  The training of Support Vector Machines may be a very difficult task when
dealing with very large datasets. The memory requirement and the time
consumption of the SVMs algorithms grow rapidly with the increase of the data.
To overcome these drawbacks, we propose a parallel decomposition algorithmic
scheme for SVMs training for which we prove global convergence under suitable
conditions. We outline how these assumptions can be satisfied in practice and
we suggest various specific implementations exploiting the adaptable structure
of the algorithmic model.
