Ubiquitous algorithms in convex optimization generate self-contracted sequences

In this work we show that various algorithms, ubiquitous in convex optimization (e.g. proximal-gradient, alternating projections and averaged projections) generate self-contracted sequences $\{x_k\}_{k\in\mathbb{N}}$. As a consequence, a novel universal bound for the \emph{length} (\sum_{k\ge0}\lVert x_{k+1}−x_k\rVert) can be deduced. In addition, this bound is independent of both the concrete data of the problem (sets, functions) as well as the step size involved, and only depends on the dimension of the space.