  The Boosted Difference of Convex functions Algorithm (BDCA) has been recently
introduced to accelerate the performance of the classical Difference of Convex
functions Algorithm (DCA). This acceleration is achieved thanks to an
extrapolation step from the point computed by DCA via a line search procedure.
In this work, we propose an extension of BDCA that can be applied to difference
of convex functions programs with linear constraints, and prove that every
cluster point of the sequence generated by this algorithm is a
Karush--Kuhn-Tucker point of the problem if the feasible set has a Slater
point. When the objective function is quadratic, we prove that any sequence
generated by the algorithm is bounded and R-linearly (geometrically)
convergent. Finally, we present some numerical experiments where we compare the
performance of DCA and BDCA on some challenging problems: to test the
copositivity of a given matrix, to solve one-norm and infinity-norm
trust-region subproblems, and to solve piecewise quadratic problems with box
constraints. Our numerical results demonstrate that this new extension of BDCA
outperforms DCA.
