  The paper is devoted to the regularization of linear Copositive Programming
problems which consists of transforming a problem to an equivalent form, where
the Slater condition is satisfied and the strong duality holds. We describe
here two regularization algorithms based on the concept of immobile indices and
an understanding of the important role these indices play in the feasible sets'
characterization. These algorithms are compared to some regularization
procedures developed for a more general case of convex problems and based on a
facial reduction approach. We show that the immobile-index-based approach
combined with the specifics of copositive problems allows us to construct more
explicit and detailed regularization algorithms for linear Copositive
Programming problems than those already available.
