  The notion of quasi-Fej\'er monotonicity has proven to be an efficient tool
to simplify and unify the convergence analysis of various algorithms arising in
applied nonlinear analysis. In this paper, we extend this notion in the context
of variable metric algorithms, whereby the underlying norm is allowed to vary
at each iteration. Applications to convex optimization and inverse problems are
demonstrated.
