  In geometry processing, numerical optimization methods often involve solving
sparse linear systems of equations. These linear systems have a structure that
strongly resembles to adjacency graphs of the underlying mesh. We observe how
classic linear solvers behave on this specific type of problems. For the sake
of simplicity, we minimise either the squared gradient or the squared
Laplacian, evaluated by finite differences on a regular 1D or 2D grid. We
observed the evolution of the solution for both energies, in 1D and 2D, and
with different solvers: Jacobi, Gauss-Seidel, SSOR (Symmetric successive
over-relaxation) and CG (conjugate gradient [She94]). Plotting results at
different iterations allows to have an intuition of the behavior of these
classic solvers.
