Zhengshan Dong

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The Penalty Decomposition (PD) method is an effective and versatile algorithm for sparse optimization, which has been used in different applications. The PD method may be slow, since it needs to solve many subproblems. The accelerated iteration hard thresholding (AIHT) method is also a powerful method for sparse optimization, but has a main drawback that it(More)
This paper proposes two homotopy methods for solving the compressed sensing (CS) problem, which combine the homotopy technique with the iterative hard thresholding (IHT) method. The homotopy methods overcome the difficulty of the IHT method on the choice of the regularization parameter value, by tracing solutions of the regularized problem along a homotopy(More)
This paper proposes a new customized proximal point algorithm for linearly constrained convex optimization problem, and further extends the proposed method to separable convex optimization problem with linear constraints. The global convergence and a worst-case convergence rate of the proposed methods are proven under some mild assumptions. Preliminary(More)
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