# 1 Fixed point acceleration

A fixed point problem is one where we look for a vector, $\hat{X} \in \Re^N$, so that for a given function $f: \Re^N \rightarrow \Re^N$ we have:

\[f(\hat{X}) = \hat{X}\]

If $f: \Re^1 \rightarrow \Re^1$ and thus any solution $\hat{X}$ will be a scalar then one way to solve this problem would be to use a rootfinder on the function $g(x) = f(x) - x$ or to use an optimiser to minimise $h(x) = (f(x) - x)^2$. These techniques will not generally work however if $f : N^a \rightarrow N^a$ where $a$ is large. Consider for instance using a multidimensional Newtonian optimiser to minimise $h(x) = (f(x) - x)^2$. The estimation of gradients for each individual dimension may take an infeasibly long time. In addition this method may not make use all available information. Consider for instance that we know that the solution for $x$ will be an increasing vector (so $x_i > x_j$ for any entries of $x$ with $i > j$) but has many entries. This information can be preserved and used in the vector acceleration algorithms that we present but would be more difficult to exploit in a standard optimisation algorithm.

**FixedPointAcceleration.jl** implements eight algorithms for finding fixed points. The first algorithm implemented in this package is the "simple" method which merely takes the output of a function and feeds it back into the function. For instance starting with a guess of $x_0$, the next guess will be $x_1 = f(x_0)$. The guess after that will be $x_2 = f(x_1)$ and so on. In some conditions $f$ will be a contraction mapping and so the simple method will be guaranteed to converge to a unique fixed point (Stokey, Lucas & Prescott 1989). Even when this is the case however the simple method may only converge slowly which can be inconvenient. The other seven methods this package implements are designed to be faster than the simple method but may not be convergent for every problem.