The root of the equation x = (1 / 2) + sin x by using the iteration method: x_{k+1} = 1/2 + sin(x_k), x_0 = 1 correct to o six decimals is x = 1.497300. Determine the number of iteration steps required to reach the root by linear iteration. If the Aitken ∆2-process is used after three approximations are available, how many iterations are required?
Relevant Theorem: The Aitken process, also known as Aitken's delta-squared process, is a numerical analysis technique used to accelerate the convergence of a sequence of approximations. It was developed by the Scottish mathematician Alexander Aitken in the 1920s. The method is particularly useful for improving the convergence rate of slowly converging sequences or for refining the results of other numerical methods.

The Aitken process is based on the idea of extrapolation, which involves using the given sequence of approximations to estimate a better approximation. The method works by constructing a new sequence from the original sequence, with the new sequence converging more rapidly to the desired limit.

Given a sequence of approximations {x_n}, the Aitken process generates a new sequence {y_n} using the following formula:

y_n = x_n - (x_{n+1} - x_n)^2 / (x_{n+2} - 2x_{n+1} + x_n)

Here, x_n, x_{n+1}, and x_{n+2} are consecutive terms in the original sequence, and y_n is the corresponding term in the new sequence generated by the Aitken process.

The Aitken process can be applied iteratively to further improve the convergence rate. However, it is important to note that the method is not universally applicable and may not always lead to faster convergence. It works best for sequences that exhibit linear convergence, and its effectiveness may be limited for sequences with erratic behavior or poor initial approximations.

In summary, the Aitken process is a numerical analysis technique used to accelerate the convergence of a sequence of approximations. It is based on the idea of extrapolation and works by constructing a new sequence with improved convergence properties from the original sequence. The method is particularly useful for refining the results of other numerical methods and improving the convergence rate of slowly converging sequences.
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