$ (1)

with $p_{n,k}'(x_{n k})$ the derivative of $p_{n,k}$ at $x_{n k}$. Having calculated $x_{n k 1}$ one calculates $f(x_{n k 1})$ and the algorithm can continue with the (n   1)th iteration. Clearly, this method requires the function $f$ to be evaluated only once per iteration; it requires no derivatives of $f$.

The iterative cycle is stopped if an appropriate stopping criterion is met. Typically the criterion is that the last calculated approximation is close enough to the sought-after root $\alpha$.

To execute the algorithm effectively, Sidi's method calculates the interpolating polynomial $p_{n,k}(x)$ in its Newton form.

Convergence

Sidi showed that if the function $f$ is (k   1)-times continuously differentiable in an open interval $I$ containing $\alpha$ (that is, $f\in C^{k 1}(I)$), $\alpha$ is a simple root of $f$ (that is, $f'(\alpha )\neq 0$) and the initial approximations $x_{1},\dots ,x_{k 1}$ are chosen close enough to $\alpha$, then the sequence $\{x_{i}\}$ converges to $\alpha$, meaning that the following limit holds: $\lim \limits _{n\to \infty }x_{n}=\alpha$.

Sidi furthermore showed that

$\lim _{n\to \infty }{\frac {x_{n 1}-\alpha }{\prod _{i=0}^{k}(x_{n-i}-\alpha )}}=L={\frac {(-1)^{k 1}}{(k 1)!}}{\frac {f^{(k 1)}(\alpha )}{f'(\alpha )}},$

and that the sequence converges to $\alpha$ of order $\psi _{k}$, i.e.

$\lim \limits _{n\to \infty }{\frac {|x_{n 1}-\alpha |}{|x_{n}-\alpha |^{\psi _{k}}}}=|L|^{(\psi _{k}-1)/k}$

The order of convergence $\psi _{k}$ is the only positive root of the polynomial

$s^{k 1}-s^{k}-s^{k-1}-\dots -s-1$

We have e.g. $\psi _{1}=(1 {\sqrt {5}})/2$ ≈ 1.6180, $\psi _{2}$ ≈ 1.8393 and $\psi _{3}$ ≈ 1.9276. The order approaches 2 from below if k becomes large: $\lim \limits _{k\to \infty }\psi _{k}=2$

Related algorithms

Sidi's method reduces to the secant method if we take k = 1. In this case the polynomial $p_{n,1}(x)$ is the linear approximation of $f$ around $\alpha$ which is used in the nth iteration of the secant method.

We can expect that the larger we choose k, the better $p_{n,k}(x)$ is an approximation of $f(x)$ around $x=\alpha$. Also, the better $p_{n,k}'(x)$ is an approximation of $f'(x)$ around $x=\alpha$. If we replace $p_{n,k}'$ with $f'$ in (1) we obtain that the next approximation in each iteration is calculated as

$x_{n k 1}=x_{n k}-{\frac {f(x_{n k})}{f'(x_{n k})}}$ (2)

This is the Newton–Raphson method. It starts off with a single approximation $x_{1}$ so we can take k = 0 in (2). It does not require an interpolating polynomial but instead one has to evaluate the derivative $f'$ in each iteration. Depending on the nature of $f$ this may not be possible or practical.

Once the interpolating polynomial $p_{n,k}(x)$ has been calculated, one can also calculate the next approximation $x_{n k 1}$ as a solution of $p_{n,k}(x)=0$ instead of using (1). For k = 1 these two methods are identical: it is the secant method. For k = 2 this method is known as Muller's method. For k = 3 this approach involves finding the roots of a cubic function, which is unattractively complicated. This problem becomes worse for even larger values of k. An additional complication is that the equation $p_{n,k}(x)=0$ will in general have multiple solutions and a prescription has to be given which of these solutions is the next approximation $x_{n k 1}$. Muller does this for the case k = 2 but no such prescriptions appear to exist for k > 2.