  Sparse learning is an important topic in many areas such as machine learning,
statistical estimation, signal processing, etc. Recently, there emerges a
growing interest on structured sparse learning. In this paper we focus on the
$\ell_q$-analysis optimization problem for structured sparse learning ($0< q
\leq 1$). Compared to previous work, we establish weaker conditions for exact
recovery in noiseless case and a tighter non-asymptotic upper bound of estimate
error in noisy case. We further prove that the nonconvex $\ell_q$-analysis
optimization can do recovery with a lower sample complexity and in a wider
range of cosparsity than its convex counterpart. In addition, we develop an
iteratively reweighted method to solve the optimization problem under the
variational framework. Theoretical analysis shows that our method is capable of
pursuing a local minima close to the global minima. Also, empirical results of
preliminary computational experiments illustrate that our nonconvex method
outperforms both its convex counterpart and other state-of-the-art methods.
