# Weak, Strong and Linear Convergence of a Double-Layer Fixed Point Algorithm

## TL;DR

This paper introduces a double-layer fixed point algorithm for convex feasibility problems in Hilbert spaces, demonstrating various convergence rates and accelerating convergence through a novel control strategy.

## Contribution

It presents a new double-layer fixed point algorithm with convergence guarantees under different regularity conditions, unifying and extending existing projection methods.

## Key findings

- Weak, strong, and linear convergence under various regularity assumptions
- The algorithm covers many known projection methods in the literature
- Numerical examples show accelerated convergence with the double-layer approach

## Abstract

In this article we consider a consistent convex feasibility problem in a real Hilbert space defined by a finite family of sets $C_i$. We are interested, in particular, in the case where for each $i$, $C_i=Fix (U_i)=\{z\in \mathcal H\mid p_i(z)=0\}$, $U_i\colon\mathcal H\rightarrow \mathcal H$ is a cutter and $p_i\colon\mathcal H\rightarrow [0,\infty)$ is a proximity function. Moreover, we make the following assumption: the computation of $p_i$ is at most as difficult as the evaluation of $U_i$ and this is at most as difficult as projecting onto $C_i$. We study a double-layer fixed point algorithm which applies two types of controls in every iteration step. The first one -- the outer control -- is assumed to be almost cyclic. The second one -- the inner control -- determines the most important sets from those offered by the first one. The selection is made in terms of proximity functions. The convergence results presented in this manuscript depend on the conditions which first, bind together the sets, the operators and the proximity functions and second, connect the inner and outer controls. In particular, weak regularity (demi-closedness principle), bounded regularity and bounded linear regularity imply weak, strong and linear convergence of our algorithm, respectively. The framework presented in this paper covers many known (subgradient) projection algorithms already existing in the literature; for example, those applied with (almost) cyclic, remotest-set, maximum displacement, most-violated constraint and simultaneous controls. In addition, we provide several new examples, where the double-layer approach indeed accelerates the convergence speed as we demonstrate numerically.