Sidi's generalized secant method

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

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 ${\displaystyle x_{1},\dots ,x_{k+1}}$ one could carry out a few initializing iterations with a lower value of k.

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

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

(1)

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

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

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

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

Sidi furthermore showed that

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

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

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

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

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

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

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

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

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

(2)

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

Once the interpolating polynomial ${\displaystyle p_{n,k}(x)}$ has been calculated, one can also calculate the next approximation ${\displaystyle x_{n+k+1}}$ as a solution of ${\displaystyle p_{n,k}(x)=0}$ 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.[3] 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 ${\displaystyle p_{n,k}(x)=0}$ will in general have multiple solutions and a prescription has to be given which of these solutions is the next approximation ${\displaystyle x_{n+k+1}}$. Muller does this for the case k = 2 but no such prescriptions appear to exist for k > 2.