  Analysis sparsity is a common prior in inverse problem or machine learning
including special cases such as Total Variation regularization, Edge Lasso and
Fused Lasso. We study the geometry of the solution set (a polyhedron) of the
analysis l1-regularization (with l2 data fidelity term) when it is not reduced
to a singleton without any assumption of the analysis dictionary nor the
degradation operator. In contrast with most theoretical work, we do not focus
on giving uniqueness and/or stability results, but rather describe a worst-case
scenario where the solution set can be big in terms of dimension. Leveraging a
fine analysis of the sub-level set of the regularizer itself, we draw a
connection between support of a solution and the minimal face containing it,
and in particular prove that extremal points can be recovered thanks to an
algebraic test. Moreover, we draw a connection between the sign pattern of a
solution and the ambient dimension of the smallest face containing it. Finally,
we show that any arbitrary sub-polyhedra of the level set can be seen as a
solution set of sparse analysis regularization with explicit parameters.
