  In recent studies on sparse modeling, $l_q$ ($0<q<1$) regularization has
received considerable attention due to its superiorities on sparsity-inducing
and bias reduction over the $l_1$ regularization.In this paper, we propose a
cyclic coordinate descent (CCD) algorithm for $l_q$ regularization. Our main
result states that the CCD algorithm converges globally to a stationary point
as long as the stepsize is less than a positive constant. Furthermore, we
demonstrate that the CCD algorithm converges to a local minimizer under certain
additional conditions. Our numerical experiments demonstrate the efficiency of
the CCD algorithm.
