  It is proved that, for an indefinite quadratic programming problem under
linear constraints, any iterative sequence generated by the Proximal DC
decomposition algorithm $R$-linearly converges to a Karush-Kuhn-Tucker point,
provided that the problem has a solution. Another major result of this paper
says that DCA sequences generated by the algorithm converge to a locally unique
solution of the problem if the initial points are taken from a suitably-chosen
neighborhood of it. To deal with the implicitly defined iterative sequences, a
local error bound for affine variational inequalities and novel techniques are
used. Numerical results together with an analysis of the influence of the
decomposition parameter, as well as a comparison between the Proximal DC
decomposition algorithm and the Projection DC decomposition algorithm, are
given in this paper. Our results complement a recent and important paper of Le
Thi, Huynh, and Pham Dinh (J. Optim. Theory Appl. 179 (2018), 103-126).
